PERMIS-2007

FOREWORD

SPONSORS

PerMIS 2009 

PERFORMANCE METRICS

FOR 

INTELLIGENT SYSTEMS (PERMIS) WORKSHOP

National Institute of Standards and Technology, Gaithersburg, Maryland USA

September 21- 23, 2009

NIST Special Publication 1112

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.SP.1112
Table of Contents 

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 

Sponsors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 

Program Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 

Plenary Addresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 

Workshop Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix 

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx 

Technical Sessions 

MON-AM1 Model-based Performance Evaluation 

Validating Extended Neglect Tolerance Model with Humanoid Soccer Robotic Tasks with 

Varying Complexities [Rajesh Elara, Carlos Acosta Calderon, Changjiu Zhou, 

Wijerupage Sardha Wijesoma] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

Modeling Multiple Human Operators in the Supervisory Control of Heterogeneous 

Unmanned Vehicles [Brian Mekdeci, Mary Cummings] . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

Internal Model Generation for Evolutionary Acceleration of Automated Robotic 

Assembly Optimization [Jeremy Marvel, Wyatt Newman] . . . . . . . . . . . . . . . . . . . . . . . 17 

Development of Top-Down Analysis of Distributed Assembly Tasks [Anthony Cowley, 

M. Ani Hsieh, C.J. Taylor] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

Context-Based Object Recognition [Shaun Edwards, Meredith Wright, Ben Abbott] . . 31

MON-AM2 Special Session I: Performance Metrics for Sustainable Manufacturing 

Manufacturing Unit Process Life Cycle Inventories (Uplci) [Michael Overcash, 

Janet Twomey, Jacqueline Isaacs] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

Conceptual Foundations of Energy Aware Manufacturing [Soundar Kumara] . . . . . . 40 

Discrete Event Simulation to Generate Requirements Specification for Sustainable 

Manufacturing Systems Design [Björn Johansson, Anders Skoogh, Mahesh Mani, 

Swee Leong] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 

Towards A New Geometric Metric for Sustainability Assessment [Gaurav Ameta] . . . . 51 

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MOM-PM1 Performance Assessment and Reliability of Unmanned Systems 

A Mission Taxonomy-Based Approach to Planetary Rover Cost-Reliability Tradeoffs 

[David Asikin, John Dolan] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Towards a Systematic Assessment of the Functions of Unmanned Autonomous Systems 

[Robin Jaulmes, Eric Moline, Laurent Vielle] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 

Performance Measures Framework for Unmanned Systems (PerMFUS): Initial 

Perspective [Hui-Min Huang, Elena Messina, Adam Jacoff] . . . . . . . . . . . . . . . . . . . . . 73 

Optimum Combination of Full System and Subsystem Tests for Estimating the Reliability 

of a System [Coire Maranzano, James Spall] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 

MON-PM2 Special Session II: Test and Evaluation of Unmanned and Autonomous Systems 

Unmanned and Autonomous Systems Mission Based Test and Evaluation [Philipp Djang, 

Frank Lopez] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 

Modeling and Simulation for Unmanned and Autonomous System Test and Evaluation 

[Mauricio Castillo-Effen, Nikita Visnevski, Raj Subbu] . . . . . . . . . . . . . . . . . . . . . . . . . 94 

Evolutionary Framework for Test of Autonomous Systems [Raj Subbu, Nikita Visnevski, 

Philipp Djang] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 

Metrics for Co-evolving Autonomous Systems [Jack Ring]. . . . . . . . . . . . . . . . . . . . . . 107 

TUE-AM1 Metrics & Measures 

The Role of Competitions in Advancing Intelligent Systems: A Practitioner’s Perspective 

[Elena Messina, Raj Madhavan, Stephen Balakirsky] . . . . . . . . . . . . . . . . . . . . . . . . . 113 

History and Evolution of Robot Rescue Competitions [Adam Jacoff]* 

Evaluating The RoboCup 2009 Virtual Robot Rescue Competition 

[Stephen Balakirsky, Stefano Carpin, Arnoud Visser] . . . . . . . . . . . . . . . . . . . . . . . . . 117 

RoboCupRescue Interleague Challenge 2009: Bridging the Gap between Simulation and 

Reality [Alexander Kleiner, Chris Scrapper, Adam Jacoff] . . . . . . . . . . . . . . . . . . . . . 123 

Mobile Microrobot Characterization through Performance-Based Competitions 

[Jason Gorman, Craig McGray, Richard Allen] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 

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TUE-AM2 Special Session III: Is an Agent Theory of Mind Valuable for Adaptive, Intelligent 

Systems? 

Is an Agent Theory of Mind (ToM) Valuable for Adaptive, Intelligent Systems? 

[Gary Berg-Cross] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Towards a Simple Robotic Theory of Mind [Kyung-Joong Kim, Hod Lipson] . . . . . . 139 

Resilient Behavior through Controller Self-Diagnosis, Adaptation and Recovery 

[Juan Cristobal Zagal, Hod Lipson] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 

Neurodynamics of Cognition and Consciousness [Robert Kozma, Walter Freeman] . .155 

Theory of Mind, Computational Tractability, and Mind Shaping [Tad Zawidzki] . . . . 157 

TUE-PM1 Ground Truth and Testbeds for Performance Testing 

Data Collection Test-Bed for the Evaluation of Range Imaging Sensors for ANSI/ITSDF 

B56.5 Safety Standard for Guided Industrial Vehicles [William Shackleford, 

Roger Bostelman] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 

Ground Truth Data Using 3D Imaging for Urban Search and Rescue Robots 

[Nicholas Scott, Alan Lytle] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 

Performance Measurements of Evaluating Static and Dynamic Multiple Human 

Detection and Tracking Systems in Unstructured Environments [Barry Bodt, Richard 

Camden, Harry Scott, Adam Jacoff, Tsai Hong, Tommy Chang, Rick Norcross, 

Anthony Downs, Ann Virts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 

Mathematical Metrology for Evaluating a 6DOF Visual Servoing System [Mili Shah, 

Tommy Chang, Tsai Hong, Roger Eastman] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 

TUE-PM2 Special Session IV: An Ontology for Robotics Science and Systems 

Ontology Formalisms: What is Appropriate for Different Applications? 

[Craig Schlenoff] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 

Universal Core Semantic Layer: A Roadmap to Semantic Interoperability 

[Lowell Vizenor, Barry Smith] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 

Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy [Ben Kuipers]* 

WED-AM1 Performance Measures for Mobile Robots 

Performance Measures of Agility for Mobile Robots [Alan Bowling, 

Shih-Chien Teng] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 

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Measuring Robot Performance in Real-time for NASA Robotic Reconnaissance 

Operations [Debra Schreckenghost, Terrence Fong, Tod Milam, Hans Utz] . . . . . . . . 208 

A Biologically Inspired Sensory Driven Method for Tracking Wind-Borne Odors 

[Brian Taylor, Brandon Rutter, Roger Quinn] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 

A Confidence Measure for Segment Based Maps [Rolf Lakaemper] . . . . . . . . . . . . . . 225 

Evaluation of Robocup Maps [Benjamin Balaguer, Stefano Carpin, Stephen 

Balakirsky, Arnoud Visser] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 

WED-AM2 Special Session V: TRANSTAC: Performance Evaluation of Speech Translation 

Systems for Military Applications 

Evaluating Speech Translation Systems: Applying SCORE to TRANSTAC Technologies 

[Craig Schlenoff, Brian Weiss, Michelle Steves, Greg Sanders, Frederick Proctor, 

Ann Virts] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 

Development and Internal Evaluation of Speech-to-Speech Translation Technology at 

BBN [David Stallard, Rohit Prasad, Prem Natarajan] . . . . . . . . . . . . . . . . . . . . . . . . . 245 

The Impact of Evaluation Scenario Development on the Quantitative Performance of 

Speech Translation Systems Prescribed by the SCORE Framework [Brian Weiss, 

Craig Schlenoff] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 

Probability of Successful Transfer of Low-Level Concepts via Machine Translation: 

A Meta-Evaluation [Greg Sanders, Sherri Condon] . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 

Automated Metrics for Speech Translation [Sherri Condon, Mark Arehart, Christy Doran, 

Dan Parvaz, John Aberdeen, Karine Megerdoomian, Beatrice Oshika] . . . . . . . . . . . 265 

Utility Assessment in TRANSTAC: Using a Set of Complementary Methods 

[Michelle Steves, Emile Morse] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 

WED-PM1: Issues in Designing Intelligent Systems 

 Performance Measurement and Its Role in Advancement for Intelligent Systems: 

Discussion Points [Danil Prokhorov, Yasuo Uehara]. . . . . . . . . . . . . . . . . . . . . . . . . . 279 

Collective Intelligence: Toward Classifying Systems of Systems 

[Alan Ramsbotham] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 

A Decision-Theoretic Formalism for Belief-Optimal Reasoning [Kris Hauser] . . . . . . 288 

Evaluation of Automatically Generated Reactive Planning Logic for Unmanned Surface 

Vehicles [Max Schwartz, Petr Svec, Atul Thakur, Satyandra Gupta] . . . . . . . . . . . . . . 295 

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WED-PM2 Special Session VI: Performance Measurements Towards Improved Forklift 

Safety 

Fork Lift Awareness [Mark Austin] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 

AGV Forklifts - Current and Future Safety Systems [Benny Forsman]* 

Where AGV's and Forklifts Roam: Preserving Operational Safety in a Shared Workspace 

[Richard Ungerbuehler] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 

Performance Measurements Towards Improved Manufacturing Vehicle Safety [Roger 

Bostelman, Will Shackleford] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 

White Paper: Towards Improved Forklift Safety [Roger Bostelman]. . . . . . . . . . . . . . 320 

Note: * Presentation Only

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FOREWORD

Welcome to PerMIS’09!

The Performance Metrics for Intelligent Systems (PerMIS) workshop is dedicated to defining measures and 

methodologies of evaluating performance of intelligent systems. As the only workshop of its kind, PerMIS has 

proved to be an excellent forum for sharing lessons learned and discussions as well as fostering collaborations between researchers and practitioners from industry, academia and government agencies.

The main theme of the ninth iteration of the workshop, PerMIS’09, seeks to address the question: “Does performance measurement accelerate the pace of advancement for intelligent systems?” In addition to the 

main theme, as in previous years, the workshop will focus on applications of performance measures to practical problems in commercial, industrial, homeland security, and military applications. 

The PerMIS’09 program consists of six plenary addresses and six general and special sessions. The topics 

that are to be discussed by the speakers cover a wide array of themes centered on many intricate facets of 

intelligent system research. The presentations will emphasize and showcase the interdisciplinary nature of intelligent systems research and why it is not straightforward to evaluate such interconnected system of systems. The three days of twelve sessions will span themes from manufacturing, mobile robotics, humansystem interaction, theory of mind, testing and evaluation of unmanned systems, to name a few.

PerMIS’09 is sponsored by NIST, DARPA and NSF, with technical co-sponsorship of the IEEE Washington 

Section Robotics and Automation Society Chapter, and in-cooperation with the Association for Computing 

Machinery (ACM) Special Interest Group on Artificial Intelligence (SIGART). The Defense Advanced Research 

Projects Agency Information Processing Technology Office graciously provided funding to help support the 

workshop. Special thanks are due to the National Science Foundation for providing funding to allow undergraduate and graduate students to attend PerMIS this year. We also thank Professor Holly Yanco of the University of Massachussetts – Lowell for organizing the student support grants program. We gratefully acknowledge the support of our sponsors.

We thank the special session organizers for proposing interesting topics and assembling researchers related 

to their sessions. These focused sessions provide an opportunity to delve deeper into specialized topics and 

to hear from experts in the field. Our thanks are also due to the Program Committee members for publicizing 

the workshop and the reviewers for providing feedback to the authors, and for helping us to put together an 

exciting program. 

The proceedings of PerMIS will be indexed by INSPEC, Compendex, ACM’s Digital Library, and are released 

as a NIST Special Publication. Selected papers from last year’s PerMIS have been published as an edited 

book volume by Springer Publishers entitled Performance Evaluation and Benchmarking of Intelligent Systems 

(Eds. Raj Madhavan, Edward Tunstel and Elena Messina). The book presents a detailed and coherent picture 

of state-of-the-art, recent developments, and further research areas in intelligent systems by drawing from the 

experiences and insights of experts gained both through theoretical development and practical implementaPerMIS 2009

vi
tion in a variety of diverse application domains. The book will be available for your perusal during the workshop.

It is our sincere hope that you enjoy the presentations, the social programs, renew old relationships, and forge 

new ones at PerMIS’09!

Raj Madhavan 	 	 Elena Messina

Program Chair 	 	 General Chair		 	

SPONSORS

 

 

 

PerMIS 2009

vii
PROGRAM COMMITTEE

General Chair:

Elena Messina (Intelligent Systems Division, NIST, USA) 

Program Chair:

Raj Madhavan (Oak Ridge National Laboratory/NIST, USA)

Publicity Chair:

Edward Tunstel (Johns Hopkins University-APL, USA)

S. Balakirsky (NIST USA)

G. Berg-Cross (EM & I USA)

F. Bonsignorio (Heron Robots Italy)

R. Bostelman (NIST USA)

S. Carpin (UC Merced USA)

P. Courtney (PerkinElmer UK)

G. Dimitoglou (Hood College USA)

J. Evans (John Evans, LLC USA)

P. Evans (SWRI USA)

J. Gunderson (GammaTwo USA)

L. Gunderson (GammaTwo USA)

S. K. Gupta (UMD USA)

J. Hallam (SDU Denmark)

T. Kalmar-Nagy (Texas A & M USA)

R. Lakaemper (Temple Univ USA)

G. Moses (UMD USA)

A. del Pobil (Univ. Jaume-I Spain)

T. Potok (ORNL USA)

E. Prassler (UApp Sci-BRS Germany)

F. Proctor (NIST USA)

D. Prokhorov (Toyota USA)

L. Reeker (NIST USA)

M. Reggiani (UPadua Italy)

C. Schlenoff (NIST USA)

M. Shneier (NIST USA)

J. Steele (CSM USA)

PerMIS 2009

viii
ABSTRACT

Order fulfillment is a multi-billion dollar business. Existing solutions range 

from the highly automated, whose 

cost effectiveness is inversely related 

to their flexibility, to people pushing 

carts around in warehouses manually 

filling orders, which is very flexible 

but not very cost effective. In this talk 

I will describe a radical new approach 

to order fulfillment that is both flexible 

and cost effective. The key idea is to 

use hundreds of networked, autonomous mobile robots that carry 

inventory-storing pods to human 

operators. The result is a distribution 

facility that is dynamic, selforganizing, and adaptive.

Various challenges had to be overcome in order to make this an economically viable system, ranging from 

design of robust autonomous mobile 

robots, real-time wireless control of 

hundreds of moving agents, the coordination of these agents, and the design of various algorithms 

that allow the system to adapt and reconfigure itself based on 

the environment and operating conditions. I will discuss these 

challenges and how they scale to future warehouses with thousands—not just hundreds—of mobile robots.

BIOGRAPHY 

Raffaello D'Andrea received the B.Sc. degree in Engineering Science from the University of Toronto in 1991, and the M.S. and 

Ph.D. degrees in Electrical Engineering from the California Institute of Technology in 1992 and 1997. He was an assistant, and 

then an associate, professor at Cornell University from 1997 to 

2007. He is currently a full professor of automatic control at ETH 

Zurich. He is also a founder of, and chief scientific advisor for, 

Kiva Systems.

He is a co-recipient of the 2008 IEEE/IFR Invention and Entrepreneurship Award, a United States Presidential Early Career Award 

for Science and Engineering, and was the faculty advisor and 

system architect of the Cornell Robot Soccer Team, four-time 

world champions at the international RoboCup competition in 

Sweden, Australia, Italy, and Japan. He is a recipient of two best 

paper awards from the American Automatic Control Council and 

the IEEE, a National Science Foundation Career Award, and several teaching awards in the area of project-based learning. A 

creator of dynamic sculpture, his work has appeared at various 

international venues, including the National Gallery of Canada, 

the Venice Biennale, the Luminato Festival, Ars Electronica, and 

ideaCity. 

PLENARY SPEAKER

Prof. Raffaello 

D’Andrea

ETH, Zurich, 

Switzerland

Towards a 

Ten 

Thousand 

Mobile Robot 

Warehouse

Mon. 8:30 am

ABSTRACT 

For robotic applications in hazardous, 

critical environments, the intelligence 

needed to provide functional value 

(i.e. reduced time, increased probability of detection, increased hazard 

source localization accuracy) cannot 

be derived from a single behavior 

(such as obstacle avoidance, mapping, or mine detection). Rarely do 

we find an integrated suite of capabilities that is capable of accomplishing an end-to-end mission. Intelligence requires not simply behavior, 

but also the ability to use behaviors 

effectively towards a highly complex 

set of real-world, mission-level requirements. If the level of robot initiative and autonomy used in real-world 

missions is to increase, the underlying mechanisms for behavior composition and human interaction must 

also change.

Many approaches to creating behaviors as well as orchestrating them 

have been offered by the community 

including a variety of machine learning based techniques. These 

methods and algorithms are often highly elegant, formalized 

methods intended to streamline the development and testing 

methodologies. Unfortunately, these all too often fail to provide 

truly intelligent systems that provide value in the real world. Why 

is this?

One clue may be found if we consider biology. Is there anywhere 

in biology where we can find an elegant, formalized, understandable method for behavior composition? Functional intelligence 

may be, in part, derived from many interwoven heuristics for sequencing and interleaving behavior. In the brain these heuristics 

are learned over time through experience and perhaps not in an 

elegant fashion. Artificial Neural Networks (ANNs) are intended to 

model the behavior derivations we find in biology, but although 

ANNs allow us to effectively capture particular perceptual and 

action pairings, we are still left with the fundamental problem of 

how to sequence and compose behaviors to get a real job done. 

Without this behavior composition, we may have capability, but 

enjoy meager intelligence.

Although this talk will not submit a solution to this fundamental 

challenge, I would like to share a variety of experiments which, 

over the past few years, have allowed us to metric various components of intelligence for mobile robots used in a variety of real 

world missions. These missions include chemical plume localization, radiological characterization, urban search and rescue, mine 

detection and defeat of improvised explosive devices. To accomplish end-to-end missions in the hands of operators with no 

or little experience with robots requires a means to fuse components of robot intelligence while hiding the behavioral complexity 

from the user.

PLENARY SPEAKER

Mr. David 

Bruemmer

5D Robotics, 

Inc., USA

Measuring 

the Benefits 

of Intelligent 

Behavior for 

Robotic 

Threat 

Detection

Mon. 2:00 pm

PerMIS 2009 ix
The Robot Intelligence Kernel (RIK) is being used to coalesce 

software components for perception, communication, behavior, 

world modeling, and human interaction into a single behavior 

architecture that can be easily transferred for use with a wide 

variety of robots and sensor-suited, low-level proprietary controls. This talk will discuss implementation strategies employed 

to integrate these components into a functional system that provides high-performance utility for various real-world tasks. Of 

particular interest is the cognitive glue, a fuzzy logic rule base, 

used to sequence and blend these behaviors into mission-level 

capabilities, such as minesweeping or radiological characterization. Lastly, the paper discusses agents within the interface that 

fuse various forms of robot and world representation. The interface agents also filter and interpret human input in order to incorporate it seamlessly into the behavioral intelligence of the 

robotic system. Our strategy is to hide sensor and behavior 

complexity while providing a means to integrate human intelligence at an appropriate level. In reviewing the benefits and limitations of the RIK approach, the talk will provide system-oriented 

results from recent hazard detection experiments. In particular, 

the talk will detail a number of measurements focused on the 

complete (i.e. human + robot + software + interface) system metrics as well as various component measurements. 

BIOGRAPHY

Mr. David J. Bruemmer is Vice President for Research and Development at 5D Robotics, Inc. where he is also a founder and 

board member. Prior to joining 5D Mr. Bruemmer was Technical 

Director for Unmanned Vehicles at the Idaho National Laboratory 

(INL.) For more than 14 years Mr. Bruemmer has enjoyed finding 

ways to fuse emerging science and engineering into innovative 

technologies that can change the way robots interact with humans and their environment. He has authored over 50 peer reviewed journal articles, book chapters and conference papers in 

the area of intelligent robotics. Mr. Bruemmer has been recognized by the President’s Office of Science and Technology Policy 

for his work to forge effective interagency research collaborations 

across the Federal government (e.g. NASA, Dept. of Energy, 

Dept. of Defense, Dept. of Commerce, Dept. of Homeland Defense). He is a winner of the R & D 100 Award, the Stoel Reeves 

Idaho Innovation Award and the Federal Lab Consortium Award 

for Excellence in Technology Transfer. 

The Robot Intelligence Kernel (RIK), developed by Mr. Bruemmer 

and his team, is being used as a framework for integrating robot 

software into a standardized, interoperable architecture. Mr. 

Bruemmer has developed robot behaviors used for a wide variety 

of robots for applications including remote characterization of 

high radiation environments, mine sweeping operations, military 

reconnaissance, IED defeat, chemical plume tracing and search 

and rescue operations. These efforts have yielded 11 Patents 

(Issued and Pending) and 10 copyrighted software inventions. 

His research in the area of countermine operations has demonstrated a four fold decrease in time necessary to find landmines 

and an improvement of over 20% in probability of detection 

when compared with the current military baseline. Before working at the INL, Mr. Bruemmer served as a consultant to the Defense Advanced Research Projects Agency, where he worked to 

coordinate development of autonomous robotics technologies 

across several offices and programs.

ABSTRACT

A robot observes the space within 

range of its sensors. In this “smallscale” space, it detects hazards 

and makes local motion plans. As it 

explores its global environment, it 

knits local spatial models together 

to build a cognitive map—a representation of the global structure of 

“large-scale” space that extends 

beyond the sensory horizon of the 

robot at any given time.

We have developed the Hybrid 

Spatial Semantic Hierarchy (HSSH), 

a model of the cognitive map that 

covers both large-scale and smallscale space, as experienced by the 

exploring robot. The key idea behind the HSSH is to combine the 

strengths of multiple different representations (ontologies) for space, 

each relatively simple: the Local Metrical, Local Topological, Global Topological, and Global Metrical 

maps.

This hierarchy of representations supports a relatively simple and 

robust way for the robot to construct a useful cognitive map from 

exploration experience. It also supports robust and efficient 

planning of routes from one place to another, as well as multiple 

ontologies for communication between a robot and a human 

directing it in how to reach a desired destination.

The structure of the HSSH allows us to factor the evaluation task 

into simpler elements. Each level of the hierarchy can be evaluated according to its ability to meet the needs of the other levels, 

and the hierarchy as a whole is evaluated according to the different ways it can meet the needs of the robot agent, and how well 

each of those ways is accomplished. As a result of this factoring, 

each component is easier to evaluate, and has a lower bar for 

successful performance.

BIOGRAPHY

Benjamin Kuipers joined the University of Michigan in January 

2009 as Professor of Computer Science and Engineering. Prior 

to that, he held an endowed Professorship in Computer Sciences 

at the University of Texas at Austin. He received his B.A. from 

Swarthmore College, and his Ph.D. from MIT. He investigates the 

representation of commonsense and expert knowledge, with 

particular emphasis on the effective use of incomplete 

knowledge. His research accomplishments include developing 

the TOUR model of spatial knowledge in the cognitive map, the 

QSIM algorithm for qualitative simulation, the Algernon system 

for knowledge representation, and the Spatial Semantic 

Hierarchy model of knowledge for robot exploration and 

mapping. He has served as Department Chair at UT Austin, and 

is a Fellow of AAAI and IEEE.

PerMIS 2009

PLENARY SPEAKER

Prof. Ben Kuipers

University of 

Michigan, USA

Evaluating the 

Robot 

Cognitive 

Mapper

Tues. 8:30 am 

x
ABSTRACT

The theme of the 2009 PerMIS is, 

“Does performance measurement 

accelerate the pace of advancement 

for intelligent systems?” Surely, 

performance measurement is 

necessary but not sufficient for the 

advancement of intelligent systems, 

and no measurement can 

compensate for badly designed 

performance tasks or for 

performance becoming an end in 

itself. AI is drunk on performing hard 

tasks at high levels. Given a choice 

between power and generality, most 

of us choose power. Our programs 

depend on designed exploits, or on 

designed search spaces in which 

programs can learn exploits. Divideand-conquer, specific function, 

power over generality, and exploits 

are valuable engineering methods in 

many disciplines. They are apt to 

build machines that do one thing 

well. Human intelligence isn't that kind 

of machine.

Fixing the current situation will require a disciplined stand against 

sophistication. It will require investments in general, child-like 

intelligence, and the investors might not see a return—high 

performance from cognitive systems—for some time. I think this 

is a deal worth making, both because it is likely to succeed and 

because the pursuit of high performance returns low dividends.

BIOGRAPHY

Paul Cohen is Professor and Head of Computer Science at the 

University of Arizona. Before that he worked at UMass Amherst 

and the USC Information Sciences Institute. His research is on 

planning, learning, cognitive development and language. He 

wrote a textbook on empirical methods for computer science 

and has worked on the evaluations of several DARPA programs, 

most recently PAL, Coordinators and Machine Reading.

ABSTRACT

Today, unmanned systems are operating in-theater with untested collaborative capabilities. The vehicles 

are heterogeneous, in that they are 

developed by different contractors, 

they have different levels of autonomy, they have different sensors and 

capabilities, and they are physically 

disparate. Unmanned air vehicles 

built by one contractor have never 

autonomously collaborated with unmanned sea surface vehicles built by 

another contractor, and no one knows 

how they would perform if deployed 

together today. Their integrated use, 

however, is rapidly growing in the 

military. As improvements in autonomy, sensing, and reasoning advance, collaborating, multi-vendor 

unmanned systems will be increasingly employed to support challenging, tactical operations. The anticipated increase in sophistication drives 

the need for an ability to robustly test, 

measure, and evaluate heterogeneous unmanned vehicles for full 

spectrum dominance and joint operations. We need to consider 

assessment methods to evaluate force-on-force and mission 

level the effectiveness of disparate unmanned systems collaborating in theater-wide scenarios. A key requirement for assessing 

autonomous unmanned systems is the realization that unmanned 

vehicles pose new challenges that are distinct from traditional 

approaches to assessing systems. These challenges stem from 

the upcoming capabilities of unmanned systems being able to 

autonomously collect and process data, turn it into valued information and knowledge, and then intelligently act upon it with 

little to no operator involvement. Autonomy at the individual vehicle level involves transitioning cognition into decisions that 

drive actions. Based on the mission or operational environment, 

these unmanned systems may execute behaviors that cannot be 

precisely predicted. Assessments need to support evaluation of 

autonomous vehicle actions and judge whether the actions are 

reasonable and acceptable, without having precisely quantifiable 

metrics. Evaluating these systems will focus more on capabilities 

and missions rather than mechanics. New approaches to measuring their effectiveness will be adopted to support advances in 

autonomy and cognition, where the metrics and methods evolve 

and adapt, just as the systems do.

BIOGRAPHY

Dr. Lora G. Weiss is a lab Chief Scientist at the Georgia Tech 

Research Institute, where she conducts research on the design, 

development, and implementation of autonomy and control for 

manned and unmanned systems. She has supported intelligent 

autonomy for unmanned underwater vehicles, unmanned air 

vehicles, and unmanned ground vehicles, and is currently 

engaged in research in exploring all aspects of the behavior of 

PerMIS 2009

PLENARY SPEAKER

Prof. Paul Cohen 

University of 

Arizona, USA

Against 

Sophistication: 

Why Worry 

About 

Performance 

Assessment

Tues. 2:00 pm

PLENARY SPEAKER

Dr. Lora Weiss 

Georgia Institute 

of Technology, 

USA

Assessing 

Autonomous 

Systems As 

They Evolve

Wed. 8:30 am

xi
these systems. Dr. Weiss has chaired sessions at IEEE 

conferences, ASA conferences, and Navy Symposiums and 

currently chairs the ASTM Standards Development 

Subcommittee F41.01, on Unmanned Maritime Vehicle 

Autonomy and Control. Dr. Weiss is on the Board of Directors for 

AUVSI, the world's largest non-profit unmanned systems 

organization. She has developed a video for IEEE Educational 

Services and has received several publication awards. Dr. Weiss 

has been Principal Investigator on numerous DoD programs 

sponsored by offices such as DARPA, the Office of Naval 

Research, and various Navy Program Executive Offices. She has 

provided over 150 technical briefs to high-ranking DoD officers 

and DoD technology offices. 

This video session serves as an introduction to the topic of 

developmental robotics (DR). It also serves to discuss some 

topics in the broader field of cognitive development, which can 

be explored by the DR research program. DR is a newly 

emerging interdisciplinary field that builds on 2 of the best tools 

we have to study cognition –robots and computer modeling. DR 

studies how autonomous robots can acquire/construct skills, 

processes & knowledge on their own, strictly through their 

interactions with the surrounding environment. A core idea is that 

intelligence is not solely explained by innate mechanisms that 

modularly organize the human brain. Instead the hypothesis is 

that much of intelligence/cognition results from a much dynamic 

process constructing cognitive ability through a long personal 

development involving “embodied interactions” in rich 

environments.

VIDEO SESSION

Dr. Gary Berg-Cross

 Knowledge Strategies, USA

Developmental Robotics in Theory and Action: 

a new way to Understand Cognition and Build 

Robots with Adaptive Abilities?

Mon. 12:45 - 1:15 pm

FOOD FOR THOUGHT: RELEASE OF WHITE PAPER

Prof. Erwin Prassler

 UAppSci. Bonn-Rhein-Sieg, Germany

The Use of Reuse for Designing and 

Manufacturing Robots

Tues. 12:45 - 1:30 pm 

ABSTRACT

How does the human brain represent 

meanings of words and pictures in 

terms of the underlying neural 

activity? This talk will present our 

research using machine learning 

methods together with fMRI brain 

imaging to study this question. One 

line of our research has involved 

training classifiers that identify which 

word a person is thinking about, 

based on their neural activity 

observed using fMRI. A more recent 

line involves developing a 

computational model that predicts 

the neural activity associated with 

arbitrary English words, including 

words for which we do not yet have 

brain image data. Once trained, the 

model predicts fMRI activation for 

any other concrete noun appearing in 

the text corpus, with highly 

significant accuracies over the 100 

nouns for which we currently have 

fMRI data. Professor Mitchell’s 

research was recently featured on a 

CBS 60 Minutes story “Reading your 

Mind.”

BIOGRAPHY 

Tom M. Mitchell is the E. Fredkin University Professor and head 

of the Machine Learning Department at Carnegie Mellon University. Mitchell is a past President of the American Association of 

Artificial Intelligence (AAAI), and a Fellow of the AAAS and of the 

AAAI. His general research interests lie in machine learning, artificial intelligence, and cognitive neuroscience. Mitchell's web 

home page is www.cs.cmu.edu/~tom.

BANQUET SPEAKER

Prof. Tom Mitchell

 Carnegie 

Mellon 

University, USA

How does 

Brain Activity 

Represent 

Word 

Meanings?

Tues. 7:00 pm 

Gaithersburg 

Hilton

PerMIS 2009

xii
PerMIS 2009 MONDAY 21

September

08:00 Welcome & Overview

08:30 Plenary Presentation: 

Raffaello D’Andrea

Towards a Ten Thousand Mobile Robot Warehouse

09:30 Coffee Break

10:00 MON-AM1 Model-based Performance Evaluation

Chairs: M. Ani Hsieh & Paul Evans

• Validating Extended Neglect Tolerance Model for Humanoid Soccer 

Robotic Tasks with Varying Complexities [Rajesh Elara, Carlos Acosta 

Calderon, Changjiu Zhou, Wijerupage Sardha Wijesoma]

• Modeling Multiple Human Operators in the Supervisory Control of 

Heterogeneous Unmanned Vehicles [Brian Mekdeci, Mary Cummings]

• Internal Model Generation for Evolutionary Acceleration of Automated 

Robotic Assembly Optimization [Jeremy Marvel, Wyatt Newman]

• Development of Top-Down Analysis of Distributed Assembly Tasks 

[Anthony Cowley, M. Ani Hsieh, C.J. Taylor]

• Context-Based Object Recognition [Shaun Edwards, Meredith Wright, 

Ben Abbott]

12:30 Lunch

12:45 - 13:15 Video Session (Gary Berg-Cross)

14:00 Plenary Presentation: 

David Bruemmer

Measuring the Benefits of Intelligent Behavior for Robotic 

Threat Detection

15:00 Coffee Break

15:30 MON-PM1 Performance Assessment and Reliability of Unmanned 

Systems

Chairs: Hui-Min Huang & Coire Maranzano

• A Mission Taxonomy-Based Approach to Planetary Rover CostReliability Tradeoffs [David Asikin, John Dolan]

• Towards a Systematic Assessment of the Functions of Unmanned 

Autonomous Systems [Robin Jaulmes, Eric Moline, Laurent Vielle]

• Performance Measures Framework for Unmanned Systems (PerMFUS):

Initial Perspective [Hui-Min Huang, Elena Messina, Adam Jacoff]

• Optimum Combination of Full System and Subsystem Tests for 

Estimating the Reliability of a System [Coire Maranzano, James Spall]

 PROGRAM PERMIS

xiii
PerMIS 2009 

September

08:00 Welcome & Overview

08:30 Plenary Presentation: 

Raffaello D’Andrea 

Towards a Ten Thousand Mobile Robot Warehouse

09:30 Coffee Break

10:00 MON-AM2 Special Session I: Performance Metrics for Sustainable 

Manufacturing

Organizers: Kevin Lyons, Mahesh Mani & Ram Sriram

• Manufacturing Unit Process Life Cycle Inventories (Uplci) 

[Michael Overcash, Janet Twomey, Jacqueline Isaacs]

• Conceptual Foundations of Energy Aware Manufacturing

[Soundar Kumara]

• Discrete Event Simulation to Generate Requirements Specification for 

Sustainable Manufacturing Systems Design

[Björn Johansson, Anders Skoogh, Mahesh Mani, Swee Leong]

• Towards A New Geometric Metric for Sustainability Assessment 

[Gaurav Ameta]

12:30 Lunch

12:45 - 13:15 Video Session (Gary Berg-Cross)

14:00 Plenary Presentation: 

David Bruemmer

Measuring the Benefits of Intelligent Behavior for Robotic 

Threat Detection

15:00 Coffee Break

15:30 MON-PM2 Special Session II: Test and Evaluation of Unmanned and 

Autonomous Systems

Organizers: Mauricio Castillo-Effen & Nikita Visnevski

• Unmanned and Autonomous Systems Mission Based Test and 

Evaluation [Philipp Djang, Frank Lopez]

• Modeling and Simulation for Unmanned and Autonomous System Test 

and Evaluation [Mauricio Castillo-Effen, Nikita Visnevski, Raj Subbu]

• Evolutionary Framework for Test of Autonomous Systems

[Raj Subbu, Nikita Visnevski, Philipp Djang]

• Metrics for Co-evolving Autonomous Systems [Jack Ring]

PROGRAM PERMIS

MONDAY 21

xiv
PerMIS 2009 TUESDAY 22

September

08:15 Overview

08:30 Plenary Presentation: 

Ben Kuipers

Evaluating the Robot Cognitive Mapper

09:30 Coffee Break

10:00 TUE-AM1 The Role of Robotics Competitions in Advancing Intelligent 

Systems

Chairs: Stephen Balakirsky & Jason Gorman

• The Role of Competitions in Advancing Intelligent Systems: A 

Practitioner’s Perspective [Elena Messina, Raj Madhavan, Stephen 

Balakirsky]

• Evaluating The RoboCup 2009 Virtual Robot Rescue Competition

[Stephen Balakirsky, Stefano Carpin, Arnoud Visser] 

• RoboCupRescue Interleague Challenge 2009: Bridging the Gap between 

Simulation and Reality [Alexander Kleiner, Chris Scrapper, Adam Jacoff]

• Mobile Microrobot Characterization through Performance-Based 

Competitions [Jason Gorman, Craig McGray, Richard Allen]

12:30 Lunch

12:45 - 13:30 Food for Thought: Release of White Paper

The Use of Reuse for Designing and Manufacturing Robots (Erwin Prassler)

14:00 Plenary Presentation:

Paul Cohen

Against Sophistication: Why Worry About Performance 

Assessment

15:00 Coffee Break

15:30 TUE-PM1 Ground Truth and Testbeds for Performance Testing

Chairs: Tsai Hong & Barry Bodt

• Data Collection Test-Bed for the Evaluation of Range Imaging Sensors 

for ANSI/ITSDF B56.5 Safety Standard for Guided Industrial Vehicles 

[William Shackleford, Roger Bostelman] 

• Ground Truth Data Using 3D Imaging for Urban Search and Rescue 

Robots [Nicholas Scott, Alan Lytle]

• Performance Measurements of Evaluating Static and Dynamic Multiple 

Human Detection and Tracking Systems in Unstructured Environments 

[Barry Bodt, Richard Camden, Harry Scott, Adam Jacoff, Tsai Hong, 

Tommy Chang, Rick Norcross, Anthony Downs, Ann Virts]

• Mathematical Metrology for Evaluating a 6DOF Visual Servoing System 

[Mili Shah, Tommy Chang, Tsai Hong, Roger Eastman] 

18:30 Banquet

19:00 - Banquet Speech

How does Brain Activity Represent Word Meanings? (Tom Mitchell)

 PROGRAM PERMIS

xv
PerMIS 2009 

September

08:15 Overview

08:30 Plenary Presentation: 

Ben Kuipers

Evaluating the Robot Cognitive Mapper

09:30 Coffee Break

10:00 TUE-AM2 Special Session III: Is an Agent Theory of Mind Valuable for 

Adaptive, Intelligent Systems?

Organizer: Gary Berg-Cross

• Is an Agent Theory of Mind (ToM) Valuable for Adaptive, Intelligent 

Systems? [Gary Berg-Cross]

• Towards a Simple Robotic Theory of Mind [Kyung-Joong Kim, 

Hod Lipson]

• Resilient Behavior through Controller Self-Diagnosis, Adaptation and 

Recovery [Juan Cristobal Zagal, Hod Lipson]

• Neurodynamics of Cognition and Consciousness [Robert Kozma, 

Walter Freeman]

• Theory of Mind, Computational Tractability, and Mind Shaping

[Tad Zawidzki]

12:30 Lunch

12:45 - 13:30 Food for Thought: Release of White Paper

The Use of Reuse for Designing and Manufacturing Robots (Erwin Prassler)

14:00 Plenary Presentation:

Paul Cohen

Against Sophistication: Why Worry About Performance 

Assessment

15:00 Coffee Break

15:30 TUE-PM2 Special Session IV: An Ontology for Robotics Science and 

Systems

Organizers: Erwin Prassler & Herman Bruyninckx

• Ontology Formalisms: What is Appropriate for Different Applications? 

[Craig Schlenoff]

• Universal Core Semantic Layer: A Roadmap to Semantic Interoperability 

[Lowell Vizenor, Barry Smith] 

18:30 Banquet

19:00 - Banquet Speech

How does Brain Activity Represent Word Meanings? (Tom Mitchell)

PROGRAM PERMIS

TUESDAY 22

xvi
PerMIS 2009 WEDNESDAY 23

September

08:15 Overview

08:30 Plenary Presentation: 

Lora Weiss

Assessing Autonomous Systems As They Evolve

09:30 Coffee Break

10:00 WED-AM1 Performance Measures for Mobile Robots

Chairs: Alan Bowling & Rolf Lakaemper

• Performance Measures of Agility for Mobile Robots [Alan Bowling, 

Shih-Chien Teng]

• Measuring Robot Performance in Real-time for NASA Robotic 

Reconnaissance Operations [Debra Schreckenghost, Terrence Fong, 

Tod Milam, Hans Utz]

• A Biologically Inspired Sensory Driven Method for Tracking Wind-Borne 

Odors [Brian Taylor, Brandon Rutter, Roger Quinn]

• A Confidence Measure for Segment Based Maps [Rolf Lakaemper]

• Evaluation of Robocup Maps [Benjamin Balaguer, Stefano Carpin, 

Stephen Balakirsky, Arnoud Visser]

12:30 Lunch

14:00 WED-PM1 Issues in Designing Intelligent Systems

Chairs: Danil Prokhorov & Satyandra Gupta

• Performance Measurement and Its Role in Advancement for Intelligent 

Systems: Discussion Points [Danil Prokhorov, Yasuo Uehara]

• Collective Intelligence: Toward Classifying Systems of Systems 

[Alan Ramsbotham]

• A Decision-Theoretic Formalism for Belief-Optimal Reasoning 

[Kris Hauser]

• Evaluation of Automatically Generated Reactive Planning Logic for 

Unmanned Surface Vehicles [Max Schwartz, Petr Svec, Atul Thakur, 

Satyandra Gupta]

16:00 Coffee Break

16:30 Adjourn

 PROGRAM PERMIS

xvii
PerMIS 2009 

September

08:15 Overview

08:30 Plenary Presentation: 

Lora Weiss

Assessing Autonomous Systems As They Evolve

09:30 Coffee Break

10:00 WED-AM2 Special Session V: TRANSTAC: Performance Evaluation of 

Speech Translation Systems for Military Applications

Organizers: Craig Schlenoff & Brian Weiss

• Evaluating Speech Translation Systems: Applying SCORE to TRANSTAC 

Technologies [Craig Schlenoff, Brian Weiss, Michelle Steves, Greg 

Sanders, Frederick Proctor, Ann Virts]

• Development and Internal Evaluation of Speech-to-Speech Translation 

Technology at BBN [David Stallard, Rohit Prasad, Prem Natarajan]

• The Impact of Evaluation Scenario Development on the Quantitative 

Performance of Speech Translation Systems Prescribed by the SCORE 

Framework [Brian Weiss, Craig Schlenoff]

• Probability of Successful Transfer of Low-Level Concepts via Machine 

Translation: A Meta-Evaluation [Greg Sanders, Sherri Condon]

• Automated Metrics for Speech Translation [Sherri Condon, Mark Arehart, 

Christy Doran, Dan Parvaz, John Aberdeen, Karine Megerdoomian, 

Beatrice Oshika]

• Utility Assessment in TRANSTAC: Using a Set of Complementary 

Methods [Michelle Steves, Emile Morse]

12:30 Lunch

14:00 WED-PM2 Special Session VI: Performance Measurements Towards 

Improved Forklift Safety

Organizer: Roger Bostelman 

• Fork Lift Awareness [Mark Austin]

• Where AGV's and Forklifts Roam: Preserving Operational Safety in a 

Shared Workspace [Richard Ungerbuehler]

• Performance Measurements Towards Improved Manufacturing Vehicle 

Safety [Roger Bostelman, Will Shackleford]

• White Paper: Towards Improved Forklift Safety [Roger Bostelman]

16:00 Coffee Break

16:30 Adjourn

PROGRAM PERMIS

WEDNESDAY 23

xviii
AUTHOR INDEX

Abbott, B. ..............MON-AM1

Aberdeen, J. ..........WED-AM2

Allen, R. ...................TUE-AM1

Ameta, G. ...............MON-AM2

Arehart, M. .............WED-AM2

Asikin,D. ................MON-PM1

Austin, M. ...............WED-PM2

Balaguer, B. ...........WED-AM1

Balakirsky, S. ..........TUE-AM1

Balakirsky, S. ...........TUE-AM1

Balakirsky, S. ..........WED-AM1

Berg-Cross, G. ........TUE-AM2

Bodt, B. ...................TUE-PM1

Bostelman, R...........TUE-PM1

Bostelman, R. ........WED-PM2

Bostelman, R. ........WED-PM2

Bowling, A. .............WED-AM1

Calderon, C. ..........MON-AM1

Camden, R. .............TUE-PM1

Carpin, S. ................TUE-AM1

Carpin, S. ...............WED-AM1

Castillo-Effen, M. ...MON-PM2

Chang, T. .................TUE-PM1

Chang, T. .................TUE-PM1

Condon, S. .............WED-AM2

Condon, S. .............WED-AM2

Cowley, A. ..............MON-AM1

Cummings, M. .......MON-AM1

Djang, P. .................MON-PM2

Djang, P. .................MON-PM2

Dolan, J. ................MON-PM1

Doran, C. ................WED-AM2

Downs, A. ................TUE-PM1

Eastman, R. .............TUE-PM1

Edwards, S. ...........MON-AM1

Elara, R. .................MON-AM1

Fong, T. ..................WED-AM1

Freeman, W. ............TUE-AM2

Gorman, J. ..............TUE-AM1

Gupta, S. ................WED-PM1

Hauser, K. ...............WED-PM1

Hong, T. ...................TUE-PM1

Hong, T. ...................TUE-PM1

Hsieh, M.A. ............MON-AM1

Huang, H.M. ..........MON-PM1

Isaacs, J. ...............MON-AM2

Jacoff, A. .................TUE-AM1

Jacoff, A. ...............MON-PM1

Jacoff, A. .................TUE-PM1

Jaulmes, R. ............MON-PM1

Johansson, B. ........MON-AM2

Kim, K.J. ..................TUE-AM2

Kleiner, A. ................TUE-AM1

Korzman, R. ............TUE-AM2

Kumara, S. .............MON-AM2

Lakaemper, R. ........WED-AM1

Leong, S. ...............MON-AM2

Lipson, H. ................TUE-AM2

Lipson, H. ................TUE-AM2

Lopez, F. ................MON-PM2

Lytle, A. ...................TUE-PM1

Madhavan, R. ..........TUE-AM1

Mani, M. .................MON-AM2

Maranzano, C. .......MON-PM1

Marvel, J. ...............MON-AM1

McGray, C. ..............TUE-AM1

Megerdoomian, K. .WED-AM2

Mekdeci, B. ...........MON-AM1

Messina, E. ............MON-PM1

Messina, E. ..............TUE-AM1

Milam, T. .................WED-AM1

Moline, E. ...............MON-PM1

Morse, E. ................WED-AM2

Natarajan, P. ...........WED-AM2

Newman,W. ...........MON-AM1

Norcorss, R. ............TUE-PM1

Oshika, B. ..............WED-AM2

Overcash, M. .........MON-AM2

Parvaz, D. ...............WED-AM2

Philipp, D. ..............MON-PM2

Prasad, R. ..............WED-AM2

Proctor, F. ...............WED-AM2

Prokhorov, D. .........WED-PM1

Quinn, R. ................WED-AM1

Ramsbotham, A. ....WED-PM1

Ring, J. ..................MON-PM2

Rutter, B. ................WED-AM1

Sanders, G. ............WED-AM2

Sanders, G. ............WED-AM2

Schlenoff, C. ............TUE-PM2

Schlenoff, C. ..........WED-AM2

Schlenoff, C. ..........WED-AM2

Schreckenghost, D.WED-AM1

Schwartz, M. ..........WED-PM1

Scott, H. ..................TUE-PM1

Scott, N. ..................TUE-PM1

Scrapper, C. ............TUE-AM1

Shackleford, W. ......TUE-PM1

Shackleford, W. ......WED-PM2

Shah, M. ..................TUE-PM1

Skoogh, A. .............MON-AM2

Smith, B. .................TUE-PM2

Spall, J. ..................MON-PM1

Stallard, D. .............WED-AM2

Steves, M. ..............WED-AM2 

Steves, M. ..............WED-AM2

Subbu, R. ..............MON-PM2

Subbu, R. ...............MON-PM2

Svec, P. ..................WED-PM1

Taylor, B. ................WED-AM1

Taylor, C.J. .............MON-AM1

Teng, S.C. ..............WED-AM1

Thakur, A. ...............WED-PM1

Twomey, J. .............MON-AM2

Uehara, Y. ...............WED-PM1

Ungerbuehler, R. ....WED-PM2

Utz, H. ....................WED-AM1

Vielle, L. .................MON-PM1

Virts, A. ....................TUE-PM1

Virts, A. ...................WED-AM2

Visnevski, N. ..........MON-PM2

Visnevski, N. ..........MON-PM2

Visser, A. ..................TUE-AM1

Visser, A. ................WED-AM1

Vizenor, L. ................TUE-PM2

Weiss, B. ................WED-AM2

Weiss, B. ................WED-AM2

Wijesoma, W. .........MON-AM1

Wright, M. ..............MON-AM1

Zagal, J.C. ...............TUE-AM2

Zawidzki, T. .............TUE-AM2

Zhou, C. .................MON-AM1

PerMIS 2009 xix
ACKNOWLEDGMENTS

These people provided essential support to make this event happen. Their 

ideas and efforts are very much appreciated.

Website and Proceedings

Debbie Russell

Local Arrangements

Jeanenne Salvermoser 

Jennifer Peyton

Conference and Registration

Mary Lou Norris

Angela Ellis

Teresa Vicente

Kathy Kilmer

PerMIS 2009 

Intelligent Systems Division 

Manufacturing Engineering Laboratory 

National Institute of Standards and Technology

100 Bureau Drive, MS 8230

Gaithersburg, MD 20899-8230 

http://www.nist.gov/mel/isd

Thank you 

PerMIS 

attendees!

xx
Validating Extended Neglect Tolerance Model for 

Humanoid Soccer Robotic Tasks with Varying 

Complexities 

Mohan Rajesh Elara 

Carlos A. Acosta Calderon, Changjiu Zhou 

Advanced Robotics & Intelligent Control Centre, 

Singapore Polytechnic, 

Singapore 139651 

MohanRajesh@sp.edu.sg 

Wijerupage Sardha Wijesoma 

Division of Control & Instrumentation, 

School of Electrical & Electronics Engineering, 

Nanyang Technological University, 

Singapore 639798 

eswwijesoma@ntu.edu.sg 

ABSTRACT

Estimating robot performance in human robot teams is a vital 

problem in human robot interaction community. In previous work, 

we presented extended neglect tolerance model for estimation of 

robot performance, where the human operator switches control 

between robots sequentially based on acceptable performance 

levels, taking into account any false alarms in human robot 

interactions. Task complexity is a key parameter that directly 

impacts the robot performance as well as the false alarms 

occurrences. In this paper, we validate the extended neglect 

tolerance model for two soccer robotic tasks of varying 

complexity levels. We also present the impact of task complexity 

on robot performance estimations and false alarms demands. 

Experiments were performed with real and virtual humanoid 

soccer robots across tele-operated and semi-autonomous modes of 

autonomy. Measured false alarm demand and robot performances 

were largely consistent with the extended neglect tolerance model 

predictions for both real and virtual robot experiments. 

Experiments also showed that the task complexity is directly 

proportional to false alarm demands and inversely proportional to 

robot performance. 

Keywords

Human robot teams, Robot performance, Task complexity, False 

alarm demand, Humanoid soccer robots, and Autonomy modes. 

1. INTRODUCTION 

Growing popularity and increasing viable application domains has 

contributed to greater presence of robots in the commercial 

marketplace. Many of these applications require humans and 

robots to interact closely and work together towards a common 

goal. Some real life examples of such scenarios include 

edutainment, service, rescue and surgical robots [1]-[3]. Robot 

autonomy is an essential component in these applications as it 

exempts human operators from the time intensive control and 

decision making processes. Most robotic applications for the 

commercial market can be categorized into tele-operation and 

semi-autonomous modes of autonomy. In tele-operation mode, the 

human operator guides the robot continuously until the given goal 

is accomplished. This mode requires complete attention of the 

human operator during the whole operation and every single 

decision is made by the operator and the robot has zero 

intelligence [4]. In semi-autonomous mode, the human and the 

robot collaboratively control parts of the functions required to 

accomplish the goal. The amount of functions left to the robot 

depends on the level of robot intelligence, in most scenarios the 

repetitive, low level tasks are handled by robots and only few high 

level tasks and decisions making steps are handled by humans [5]. 

Therefore, the operator work load is greatly reduced in semiautonomous mode as compared to tele-operation mode. The 

experiments presented in this paper were performed across teleoperation and semi-autonomous modes of autonomy. 

Robot performance in human robot teams is complex and multifaceted reflecting the capabilities of the robot(s), the operator(s) 

and the quality of interactions [6]. Neglect tolerance model 

presented in [7] is used as a general index for estimating robot 

performance in relation to autonomy in human robot interaction 

community. This model is employed in [8] to predict the 

optimized number of robots that should be utilized in human 

robot teams and robot system effectiveness. Neglect tolerance 

model is applied in [9] to estimate instantaneous robot 

performance, evaluate and compare three human robot interaction 

systems. Neglect tolerance model is applied in [10] to evaluate 

human robot interaction systems with special focus on the role of 

a collaborative workspace in enabling mixed initiative interaction 

between humans and heterogeneous teams of robotic vehicles. 

Neglect tolerance model is also adopted in [11] to derive model 

that approximates absolute autonomy and power in agent systems. 

Neglect tolerance model is extended in [12] to investigate human 

interaction in cooperating human robot teams within a realistically 

complex environment. Neglect tolerance model assumes ideal 

conditions while estimating performances, ignoring any false 

alarms due to erroneous interactions between the human operator 

and robot. But, in most real life applications erroneous 

interactions between the human operator and the robot are 

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1
common due to uncertainties in both human operators as well as 

in robots. These erroneous interactions lead to false alarms which 

can be classified into two categories namely, the false positives 

wherein a robot rejects a "correct" interaction and false negatives 

wherein a robot fails to reject an "incorrect" interaction. False 

alarms negatively impact the performance of human robot teams. 

This zero false alarm assumption results in a less accurate 

estimation of robot attention demand and robot performance, not 

only leading to the operator’s failure in accomplishing the task as 

scheduled due to higher attention demands in actual situation, but 

leading to operator’s inability to achieve the performance level set 

for that task due to the drop in performance attributed to the false 

alarms. In our earlier work [13] [14], we presented the extended 

neglect tolerance model to estimate robot performance taking into 

account the additional demands required due to false alarms. We 

also showed that extended neglect tolerance model offers better 

estimations of robot attention demand, and robot performances as 

compared to neglect tolerance model. For any robotics 

applications, task complexity is one of the critical factors directly 

impacting the performance of the robot and occurrence of false 

alarms in human robot teams. In our previous work, we only 

experimented and estimated robot performance using the extended 

neglect tolerance model over a single task complexity and the 

relationship to task complexity was ignored. But, extended neglect 

tolerance model can be applied to any robotics application 

irrespective of the task complexity, robot platform, or domain. 

Neglect tolerance and interface efficiency are proportional to the 

task complexity, therefore deriving the influence of latter on robot 

performance and false alarm demands is necessary for the robot 

operators to better gauge and optimize resources for the robot task 

on hand. In this paper, we validate the extended neglect tolerance 

model for two real and virtual humanoid soccer robotic tasks with 

varying complexity levels across two levels of robot autonomy. 

We also present the impact of task complexity on robot 

performance estimations and false alarms demands. 

In this paper, we will first discuss the extended neglect tolerance 

model. In Section III, we will present a brief description of our 

real and virtual Robo-Erectus Junior humanoid robots used in the 

experiments. In Section IV, we will present the experiments 

involving twenty test subjects to validate the extended neglect 

tolerance model for two tasks with varying level of complexities 

across two autonomy modes. Finally, Section V presents some 

concluding ideas. 

2. EXTENDED NEGLECT TOLERANCE 

MODEL 

Neglect tolerance model exploits neglect tolerance and interface 

efficiency parameters for estimating robot performance in human 

robot teams [15]-[17]. Neglect tolerance is a measure of how the 

robot’s performance drops over time when the robot is neglected 

by the user. Interface efficiency is a measure of how the robot’s 

performance varies over time when the robot is being serviced by 

the human operator. Neglect tolerance model assumes zero 

erroneous interactions during robot operation while estimating 

robot performance in human robot teams. But, in most real life 

situations uncertainties in both human operator and the robots 

result in erroneous interactions. For example, in a manipulator 

control task the human operator may select a incorrect co-ordinate 

points leading to a “false negative” as the manipulator would fail 

to reject the false interaction or there may be cases where the 

human operator select a correct co-ordinate points but the robot 

chooses a wrong co-ordinate points/ignores the human operator 

controls due to uncertainties in robot software/hardware leading to 

a “false positive” as the robot rejects a true interactions. 

To incorporate the demands due the false alarms, in our earlier 

work we extended the neglect tolerance model by introducing the 

notions false alarm time (FAT) and false alarm demand (FAD) as 

illustrated in Fig. 1. FAT is defined as the time spent over false 

alarm identification and robot performance recovery to the prefalse alarm level. 

IT NT

Acceptable 

performance

Performance 

Time

FATTO

FATSA

IT : Interaction Time 

NT : Neglect Time

FATTO : False Alarm Time (Tele-operation) 

FATSA : False Alarm Time (Semi-autonomous) 

IT

IT IT

 

Figure 1.Extended Neglect Tolerance Model for Measuring 

Robot Performance in Human Robot Teams 

The scenario depicted in Fig. 1 starts just after the operator starts 

to service the target robot. The robot performance increases with 

human operator servicing the robot over time and saturates at 

some point for both tele-operation and semi-autonomous 

autonomy modes. In Fig. 1, IT is the period of interaction between 

human operator and the robot, NT is the period of neglect where 

the human operator ignores the robot, FATTO is the time spent 

over false alarm identification and recovery for tele-operation 

mode and FATSA is the time spent over false alarm identification 

and recovery for semi-autonomous mode. Acceptable performance 

is the minimum performance level that can be tolerated by the 

operator for a given task. 

False alarms, both false negatives as well as the false positives 

negatively affect the robot performance, but the FATs are larger 

for semi-autonomous mode as compared to tele-operation mode 

due to the delay in the false alarm identification process in the 

latter. In tele-operation mode, as the operator controls the robot 

continuously any occurrence of false alarm is identified and 

rectified in a shorter time period whereas in semi-autonomous 

mode the operator controls the robot by specifying waypoints for 

the latter to navigate and so any false alarm that occurs during the 

neglect period can only be identified and rectified during next 

period of service thereby resulting in larger FAT. 

FAD is the additional demand placed on the robot operator due to 

false alarms, it is defined as: 

2
∑

∑

+

=

IT FAT

FAT

FAD (1) 

FAT in Eq. 1 can be expanded as: 

∑ ∑

∑ ∑

+ +

+

=

P N

N

IT FAT FAT

FAT FAT

FAD P

 (2) 

where, FATp is the false alarm time contributed by a false positive 

and FATn is the false alarm time contributed by a false negative. 

Robot attention demand (RAD) is the robot’s average 

performance over an interaction cycle [23] and extended neglect 

tolerance model redefines RAD as: 

∑

∑

++

+

=

NTIT FAT

IT FAT

RAD (3) 

Task complexity is a measure of difficulty level of the robot task 

and it remains as a key factor in deciding the robot performance 

level and number of occurrences of false alarms. In our 

experiments in this paper, the task complexity is only a function 

of static obstacle density. But, it can be further extended to 

include active obstacle density and terrain factors. We conducted 

experiments with our real and virtual humanoid soccer robots, 

Robo-Erectus Junior to validate the extended neglect tolerance 

model for two tasks with varying complexity levels across teleoperation and semi-autonomous modes of autonomy. 

3. ROBO-ERECTUS JUNIOR- A SOCCER 

PLAYING HUMANOID ROBOT 

This section introduces the Robo-Erectus Junior humanoid robot 

that we employed for our experiments for validating extended 

neglect tolerance model in this paper. Robo-Erectus Junior is one 

of the foremost leading soccer playing humanoid robots in the 

RoboCup humanoid leagues. The objective of the Robo-Erectus 

Junior development team is to develop a low cost humanoid 

platform for soccer robotics [18] and human robot interaction 

[19]. The mechanical structure, electronic control system and gait 

movement control of Robo-Erectus Junior has evolved through 

many stages to cope with the increasing complexity of the 

RoboCup humanoid leagues. Fig. 3 shows the physical design of 

Robo-Erectus Junior. 

Robo-Erectus Junior has been designed to cope with the 

complexity of a 3 versus 3 soccer game. Robo-Erectus Junior is 

equipped with three processors each for vision, artificial 

intelligence and control. Table 1 shows the specification of the 

processors used in Robo-Erectus Junior. The robot platform is 

equipped with three sensors: an USB camera to capture images, a 

tilt sensor to detect a fall, and a compass to detect their direction. 

The servomotors used send back the feedback data including 

angular positions, speed, voltage, and temperature. To 

communicate with its teammates, Robo-Erectus Junior uses a 

wireless network connected to the artificial intelligence processor. 

The vision processor performs recognition and tracking of objects 

of interest including ball, goal, field lines, goal post teammate and 

the opponents based on a blob finder based algorithm [20]. The 

further processing of detected blobs, wireless communications and 

decision making are performed by the artificial intelligence 

processor which selects and implements the soccer skills (like 

walk to the ball, pass ball, and dive) the robot is to perform. 

Figure 2.Robo-Erectus Junior, the Latest Generation of the 

Family Robo-Erectus 

Table 1. Processor Specification of Robo-Erectus Junior 

Features Artificial 

Intelligence 

Vision 

Processor

Control 

Processor

Processor Intel ARM 

XScale 

Intel ARM 

XScale 

ATMEL 

ATmega-128 

Speed 400Mhz 400Mhz 16Mhz 

Memory 16MB 32MB 4KB 

Storage 16MB 16MB 132KB 

Interface RS232, WIFI RS232, 

USB 

RS232, 

RS485 

Finally, the control processor handles the low level control of 

motor, based on the soccer skill selected by the artificial 

intelligence processor. Table 2 shows the physical specifications 

of Robo-Erectus Junior. It is powered by two high-current Lithium 

polymer rechargeable batteries, which are located in each foot. 

Each battery cell has a weight of only 110g providing 12v which 

means about 15 minutes of operation. 

Our Virtual-RE simulator was used to perform experiments with 

virtual Robo-Erectus Junior humanoid robots providing several 

possibilities of visualization and interaction with the simulated 

world [21]. Fig. 3 shows the virtual Robo-Erectus Junior 

humanoid robot and its environment. Virtual-RE simulator uses 

the Open Dynamics Engine (ODE) to simulate rigid body 

dynamics, which has a wide variety of features and has been used 

successfully in many other projects [22]. OpenGL libraries were 

used for both visualization and computation of imaging sensory 

3
information due to its effectiveness in accommodating modern 

hardware on a range of platforms. Client-server based architecture 

was adopted for the realization of the simulator as it allows 

halting and stepwise execution of the whole simulation without 

any concurrencies. It also permits detailed debugging of the 

executed robot software. The simulation kernel models the robots 

and the environment, simulates sensor readings, and executes 

commands given by the controller or the user. The graphic user 

interface not only serves as a tool for interaction between the 

robot and the user but also visualize the robot status and feedback 

information whereas the all behavioral controls are handled by the 

robot controllers. 

In each simulation step, the controller reads the available sensors, 

plans the next action, and sets the actuators to the desired states. 

Virtual-RE provides each robot with a set of simulated sensors, 

i.e. tilt, compass, gyroscopes, camera images, and motor feedback. 

The motor states are also simulated as in the real robot with 

feedback information that includes the joint angles as well as the 

velocities of motors. 

Figure 3. Robo-Erectus Junior in Virtual-RE simulator 

Table 2. Processor Specification of Robo-Erectus Junior 

Dimension Speed 

Weight 

Height Width Depth Walking 

3.2 Kg 480 mm 270 mm 150 mm 5 m/min 

4. EXPERIMENTS 

4.1 Experimental Design 

In these experiments, we validated the extended neglect tolerance 

model for two different levels of task complexities with our real 

and virtual Robo-Erectus humanoid robots across tele-operation 

and semi-autonomous modes of autonomy. We selected the task 

of navigating Robo-Erectus Junior in the soccer field towards a 

ball position. The robot and the ball were randomly placed in the 

soccer field. Operator used the graphic user interface to control 

the robots so as to navigate to the ball. Upon reaching the ball, the 

robot was placed at the initial position and the ball at another 

random position on the field for the next session. 

To validate the extended neglect tolerance mode for different task 

complexity levels, obstacles were placed in the path between the 

robot and the ball. The complexity was increased by increasing 

the number of obstacles. The secondary task for the operator was 

to control a second robot during the neglect time of the target 

robot so as to collect twice as much data per test session. 

4.2 Instantaneous Performance 

In this paper, we redefined the instantaneous performance 

presented in [23] to suit the task under study as ratio of current 

capability of the robot at a given time to the maximum capability 

of the robot. Instantaneous performance can take any value 

between 0 and 1. It is given by: 

)(

)(

)(

tM

tC

tP

C

C

I = (5) 

Where Cc(t) is the current capability of the robot at time t for a 

task, Mc(t) is the maximum capability of the robot or other objects 

at time t for the same task and PI(t) is the instantaneous 

performance at time t. In our experiments, the objective for the 

robot is to navigate to the ball position so the maximum capability 

would be the distance travelled by the robot moving optimally 

towards the ball position at top speed. We define maximum 

capability of the robot as: 

C KtM δ t = .)( (6) 

where t δ is a small interval of time and K is the maximum speed 

of the robot. Since, Robo-Erectus Junior humanoid robots can 

travel at the speed of 8.33cm per second, K value used was 8.33. 

The current capability of the robot is the actual distance travelled 

by the robot in the time t δ , 

t

C DDtC tt −δ

)( = − (7) 

where Dt

is the distance travelled by the robot at time t and 

t

Dt −δ

is the distance travelled by robot at time t- t δ . The 

instantaneous performance is computed as: 

t

tt

C

C

I

K

DD

tM

tC

tP

t

δ

δ

.)(

)(

)(

−

−

== (8) 

4
4.3 Participants & Procedure 

The test subjects were first trained on the use of the graphic user 

interface to control the humanoid robots. Sufficient training was 

provided until the test subjects felt confident in using the user 

interface upon which the test session with real and virtual robots 

across tele-operation and semi-autonomous modes of autonomy 

for the two tasks were conducted. We recruited 20 test subjects 

aged between 18 and 51 and each of them took part in two 10 

minute session with real and virtual robots, so a total of 80 test 

sessions were performed. Of the 40 sessions each with real and 

virtual robots, 20 were dedicated to the tele-operation mode and 

remaining 20 to the semi-autonomous mode. 

Out of the 20 test sessions for both the autonomy modes, 10 

sessions involved task 1 where the human operator navigated the 

robot to the ball with five obstacles in its path, and the other 10 

sessions were dedicated to task 2, where fifteen obstacles were 

placed in the path between the robot and the ball. In each test 

session, the operator first serviced the target robot to accomplish 

the task of navigating to the ball. After servicing the target robot, 

he/she switches to the secondary task of navigating the second 

robot to the ball. The operator performed the navigation task as 

many times as possible with the two robots during each ten 

minutes test session. The instantaneous performance 

measurements together with the time, operator controls, and robot 

state information were recorded for each test session. 

4.4 Results 

Fig. 4 shows the performance of the real and virtual robots for 

task 1 across tele-operation and semi-autonomous modes for all 

the twenty test subjects. False alarms were witnessed in 39 out of 

the total 40 test sessions with task 1 involving both real and 

virtual robots across tele-operation and semi-autonomous modes. 

A total of 68 false alarms were recorded out of which 22 were 

false negatives and 46 were false positives. Fig. 5 shows the 

performance of the real and virtual robots for task 2 across teleoperation and semi-autonomous modes for all the twenty test 

subjects. False alarms were witnessed in all the 40 test sessions 

with task 2. 

(a) 

(b) 

(c) 

(d) 

Figure 4.Robot Performance in Human Robot Teams for Task 

1: (a) Real Robot in Semi-autonomous, (b) Virtual Robot in 

Semi-autonomous, (c) Real Robot in Tele-operation and (d) 

Virtual Robot in Tele-operation. 

5
A total of 103 false alarms were recorded out of which 35 were 

false negatives and 68 were false positives. The false positives 

were mainly due to errors in the graphic user interaction scheme, 

software faults in robot's control and artificial intelligence 

modules, and hardware failures in sensor/actuator systems. The 

false negatives were mainly due to the human error pertaining to 

lack of understanding of the interaction scheme, and the task of 

interest. As postulated in the extended neglect tolerance model, 

the FADs for tele-operation were found to be shorter than those 

for semi-autonomous experiments for both task complexities. The 

performance results for real and virtual robots were found to 

follow similar pattern for both task complexities across both teleoperation and semi-autonomous modes. From the figures, it is 

evident that due to the increased complexity in task 2 attributed to 

the presence of additional ten more obstacles in the path of the 

robot, the performance has significantly dropped in both real and 

virtual experiments. 

(a) 

(b) 

(c) 

(d) 

Figure 5. Robot Performance in Human Robot Teams for Task 

2: (a) Real Robot in Semi-autonomous (b) Virtual Robot in 

Semi-autonomous. (c) Real Robot in Tele-operation Mode, and 

(d) Virtual Robot in Tele-operation Mode 

For both the tasks complexities, tele-operation mode is efficient in 

increasing the performance of the robot upon servicing after a 

neglect period as compared to point to point mode. The robot 

performance dropped abruptly to zero within 2 seconds of neglect 

period for both the tasks across tele-operation experiments 

whereas the performance drop during neglect period was more 

gradual for both the tasks across semi-autonomous experiments. 

During the neglect period, the rate of performance drop was 

slower in semi-autonomous mode as compared to tele-operation 

mode. Occurrence of false alarm degrades performance in all the 

experimental cases. The performance drop due to false alarms are 

more prominent in semi-autonomous mode as compared to teleoperation mode as the period for performance recovery to prefalse alarm level is shorter in latter. The trend of the graphs in Fig. 

4, and Fig. 5 validate the extended neglect tolerance model for 

varying levels of task complexities. From the figures, it is evident 

6
that irrespective of the task complexity the tele-operation mode 

requires the operator to interact continuously with the robot as in 

the case of neglect the robot performance drops rapidly to zero. 

We also computed and compared the FADs for real and virtual 

robot experiments across the two autonomy modes for both the 

tasks. Fig. 6 shows the average FADs for different experimental 

cases. The mean FAD for task 2 in semi-autonomous mode was 

highest for both real and virtual robot experiments. It is clear from 

the figure that increasing task complexity increases the number of 

occurrence of false alarms and therefore resulting in higher FAD. 

The number of false alarms increased from 68 in task 1 to 103 in 

task 2, in specific the increase in false positives was more 

prominent and the results for real and virtual robot experiments 

followed the similar patterns. FADs can be used as a performance 

metric to gauge the additional operator efforts required for tasks 

of varying complexities and scenarios. Table 3 presents the 

percentage performance drop in task 2 in comparison to task 1 for 

all experimental cases. 

Table 3. Percentage Performance Drop in Task 2 in 

Comparison to Task 1 For All Experimental Cases 

Experimental Cases Performance Drop 

Real Robot Tele-operation 6.78% 

Virtual Robot Tele-operation 10.75% 

Real Robot Semi-autonomous 12.71% 

Virtual Robot Semiautonomous 11.27% 

From, the table it is evident that an increase in task complexity 

results in performance drop and deriving this relationship for task 

of interest can aid robot operator optimize resources and time. 

5. CONCLUSION 

In this paper, we validated the extended neglect tolerance model 

for two robot navigation tasks with different complexity levels 

across tele-operation and semi-autonomous modes of autonomy. 

Results of our experiments with real and virtual robots were 

largely consistent with the proposed extended neglect tolerance 

model predictions for both tasks across the two autonomy modes. 

FADs were found to be directly proportional to the task 

complexity, as the results showed that an increase in task 

complexity resulted in an increase in FAD. Irrespective of the task 

complexity, FADs were found to be higher in semi-autonomous 

mode as compared to tele-operation mode for experiments with 

real and virtual robots. Results from both the tasks showed that 

tele-operation mode offers higher robot performance than semiautonomous mode but the latter requires lower RAD and offers 

better performance deterioration rate during neglect times. The 

experiments in this paper were limited to a human operator 

navigating a single robot towards a randomly placed ball. Future 

work would include extending these results to estimating robot 

performance in multi-robot teams involving homogeneous and/or 

heterogeneous robots working together with a human operator 

towards accomplishing tasks of varying complexities. A second 

possibility of future work is to use FAD as a metric to compare 

performances of robot platforms, autonomy modes and interaction 

schemes for tasks of varying complexities. Another possibility of 

future work is to study the effects of task complexities on robot 

performances and FADs for multi-tasking problems. 

Figure 6. Average FADs for different experimental cases

7
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[12] J. Wang, “Human control of cooperating robots,” Ph. D 

dissertation, University of Pennsylvania, Pennsylvania, 

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“Experimenting false alarm demand for human robot 

interactions in humanoid soccer robots” International Journal 

of Social Robotics, Accepted for publication. 

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"False alarm demand: A new metric for measuring robot 

performance in human robot teams", in Proc. IEEE 

International Conference on Autonomous Robots and 

Agents, New Zealand, 2009. 

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Tolerant Teaming: Issues and Dilemmas", in Proc. AAAI 

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Systems in Complex Environments, U.S.A, 2003. 

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Schultz, and M. A. Goodrich, “Common metrics for human 

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and L. Hu, "Modelling human humanoid robot interaction in 

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Interactive Communication, Germany, 2008. 

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Hu, and B. Iniya, "An embedded vision system for soccer 

playing humanoid robot: Robo-Erectus Junior", in Proc. 

IEEE International Conference on Signal Processing, 

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8
Modeling Multiple Human Operators in the Supervisory 

Control of Heterogeneous Unmanned Vehicles 

Brian Mekdeci 

Massachusetts Institute of Technology 

77 Massachusetts Ave, Rm 33-407 

Cambridge, MA, 02139 

1-617-452-3044 

mekdeci@mit.edu

ABSTRACT 

In the near future, large, complex, time-critical missions, such as 

disaster relief, will likely require multiple unmanned vehicle (UV) 

operators, each controlling multiple vehicles, to combine their 

efforts as a team. However, is the effort of the team equal to the 

sum of the operator’s individual efforts? To help answer this 

question, a discrete event simulation model of a team of human 

operators, each performing supervisory control of multiple 

unmanned vehicles, was developed. The model consists of 

exogenous and internal inputs, operator servers, and a task 

allocation mechanism that disseminates events to the operators 

according to the team structure and state of the system. To 

generate the data necessary for model building and validation, an 

experimental test-bed was developed where teams of three 

operators controlled multiple UVs by using a simulated ground 

control station software interface. The team structure and interarrival time of exogenous events were both varied in a 2x2 full 

factorial design to gather data on the impact on system 

performance that occurs as a result of changing both exogenous 

and internal inputs. From the data that was gathered, the model 

was able to replicate the empirical results within a 95% 

confidence interval for all four treatments, however more 

empirical data is needed to build confidence in the model’s 

predictive ability. 

Categories and Subject Descriptors

I.6.3 [Computing Methodologies]: Simulation and Modeling – 

applications. 

General Terms

Performance, Experimentation, Human Factors. 

Keywords

Discrete event simulation, human factors, modeling, team 

performance, supervisory control, unmanned vehicles. 

M.L. Cummings 

Massachusetts Institute of Technology 

77 Massachusetts Ave, 33-311 

Cambridge, MA, 02139 

1-617-252-1512 

missyc@mit.edu

1. INTRODUCTION 

Unmanned vehicles (UVs) are currently in use for numerous 

military operations, but they are also being considered for many 

non-military applications as well, including mining, fighting 

forest fires, border patrol and supporting police [1]. Currently, 

several human operators are required to control many of today’s 

UVs, but futuristic systems will invert the operator-to-UV ratio so 

that one operator can control multiple UVs [2]. To accomplish 

this goal, the level of automation will have to increase such that 

operators will give high-level, supervisory instructions to the UVs 

instead of manual control [3]. However, previous research has 

shown that even under supervisory control, there is a cognitive 

limit as to the number of UVs a single human operator can 

effectively manage [4, 5]. Large, complex, time-critical 

missions, such as disaster relief, will likely exceed that limit and 

will require multiple operators, each controlling multiple UVs, to 

combine their efforts. Since such systems do not currently exist, 

many questions arise, including: (1) How many operators are 

necessary to achieve a set of mission objectives? (2) How should 

the operators combine their efforts in the most effective way? (3) 

Will the group performance be more than, equal to, or less than 

the sum of the individual contributions? 

2. RESEARCH OBJECTIVE 

The goal of this research is to develop a quantitative model of a 

team of human operators, each performing supervisory control of 

multiple unmanned vehicles, in time-critical environments. This 

model would allow stakeholders, such as vehicle designers and 

battlefield commanders, to vary input parameters, such as vehicle 

speed and number of human operators, in order to determine their 

impact on system performance. 

3. PREVIOUS RESEARCH 

3.1 Queuing Model of Supervisory Control of 

Unmanned Vehicles 

Supervisory control of unmanned vehicles involves an operator 

handling intermittent events via an automated system by giving 

high-level commands to UVs. As such, supervisory control of 

unmanned vehicles has been previously modeled as a queuing 

system where the vehicles requesting assistance are regarded as 

users and the human operators are regarded as servers [6]. For 

instance, in a simple surveillance scenario whose timeline is 

shown in Figure 1, an unidentified contact suddenly emerges at 

time t. This event, labeled A, requires that the operator perform a 

task, in this case, assign an UV to the contact location for further 

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9
investigation. Since this event is not directly controllable by the 

operator or vehicle, it is considered to be an exogenous event to 

the system. Ideally, the operator would notice this event and start 

“servicing” it immediately by performing the associated task. 

However, because of inherent inefficiencies of human attention, 

the operator will inadvertently introduce a delay between the 

arrival of this event and the moment he starts to service it (marked 

by event B in the timeline). This delay is due to a combination of 

the Wait Time due to loss of Situational Awareness (WTSA) and 

the Wait Time due to Interaction (WTI) [4]. WTSA occurs when 

the operator is not aware that the event requires his attention, 

whereas WTI occurs when the operator has noticed the event, but 

has not measurably started the associated task yet (perhaps due to 

deciding between the right course of action from a number of 

options). Since it is extremely difficult to separate WTSA from 

WTI, the measured time between when an event emerges and 

when the operator starts the associated task (assuming the 

operator is not busy and has the resources available to service the 

event) will be considered WTOD – wait time due to operator 

delay. Cummings and Mitchell [4] have shown that this delay can 

be quite significant particularly when operators are controlling 

multiple vehicles simultaneously and have degraded situational 

awareness. 

Figure 1: Timeline of events for simple UV scenario. 

The task of assigning a vehicle to a location also takes a finite 

amount of time known as the Service Time (ST). At the moment 

when the operator finishes assigning a vehicle (C in Figure 1), that 

vehicle will begin to travel the assigned location. The time 

during which the vehicle is travelling is referred to as the Travel 

Time (TT) and in this scenario also represents the Neglect Time 

(NT) of the vehicle, since the vehicle acts autonomously during 

this period without requiring the operator’s attention [7]. After 

some time, the vehicle will eventually arrive at the contact 

location, denoted by event D. Similar to the time between A and 

B, the vehicle must wait a finite period of time before the operator 

begins to interact with the vehicle’s camera, denoted by event E. 

Finally, after another service time, the operator finishes 

identifying the contact (labeled event F) which may more may not 

spawn additional endogenous events, depending upon the 

scenario. If the final objective of the operator is to simply identify 

unknown contacts, then the difference in time between event F 

(when the final objective is met) and event A (when the contact 

emerged) is known as the Objective Completion Time (OCT). 

Since time is of the essence in many UV applications, the goal of 

many UV system designers and decision makers it to minimize 

the average OCT for a given scenario. 

3.1.1 Multiple Event Handling 

3.1.1.1 Wait Time due to Queuing 

If an operator is busy interacting with a vehicle and another event 

emerges that requires the operator’s attention, then that event must 

wait for the operator to become available. This additional time, 

not represented in Figure 1, is known as the wait time due to 

queuing (WTQ) since the event is considered to be in the queue 

for the operator’s attention. Since vehicles tend to produce 

endogenous events (such as requiring new waypoints when they 

have reached the old ones), as the number of vehicles or 

exogenous events in the system increases, the probability of an 

event experiencing WTQ grows. Additionally, it has been shown 

that operators may take longer to respond to events as they 

emerge due to high workload and a loss of situational awareness 

[4]. Thus, as more events require the operator’s attention, the 

OCT will continue to grow until it reaches an unacceptable level, 

at which point a team of multiple operators will likely be required. 

3.1.1.2 Switching Strategy 

If more than one event is in the operator’s queue, the operator 

must select which event he will service next. There are several 

strategies an operator can use, including first-in-first-out (FIFO), 

highest-priority-first or even random selection. Switching strategy 

affects the total time tasks spend waiting for service not only 

because of the ordering of the tasks (queuing policy), but also 

because of the time required for the mental model change of the 

operator (switching cost) if the tasks are dissimilar [8]. It has 

been demonstrated that for operators of multiple, unmanned 

vehicles, the switching cost can be substantial [9]. 

3.2 Single Operator Discrete Event 

Simulation Model 

Solving traditional queuing models can yield results of interest to 

the study of supervisory control such as the average time an event 

will spend waiting in a queue and server (operator) utilization. 

Although analytical solutions are possible for simple supervisory 

control systems, often the assumptions required for closed-form 

solutions, such as steady-state behavior and independent arrivals, 

are not met. Discrete event simulations (DES) overcome many of 

the limitations of analytical models by using computational 

methods that do not require such strict assumptions [10] and 

therefore allow a richer set of complex UV-operator systems to be 

modeled. 

A single human operator controlling heterogeneous unmanned 

vehicles was successfully modeled using a Multi-UV Discrete 

Event Simulation (MUV-DES) model [8]. A Multi-UV, MultiOperator Discrete Event Simulation (MUVMO-DES) model that 

builds upon this work, but also considers multiple operators 

combining their efforts, is the focus of this research. This new 

model consists of exogenous and internal inputs, operator servers 

and their interactions, and a task allocation mechanism that 

disseminates events to the operators according to the team 

structure and state of the system. The inputs to the model are both 

exogenous, such as the arrival rate of new contacts, and also 

internal, such as the length of time an operator spends interacting 

with a vehicle. These inputs are also stochastic due to the large 

amount of uncertainty in environmental conditions and human 

behavior. 

4. METHODS 

4.1 Multi-UV, Multi-Operator Discrete Event 

Simulation Model 

Expanding the MUV-DES model to multiple operators required 

several new considerations, in particular a model of team 

10
communication, mutual performance monitoring and task 

allocation. 

4.1.1 Modeling Communication 

Geographically-disperse UV operators communicate through 

voice, chat or a combination of both. Voice communication is 

typically the fastest and allows operators the ability to control the 

UVs while simultaneously communicating via a headset. Voice 

communication is effective for small teams but can become 

problematic as the number of operators becomes large, due to 

multiple voice messages that occur simultaneously. Thus, voice 

communications are typically serial in nature, meaning only one 

operator can speak at a time. Chat messages allow operators to 

send messages to each other asynchronously and in parallel. Due 

to software’s ability to parse text and apply sorting filters in realtime, chat communication often scales well with large teams. 

Chat messages also tend to be clearer than voice communication, 

in that they are not as susceptible to noisy communication 

channels, background noise, volume or operator accents. 

Furthermore, chat messages automatically create a real-time 

transcript of the communication, something that is typically not 

possible with voice. For the initial MUVMO-DES model, 

communications are assumed to be chat for data gathering 

purposes, but given the widespread use of chat by operational 

command and control personnel, this assumption also carries 

external validity. Modeling voice communications is left for 

future work. 

4.1.2 Mutual Performance Monitoring 

In addition to explicit communications, operators may also 

coordinate by mutual performance monitoring, recognized as one 

of the core components of teamwork [11]. Through a user 

interface, operators can typically view each other’s vehicles and 

commands to gain situation awareness of what the team is doing. 

For instance, instead of explicitly communicating, an operator 

may take a quick look at the interface to see if any other 

operator’s vehicles are already heading to a new contact before 

assigning their own. However, because this form of coordination 

is unilateral, teammates must make assumptions about the actions 

and intentions of other teammates which may or may not be valid. 

4.1.3 Modeling Coordination 

Communication and mutual performance monitoring can be 

represented by discrete endogenous events that the operators 

generate. For instance, in Figure 2, instead of servicing an event 

once it arrives (event A), an operator may choose to send a chat 

message to other operators by first starting a chat message, 

composing it for a finite period of time (labeled COORD) and 

then sending it before starting to service the task (event C). 

Similarly, an operator may perform a mutual performance 

monitoring task that also takes a finite period of time. However, 

if an operator is composing a chat message or monitoring the 

performance of other operators, then the operator is considered to 

be busy and as such, any event that is waiting for the operator’s 

attention while he is communicating or monitoring will incur a 

WTQ for that period of time. This additional WTQ represents a 

quantitative measurement of the coordination cost (process loss) 

associated with the team performance. 

The timeline shown in Figure 2 is a simple example of 

coordination but more complex coordination scenarios exist as 

well. For simple tasks, a single communication message may be 

all that is needed, such as claiming responsibility for a target that 

emerges. For more complex tasks, the communication may 

involve a conversation that spawns several iterations of 

communication messages. This initial model will only assume 

single communication messages and as such, will only be able to 

model simple coordination between the team members. 

Figure 2: Timeline of events with coordination. 

4.1.3.1 Coordination Strategies 

Similar to switching strategies, an operator will also have a 

coordination strategy that dictates the type and timing of the 

coordination he will perform when faced with a task that can be 

serviced by more than one operator. One such strategy is to not 

coordinate at all, but this would require the team to have 

predefined roles and responsibilities (such as mechanistic teams) 

or run a high risk of task allocation errors. A task allocation error 

occurs when more than one operator or no operator attempts to 

service a particular task. 

If an operator choose to coordinate her actions, she typically must 

choose the type of coordination first, i.e. whether or not to 

communicate, monitor or both. In addition to the type of 

coordination, the timing of the coordination is very important as 

well. A common strategy would be to coordinate first and then 

service the task. This type of coordination strategy is the least 

likely to incur task allocation errors. This coordination strategy 

was assumed for the initial MUVMO-DES model. However, other 

coordination strategies exist. For instance, an operator could 

service the task first and then send a courtesy message to other 

operators. This strategy allows the operator to give the fastest 

response to an event, but raises the possibility that another 

operator will also begin servicing the task before the first operator 

gets a chance to send the coordination message. 

4.1.3.2 Team Structure and Task Allocation 

Although the model was designed to be general and handle a 

variety of team structures, mechanistic and organic teams 

structures were chosen to be modeled initially since they represent 

two polar opposites of the organizational spectrum [12]. A 

mechanistic team is one where the operators have rigidly defined 

roles and responsibilities. For instance, when all of the vehicles of 

one type are assigned to one and only one operator, then that 

operator is given the full responsibility for performing the tasks 

that only that vehicle can do. If one of each vehicle type is 

allocated to each operator instead, then that team structure would 

be considered organic since any operator can perform any task 

that arises, provided that he has an appropriate vehicle available. 

Both team structures suffer from inefficiencies, or what Steiner 

[13] refers to as a “process loss” which is the differential between 

the performance of a team and the theoretical maximum achieved 

if the efforts of the individuals were combined ideally. In 

mechanistic teams, process loss occurs when task loads are 

uneven and some operators are too busy while others are idle. In 

organic teams, process loss occur when operators have to spend 

11
time coordinating how they will share the common queue and/or 

allocate the tasks amongst themselves in a sub-optimal manner. 

Due to the clear task allocation roles, extending the MUV-DES 

model for mechanistic teams involved having a separate queue 

and server for each operator. Since each task was unique to an 

operator, every event that arose was automatically assigned to the 

appropriate operator. 

For the organic team, a different task allocation mechanism was 

needed. Since the model is merely an abstraction of the actual 

scenario, the first attempt at an organic model randomly assigned 

the tasks to the operators based on who was available at that 

moment to service the event. If more than one operator was 

available, the event was randomly assigned to one of the available 

operators. If no operator was available, the event waited in a 

common queue (incurring a WTQ cost) until an operator became 

available. This form of modeling assumes that there will be no 

task allocation errors, i.e. one and only one operator will service 

or attempt to service any particular task. In real organic teams, 

this will likely only happen if the teams coordinate their actions 

through communication or mutual performance monitoring. 

4.2 Data Gathering 

The MUVMO-DES model utilizes stochastic processes to account 

for the uncertainty within the system. Therefore, random values 

are drawn for WTOD, service time, communication time, 

monitoring time, travel times and travel time in the model. These 

probability density functions (pdfs) need to be generated by 

binning empirical data into histograms and fitting an appropriate 

curve. 

To generate the stochastic inputs necessary for model building 

and to validate the model’s outputs against actual team 

performance metrics, real data must be gathered. Since there are 

no extant systems of teams of operators each controlling multiple 

unmanned vehicles, there is no “real world” data to collect. 

Hence, an experimental test-bed where teams of operators 

controlled multiple UVs was specifically developed and 

experimental trials were conducted to gather the data used for 

model building and validation. 

Figure 3: Main display of the ground control interface. 

4.2.1 Experimental Test-Bed 

The experimental test-bed consisted of a video game-like 

simulation of unmanned vehicle control by a team of operators. 

The simulation included three ground control stations, with one 

subject assigned to each station. 

4.2.2 Ground Control Interface 

Subjects interacted with the ground control stations via a 

computer monitor display using standard keyboard and mouse 

inputs. The main display of the ground control station featured 

three sections – a large map, a chat panel and a system panel 

(Figure 3). The map represented the geographical area that the 

operators were responsible for, as well as all the vehicles under 

their control and contacts that they needed to handle. Contacts and 

vehicles were represented using MIL-STD-2525B icons [14] and 

the operators assigned vehicles to contacts by clicking on the map 

interface with the mouse. The operators were also able to 

communicate with each other via instant messaging within the 

chat interface window. Operators would type messages into the 

chat, which would then appear on all the other operator’s chat 

panels instantly. Chat messages were labeled with the operators 

unique IDs, which corresponded to the labels for each operator’s 

vehicle icons. In addition to the map and chat display, there was 

also a system panel where the system would occasionally send 

messages to a particular operator, such as a confirmation message 

that the operator had assigned a particular vehicle to travel to a 

particular location. 

4.2.3 Tasks 

Each mission scenario required a team of operators to “handle” 

contacts that appear intermittently over the map. To do this, the 

team of operators needed to perform both assignment and payload 

tasks. 

4.2.3.1 Assignment Tasks 

Assignment tasks required the operators to send their vehicles to 

the contacts on the map as they emerged. Once assigned, the UV 

would start to travel to that particular contact location on the map 

in a straight line and would continue until either the vehicle 

reached its assigned destination or the operator re-assigned the 

vehicle elsewhere. There were no obstacles on any of the maps 

and no path-planning required. 

Although assignments were done by individual operators, they 

can be considered a “team task” since the operators had to 

coordinate their assignments to ensure that one and only one 

vehicle was assigned to each and every contact. Furthermore, 

subjects were instructed that vehicles should be chosen in the 

interest of minimizing travel times, i.e. typically the closest 

available vehicle to the contact location. 

4.2.3.2 Payload Task 

Once a vehicle reached a contact, the operator performed a simple 

task by interacting with the vehicle’s payload. This task was 

unique to the vehicle and contact type, but involved either visual 

identification (e.g., where is the red truck in the parking lot?) or a 

simple hand-eye coordination task. Since all three vehicles were 

aerial of some sort, all payload tasks involved a birds-eye view of 

the terrain. An example of a hand-eye coordination task is shown 

in Figure 4 where the operator must destroy a contact by centering 

the crosshairs over a stationary target on the ground and pressing 

the fire button three times. The difficulty in this task was that the 

crosshairs are subject to jitter due to the motion of the UV. The 

other hand-eye coordination task involved dropping aid packages 

to victims on the ground. This task was similar to the destruction 

task except that the crosshairs were steady but the projectiles were 

slow-falling and susceptible to the wind. Thus, players had to 

compensate for a light north-east wind, for instance, by aiming 

packages slightly to the southwest of the target location and 

12
pressing the drop button once. Payload tasks are considered an 

“individual task” as they do not require any coordination or 

assistance from any of the other operators. 

Figure 4: Missile firing payload task. 

4.2.3.3 Scenario Objectives 

The objective of each scenario was to identify all unidentified 

contacts and either rescue them (if friendly) or destroy them (if 

hostile) as quickly as possible. There were three vehicle types, 

one that handles each type of contact (unidentified, friendly, 

hostile) exclusively. Although any UV of the appropriate type 

could be assigned to a contact, only the first vehicle to start the 

payload task could successfully complete it. When a contact first 

appeared on the map, it was always of the unidentified type, 

which required a scouting UV (Type A). Once the scouting UV 

arrived, the operator performed a visual identification task which 

transformed the contact from unidentified to either hostile or 

friendly. If the contact was identified as being hostile, a tactical 

UV (Type B) was sent by an operator to the contact location to 

destroy it via the missile firing task. Similarly, if an unidentified 

contact was identified as being friendly, a rescue UV (Type C) 

was sent by an operator instead to drop aid packages to the 

contacts’ location, thereby “rescuing” the contact. The time a 

contact spent in the system, from the moment it arrived, until the 

moment it was successfully handled, was the objective completion 

time. Since a scenario consisted of multiple contacts, the Average 

Objective Completion Time (AOCT) was the metric of interest, 

where the average was simply the mean of all the OCTs for that 

scenario. 

4.2.3.3.1 Design of Experiments 

A 2x2 repeated measures experiment was conducted where the 

independent variables were team structure (mechanistic, organic) 

and the inter-arrival time of unidentified contacts (constant, 

erratic). Ten teams of three participants each completed all four 

treatments. The order of trials was counter-balanced and randomly 

assigned to the teams. An alpha value of 0.05 was used for 

significance. 

4.2.4 Independent Variables 

4.2.4.1 Inter-Arrival Times of Exogenous Events 

Previous research has demonstrated that optimal UV operator 

performance occurs when the operator has a utilization lower than 

70% [15]. Thus, all scenarios were designed to have an operator 

utilization of about 50%, meaning that operators spent 

approximately 50% of their time, on average, performing 

assignment or payload tasks. This was achieved in pilot studies 

by fixing the payload tasks and manipulating the number of 

exogenous events and their inter-arrival times until the average 

operator utilization was about 50%. 

The experimental trials had a total of 16 exogenous events 

(unidentified contacts emerging). The time between successive 

exogenous events (the inter-arrival time) was 30 seconds for the 

constant treatment. For the erratic factor level, the inter-arrival 

times were generated from a bimodal distribution where the 

means of the modes were set at 75 seconds and 225 seconds from 

the start of the trial, with a standard deviation of 15 seconds. In 

both the constant and bimodal treatments, the first exogenous 

event always appeared at time 0, thus only 15 events were drawn 

from the bimodal distribution for the erratic condition. The interarrival of exogenous events was varied between constant and 

erratic to determine if team structure had an effect on how 

operators performed under different task load distributions. 

4.2.5 Participants 

Participants were recruited via e-mail and paper advertisements 

and through word-of-mouth. All of the participants were between 

the ages of 18 and 35, with the mean age being 21.7. Some 

participants had military, video game or previous UV experiment 

experience. Due to scheduling concerns, some teams were 

composed of individuals who knew each other while most teams 

were composed of individuals who were randomly assigned. The 

level of inter-personal relationships between team members 

(stranger, casual acquaintance, friend, romantic, etc) was not 

recorded. 

4.2.5.1 Training 

Prior to the experimental trials, the participants completed an 

individual 20-minute PowerPoint® training session. Afterwards, 

the participants completed two practice scenarios (one 

mechanistic and one organic) as teams, each one taking about 10 

minutes to complete. Thus, the total training time was 

approximately 40 minutes. 

5. RESULTS 

The order of the trials was checked to determine if a learning 

factor occurred across the four team sessions. Given that the 

training time was minimal, and previous research has shown that 

four or more training sessions is needed for teams to achieve 

stable performance [16], testing order was of concern, and showed 

a significant effect (F(3, 24) = 4.12, p=.02). Most teams did worse 

on the first trial, regardless of the treatment, than on subsequent 

trials (Figure 5). Thus, the final statistical model included a two 

factor, repeated measures ANOVA with blocking on the trial 

order. 

Team structure was significant (F(1, 24) = 1.484, p < 0.01), with 

mechanistic teams performing better than organic teams overall, 

although there was no significant difference when the inter-arrival 

rate was erratic. Mechanistic teams performed worse when the 

inter-arrival rate was erratic as opposed to constant (t(15.8) = 

13
2.47, p = 0.03). However the inter-arrival rate had no significant 

effect on the organic teams. The inter-arrival rate by itself was 

not significant, but the interaction of the independent variables 

was (F(1, 24) = 10.47, p = 0.04). 

Figure 5: Effect of AOCT vs trial order. 

5.1 Model Results 

The model was run 1000 times for each treatment condition. For 

the organic team, the model predictions were within the 95% 

confidence interval of the empirical results for all four treatments 

(Figure 6). Since the mechanistic teams did not have to coordinate 

their actions due to their rigid role structure, they were initially 

modeled without any communication or monitoring behavior. In 

the erratic inter-arrival condition, the model predictions for the 

mechanistic team was within the 95% confidence interval, 

however for the constant inter-arrival condition, the model’s 

predictions were low (Figure 6). 

Figure 6: Initial empirical results. 

Upon further investigation of the experimental transcripts, the 

mechanistic team did communicate and monitor each other’s 

actions, even though it was not necessary. Thus, a coordination 

strategy similar to that used by the organic team was implemented 

in the mechanistic model and new outputs were generated. Not 

surprisingly, the additional cost associated with coordination 

increased the OCT of the mechanistic team. Thus, with the 

coordination strategy implemented in both teams, the model 

predictions were within the 95% confidence interval for all four 

treatment conditions (Figure 7). 

Figure 7: Revised empirical results. 

6. DISCUSSION 

It was not surprising that the mechanistic teams performed worse 

under erratic inter-arrival times than they did when the interarrival times were constant, since the erratic inter-arrival times 

caused events to arrive in batches, thereby increasing the queues. 

However, it was interesting that there was no significant 

difference in the performance of the organic team under the 

different inter-arrival rates of exogenous events. This suggests 

that even though events arrived in clusters during the erratic interarrival treatment, the organic team was able to handle the 

workload spike without increasing the AOCT. This suggests that 

the organic team is more robust to environmental uncertainty than 

mechanistic teams due to their flexible structure and the ability to 

spread tasks across the team. 

It was predicted that mechanistic teams would perform better than 

organic teams, which they did, but not necessarily for the same 

reasons. Originally, mechanistic teams were thought to have an 

advantage over organic teams because they did not incur 

coordination costs. As shown in the results, mechanistic teams do 

incur coordination costs and without taking these costs into 

consideration, the performance predictions are too low in the 

constant inter-arrival case. This is interesting because the 

communications are theoretically unnecessary. However, this 

highlights the importance of understanding the intrinsic need for 

communication between team members, even if it is not 

necessary. Future work should look at how to mitigate such 

communication overhead. 

So, if mechanistic teams are also incurring coordination costs, 

how are they still managing to perform better overall than organic 

teams? The answer to this question perhaps lies in the fact that 

the empirical data used to generated the pdfs for the different 

sources (e.g. travel times, WTOD, service times) was separated 

into the four different treatment conditions. Although there was no 

statistically significant difference between the values and the 

14
differences could be attributed to sampling error, there were small 

differences in nearly every input condition. Since the OCT is the 

sum of all of these individual times, then these differences (or 

errors) combine into a statistically significant result. 

Other factors may play a role as well, such as the switching 

strategy of the operators. The switching strategy assumed for all 

of the operators was FIFO, although in many cases, operators did 

not adhere to this strategy. Thus, future analysis should determine 

the actual switching strategies observed in the experimental trials 

and implement those instead. 

Another issue is that statistical significance for data such as 

WTOD was difficult to obtain due to a number of factors. First, 

the sample size of the experiment was small (n = 10) but this is 

not unusual for team studies since it takes multiple participants to 

form a single experimental unit. Increasing the sample size should 

reduce the standard error of the experimental results. 

Additionally, previous research has shown that UAV teams do not 

reach asymptotic performance levels until after they have 

completed around four sessions together [16]. Although this is 

likely to be highly contingent upon a number of factors such as 

the difficulty of the task, the inter-operability required for success 

and the length of the sessions, it does seem to be consistent with 

our results. Thus, to further reduce variability in the experimental 

results, additional practice sessions should be added. Finally, the 

experiment was not controlled for the skill level or the 

relationships of the individuals. Factors such as age, video game 

experience and military background could have had an effect on 

individual performance. If a reduction in the variability of the 

team’s performance is desired, then future experiments could 

select for and block on particular individual traits. However, 

teams of futuristic UV operators may be just as diverse as the 

sample population, particularly if they are composed of 

individuals from different agencies or even nations operating via 

an interoperability standards [17]. These operators may have 

different levels of training, skills and attitudes which may result in 

significantly different levels of individual performance. Thus, it 

is not necessarily a flaw in the experimental design to have 

diversity in regards to the individual traits, as it can be argued that 

such diversity will be likely in future UV systems. 

7. FUTURE WORK 

The model in this paper has successfully replicated the results of 

experimental trials, but it has not been used to predict the 

performance of teams in hypothetical situations. Future work will 

look at developing the model to predict the performance of teams 

in new scenarios and then verify those results empirically. One 

such scenario could be if the teams had an additional member or 

decision support tool that aided in task allocation. While the 

mechanistic teams performed better than organic teams overall, 

the fact that the mechanistic teams were more sensitive to 

variations in the environment suggests that this team architecture 

may not be ideal for volatile environments such as those found in 

command and control settings. If an organic team had the benefit 

of a leader or decision support tool, then its coordination costs 

might drop significantly, whereas a leader or decision support tool 

would likely have little or no effect on a mechanistic team. Thus, 

the team model could be updated to see just how much of a 

performance difference one could expect by having a leader or 

decision support tool in both team structures. 

8. ACKNOWLEDGMENTS 

This research is sponsored by the Office for Naval Research and 

the Air Force Office of Scientific Research. We would also like 

to acknowledge M.I.T. undergraduates Tony MacDonald, Kyle 

Miller, Anrae Jamon Motes, Ben Sedat, Jieyun Fu and An V. Ho 

for their programming contributions to this project and postdoctorate associate Birsen Donmez for her assistance with 

statistical analysis. 

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[12] T. Burns, and G. Stalker, The management of 

innovation: Oxford University Press, USA, 1994. 

[13] I. D. Steiner, Group process and productivity: 

Academic Press New York, 1972. 

[14] "Common Warfighting Symbology," MIL STD 2525B, 

Defense, ed., 2005. 

[15] B. Donmez, C. Nehme, M. Cummings et al., “Modeling 

situational awareness in a multiple unmanned vehicle 

supervisory control discrete event simulation,” Journal 

of Cognitive Engineering and Decision Making, Special 

Issue on Computational Models of Macrocognition (in 

review), 2008. 

[16] N. J. Cooke, P. A. Kiekel, and E. E. Helm, “Measuring 

team knowledge during skill acquisition of a complex 

task,” International Journal of Cognitive Ergonomics,

vol. 5, no. 3, pp. 297-315, 2001. 

15
[17] M. Cummings, A. Kirschbaum, A. Sulmistras et al., 

“STANAG 4586 Human Supervisory Control 

Implications,” Air and Weapon Systems Dept, Dstl 

Farnborough & the Office of Naval Research, 2006. 

16
Internal Model Generation for Evolutionary Acceleration of 

Automated Robotic Assembly Optimization 

Jeremy A. Marvel 

Case Western Reserve University 

Cleveland, OH, USA 

jeremy.marvel@case.edu

Wyatt S. Newman 

Case Western Reserve University 

Cleveland, OH, USA 

wyatt.newman@case.edu

ABSTRACT

While machine learning algorithms have been successfully 

applied to a myriad of task configurations for parameter 

optimization, without the benefit of a virtual representation to 

permit offline training, the learning process can be costly in terms 

of time being spent and components being worn or broken. 

Parameter spaces for which the model is not known or are too 

complex to simulate stand to benefit from the generation of model 

approximations to reduce the evaluation overhead. In this paper, 

we describe a computational learning approach for dynamically 

generating internal models for Genetic Algorithms (GA) 

performance optimization. Through the process of exploring the 

parameter gene pool, a stochastic search method can effectively 

build a virtual model of the task space and improve the 

performance of the learning process. Experiments demonstrate 

that, in the presence of noise, neural network abstractions of the 

mappings of sequence parameters to their resulting performances 

can effectively enhance the performance of stochastic parameter 

optimization techniques. And results are presented that illustrate 

the benefits of internal model building as it pertains to simulated 

experiments of complex problems and to physical trials in robot 

assembly utilizing an industrial robotic arm to put together an 

aluminum puzzle. 

Keywords: Genetic algorithms, parameter optimization, model 

building, robotic assembly 

1. INTRODUCTION

Conceptualizing the transformation from knowing what needs to 

be accomplished in an optimizeable task to knowing how to 

actually go about accomplishing said undertaking is an expensive 

and time-consuming process. These costs are further 

compounded when the tasks being optimized are susceptible to 

complex, external influences such as the gross uncertainty of 

physical systems caused by friction, pressure and temperature. 

These tasks become problematic because the limitations of virtual 

models fail to fully capture the complexity of the operational 

environments and conditions, and thus necessitate the utilization 

of physical trials for learning. 

In previous work at Case Western Reserve University [5] it was 

demonstrated that Genetic Algorithms can effectively and safely 

perform rigid-body assembly optimizations by using physical 

robot systems for parameter evaluation. Using a simple metric of 

success based on assembly trial speed and contact force, the 

system was capable of learning how to perform a variety of 

assemblies quickly and within the bounds of defined safety 

parameters. Though this implementation was highly successful, it 

was noted that, by the very nature of the learning method used, 

the process of optimization was often wasteful. Specifically, 

parameter sequences that were incapable of even completing the 

assemblies still had to be tested and allowed to time out before 

ultimately being discarded. 

Numerous attempts to minimize this waste have been attempted, 

though their methods focus on experimenting with the learning 

rates [2], population sizes, mutation and crossover rates, child 

succession rates [4], and competition metrics [9]. In this paper, an 

augmentation to Genetic Algorithms implementations is described 

that utilizes the system’s experiences in performing an 

optimizeable function to generate an internal model of the task 

space. 

2. DEVELOPMENT OF INTERNAL MODELS

Genetic Algorithms, stochastic methods of parameter space 

exploration, follow the biological model of random gene 

mutations and Darwinian survival to evolve competitive gene 

vectors of parameters for optimization. While certain 

implementations may preserve information regarding the 

evolutionary genetic lineage, the competitive nature of the system 

does not maintain any history of the gene strains that are deemed 

unsuitable for survival. As a result, massive amounts of useful 

knowledge generated by the random search are discarded without 

actually benefiting the system. Many biological organisms 

maintain a memory of previous experiences—both positive and 

negative—and effectively learn from them by altering their future 

behaviors based on the results from the past. When applied to 

Genetic Algorithms, these memories could provide a basis for 

predicting the survivability of the progeny gene sequences, and 

may actually preempt the necessity of actually running trials 

doomed to fail. 

Within the context of Genetic Algorithms, models are defined as 

functional mappings from the gene sequence parameters to their 

respective resulting performances. Explicitly, for the query 

parameter vector g, executing the gene sequence through the 

evaluation function f (such as physically performing an assembly 

task) produces a resulting performance r. By developing an 

enhanced filter function h to approximate the mapping of the full 

genetic parameter pool G to its respective output mapping R such 

that h(G)→R’ ≈ f(G)→R, where R’ is an approximation of R, one 

should be able to effectively accelerate the convergence on an 

optimal solution by evaluating only those parameter sequences 

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17
that are predicted to surpass the performance of their originating 

parents. 

Assuming that the model is an effective predictive filter and that 

each trial run by the GA has a constant evaluation cost c, by 

evaluating only the K projected best-performing parameter 

sequences of the N total child genes produced by the Genetic 

Algorithms driver program per generation, one can expect an 

average convergence performance enhancement cost of cK/N. Of 

course, in the trivial case where h = f (i.e. there is a perfect model 

that precisely maps all possible g to their respective r), K

effectively becomes 0 since one can effectively eliminate the need 

to actually run the gene sequences since they can manifest and be 

evaluated in silico because the outcome is already known with 

certainty. 

One of the chief underlying inspirations for this research was the 

desire to maintain a safe working environment. Manual tuning of 

assembly parameters is frequently employed, often using the 

manipulator, itself, as an input device for characterizing 

parameters and their subsequent performances. While methods 

such as Design of Experiments [3] have been developed, their 

implementations require the optimization experts and robot 

programmers. In an effort to minimize the expertise cost 

associated with these tuning methods, however, automated tools 

for the same processes are actively being developed [10]. 

For robot automation it is preferable to perform as much of the 

parameter optimization as possible offline due to the inherent risk 

of damaging the robots and their operational environments given 

suboptimal or dangerous inputs. For example, a similar approach 

was used in [6], which utilized existing simulators for 

reinforcement learning of helicopter flight prior to code 

deployment on the robot. Customizable simulators such as those 

used by [8] for clutch assembly modeling, and [7] for mobile 

robotics benchmarking and analysis, utilize real-world data to 

construct more realistic representations of the robots and their 

environments. These solutions, however, are useful only when all 

of the specified environmental constraints are known. In 

unknown conditions, [1] developed topological maps for peg-inhole localization strategies. However, this approach required that 

the exploration be exhaustive, and that it occur entirely before the 

localization could begin. What we hope to gain from this work is 

inline knowledge acquisition and representation for optimization 

acceleration purposes. 

To this end an inline helper function is proposed to selectively 

prune the child gene pool prior to being executed. This function 

would take the N children produced by the Genetic Algorithms 

software and rank-order them according to their predicted 

performances. The GA implementation would then select the top 

K projected child gene sequences for trial evaluation, and 

afterward report to the helper function the results of running those 

genes such that the helper might then adjust its mapping of the 

world to further improve the model’s predictive abilities. 

Because an analytical model is not always readily available, what 

were tested in this study are two simple numerical approximations 

of the data parameter-to-performance mapping trends. The first 

was a standard gradient descent approach to a least-squared linear 

fitting of the data. With this, we attempted to fit a highdimensional plane to the surface plot of the observed system 

outputs for the known parameter sequences. Given an Mdimensional gene sequence, gi, and a set S ⊆ G of previouslyexecuted gene sequences, selected from S are the M+1 closest 

(based on Euclidean distance) distinct sequences to g in the 

parameter space to form the (M+1)×M matrix X, illustrated as 

follows: 

1,1 1,

1,1 1,

M

M M M

g g

X

g g + +

⎛ ⎞ ⎜ ⎟ =

⎝ ⎠

...

#% #

"

1

M 1

r

y

r +

⎛ ⎞ ⎜ ⎟ = ⎜ ⎟ ⎜ ⎟ ⎝ ⎠

, #

Taken from the set of known outputs Y ⊆ R paired with their 

respective inputs S are the M+1 resulting performances y (see 

above). From the M+1 sampled sequences, a linear hyperplane is 

generated that best approximates the slope of the data trends. 

This hyperplane is equivalent to the estimated tangent of the 

model surface, and can thus be described by the normal vector 

definition given in Equation 1. In particular, the hyperplane 

solution is identified as the best-guess vector, b, that most 

accurately explains the observed model such that it minimizes the 

position and orientation error, ε. The value of b can be computed 

such that y = Xb+ε by Equation 2. 

( ) T T X Xb Xy = (1)

( ) 1 T T b XX Xy −

= (2)

The second approach employed yet another layer of abstraction 

by means of a standard feed-forward neural network trained via 

back propagation in order to generate a best-estimated fit to the 

explored parameter space. The neural network utilized followed a 

standard three-layer model (see Figure 1) that consisted of an 

input layer, I, composed of M nodes, an arbitrary number of 

“hidden” layer nodes, J, and an output layer, K, consisting of one 

or more nodes. Here, nodes are defined as equations that take as 

arguments a single scalar input, u, composed of the summed, 

weighted outputs from the layer before it, and produce a scalar 

output, o = t(u), where t is a defined nonlinear “activation 

function.” The links connecting nodes in Figure 1 are the 

weighted “synapses” that scale the output of the presynaptic node 

before feeding it into the postsynaptic node. 

Figure 1: A standard three-layer neural network topology 

consisting of input-, hidden- and output-layer neurons 

The full set S is used to train the network using the associated set 

of known outputs, Y, to compute the resulting error values, which 

are then utilized in adjusting the weights of the links connecting 

adjacent layers of hyperbolic tangent activation function nodes. 

The sensitivity factor (or how much effect a change in the current 

value will have in the total network error) for every synapse, w, is 

computed for each training sequence, p, and the weights are 

18
adjusted accordingly. For the synapses linking the hidden and 

output layer neurons (wk,j) the sensitivity factor is computed using 

Equation 3, while the sensitivity factor for the synapses linking 

the input layer neurons and hidden layer neurons (wj,i) are 

computed using Equation 4. Training the network adjusts the 

resulting outputs for a given input parameter sequence, and 

approximates the surface of the mapping from parameters to 

solutions. 

( )( ) ,, ,

,

' pk pk pk p j

k j p

E

y o tu o w

∂ =− − ∂ ∑ , (3)

( )( ) ( ) , , ,, ,

,

' ' pk pk pk k j p j pi

j i k p

E

y o tu w tu o w

∂ =− − ∂ ∑∑ , (4)

In the above equations, the sensitivity factors are dependent on 

the errors between the known performance outputs, yp, and the 

outputs projected by the neural network, op. The function t’ is 

computed as the first-order derivative of the activation function t 

discussed previously. For experimentation, this function was 

defined as the hyperbolic tangent, t(u) = 1.7159tanh(2u/3). 

3. RESULTS: SIMULATED HARD PROBLEMS

Given the time constraints of training massive instances of 

assemblies, and the inherent uncertainties of the existence of a 

global optimum of physical configurations, initial trials consisted 

of a reconfigurable simulator of nonlinear problems. Preliminary 

experiments with mathematical simulators illustrated that simple 

problems with low-dimensional parameter spaces were too 

quickly solved by the GA, and that the dichotomy between 

assisted and unassisted implementations was difficult to 

distinguish visually. Because of this, it was decided to implement 

the simulation as a scalable M-dimensional Gaussian that 

computes a scalar output score, rj, as defined by Equation 5. 

( )

( ) ( ) ( ) 22 2

11 2 2

22 2

1 2 22 2

M M

M

gg g

r f g Ae

μμ μ

σσ σ

β

⎛ −− − ⎜− − −− ⎜

⎝ = = +

...

⎞

⎟

⎟

⎠ (5)

The value A is an arbitrary scaling constant to determine the 

maximum value of the Gaussian. For this research, it is assumed 

to be 1.0 in order to reduce the number of variables. The origin, 

μ, was centered at {π, π, π, ..., π} and was configured for a 

variance σi = i (specifically, σ = {1, 2, 3, ..., M}). The individual 

gene sequence elements gi were all initialized to 0.5 for all 

simulator trials, but could be mutated by the GA to be any real 

value in the range [-∞, ∞]. The addition of a noise parameter, β, 

allowed for the inclusion of varying levels of static or dynamic 

noise upon each trial evaluation if such noise is desired. 

This model was chosen because it is demonstrably learnable, has 

a known global optimum, is sufficiently difficult in high 

dimensions, is repeatable and testable, and is easily augmented 

given the added noise value β. In the trials run with noise present 

within the simulator, β was assigned a random value with 

Gaussian distribution and variance 0.03. This value was 

dynamically generated upon each query to the simulator, such that 

for N different queries given a parameter sequence, g, N different 

random values would be assigned to β. Even in the presence of 

the random noise, however, the shape of the Gaussian was still 

clearly visible. Though, given the noisy nature of the problem, 

the surface became jagged and rife with local optima (as is seen in 

Figure 2). 

Figure 2: Low-dimension simulator surface plots in both noiseless 

(left) and noisy (right) operating environments 

Because initial tests began with a known application model, there 

existed the benefit of also being able to demonstrate what 

advantage having omniscience would grant a Genetic Algorithms 

implementation for parameter optimization. By using the 

simulator itself as a predictor of its own outcome, a perfect model 

was gained which could thus establish a baseline for the best 

possible expected convergence on a solution with an assisted GA. 

Naturally, because perfect model of the problem domain existed, 

the number of performance queries to the simulator essentially 

dropped to zero due to the reasons discussed in the previous 

section. However, for the sake of argument, it was assumed the 

Genetic Algorithms driver program was unaware that the filter 

function was perfect, and thus it treated the function as it would 

any other assistant method. 

Similarly, because perfect knowledge of the system existed, an 

analytical approach toward gradient descent for comparative 

purposes was provided. Using an analytical approximation of the 

slope of the known model system, performances considerably 

better than those of an unassisted GA could be expected. The 

results, however, would not be quite as effective as the perfect 

model. For the simulator problem, the analytical approximation 

consisted of an instantaneous tangent plane that passed through 

the best-performing child of the previous generation’s stochastic 

search. This plane was defined by the solution of the gradient at 

the origin point gˆ computed by the function described in 

Equation 6 (which can then effectively be reduced to Equation 7). 

For example, if the M-dimensional gene gk performed such that rk

≥ rj ∀j ∈ N in the previous time step, the analytical tangent model 

would be that which passed through gˆ = gk. 

( )

( ) ( ) ( ) 22 2

11 2 2

22 2

1 2 22 2 ˆ

M M

M

gg g

i i

fg e

g g

μμ μ

σσ σ

β

⎛ ⎞ −− − ⎜ ⎟ − − −−

⎝ ⎠

⎛ ⎞ ⎜ ⎟ ∂ ∂

= +

∂ ∂

⎝ ⎠

...

(6)

( ) ( )2

2 2 ˆ 2

i i i i

i i i i

g g f g r

g g

μ μ

σ σ

⎛ ⎞ ∂ ∂ − − =− = ⎜ ⎟

∂ ∂ ⎜ ⎟ ⎝ ⎠

r (7)

Associated with each M-element gene vector, g, is an equitable 

M-element mutation vector, h, that specifies the possible variance 

of random perturbation around the current set value. Upon each 

successive generation of training, the mutation variances for 

generation t+1 are multiplicatively reduced by constant, bimodal 

“learning rates” according to Equation 8. These learning rates are 

applied based on either gene succession (i.e. a child gene is 

selected as the next generation’s parent), η+

, or genetic failure (no 

child performs better than the parent gene), η-

. These learning 

rates can either broaden the variance to allow for larger search 

19
spaces, or can narrow the variance to hone in on some optimum 

configuration. Gene elements that have an associated mutation 

variance of 0 are considered “locked,” and can not be modified 

further. 

( 1) ( ) i i ht ht + = η (8)

For the simulator trials we set M = 15. The Genetic Algorithms 

implementation was executed with a single clan for over 30 

iterations of learning with 10 children in the clan, for over 300 

trial inquiries to the simulator interface. The assisted GA was 

allowed to generate 1,000 child gene sequences for the single 

clan, which were then rank-ordered by the filter methods and the 

10 children with the best projected scores were selected for trials. 

The neural network topology consisted of three layers of 

hyperbolic tangent activation function nodes: an initial layer of 

15 input nodes, a hidden layer of 20 nodes, and an output layer 

consisting of a single node. 

Simulation Results: Noiseless Learning

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

10

30

50

70

90

110

130

150

170

190

210

230

250

270

290

Number of Trials

J

Perfect Model

Unassisted GA

Analytical Tangent

Numerical Tangent

Neural Network

Figure 3: Results of internal modeling enhancement for a highdimensional simulated Gaussian problem.

The subsequent expected performances of the five optimization 

techniques (unassisted Genetic Algorithms, perfect model, 

analytical tangent, numerical tangent, and neural network surface 

abstraction)—as computed by the averaged results over numerous 

trials—for the noiseless simulator model are illustrated in 

Figure 3. Each method is demonstrably monotonically decreasing 

toward the convergence of some optimal solution. In this 

instance, the optimum was defined as the apex of the Gaussian 

curve. Performance improvements are marked by a movement 

toward convergence (specifically, a smaller value of the error J, 

which is defined here as the distance from the known maximum 

value of 1.0) in the fewest number of inquiries to the simulator as 

possible. 

The unassisted Genetic Algorithms implementation faired the 

worst of all five, while the perfect knowledge model moved 

toward convergence of the optimal solution the fastest as 

anticipated. The analytical tangent closely followed the rate of 

convergence of the perfect model initially, but diverged as it 

approached the optimal solution and slowed to a rate comparable 

with that of the GA search. This divergence is likely due to the 

tangent plane projecting beyond the optimal value, and thus the 

stochastic search took precedence as the internal model bounced 

back and forth over the zenith of the Gaussian curve. Both the 

numerical tangent and the neural network approximations faired 

considerably better than the unassisted Genetic Algorithms 

method, but came shy of the performance improvements granted 

by omniscience. 

When noise was added to the simulator, however, the possibility 

of this omniscience from the GA’s perspective was effectively 

lost. Because of this, the analytical model was thus omitted from 

this line of testing. The perfect model results from the noiseless 

experiment, however, are included in the test results as a basis for 

comparison. Each query to the simulator was performed 10 times, 

and the resulting noisy outputs of those queries were then 

averaged to produce an expected result for a given parameter 

sequence for a single trial. These results were thus used by the 

Genetic Algorithms program to select which gene sequences 

would be chosen for parental succession on the subsequent 

generation of training. 

It should be noted, however, that the added noise β distinguishes 

what the Genetic Algorithms implementation observes and what 

are the actual outputs for a given set of parameter sequences. The 

values reported in Figure 4 are based on the performances of the 

observed best-performing gene sequences per generation as they 

would actually fare with the noise removed from the equation. 

Simulation Results: Noisy Learning

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

10

30

50

70

90

110

130

150

170

190

210

230

250

270

290

Number of Trials

J

Perfect Model

Unassisted GA

Numerical Tangent

Neural Network

Figure 4: Results of internal modeling enhancement for a highdimensional simulated Gaussian problem in the presence of noise 

Running the simulator with noisy reported outputs demonstrated 

that the numerical tangent hyperplane performed no better than 

the unassisted Genetic Algorithms implementation for 

convergence improvements. One could legitimately argue that 

the performance of the numerical tangent actually had the 

potential for being inferior, because one might naturally expect 

that a stochastic search being run with bad additional information 

(such as that produced by line-fitting with erroneous data) would 

likely be worse off than one running with no extra information at 

all. The neural network approach, however, provided enough 

data abstraction to perform better than both the unassisted GA and 

the numerical tangent approximation. 

When we increased the range of the noise value β such that the 

surface of the Gaussian curve became almost indistinguishable 

(see Figure 5), the dichotomy in performance became even more 

pronounced. By making the random noise uniformly distributed 

in the range of [-0.5, 0.5], the surface plot resembles a field of 

needles more than it does a gently-sloping hill. To the human 

eye, the curvature of the Gaussian is still barely visible, but to the 

stochastic search it is little more than a sea of noise. This is made 

evident by the noticeably worse performance of the unassisted 

Genetic Algorithms implementation. A similar performance of 

the gradient descent approach with the numerical tangent 

approximation is observed, as the resulting fitness was only 

marginally better, as is illustrated in Figure 6. The neural 

network model, while being far from converging on the optimum 

20
solution of the Gaussian, managed to guide the GA to a solution 

that was far superior to that which the Genetic Algorithms 

program would have found on its own. Without some additional 

insight into the nature of the model, it is possible that the results 

achieved by the neural network may even be the best possible 

practicable by a stochastic search method. 

Figure 5: Comparison of the low-dimensional simulator surface 

plots in the presence of varying degrees of noise

Simulation Results: Noisy Learning

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

10

30

50

70

90

110

130

150

170

190

210230

250

270

290

Number of Trials

J

Perfect Model

Unassisted GA

Numerical Tangent

Neural Network

Figure 6: Results of internal modeling enhancement for a highdimensional simulated Gaussian in the presence of massive noise 

From these initial simulated trials, we can infer three things. 

First, that the presence of an internal model—whether developed 

analytically or by means of exploration of the search space—has 

the potential of greatly improving the performance of a stochastic 

search in its search for an optimal solution. Second, that in the 

presence of output noise, gradient descent approaches for 

traversing the parameter space may not perform any better than 

random walks. More intelligent models than simple hill-climbing 

are clearly needed. And third, in the early stages of training, the 

GAs with internal models typically see faster rates of performance 

improvement than those without internal models. 

4. RESULTS: PHYSICAL ASSEMBLIES

While informative, the results of simulations run in Section 3 do 

not provide sufficiently definitive empirical proof that the internal 

modeling methods proposed are effective for the acceleration of 

convergence for stochastic searches. For the virtual problem, 

there was a known parameter sequence that resulted in a global 

optimum solution reachable by a simple hill-climbing algorithm. 

How does the system fare in an environment where the 

cumulative uncertainties rule out such an ascent, and where there 

may exist numerous global optima? To this end, a physical 

assembly trial was configured and implemented to test the 

proposed internal model generation. 

For the assembly configuration, an aluminum pentagonal puzzle 

(see Figure 7) was set up to be put together using an ABB IRB140 industrial robotic arm outfitted with an ATI GAMMA 

force/torque sensor for force feedback in order to facilitate 

compliant motion control. The puzzle consisted of two stages of 

assembly: a peg-in-hole search that locked the circular lip of 

puzzle piece in the inner circumference of the pentagonal hole, 

and a rotational search that aligned profile of the puzzle piece 

with the pentagon orientation such that it could be fully inserted. 

Each search was represented by a parameterized numeric vector 

of arguments that were generated and mutated by a host 

computer, and communicated to the ABB IRC5 robot controller 

using a 4ms fast Ethernet connection for interpretation and 

execution. Each vector was of fixed length, and the distinct 

searches are concatenated to form a single input vector to the 

internal model for training. The GA configuration is identical to 

the one introduced in previous work [5], with the exception of the 

addition of the internal model filter method. 

Figure 7: Aluminum pentagonal puzzle insert (right) to be 

assembled by the ABB IRB-140 open-chain manipulator (left). 

The gene sequence fitness score for the physical assembly 

problem, Equation 9, was a function of the resulting assembly 

times, T, and incidental forces, F, encountered while performing 

the assembly task. Here the value of T is equal to the amount of 

time passed before either the assembly has been completed or the 

assembly attempt timed out, and F is equal to the average value of 

the maximum force recorded on the X, Y or Z axes of the torque 

sensor. Both time and force were bound by pre-defined 

maximum values (Tmax and Fmax, respectively), and if the task 

exceeded either value the assembly process would be immediately 

aborted and the trial given a score of 0. Given that different 

assemblies may have different requirements regarding time and 

force, the scaling factor, 0 ≤ α ≤ 1, was used to shift the weight of 

the score accordingly with regard to where the process importance 

was focused. 

( ) ( ) max max

max max

max 1 , 0 TT FF r fg T F α α

⎛ ⎞ ⎛⎞⎛ ⎞ − − = = ⎜ ⎟ ⎜⎟⎜ ⎟ + −

⎝ ⎠ ⎝⎠⎝ ⎠

(9)

The assembly process for the pentagonal puzzle consisted of three 

phases of distinct search strategies. Each search strategy was 

defined by a vector gene consisting of 20 floating point numbers 

that identified the search strategy, the termination conditions, and 

the search parameters. For searches that required fewer than 20 

numerical values to be fully defined, all unused vector elements 

were set to 0 in order to maintain a unit gene length. 

The first phase was essentially little more than a localization 

offset that sought to minimize both 1) the search time necessary to 

engage the circular insert caused by position uncertainty, and 2) 

the profile orientation caused by rotational uncertainty. In short, 

the first phase was little more than lateral offsets and a rotation 

21
around the tool’s Z axis to move the robot to what it believed was 

an optimal initial configuration for assembly. The second phase 

performed a spiral search to perform a peg-in-hole assembly of 

the circular insert. Parameters to be optimized included the spiral 

radius, search speed, and number of turns per spiral. And the 

third phase was a rotational search to engage the pentagonal 

profile of the puzzle piece. Optimizeable parameters for the 

rotational search included the rotational arc size, search speed, 

hopping frequency, and hopping amplitude. The hopping 

parameters controlled an oscillating vertical force profile and 

were included to minimize the likelihood of the edges of metal 

components seizing while being assembled. While the puzzle 

assembly had sub-millimeter tolerances, no great amount of force 

was necessary to join the insert and the puzzle housing. Because 

of this, the applied downward force for the assembly task was 

fixed at 5 N. Initial trials of the unassisted GA with the puzzle 

assembly demonstrated that the encountered forces never 

approached the 80 N value of Fmax. It was thus decided to set 

value of α in Equation 8 to 1.0, effectively eliminating the force 

term and restructuring the fitness function to take into account 

only the assembly time. 

As mentioned previously, training for the Genetic Algorithms 

implementation for the puzzle assembly task was divided into two 

unique search stages: the spiral search, and the rotational search. 

The piece components being aligned for assembly are bolded in 

solid red in Figure 8 for each of the two searches. Stage 1 

consisted of learning the optimal position offsets for the first stage 

of insertion, and then optimizing the spiral search for the circular 

insert (Figure 8-A). Phase 2 locked the values from the first 

phase in place, and optimized the orientation offset and rotational 

search parameters (Figure 8-B). For the assembly trials, Phase 1 

training was performed without the assistant filter function and 

was evolved for twenty-five generations independently before 

being parametrically fixed. The internal model method was then 

applied to the second phase of training for performance gauging. 

A B 

Figure 8: Pentagonal assembly representations of the peg-in-hole 

lateral search of the circular component insertion (A), and the Zaxis rotational piece profile meshing (B) 

Given the results of the simulator with noise and the sub-par 

performance of the numerical tangent approximation, only the 

neural network filter method was used in physical testing as an 

assistive model. The neural network topology from Section 3 was 

augmented to accept the increased number of search parameters 

of all three stages, with the number of input-layer neurons being 

increased to 60. The numbers of hidden-layer and output-layer 

nodes were maintained at 20 and 1 respectively, however, with 

the single output representing the time for assembly completion. 

The first three generations of parameter optimization were 

evaluated without the assistive model. The resulting inputs and 

outputs were used to train the neural network model offline. The 

assisted and unassisted Genetic Algorithms implementations were 

then started at the same point given the best-performing 

parameters after the third generation. The assisted GA model was 

again allowed to generate 1,000 children, but only the top 10 

projected performers were actually evaluated. The results of 

adding an assistive function to the second sequence are discussed 

presently. 

Physical Results

2

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

3

10

20

30

40

50

60

70

80

90

100

110

120

Number of Trials

Stage 2 Assembly Time (s)

Unassisted

Unassisted Average

Assisted

Assisted Average

Figure 9: Sample results of internal modeling enhancement for a 

multidimensional physical assembly. 

For each trial, the robot began searching for the assembly attempt 

at the same location and orientation in space. Plotted in Figure 9 

are the results of running the Genetic Algorithms implementations 

with and without the assistive method. The solid blue lines 

represent the numerous test results of unassisted stochastic 

learning, while the dashed red lines show the results of stochastic 

learning with selective pruning of the genetic parameter pool. 

With only a few exceptions, the trials had quickly converged to 

some optimum performance around which subsequent trials 

oscillated. While the two fared quite well, the assisted model 

performed, on average, slightly better than the unassisted 

stochastic search. These results are reminiscent of those 

comparing the unassisted and assisted GA implementations of the 

high-dimensional simulated problem in the presence of noise 

discussed in Section 3. 

As an additional test, random noise was introduced to the system 

in the form of random robot configurations. To simulate 

positional and rotational uncertainty, random perturbations were 

added to the robot’s initial pose for each trial. Positional noise 

consisted of uniformly-distributed lateral offsets in the range of 

[-2.0 mm, 2.0 mm] for both the X and Y axes, and rotational noise 

took the form of a random rotation in the range of [-5.0°, 5.0°] 

along the Z axis. The results for these tests are illustrated in 

Figure 10. 

The performance of the physical testing was comparable to the 

simulator results with massive added noise, which lends credence 

to the simulator model for performance testing. With assistance, 

the training performed better than when it was unassisted, with 

each training sequence performing better than or equal to the 

22
6. ACKNOWLEDGMENTS average performance of the unassisted model. Based on the 

simulated results, the expected performance of the unassisted 

Genetic Algorithms implementation was projected to improve at a 

slower rate than it would with assistance. Indeed, as was 

observed the assisted model’s rate of improvement increased as 

the stochastic search explored more of the parameter space. 

Support for this research was provided by the Robot and 

Automation division of the ABB Inc. U.S. Corporate Research 

Center. 

7. REFERENCES 

Physical Results

1

2

3

4

5

6

7

10

20

30

40

50

60

70

80

90

100

110

120

Number of Trials

Stage 2 Assembly Time (s)

Unassisted

Unassisted Average

Assisted

Assisted Average

[1] Chhatpar, Siddharth R., and Michael S. Branicky. 

“Localization for Robotic Assemblies with Position 

Uncertainty.” Proceedings of the 2003 IEEE RSJ 

International Conference on Intelligent Robots and Systems. 

October, 2003. Pp. 2543-2540. 

[2] Gheorghies, O., Luchian, H., and Gheorghies, A. “A Study 

of Adaptation and Random Search in Genetic Algorithms.” 

IEEE Congress on Evolutionary Computation. 16-21 July, 

2006. Pp. 2103-2110. 

[3] Gravel, David P. “Efficient Method for Optimization of 

Force Controlled Robotic Assembly Parameters.” Control 

and Applications. 30 May – 1 June, 2007. 

[4] Grefenstette, John J. “Optimization of Control Parameters 

for Genetic Algorithms.” IEEE Transactions on Systems, 

Man, and Cybernetics. January/February 1986. Vol. SMC16, No. 1. Pp. 122-128. 

Figure 10: Sample results of internal modeling enhancement for a 

multidimensional physical assembly. 

5. CONCLUSIONS

Discovering an optimal set of parameters for any given task is 

often a long, slow process that frequently requires repeated testing 

and adjustment. The research presented in this paper 

demonstrates that a stochastic approach for parameter 

optimization can be accelerated by means of internal model 

building. As the search explores the parameter space, an internal 

modeling method can extrapolate useful information about its 

operational environment, and essentially learn from the successes 

and failures of the system to aid in the guidance of parameter 

testing and effectively accelerate the rate of optimization. 

[5] Marvel, Jeremy A., et al. “Automated Learning for 

Parameter Optimization of Robotic Assembly Tasks 

Utilizing Genetic Algorithms.” Proceedings of the 2008 

IEEE International Conference on Robotics and 

Biomimetics. 21-26 February, 2009. Pp 179-184. 

[6] Ng, Andrew Y., H. Jin Kim, Michael I. Jordan, and Shankar 

Sastry. “Autonomous Helicopter Flight via Reinforcement 

Learning.” Advances in Neural Information Processing 

Systems. No. 16. 2004. Pp. 799-806. 

[7] Taylor, Brian K., Stephen Balakirsky, Elena Messina, and 

Roger D. Quinn. “Analysis and Benchmarking of a 

WhegsTM Robot in USARSim.” Proceedings of the 2008 

International Conference on Intelligent Robots and Systems. 

22-26 September, 2008. Pp. 3896-3901. 

The results have demonstrated that, even in noisy performance 

environments, intelligent systems have the ability to successfully 

extrapolate useful information and effectively improve the rate of 

improvement. The fact that both virtual and physical applications 

both benefit from the utilization of internal modeling has 

considerable implications for the automation of robotic 

optimization tasks. While these results are still largely 

preliminary, the potential of internal model generation 

implementations is clear. Further research into knowledge 

extraction and intelligent mutation, however, would seem to be 

warranted. For example, neural networks are only as effective as 

their input pattern distributions and architectural constructions. 

Clustering of randomly-generated input parameter sequences may 

adversely skew the surface model of certain applications away 

from a global representation and move toward a localized 

snapshot of the sampled regions with the greatest density, while 

other application solutions may be independent of such sample 

densities. In future work, will investigate the nature of automated 

problems and their parameter spaces that benefit the most from 

internal modeling. 

[8] Yamanobe, Natsuki, et al. “Optimization of Damping 

Control Parameters for Cycle Time Reduction in Clutch 

Assembly.” Proceedings of the 2005 IEEE/RSJ International 

Conference on Intelligent Robots and Systems. 2-6 August, 

2005. Pp. 3251-3256. 

[9] Yip, Percy P. C., and Yoh-Han Pao. “A Guided 

Evolutionary Computation Technique as Function 

Optimizer.” IEEE World Congress on Computational 

Intelligence. 1994. Vol. 2. Pp. 628-633. 

[10] Zhang, George Q., et al. “On-Pendant Robotic Assembly 

Parameter Optimization.” IEEE World Congress on 

Intelligent Control and Automation. 25-27 June, 2008. Pp. 

547-552. 

23
Development of Top-Down Analysis of Distributed

Assembly Tasks

Anthony Cowley

GRASP Laboratory

University of Pennsylvania

acowley@seas.upenn.edu

M. Ani Hsieh

SAS Laboratory

Drexel University

mhsieh1@drexel.edu

C.J. Taylor GRASP

Laboratory

University of Pennsylvania

cjtaylor@seas.upenn.edu

ABSTRACT

Distributed assembly tasks, in which large numbers of agents

collaborate to produce composite objects out of component

parts, require careful algorithm design to ensure behavior

that scales well with the numbers of agents and parts. Yet algorithm evaluation, through which design is guided, is complicated by the combinatorial nature of system states over

the course of execution. This leads to a situation in which

the algorithm design space is often severely cramped by the

inefficiency of available analysis techniques. We review several available analysis strategies, and present two techniques

for designing distributed algorithms that lend themselves to

continuous differential analysis while avoiding catastrophic

deviation between discrete and continuous system models.

This methodology aims to allow optimization at the macro

continuous level to inform parameter choice for discrete, real

world systems.

Categories and Subject Descriptors

I.2.11 [Distributed Artificial Intelligence]: Multiagent

systems

1. INTRODUCTION

In this work, we describe a flexible manufacturing system based on a robot swarm tasked with assembling composite products from distinct parts. The objective is to develop “top-down” design techniques for decentralized control

policies that are invariant to changes in team size and part

quantities while satisfying workspace and task constraints.

To this end, we consider a distributed assembly task where

heterogeneous parts are randomly placed within the environment. Assembly is achieved by tasking robots to wander

the workspace, picking up parts as they encounter them, and

assembling composite objects when they encounter other

robots with complementary parts. The dynamics of the assembly task may be modeled as a chemical reaction network

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since robot-part and robot-robot interactions can be treated

as chemical reactions between different molecules.

Our proposed approach is close in spirit to several previous works in which system dynamics are modeled as chemical reaction networks. Hosokawa et al. used such a model to

predict the yield of full assemblies from a collection of vertically stirred modules [3]. Klavins et al. achieved distributed

self-assembly from component parts through random collisions of parts that bind and detach from each other based

on pre-programmed probabilistic rules [7]. Here, the chemical reaction based model was used to maximize assembly

yield by optimizing the spontaneous detachment probabilities of the various components at equilibrium. However,

the proposed optimization strategy required the enumeration of all reachable system configurations, which does not

scale well with the number of parts. Similarly, Matthey

et al. developed stochastic control policies from chemical

reaction-based models that enabled a robot swarm to assemble distinct products from a collection of heterogeneous

parts [10]. The control policies obtained here provided theoretical guarantees on overall system performance. The use

of mobile robots to manipulate and assemble passive parts

decentrally is similar to other work [11] where the objective

was to derive a rule set to enable the construction of an

entire structure out of simple building blocks.

Similar to earlier works by Hsieh et al. [4] and Matthey et

al. [10], we propose to develop a “top-down” design methodology for generating stochastic agent-level control policies for

a robot swarm based on the mathematical framework used

to model chemical reaction networks. Other works have analyzed collective behavior in cooperative robotic tasks [6].

Macroscopic swarm models have been derived to study the

performance of a distributed foraging strategy under varying conditions [9], while a similar approach has been used to

analyze and study the effects of specialization within large

robot teams [5]. In all these works, robots are treated as single molecules and assumed to be capable of simple atomic

behaviors, with local interactions between robots governed

by a set of reaction rates. Since individual robotic agents

can only assume a finite set of basic behaviors, it is possible

to model system dynamics solely by considering the population distribution across the set of behaviors. By describing

the swarm dynamics via a macroscopic analytical model,

these works have shown that it is possible to derive stochastic agent-level control policies to meet a particular desired

group-level outcome [1, 4, 10], thus providing a “top-down”

versus the traditional “bottom-up” approach to designing

group behavior.

24
Figure 1: An example of heterogeneous primitive

parts A, B, and C that can be assembled into a microrobot.

Figure 2: An example of allowable sub-assemblies

obtained from the assembly of primitive parts A, B,

and C.

We investigate methods to simultaneously adapt the development of these macroscopic analytical models alongside

the discrete behavioral algorithms they are to be applied to

in order to maximize the fidelity of the models. Improved

compatibility between implementation and analysis creates

a virtuous cycle where carefully designed algorithms lead to

higher fidelity modeling which leads to improved algorithm

refinement strategies. Specifically, we consider the execution of collaborative tasks by a swarm of robots whose goal

is to assemble composite widgets made of several smaller

parts. This is relevant to applications in areas such as flexible manufacturing where it may be desirable to have a system capable of assembling significantly different products

on-demand. Other applications include automation of recycling plants and nanoscale assembly where stochasticity is

often the norm rather than the exception.

2. PROBLEM FORMULATION

Consider the problem of deploying a swarm of N robots

to assemble complex products from a set of heterogeneous

parts. For example, consider the problem of assembling a

micro-robot from a pair of wheels, chassis, and a sensor. As

such, the set of possible part types is given {A, B, C} where A

corresponds to the sensor, B corresponds to the chassis, and

C corresponds to the pair of wheels as shown in Figure 1.

The assembly of the micro-robot can be broken down into

the assembly of intermediate products: either an AB, the

attachment of the sensor to the chassis, or a BC, the attachment of the chassis to the wheels, sub-assembly as shown in

Figure 3: An example of an assembled micro-robot

composed of primitive parts A, B, and C.

Figure 2. The sub-assemblies may then be mated with the

missing primitive part, either the set of wheels or the sensor, to complete the assembly of the micro-robot as shown

in Figure 3. Rather than focus on the details of assembling

micro-robots, this work will consider the analogue problem

of assembling ABC widgets since it provides a nice abstraction

for more general assembly tasks.

In particular, we assume uniform distributions of each

part type, A, B, and C, within the workspace. Robots navigate the environment by following a trajectory chosen randomly at start-up and upon encountering an environment

boundary. As robots wander the workspace, they are tasked

to pick-up and assemble intermediate parts, i.e. AB’s and

BC’s, and/or ABC widgets as they encounter parts and other

robots. For simplicity, we assume that the primitive parts,

A, B, and C are replaced in the environment as soon as they

are picked up for any reason. Furthermore, the intermediate objects AB and BC are dropped in the environment upon

production, while a successful assembly of an ABC widget is

immediately removed from the workspace, returning each

agent involved in its construction to a free state identical

to that in which it started. Finally, it is important to note

that agents performing these assembly operations have no a

priori knowledge of their workspace: neither its geometry,

the availability of parts, nor the disposition of other agents.

This abstract assembly task requires cooperation between

at least two agents, without any high-level coordination.

However, while agent-level behaviors that result in cooperative widget assembly are easy to express, and immediately suggest the opportunity for great parallelism through

a simple scaling of the number of parts and agents in the

environment, system-level performance is contingent on benign interactions between concurrent assembly operations.

Toward this goal, we propose to develop robust concurrent

assembly strategies that lend themselves to rigorous analysis

for the purposes of tuning high-level algorithm parameters.

These parameters may include such features as agent-level

preference for certain parts in particular situations. Any

such biases can have a dramatic effect on system performance, and thus represent important tuning parameters for

the system as a whole. Yet, while the effect of such biases

at the agent level may be clear from inspection, the effect

of their interactions when embodied by hundreds of concurrently operating agents is less clear. In this way, the system

tuning process relies on agent-level tuning, and may only be

directed by considering multitudes of interacting agents.

To achieve this, we will first develop a baseline approach

using a swarm of N non-communicating robots with limited

sensing capabilities. In this baseline case, a free robot discovers and picks up a part by physically bumping into it.

Robots encounter each other by a similar physical interaction, at which time they will produce a new composite part

if such an assembly is possible given the parts held by each

agent.

The second variation is similar to the first, but involves

equipping each agent with a sensor that allows for the detection of parts, be they of type A, B, C, AB, or BC, within

some fixed sensing radius of the agent. Additionally, each

agent is capable of coordinating with any other agent within

its communication radius in order to perform an assembly

operation.

The third variation considered here is one in which agents

do not speculatively pick up parts at all. In terms of the

25
identification scheme of discrete agent states implied by the

previous variations, e.g. an agent holding some part of type

A, or some part of type C, this variation is as if agents are

allowed to exist in several overlapping states simultaneously.

An agent may be aware of multiple parts in the environment,

e.g. an agent aware of both a part of type A and a part of

type C, but does not commit to any subsequent operation

until said operation is known to be terminating. That is,

when the agent comes into contact with another agent such

that the two may combine the parts they are aware of to

produce a composite object. Put another way, the previous

two problem formulations force an agent to commit to a

course of action when it encounters a part: should an agent,

upon discovering a part of type A, pick up said part, it has

preordained its immediate future to consist of an assembly

operation in which it contributes a part of type A.

3. ANALYSIS TECHNIQUES

Given an assembly algorithm defined over a set of parameters, a frequent objective is to determine the optimal set or

subset of parameters that can satisfy specific performance

metrics, i.e. maximize widget production. Perhaps the most

intuitive approach is to search for these optimal parameters

by simulating the assembly process. This is most commonly

achieved via an agent-based simulation (ABS) where each

robot agent is simulated individually, and time is a synchronous signal used to advance each agent’s state of execution simultaneously. While this approach has the advantage

of faithfully modeling both the finite individuality of swarm

members and the constant advance of time, it ignores many

opportunities for improved efficiency. First, if many agents

are executing the same behavior, it is not always clear how

much is gained by simulating N copies of the agents. Second, the regular sampling of time must be fine enough such

that each robot is only expected to be involved in one interaction between simulation samples. Otherwise, the order of

events during a time interval is unspecified, which can lead

to undefined behavior. However, this fine-grained, regular

sampling typically results in many samples when nothing

interesting happens. Such intervals are identified by purely

deterministic behavior that could be perfectly modeled in

a more computationally efficient manner. For instance, the

position of a particle moving with constant velocity under

the influence of no external forces may be accurately predicted by simply integrating the known velocity, rather than

simulating the movement by a sequence of identical discrete

jumps through space.

To address the efficiency of time sampling while still explicitly representing the discrete agents that make up the

system, one may employ a macro-discrete model [2]. In this

model, one arranges to only sample the simulation when an

interaction occurs. This is achieved by modeling the rate at

which events happen with a stochastic process, typically a

Poisson distribution, and advancing the simulation directly

between the times at which events occur. A Poisson process with time constant k fires at random times with the

firing probability per unit time given by k. The process is

Markov since the firing probability is independent of past

history. The distribution of intervals between two firings

can be derived analytically and is given by p(t) = ke−kt.

Thus, one can simulate Poisson transitions in two mathematically equivalent ways. (1) Run iterations with a small

time step ∆t << 1/k; at each iteration, the probability of

transition is ∆p = k∆t. The transition is triggered in the

current iteration if r < ∆p, where 0 < r < 1 is a uniformly

distributed random number. This implementation is exact

in the limit k∆t → 0. (2) Generate a random number tr

distributed according to p(t) = e

−kt and take the transition

at time tr. This second implementation has been shown to

be mathematically equivalent to an agent-based simulation

[2]. Since each agent is modeled individually, system dynamics dependent on small numbers of individuals may be

faithfully captured, while overall simulation performance is

greatly increased.

The final strategy considered here is a continuous model

of system dynamics. For large enough numbers of robots

and parts, it is possible to derive an analytical macroscopic

description of the dynamics of the assembly process. Such

a model stands in stark contrast to the previous two as it

adopts a continuous model of the passage of time, but also

takes the drastic step of abstracting discrete parameters into

continuously varying values, such as mapping the number of

agents engaged in a particular behavior to fractions of the

total population. The distinct advantage of this approach

is that it brings to bear all the long established analysis

tools and techniques from the study of differential equations, and allows for the immediate numerical solution to

parameter optimization for most systems. The performance

of such methods vastly outpaces the alternate simulation

techniques, and thus allows for significantly more rapid iteration of algorithm design since algorithm performance can

be easily approximated.

4. ALGORITHM DESIGN

While continuous models are incapable of reflecting the

discrete dynamics of a distributed assembly task, they are

often good enough, and represent such a compelling performance advantage over other simulation techniques that investigating their applicability is a worthy endeavour. While

continuous models treat inherently discrete quantities as

continuously varying, their behavior may closely mimic that

of the discrete system when large numbers of particles are

considered. For example, a continuous model may indicate

that, at some point in time, 50.02% of agents are engaged

in a particular behavior. If there are one thousand agents in

the actual system, then such a configuration is impossible:

the assignment of agents to behaviors is entirely discrete.

However, if the true performance of the system would have

exactly five hundred agents engaged in the behavior, then

the deviation of the continuous model from the discrete system may not introduce significant error.

For the specific application of assembly tasks, one may

identify the key areas where continuous models break down.

One such area is the possibility of deadlock. A deadlocked

configuration is one in which no agent is able to change

its own state with respect to an assembly task. That is,

agents may move about the environment, but none makes

any productive contribution to an assembly operation. Such

a configuration may result in the example problem described

above if every agent should pick up a part of type A, for

example, and not release it until it is able to contribute that

part to an assembly operation. In this case, no agent is able

to find another agent with a complementary part, thus no

progress is made in the assembly task. In comparison, a

continuous model may suggest that some fraction, say 10%,

of a single agent is still free to take productive action, thus

26
allowing some non-zero probability that the system should

recover. Yet the discrete, real system does not allow this

configuration: if zero agents are able to make progress, then

the system is in a stable state of non-production.

Crucially, such a configuration never occurs in a continuous model of the system. This is because each agent exists

in all possible states at once according to some probability

distribution; no commitment need be made. This deviation

of the continuous model from the real system is catastrophic

when it occurs, and arguably invalidates any proposed utility

of the entire approach. However, this deviation of the continuous model from real system performance may be precluded

by designing the algorithm such that deadlock is not a reachable configuration. An example of such a design technique is

a transactional approach to resource-consuming operations.

One may view a productive assembly as a sequence of

robot resource acquisitions followed by an assembly operation. Note that the action of a robot picking up a part consumes a robot resource. While it is unknown precisely what

assembly operation will ultimately exploit that resource, the

robot may retro-actively be classified as locked by the abstract assembly operation that eventually uses it. Viewed

against time, the interval during which a robot resource is

held begins when a free robot encounters a part, and ends

when that robot contributes the part to an assembly operation. But this time is unbounded! If instead one is able

to bound the time any resource is exclusively held by some

operation, then deadlock is avoided. This may be achieved

in distributed assembly tasks by taking advantage of the

fact that robots are not molecules, and are typically imbued with remote sensing and communication capabilities.

Thus, a robot may simply transition between various states

of awareness of components in its vicinity without physically acquiring exclusive domain over any one part. Note

that the exclusive domain in this case is mutual: the part

may be viewed as having exclusive ownership of the robot

that has picked it up. The robot resource is consumed by

the part until it is able to integrate the part into a composite

object.

When a communicating group of robots decides that, collectively, they know how to obtain the components necessary

to assemble a composite part, then, and only then, do they

move to physically pick up the necessary pieces before engaging in a cooperative assembly operation. Part acquisition

may fail, but such an eventuality may now be reasonably detected by a simple time limit on the action of a robot picking

up a sensed part. That is, one may assume that a robot actively sensing a part may acquire, or fail to acquire, that part

in bounded time. In this way, the entire assembly operation

occurs in bounded time, and may be viewed as transactional

– the full assembly occurs instantaneously or not at all – by

an external observer.

A shortcoming of this approach is that it may be too conservative. In fact, some speculative execution of the assembly task may suffice to overcome an environmental condition

such as an excessive sparsity of parts. Specifically, if parts

are spaced farther apart than twice the robots’ communication radii, then no two robots will ever be able to share

their concurrent knowledge of part locations. This represents a type of live lock, wherein individual robot state,

vis-`a-vis the set of parts sensed by the robot, changes as

time progresses, but no productive work is done. However,

if a robot should optimistically acquire a part, thus locking

Algorithm 1 A simple probabilistic behavior causing

robots to switch between driving to the left or to the right

for τ time.

while true do

if random() < k then

driveLeft(τ )

else

driveRight(τ )

end if

end while

itself to an assembly operation, it may move within communication range of another robot either sensing or holding a

complementary part.

In order to avoid deadlock, while maintaining the benefits

of optimistic execution, one may specify that some robots

act optimistically while other act pessimistically. This amounts

to a hedging strategy against unforeseen environmental conditions: the pessimistic strategy is advisable when parts are

densely packed as it maximizes potential parallelism, while

the optimistic strategy is necessary to make any progress

when parts are few and far between. Such a heterogeneous

behavior population may be arrived at by programming agents

to probabilistically assign themselves one behavior or the

other. This stochasticity may be added without affecting

the execution of either behavior by wrapping the two deterministic behaviors in a probabilistic conditional expression.

5. EXAMPLE ANALYSIS

One may consider a single probabilistic deterministic algorithm that induces a specific population distribution over

discrete classes. This is useful because the single algorithm

can be duplicated an arbitrary number of times while always

maintaining the desired population statistics. However, for

analysis, one can strip off a layer of randomization by representing the algorithm as two distinct sub-populations, each

of whose relative prevalence is defined by the statistics of the

probabilistic element of the original algorithm. This may be

demonstrated by a simple example.

Consider Algorithm 1, which, when executed by a population of N agents equipped with a suitable random number

generator approximating a uniform distribution, can be expected to yield kN agents driving to the left, and (1 − k)N

agents driving to the right. This same algorithm can be

deconstructed by lifting the impact of the if...then...else

construct into the top-down population specification. Alternatively, this algorithm may be viewed as an implementation

of a top-down design directive. Concretely, the chemical kinetics specification,

Rright

k

τ−→ Rleft

Rleft

1−k

τ −−−→ Rright

may be used to model Algorithm 1, or, from the other direction, the above reaction equations may be implemented at

the agent level by Algorithm 1. Here, τ can be interpreted

as a simple scale factor in the time domain. These reaction

equations result in a differential model of the population

distribution,

27
R ̇

left =

k

τ

Rright −

1 − k

τ

Rleft,

R ̇

right =

1 − k

τ

Rleft −

k

τ

Rright.

In order to evaluate steady state production levels of an assembly algorithm, one would typically be interested in equilibrium conditions of this system,

1

τ

»

k 1 − k

1 − k k – » RrightRleft –

= 0

The equilibrium condition of the linear system induced

by Algorithm 1 indicates the relative proportion of agents

driving left, and those that are driving to the right. Given a

value for k, Rright and Rleft can be solved for by imposing

a conservation condition that Rright + Rleft = 1. That is,

the sum of the population fractions must be one. Thus one

arrives at the expected result that Rright = Rleft = 0.5 if

k = 0.5, for example. Assembly tasks do not often lend

themselves directly to description by linear system, however

the resulting system of differential equations may still be

solved numerically when no analytic solution is available.

5.1 Results

While the continuous model of the example widget assembly task is free to consider nonsensical concepts such as a

fraction of a robot, its performance still matches the agentbased simulation at large population sizes, Figure 4. These

graphs show that solutions of the differential equations modeling system dynamics, number of robots holding a part of a

particular type and total ABC widget production rate, closely

match the behavior of the discrete agent based simulation.

The assumption of the existence of partial robots does,

however, deviate strongly from reality in the presence of

deadlocked behaviors. This is best seen by considering smaller

populations executing locking behaviors where deadlocked

configurations are statistically probable events. Figure 5

shows the agent-based simulation’s production rate of ABC

widgets dropping to zero due to the population working itself

into a deadlocked configuration. In this scenario, the most

common cause for deadlock proved to be too many agents

holding onto intermediate parts of type AB or BC while the remaining agents held parts of type B. If all the agents in the

system assume one of those roles, then overall production

ceases. The cessation of production is stable and unrecoverable: the controller makes no allowance for detecting and

escaping a system-wide deadlocked behavior.

This type of deadlock, where all robot resources are consumed, may be avoided by a transactional assembly approach that does not speculatively lock a robot to a particular part. The steady state production rate for the transactional assembly technique, as seen in Figure 6, dwarfs the

steady state of the locking approach shown in Figure 4, even

when just considering the continuous model. This is due

to the greater extent to which the transactional assembly

behavior exploits potential concurrency in the system: the

duration for which a robot resource is exclusively locked is

strictly bounded. Turning to the agent-based simulation results shown in Figures 4 and 6, the danger of deadlock in

the speculative locking strategy is starkly apparent. While

the continuous model is not susceptible to falling into a staFigure 4: Comparison of system states over time between an agent-based simulation (ABS) and a continuous differential model (Cont) for a locking assembly strategy. Agent population size of 1000; 900

of each type of primitive part.

ble “stuck” configuration, the discrete system is, and may do

so at any time.

However, the conservative approach taken by the transactional assembly behavior has its own drawbacks. While it

avoids prematurely locking a robot resource, it is far more

susceptible to production shortfalls due to unfortunate environmental conditions. Namely, if part density is low, there

is a chance that individual parts may be so widely spaced

that two robots can never simultaneously sense two different, compatible parts and be in communication range of

each other. This occurrence is, once again, not seen in the

continuous model since, in such a model, a low part density

means that a productive configuration, in which two robots

sense two parts and can communicate, is unlikely, but never

impossible. In a discrete system, however, the transactional

assembly behavior may yield a production rate of zero if the

parts are distributed with too much distance between them.

This second form of deadlock, in which no positive progress

may be made by the system, can be avoided by a mixed

population.

Without complicating agent-level behaviors, one may avoid

both forms of deadlock by hedging against either a sparsity

of robots or a sparsity of parts. The performance advantage

28
Figure 5: Composite widget productions rates for

a team whose agents have effectively zero sensing

and communication radii and a team whose agents

have finite, non-zero sensing and communication

radii, Rp and Rc respectively. Results for the agentbased simulation (ABS) are compared with those

from the continuous differential model (Cont). Sensing and communication increase theoretical performance, but quickly deadlock in the agent-based simulation. Agent population size of 100; 900 of each

type of primitive part.

of a mixed population is shown in Figure 7, in which 100

robots are operating in an environment with an uneven part

distribution. Namely, there are regions of the environment

densely packed with parts, and there are regions with very

low part density. The production rate of the transactional

assembly behavior, shown with a solid red line, plummets

when a significant fraction of agents find themselves in low

density regions, while the heterogeneous team has a much

higher minimum production rate.

6. DISCUSSION

One can see, by comparing Figure 6 with Figure 5, the difference in absolute widget production rates for a given set

of system parameters. Even if one ignores absolute productivity measures, the deadlock-freedom of the transactional

assembly behavior trumps any other performance considerations of different agent-level behaviors.

Modifications to agent-level behavior that avoid deadlocked

configurations have typically been implemented as spontaneous decay reactions in other robot swarm simulations based

on chemical reaction networks [8, 7, 5, 10]. However, such

spontaneous reactions, in which a robot has some non-zero

probability of simply dropping a part it is carrying, may not

be necessary in order to avoid deadlock configurations. Instead, deadlock freedom may be attained by more deterministic behavior specification, as in the transactional assembly

scheme that avoids robot starvation.

By working with agent-level behaviors that preclude deadlocked scenarios, one is able to leverage computationally efficient differential models of system dynamics. This ability

Figure 6: Composite ABC widget production rates for

the transactional assembly behaviors. The fraction

of agents aware only of a part of type A is also shown

for reference. Agent population size of 100; 900 of

each type of primitive part.

Figure 7: A purely conservative approach can be

stymied by a sparsity of resources. The homogeneous team is adversely affected by regions of the

environment with a low part-density. To counter

this, an even mix of eager and conservative agents

(100 of each) avoids the production fall-offs of each

in an agent-based simulation.

means that one can quickly predict system performance for

a given set of parameters, e.g. part density, and take some

action to adjust these parameters if system performance

is insufficient. Hedging strategies, as demonstrated in the

mixed transactional-speculative population above, may be

efficiently implemented by a probabilistic role assignment

mechanism in which agent’s adopt a particular behavior

based on a desired distribution. This assignment technique

is robust to changes in population size, and requires no peragent customization, thus making it suitable for swarm deployment scenarios. The probabilistic combination of deterministic behaviors represents a sweet spot of easily understood agent-level behaviors, scalability to large, varying

29
population sizes, and amenability to differential modeling

techniques.

7. REFERENCES

[1] S. Berman, A. Halasz, V. Kumar, and S. Pratt.

Bio-inspired group behaviors for the deployment of a

swarm of robots to multiple destinations. In

Proceedings of the 2007 International Conference on

Robotics and Automation (ICRA07), pages 2318–2323,

Rome, Italy, April 2007.

[2] D. Gillespie. Stochastic simulation of chemical

kinetics. Annual Review of Physical Chemistry,

58:35–55, 2007.

[3] K. Hosokawa, I. Shimoyama, and H. Miura. Dynamics

of self assembling systems: Analogy with chemical

kinetics. Artificial Life, 1(4):413–427, 1994.

[4] M. A. Hsieh, A. Halasz, S. Berman, and V. Kumar.

Biologically inspired redistribution of a swarm of

robots among multiple sites. Swarm Intelligence,

2(2):121–141, 2008.

[5] M. A. Hsieh, A. Halasz, E. D. Cubuk, S. Schoenholz,

and A. Martinoli. Specialization as an optimal strategy

under varying external conditions. In Accepted the

2007 International Conference on Robotics and

Automation (ICRA07), Kobe-Japan, May 2009.

[6] E. Klavins. Programmable self-assembly. Control

Systems Magazine, 24(4):43–56, 2007.

[7] E. Klavins, S. Burden, and N. Napp. Optimal rules for

programmed stochastic self-assembly. In Proc.

Robotics: Science and Systems II, pages 9–16, Atlanta,

GA, 2007.

[8] K. Lerman, A. Galstyan, A. Martinoli, and A. J.

Ijspeert. A Macroscopic Analytical Model of

Collaboration in Distributed Robotic Systems.

Artificial Life, 7(4):375–393, 2001.

[9] K. Lerman, C. V. Jones, A. Galstyan, and M. J.

Mataric. Analysis of dynamic task allocation in

multi-robot systems. International Journal of Robotics

Research, 25(4):225–242, 2006.

[10] L. Matthey, S. Berman, , and V. Kumar. Stochastic

strategies for a swarm robotic assembly system. In

2009 IEEE International Conference on Robotics and

Automation (ICRA’09), Kobe, Japan, May 2009.

[11] J. Werfel and R. Nagpal. Three-dimensional

construction with mobile robots and modular blocks.

International Journal on Robotics Research,

27(3-4):463–479, 2008.

30
Context-Based Object Recognition 

Shaun Edwards 

Southwest Research Institute 

6220 Culebra 

San Antonio, TX 

1-210-522-3277 

shaun.edwards@swri.org

Meredith Wright 

Southwest Research Institute 

6220 Culebra 

San Antonio, TX 

1-210-522-6247 

meredith.wright@swri.org

Ben A. Abbott, Ph.D. 

Southwest Research Institute 

6220 Culebra 

San Antonio, TX 

1-210-522- 2802 

ben.abbott@swri.org

ABSTRACT

Several methods have been developed for context-based object 

recognition within aerial imagery. These methods were inspired 

by human object recognition, which has been shown to rely on 

contextual information as opposed to classical appearance based 

methods. While this concept may not be new, this research sought 

to develop generic methods that leveraged recent developments in 

cognitive systems research, and more specifically large scale 

ontologies or knowledge bases. The results of the research have 

shown that context-based methods, supported by an ontology, can 

increase recognition rates versus classical appearance based 

methods. These methods have the potential to automate many 

complex object recognition tasks, aerial imagery analysis being 

one of them, that currently require human analysis. 

Categories and Subject Descriptors

J.2 [Physical Sciences and Engineering]: Engineering

General Terms

Algorithms, Performance. 

Keywords

Computer vision, image processing, object recognition, ontology, 

context. 

1. INTRODUCTION 

The process of object recognition, within the context of computer 

vision, is the method by which an object is identified within 

visual images. The applications of object recognition include 

robotics, image storage and retrieval, automated surveillance, and 

aerial imagery analysis. In each of these applications areas, 

accurate object recognition is critical. Unfortunately, the 

performance of current techniques is not sufficient for the bare 

minimum functionality required for those applications. 

This lack of performance is not due to a lack of research. To the 

contrary, object recognition has been well researched, with 

several texts dedicated to the topic [1]. A popular approach is to 

isolate objects within an image, calculate a set of features for that 

object, and then compare those features to known object types 

using pattern recognition techniques. While generic and 

powerful, this pattern recognition approach is inherently 

appearance based, and as such, tends to fail when object types 

look very similar. This weakness is exacerbated when the number 

of object types becomes large, thus increasing the likelihood that 

two object types will look very similar. Unfortunately, future 

applications of object recognition are moving in this direction. 

Without a significant leap in technology, current methods will not 

be able to address future challenges. 

The current pattern-based approaches to date have shown limited 

success, but are still grossly inadequate, especially when 

compared to the object recognition capabilities of people. It 

stands to reason, that the performance of current techniques could 

be improved by borrowing from those methods that have been 

shown to be used by people. A key aspect used by people, and all 

but ignored by pattern matching techniques, is the image context 

[2]. The use of context for object recognition is not new [1]. It 

could also be argued that context is implicitly “hard coded” into 

an application. For example, a method for an outdoor scene 

might assume the sky is higher in an image than the ground. This 

“hard coded” approach is inflexible. A generic context-based 

object recognition approach will require flexible and efficient 

methods for storing and reasoning over contextual information. 

Such methods, while not developed specifically for computer 

vision and object recognition, have been developed for cognitive 

systems. 

In this paper, we present several methods for using contextual 

information stored within an ontology to aid in object recognition 

in aerial imagery. The ability to capture aerial images is far 

outpacing the ability to analyze them, a task that is typically 

performed by people. The data resulting from this image analysis 

is used for anything from city planning to intelligence gathering. 

In order to be useful, this data must be up to date and accurate. 

The developed methods include two heuristics and one 

mathematical approach. The results of these methods are 

compared to classical generic object recognition algorithms to 

determine the relative effectiveness of context when combined 

with traditional pattern recognition techniques. 

2. METHODS 

2.1 Technical Approach 

The software developed for this research contains three core 

components, an ontology, a classifier and a simulator. The 

ontology software component captures the system knowledge 

base. It stores the object features and relationships. The classifier 

software component contains all the classifiers developed or used 

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personal or classroom use is granted without fee provided that copies are 

not made or distributed for profit or commercial advantage, and that 

copies bear this notice and the full citation on the first page. To copy 

otherwise, to republish, to post on servers or to redistribute to lists, 

requires prior specific permission and/or a fee. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

Copyright © 2009 ACM 978-1-60558-747-9/09/09...$10.00

31
for this project. This includes classical feature only classifiers 

and the newly developed context-based classifiers. The final 

software component, the simulation environment, generates 

objects to be classified and computes the resulting classifier 

statistics. The use of a simulated environment removes the need 

for image processing and segmentation, both complex problems. 

The simulation instead focuses this research on pattern 

recognition. 

2.2 Ontology Structure 

The Cyc (pronounced “psych”) ontology was used for this 

project[5]. Cyc contains a general purpose framework for 

representing common sense knowledge. An initial survey of the 

topics stored within Cyc, found that it contained many of the 

concepts relevant to aerial imagery. Many of the object types 

found in an aerial image were already stored in Cyc. In fact, with 

only one or two exceptions, no new object types had to be entered 

into the ontology. In total, 49 object types, many of which looked 

very similar, were identified in the Cyc ontology for classification 

of overhead imagery. 

Cyc also contained relevant spatial relations such as, near, 

parallel, spatially contains and others. Six relations were 

identified for use. The six base relations required mathematical 

and geometrical grounding. Given two objects, a mathematical 

formula was required to determine if the two objects were indeed 

related. For example, the Cyc relation spatiallyContains requires 

that one object contain another. Given location and size 

information for each object, determining if one object contains 

another is straight forward. Other relations, such as near,

required more complicated formulations given the ambiguity in 

the relation itself. 

Unfortunately, Cyc was missing the necessary assertions about 

the object types and their relations to be useful for aerial imagery 

analysis without modification. Over 100 assertions were made to 

the ontology. All assertions were limited to relating two objects. 

Positive assertions were made if, in general, a relation was found 

to hold. For example, in general, “Road vehicles are found near 

other road vehicles”. Negative assertions were made if a relation 

was found not to hold. For example, “Modern-Houses are not 

found near highways”. By way of Cyc’s inheritance rules the 100 

assertions resulted in almost 500 defined spatial relations between 

individual object types. 

2.3 Classifiers 

One of the goals of this research was to evaluate classical pattern 

matching methods against the newly developed contextual 

matching methods. For the purposes of this research two classical 

methods were chosen, Naïve Bayes[3] and Nearest Neighbor[4]. 

Both methods were taken from the Weka Data Mining library[6]. 

These methods are very similar in that both use training data to 

perform classifications. The training data was obtained from 

extracting images (a maximum of 10 images per object) of the 

desired objects from overhead images. 

2.3.1 Generic Classifier 

Figure 1 shows the generalized classifier. This implementation is 

similar to the Weka implementation; except that this 

implementation requires an entire object set for classification, 

whereas the Weka implementation works only on single objects. 

This is because, in order to make use of context, the object to be 

classified as well as the related objects must all be classified at 

once. 

Figure 1: Generalized classifier 

32
Each classifier takes a set of unknown objects as an input. An 

initial seed classification is performed. For the purposes of this 

research, the seed classification is a feature classification, but 

nothing within the generic structure requires this. The seed 

classification could just as easily be another generic classifier. 

The seed classification is then refined by applying contextual 

information provided by the ontology. In order for contextual 

information to be applied, it must both be logical true and 

physically true. For example, given two objects, an airplane and 

an airport terminal building, the two are only contextually 

significant if logically the two objects would be found “near” 

each other (which is true) and if physically the objects are “near” 

each other (which may or may not be true). A classified object 

set is produced by the classifier. The classified set consists of the 

list of possible classes along with some measure of the match. 

Typically, this list is normalized so that the measure represents 

the probability of a match, but this isn’t required. 

2.3.2 Combined Context Classifier 

The combined classifier was the first and most simplistic context 

classifier developed. It is a heuristic for modifying the initial seed 

classifier given the number of arguments for and against the 

classification (as determined by the ontology). 

In the first step, the results of the seed classifier are compared to a 

threshold constant. Those classifications that exceed the 

threshold are considered complete. Those that do not, are updated 

based on contextual information provided by the previously 

determined complete classifications. Two types of contextual 

information are used, supporting (represented by positive 

assertions within the ontology) and opposing (represented by 

negative assertions within the ontology). A counter is kept of 

arguments. Every piece of true (logical and grounded) supporting 

evidence increments the counter, and every piece of true opposing 

evidence decrements the counter. The seed classification is then 

scaled by the following factor shown in Figure 2. 

nf

nif

nf

nif

evidencen counter

where

1

1

1

:

=

<

=

≥

=

Figure 2: Combined classifier scaling factor definition 

The scaling factor calculation multiplies the seed classification, if 

there are more arguments for a classification, and divides it if 

there are more arguments against the classification. The resulting 

values are then normalized in order to return probabilities of a 

match. 

The pseudo code implementation for the combined context 

classifier is shown below. 

for classification in seedClassification 

 if classification >= threshold 

 classifiedObjects.add(object) 

 else 

 unclassifiedObjects.add(object) 

for object in unclassifiedObjects 

 for relatedObject in classifiedObjects 

 for relation in contextRelations 

 for possibleType in objectClasses 

if positive relation holds for (object, 

possibleType, relatedObject) 

 argCount++ 

if negative relation holds for (object, 

possibleType, relatedObject) 

 argCount- - 

 

 updateSeedClassification(type, scaleFactor(argCount)) 

return updatedSeedClassification 

2.3.3 Probability Combined Context Classifier 

The probability combined classifier is very similar to the 

combined classifier (see section 2.3.2), except that classifications 

are not considered complete after the seed classification. Instead, 

all possible classifications and their corresponding probabilities 

are considered when tallying the number of supporting and 

opposing arguments. The amount the argument counter is 

incremented/decremented is the probability of a match (instead of 

+/- 1 as with the combined classifier). 

The resulting classifier improves upon the combined classifier by 

including the probabilities of classification of other objects. In 

other words, a strong classification of a related object is more 

relevant than a weak classification. In addition, because a 

classification is not considered complete after the initial seed 

classification, this method can reject a false positive 

classification, even if it is a strong classification. 

The pseudo code implementation for the probability combined 

context classifier is shown below. 

 for object in unclassifiedObjects 

 for relatedObject in unclassifiedObjects 

 for relation in contextRelations 

 for possibleType in objectClasses 

for relatedType in 

seedClassification(relatedObject) 

 if positive relation holds for ( 

object, possibleType, relatedObject, 

relatedType) 

 argCount = 

 argCount + relatedType.measure 

 if negative relation holds for ( 

object, possibleType, relatedObject, 

relatedType) 

 argCount = 

 argCount - relatedType.measure 

updateSeedClassification(possibleType, 

scaleFactor(argCount)) 

33
return updatedSeedClassification 

2.3.4 Bayesian Context Classifier 

The Bayesian context classifier represents a more mathematical 

approach to using context for classification whereas the two 

previous methods were heuristics. In the simplest of terms, an 

object classification is either supported or opposed by the 

relations to the objects around it. In terms of probability, if a 

particular object classification is reaffirmed by the surrounding 

objects, then the probability of the classification should be 

increased. Bayes theorem (shown in Figure 3) describes 

mathematically how an existing probability is updated; given the 

observation of new evidence. 

( ) ( ) ()

( )

( )

( )

( )

( )

other allgiven shypothesie possible

occurring evidence theofy probabilit or the

 evidence theofy probabilit marginal theis 

hypothesis given the occuring

 evidence theofy probabilit theis |

evidence thebefore 

prior theis hypothesis theofy probabilit

given evidence given the hypothesis

 theofy probabilit updated theis |

:

*| |

EP

HEP

H

HP

H E

P EH

where

EP

HPHEP EHP =

Figure 3: Bayes thereom 

Bayes theorem, in its standard form, does not apply to the specific 

application of using context for object recognition. Bayes 

theorem requires that the evidence must be observed, implying a 

probability equal to one. In object recognition, the probability of 

the evidence (or a particular classification of the surrounding 

object) is uncertain. This problem is common to other 

applications, and methods for adapting Bayes theorem have been 

developed[7]. The equations for updating the probability of a 

classification have been adapted to our application and are shown 

in Figure 4. 

( )

( ) ()

( )

() ( )

( ) ( )( )

( )

( ) ( ) () ⎪ ( )

⎪

⎪

⎪

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−

− ′

+ −

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≤ ′ ≤

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given occurring evidence theofy probabilit theis 

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assertion negative a Ifexists. exists

unknown object classification 

object related the classifica andtion 

assertion positive a if between

ontology w 0.75 is | thehere

particular given is This type. by the

unknown that the a isobject 

given observed isobject related

probabilit theis | y that the

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particular a given type given , evidence

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classification)

object related theof (given seed by the

classification features given the

object related theofy probabilit theis |

classifica seedby tion)

particular a of isobject (given type

prior theis probabilit unknown y that the

object related theof 

features given the updated theis |

:

HPHEP ii

EP

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HEP

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E

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E

EEP

HP

E

EHP HP

where

∑

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Figure 4: Probability of object classification given the features 

of a related object. 

The Bayesian context classifier works very similarly to the 

probability combined context classifier in that all possible 

classifications of the unknown object and the related object are 

taken into account. However, the Bayesian approach allows the 

probability of an unknown object classification to be updated 

directly and recursively, removing the need for counting the 

number of positive and negative arguments. 

The pseudo code implementation for the Bayesian context 

classifier is shown below. 

for object in unclassifiedObjects 

 for relatedObject in unclassifiedObjects 

 for relation in contextRelations 

 for objType in objectClasses 

 for relatedType in 

seedClassification(relatedObject) 

 if positive relation holds for ( 

 object, objType, relatedObject, relatedType) 

34
updateSeedClassification(object, objType, 

relatedObject, relatedType) 

 if negative relation holds for ( 

 object, objType, relatedObject, relatedType) 

updateSeedClassification(object, 

objType,relatedObject, relatedType) 

return updatedSeedClassification 

2.4 Simulation 

The simulation environment (shown in Figure 5) generates the 

objects within the scene and provides a user interface in which to 

test and debug classification algorithms. Each scene is described 

by an xml file. The xml file is generated by hand classifying 

overhead images and contains the type (for evaluation of the 

classifiers), location (x, y) and orientation of each object. The 

simulator generates the object features for each object in the xml 

file; using information about the distribution of each feature 

obtained from the training data. In addition, the simulation can 

add uniform noise to each feature. The uniform noise is 

represented as a percentage of the feature value. It is important to 

note that although specific objects are simulated from statistics of 

an object sample set, the contextual relations are not. This is 

because context is determined by the location and orientation of 

each object, which is determined from actual overhead imagery. 

 

 

3. RESULTS 

The three context-based classification methods were each 

compared to the classification results of the seed classifier alone. 

This comparison shows the increase or decrease in recognition 

that context provides. The classifiers were tested on aerial 

imagery of different types of areas, including an airport, industrial 

area, military base, downtown, and an agricultural area. 

3.1 Classifier Optimization 

The classifiers were optimized at three different random noise 

levels in the simulation. By optimizing the classifiers, the best 

classifier performance is achieved, allowing meaningful 

comparisons to be made between classifiers. The classifiers were 

optimized by finding the optimum points along the receiveoperator characteristic (ROC) curve[8]. The ROC curve shows 

the tradeoff between correct classifications and incorrect 

classifications as some classifier parameter is varied (in this case, 

the threshold probability for a classification). 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0% 10% 20% 30%

% Incorrect

% Correct

Figure 6: Typical ROC curve showing the three context-based 

recognition algorithms vs the seed classifier (Naïve Bayesian) 

alone 

3.2 Classifier comparison 

Table 1 and Table 2 show the optimized classifier comparisons, 

relative to their seed classifiers (Naïve Bayes and Nearest 

Neighbor, respectively) at different random noise levels. Two 

intermediate metrics are used to evaluate the difference between 

classifiers, the percent difference correct and percent difference 

incorrect. As can be observed from the ROC curves (Figure 6), 

there is typically a tradeoff between these two metrics. Assuming 

equal weight to these two metrics, taking the difference between 

them (%difference correct - %difference incorrect) indicates the 

value of the tradeoff. For instance, a reduction in both percent 

correct and percent incorrect would still have superior recognition 

as long as the %difference incorrect was greater than the 

%difference correct. 

Figure 5: Simulation screen shot (San Antonio Airport test 

case). Markers indicate objects for classification. Blue 

markers indicate unknown objects, green indicate correctly 

classified, and red indicated incorrectly classified. 

Combined Classifier 

Probability Combined Classifier 

Bayesian Combined Classifier 

Naïve Bayesian Classifier (Feature Only) 

35
Table 1: Results of classifier simulations at varying noise levels versus a naïve Bayesian classifier. In almost all cases the contextbased classifiers show better recognition (increased percent correct, decreased percent incorrect, or both). 

Noise Classifier 

Sum of Number 

Correct 

Sum of Number 

Incorrect 

% Difference From 

Number Correct 

Number Incorrect 

% Difference From 

Total Percent 

Difference 

0% Naïve Bayes 2938 1930 

 Bayesian Context Classifier 2496 1221 -15.04% -36.74% 21.69% 

 Combined Context 3165 1543 7.73% -20.05% 27.78% 

 Probability Combined Context 3909 1100 33.05% -43.01% 76.05% 

10% Naïve Bayes 1976 1523 

 Bayesian Context Classifier 2349 1932 18.88% 26.85% -7.98% 

 Combined Context 2534 1928 28.24% 26.59% 1.65% 

 Probability Combined Context 2582 1775 30.67% 16.55% 14.12% 

25% Naïve Bayes 1010 1357 

 Bayesian Context Classifier 1138 1445 12.67% 6.48% 6.19% 

 Combined Context 1047 1367 3.66% 0.74% 2.93% 

 Probability Combined Context 1115 1062 10.40% -21.74% 32.14% 

Table 2: Results of classifier simulations at varying noise levels versus nearest neighbor classifier. In almost all cases the contextbased classifiers show better recognition (increased percent correct, decreased percent incorrect, or both). 

Noise Classifier 

Sum of Number 

Correct 

Sum of Number 

Incorrect 

% Difference From 

Number Correct 

Number Incorrect 

% Difference From 

Total Percent 

Difference 

0% Nearest Neighbor 3829 1020 

 Bayesian Context Classifier 3798 900 -0.81% -11.76% 10.96% 

 Combined Context 3870 771 1.07% -24.41% 25.48% 

 Probability Combined Context 3832 741 0.08% -27.35% 27.43% 

10% Nearest Neighbor 3618 975 

 Bayesian Context Classifier 3658 915 1.11% -6.15% 7.26% 

 Combined Context 3695 847 2.13% -13.13% 15.26% 

 Probability Combined Context 3617 790 -0.03% -18.97% 18.95% 

25% Nearest Neighbor 2313 1163 

 Bayesian Context Classifier 2248 813 -2.81% -30.09% 27.28% 

 Combined Context 2400 1350 3.76% 16.08% -12.32% 

 Probability Combined Context 2514 1210 8.69% 4.04% 4.65% 

36
4. Conclusion and Discussion 

Table 1 and Table 2 show that, in most cases, recognition rates of 

the context-based classifiers are superior to the feature only 

classifiers. It was hoped that the context classifiers would show 

robustness under increasing amounts of noise. However, 

recognition actually decreased with increasing noise. The 

application of noise causes a large proportion of the seed 

classifications to be weak classifications. That is, instead of a 

single strong match, several types are matched to a single object. 

Strong matches are required in order to establish context and in 

turn reinforce weaker matches. It stands to reason that fewer 

strong matches will result in weaker reinforcement of weaker 

matches, and thus fewer correct classifications. 

The data does not support a single best implementation of the 

context classifier. This is not unexpected given that is the nature 

of most classifiers, with each performing differently under 

different applications. The popularity of the Bayesian approaches 

to classification could be attributed to the strict mathematical 

approach. While the Bayesian context classifier has a similar 

mathematical basis, the probabilities associated with ontological 

assertions were arbitrary. Better results may have been achieved 

if more accurate probabilities were used. Accurate probabilities 

for relations could be developed over time by adding a learning 

component to the ontology. 

The focus of this work was to evaluate methods for integrating 

contextual knowledge into object recognition techniques. The 

Cyc ontology was chosen for this task due to its breadth and depth 

of knowledge. While some knowledge was added for the 

purposes of this research, the criticality of that knowledge was not 

evaluated. For the application of object recognition in aerial 

imagery, real-time performance was not a constraint. However, 

for most object recognition applications real-time performance 

will be required. For these applications, the size and structure of 

the knowledgebase will be critical. More research is required to 

answer the questions: What is the optimum type and amount of 

information required for context-based recognition? 

5. ACKNOWLEDGMENTS 

The authors would like to acknowledge Southwest Research 

Institute for funding this research. In addition, they would like to 

recognize the contributions of Ian Mienzen for developing much 

of the software that enabled this research. 

6. REFERENCES 

[1] D. H. Ballard and C. M. Brown, Computer Vision. 

Englewood Cliffs, NJ. PrenticeHall, 1982 

[2] Bar, M. (2004). Visual objects in context. Nature 

Neuroscience Reviews, 5, 617-629. 

[3] George H. John, Pat Langley: Estimating Continuous 

Distributions in Bayesian Classifiers. In: Eleventh 

Conference on Uncertainty in Artificial Intelligence, San 

Mateo, 338-345, 1995. 

[4] D. Aha, D. Kibler (1991). Instance-based learning 

algorithms. Machine Learning. 6:37-66. 

[5] Lenat, D. B. 1995. Cyc: A Large-Scale Investment in 

Knowledge Infrastructure. The Communications of the ACM 

38(11):33-38 

[6] Ian H. Witten and Eibe Frank (2005) "Data Mining: Practical 

machine learning tools and techniques", 2nd Edition, Morgan 

Kaufmann, San Francisco, 2005. 

[7] R. Duda, P. Hart, and N. Nilsson, aSubjective Bayesian 

Method for Rule-Based Inference System,o AFIPS, vol. 45, 

pp. 1,075-1,082, 1976. 

[8] T. Fawcett, Roc graphs: Notes and practical considerations 

for researchers," http://www.hpl.hp.com/personal/Tom 

Fawcett/papers/ROC101.pdf 

 

37
Manufacturing Unit Process Life Cycle Inventories (uplci)

Michael Overcash 

Wichita State University 

Wichita, Ks 67260 

mrovercash@earthlink.net

Janet Twomey 

Wichita State University 

Wichita, KS 67260 

janet.twomey@wichita.edu 

Jackie Isaacs 

Northeastern University 

Boston, MA 

jaisaacs@coe.neu.edu 

ABSTRACT

Tools to make environmentally informed product decisions 

at the design stage have long been an identified need of the 

manufacturing community. Potential solutions to address 

this need have been one of the topics of a series of National 

Science Foundation funded international workshops in the 

area of environmentally benign design and manufacture 

(EBDM). This paper reports one outcome of those 

workshops and the progress toward the development of a 

new approach to use manufacturing unit processes as the 

basis for evaluating environmental impacts at the 

manufacturing phase of a product’s life cycle. The research 

presented here is funded through a Department of Energy 

award DOE DE-FG36-08GO88149. 

General Terms

Measurement, Documentation, Design. 

Keywords

Manufacturing, environmental, unit process, life cycle inventory, 

sustainability. 

1. INTRODUCTION 

Tools to make environmentally informed product decisions 

at the design stage have long been an identified need of the 

manufacturing community. Potential solutions to address 

this need have been one of the topics of a series of National 

Science Foundation (NSF) funded international workshops 

in the area of environmentally benign design and 

manufacture (EBDM). Dr. Delcie Durham, Program 

Officer at the NSF (now at University of South Florida) 

created the workshops as a means for the international 

community to collaborate in the creation of new knowledge 

in EBDM. This paper reports on a collaboration formed as 

an outcome of those workshops and the progress toward 

the development of a new approach to use manufacturing 

unit processes as the basis for evaluating environmental 

impacts of a product’s life cycle at the manufacturing 

phase. 

2. uplci PROJECT 

The new approach is developed to use the manufacturing 

unit process, commonly outlined in manufacturing process 

taxonomy systems, as the basis for life cycle inventory. 

This will initially involve 50-70 unit processes from the 

taxonomy and will generate energy and mass profiles for 

each unit process life cycle (uplci). These uplci can be 

adjusted for each case to include the major variables 

affecting such operations as related to any specific product. 

The sum of the performance of a sequence of uplci thus 

provides the life cycle of the specific product from a 

defined set of plant process inputs [1]. 

The research supporting the development of the uplci is 

located at Wichita State University, Wichita, KS. A 

website (www.wichita.edu/sustainability) has been established 

to allow access to the uplci (see Figure 1). The website is 

also intended for the community to supply comments and 

submit it additional unit processes. 

The Wichita State University project is currently funded 

through a U.S. Department of Energy award (DOE DEFG36-08GO88149) in wind energy. The DOE has interest 

in this area because the primary environmental impacts of 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies are 

not made or distributed for profit or commercial advantage and that 

copies bear this notice and the full citation on the first page. To copy 

otherwise, or republish, to post on servers or to redistribute to lists, 

requires prior specific permission and/or a fee. 

Permis’09, September 21–23, 2009, Gaithersburg, MD, USA. 

Copyright 2009 ACM 978-1-60558-747-9/09/09...$10.00. 

38
wind energy are at the manufacturing phase. uplci will be 

used as means for making comparisons of wind energy 

with sources other than coal and throughout the turbine life 

cycle. 

Figure 1. Snap shot of uplci website 

3. REFERENCES 

[1] Overcash, M., Twomey, J. and Kalla, D. (2009). ASME 

International Manufacturing Science and Engineering 

Conference October 4-7, 2009, West Lafayette, IN, USA 

39
Conceptual Foundations of Energy Aware Manufacturing

Soundar Kumara 

The Pennsylvania State University 

310 Leonhard Building 

University Park, PA 16802 

(814)863-2359 

skumara@psu.edu 

ABSTRACT

In this paper we define “Energy Aware Manufacturing (EAM),” 

as the “Manufacturing paradigm that concentrates on integrated 

optimal usage of energy in manufacturing right from material 

procurement to part disposal.” The main emphasis of EAM is on 

an integrated optimal energy usage. The focus is to model energy 

related issues in the interactions between suppliers, production 

machines, part manufacturing and plant physical facilities. There 

is a need to develop quantitative models and information models 

and implementing a simulation test bed to accomplish the 

objective of EAM. In this paper, we develop a conceptual 

roadmap for accomplishing the objective of energy efficiency. We 

discuss the conceptual foundations of our approach. 

Categories and Subject Descriptors

Performance, Management

General Terms

Management, Measurement, Performance 

Keywords

Sustainability, Energy Aware Manufacturing, Energy efficiency 

1. INTRODUCTION 

If manufacturing sector in the USA were to be a country by itself, 

it would be the 8th largest economy in the world. The 

manufacturing sector in the USA in 2005 accounts for 16% of the 

national GDP, and is still the world’s largest manufacturer. In 

2004, it accounted for a quarter of the global production. Though 

the current figures are slightly dipping (by about 1.1%) still USA 

holds its superiority in the manufacturing sector. China and South 

Korea are increasing their share in manufacturing which is mainly 

due to the increase in non-direct material related costs such as 

health care, labor, and energy in the USA. Given the increase in 

global demand for energy any attempt to improve energy 

efficiency will lead to considerable value addition to not only to 

the US economy but to the global welfare (World Bank and 

Economy.com, 2009; US EPA, 2007). 

Energy efficiency is a global issue and cannot be solved by 

unilateral and myopic approaches. Energy efficiency can be 

achieved by proper energy management, and is a 

multidisciplinary challenge involving science, technology, 

ecology, information technology and common sense (ICT, 2009). 

Energy management is critical to the sustainability of the future of 

US and Global economy. Optimizing energy usage and 

minimizing energy loss are central to energy management. It is 

important that manufacturing integrate this concept into its 

operations from procurement to maintenance or from cradle to 

grave. Energy Aware Manufacturing (EAM), which focuses on 

the energy management in manufacturing, is central to reducing 

the carbon footprint of manufacturing industries. We define EAM 

as the “Manufacturing paradigm that concentrates on optimal 

usage of energy in manufacturing right from material 

procurement to part disposal.” We believe that the state-of-theart research in commercial as well as academic domains though 

focuses on sustainability, has not addressed an integrated 

methodology development for efficient use of energy from 

conception to grave of a product. Such an integrated development 

must deal with information models, quantitative models, metrics 

for energy usage, and development of standards. Efficient Tools 

and methods for estimation of the total energy consumption of a 

complete manufacturing system are missing today. To be able to 

calculate the most optimal use of energy, new tools, and methods 

must be developed (Asnafi et al., 2008). This forms the central 

theme of our conceptual paper. Energy labeling is already in 

progress in Europe. In this paper we propose the concepts for 

developing manufacturing part label which will incorporate 

manufacturing energy as a part of it so that we can track the 

carbon footprint of each part. 

2. THE NEED 

We argue that energy is an important consideration in 

manufacturing, both from process as well as plant operational 

view. Though energy efficiency has become important in the last 

decade, it is necessary to establish a scientific basis for including 

reduction in energy consumption as one of the focuses of 

manufacturing. We need to define, develop, and establish 

standards to evaluate the total energy consumption by the entire 

manufacturing operation. With out consideration to the legal and 

political issues, it may be beneficial for the society to have 

“Carbon Labeling” for every manufactured part, akin to food 

labeling. Due to the complexity of this problem, there is a clear 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies are 

not made or distributed for profit or commercial advantage and that 

copies bear this notice and the full citation on the first page. To copy 

otherwise, or republish, to post on servers or to redistribute to lists, 

requires prior specific permission and/or a fee. 

Permis’09, September 21–23, 2009, Gaithersburg, MD, USA. 

Copyright 2009 ACM 978-1-60558-747-9/09/09...$10.00. 

40
and urgent need to develop quantitative and information based 

models for energy management. 

In order to realize EAM, we postulate three important research 

streams. We do not address the process efficiency aspects. 

Though they are critical we consider EAM from a systems view 

point. 

Research Stream1: Define and implement “Manufacturing 

Energy Computations” that will help in generating quantitative 

models to realize EAM. 

Research Stream 2: Define and implement “Manufacturing 

Energy Information Modeling” that will help in representing the 

models from stream 1. 

Research Stream 3: Design, develop and implement 

“Manufacturing Energy Simulation Modeling” to establish a 

simulation framework and a platform that will use the information 

and the quantitative models developed to study the dynamics of 

manufacturing. 

3. THE NEED 

Environmental regulations, such as European Union’s Integrated 

Product Policy (IPP) and the EU’s directive on the Ecodesign of 

Energy-Using Products (EUPs), will directly regulate the negative 

contribution to the environment across the entire lifecycle of the 

product, not just the use phase. This implies that the 

environmental impact of any given product will be addressed by 

taking all aspects of its supply chain and life cycle into account: 

raw materials, components/part sub-assembly/final product 

manufacturing, transportation, distribution, marketing, sales, 

delivery and waste treatment at the end of life. Therefore, the 

carbon footprint and Green House Gas (GHG) emission 

accounting for a product will be directly affected by: 

1. The manufacturing processes as well as the equipment 

used for producing the product. For example, in the 

vehicle body shop in an automobile manufacturing 

company, the energy consumed and GHG emitted by 

spot welding process will be different compared to the 

ones by laser welding process. It should be noted that a 

product can be a complete vehicle or a component (e.g., 

a wheel) or a sub-assembly (e.g., an instrument panel). 

2. The supplier footprint and logistics (transportation) of 

components/parts for the final product are integral to the 

carbon foot print of the part. Take engine assembly as 

an example, if the engine block and cylinder head are 

manufactured in an engine assembly plant located in 

US, whereas the other components such as pistons, 

crankshaft, and camshaft, could be purchased and 

shipped from supplier(s) located in US, India, or China. 

Therefore, the final carbon footprint and GHG emission 

accounting for the engine assembly will depend on 

where each of all the engine components is made and 

how it is shipped from its supplier to the final engine 

assembly plant. 

Another analysis and implementation exercise is closely tied 

to the second point stated above, i.e., the carbon 

footprint/GHG emission accounting based on the 

components/parts’ supplier footprint. With the 

calculation/measurement of the carbon footprint/GHG 

emission of each component/part/sub-assembly, product 

manufacturer can implement its “green procurement” 

strategy to set up and maintain its supply chain to be the one 

having lowest energy consumption and safest impact on the 

environment. 

4. RESEARCH STREAM 1: ENERGY 

COMPUTATION 

Increasing raw material prices, necessary investments in the 

environmental technologies, potential penalties for not complying 

with regulations as well as ability to attract incentives and public 

image is forcing manufacturing companies to rethink about energy 

related issues (Hesselbach et al., 2008). Manufacturing plants 

from an energy perspective are integrated units of building and 

production machines. Energy efficiency mainly relates to 

optimizing the ratio of production output to the energy input for 

the technical building services (heating and cooling) and 

production machines. Energy efficiency is a generic term and 

there is no one uniform quantitative measure of “energy 

efficiency.” instead one must rely on a series of indicators. 

Patterson (Patterson, 1996) discussed four indicators 

(Thermodynamic, Physical-Thermodynamic, Economicthermodynamic, and Economic) of energy efficiency. These 

cannot be used directly in manufacturing; however, we can use 

some variants of the concepts. We extend the traditional definition 

of energy efficiency by including the supply chain also into its 

computation. Figure 1 reproduced from Hesselbach (Hesselbach 

et al., 2008) shows a holistic view of the facility-production 

machines without supply chain interface from an energy 

awareness viewpoint. 

Figure 1: Facility-Production Machines Holistic View 

(Reproduced from (Hesselbach et al., 2008)) 

We propose a part energy profile comprising of three components 

(see Figure 2), namely: 

1. Procurement component 

2. Production component 

3. Delivery component 

These energy component values can be calculated from either top 

down or bottom up fashion. In the first method, the aggregate 

energy for the procurement, production, and delivery of a batch of 

41
products and assemblies is obtained from energy metering and 

apportioned to individual components. In the second approach 

individual component, energy usage values are obtained through 

individual unit procurement, production, and delivery and 

aggregated for the assembly. In both the cases due to the inherent 

uncertainties in the operations, stochastic estimation methods are 

needed. Bottom up approach though extremely cumbersome may 

yield results that are more accurate. 

Figure 2: Part Energy Profile 

For an identified critical part we generate the part energy profile 

based on the following steps: 

1. Generate alternate process plans 

2. Identify the alternate processes 

3. From material removal rate computations calculate 

energy requirements using alternate processes (and 

hence alternate production machines) 

4. Identify alternate suppliers and if possible next tier 

suppliers 

5. Identify alternate transportation facilities(modes and 

equipment) and routes 

6. Compute energy expenditures (Kwh) 

The energy parameters to consider are: 

1. Process plans 

2. Manufacturing Processes 

3. Production Machines 

4. Suppliers 

5. Transportation Modes 

6. Transportation Facilities (equipment) 

7. Packaging modes 

Metrics for EAM: The metrics needed to evaluate EAM are: 

Supply Chain Related, Physical Facility Related, Production 

Machine Related, and Production Cell Related. In all these 

categories, we need to formulate metrics for: 1. Energy Wastage 

due to not utilizing set-up properly on a machine (a batch of 100 

parts would have for example made the optimal use of a set up as 

opposed to producing 50 units), 2. Energy Wastage due to not 

utilizing set-up properly in a production cell (a batch of 100 parts 

would have for example made the optimal use of a set up as 

opposed to producing 50 units), 3. Energy wastage due to 

improper re-ordering, and 4. Cost of non-compliance to 

regulations (now most of these are voluntary), and 5. Return on 

Investment.

In general, in the industrial sector the total energy used per unit 

time (month or a day) is expressed as a combined component of 

the facility energy expenditure (EF) and production machines 

energy expenditure (EP). 

+ = EEE TPF

This is basically the power consumed in Kilo-watt-hours (Kwh). 

For example, EF can be 0.4 of ET; and EP therefore can be 0.6 of 

ET. This when related to the total production in unit time 

U

T E

Total oduction

E = Pr

6.0

Where EU is Energy consumed per unit of the product. This can be 

weighted by individual processes and machine rating. This of 

course is a gross measure. Detailed methodological aspects need 

to be developed. 

5. RESEARCH STREAM 2:INFORMATION 

MODELING 

We view manufacturing systems as comprising of five levels (we 

do not define them as hierarchically related):1. Supply chain level, 

2. Company level, 3. Production system level, 4. Production cell 

level, and 5. Machine level 

Thought there are several commercial systems available to 

represent and analyze each of these systems from different 

viewpoints there is very little attention paid to the energy related 

aspects. Similarly there are commercial (Sahlin et al., 2004; Chen 

et al., 2001) building and HVAC simulators, but each one with a 

specific objective of computing total energy consumed vis-a-vis 

wasted. Very little work exists in relating production machines to 

the facility. To the best of the author’s knowledge, no work exists 

in relating manufacturing with supply chain with respect to energy 

issues. We need to define a robust information model before we 

can build a tool and standards for analyses. We suggest an 

integrated information model based on Service Oriented 

Computing (SOC), which is an increasingly popular and an 

efficient paradigm for collaborative planning and execution. 

Services are self-describing, open components that support low 

cost composition of distributed applications. Services are offered 

by service providers who are distributed over the web (web 

services). Service descriptions are used to advertise the service 

capabilities, interface, behavior, and quality. Service description 

provides the conceptual purpose and expected results of the 

service. The service interface description publishes the service 

signature (input, output, error parameters, and message types). 

The expected behavior of the service during its execution is 

described by service behavior description (for example aggregate 

energy consumption trace). Quality refers to service cost, 

performance metrics and security attributes (Papazoglou & 

Georgakopoulos, 2003). When these are offered through the web, 

they become web services. 

Our vision is to utilize Service Oriented Architecture (SOA), 

which is the most advanced Web-based service system 

architecture (Erl, 2004), to formalize an IT framework to support 

EAM. The main aspects to consider are: 

1. Develop an interface-oriented machine-readable 

representation scheme for components; 

2. Formalize an accessible cyberinfrastructure-based 

framework that enables global users to describe, 

publish, and discover component information in a 

standardized way; and 

42
3. Adapt Artificial Intelligence (AI) planning algorithms to 

support energy computations. 

The National Institute for Standards and Technology (NIST) has 

proposed the use of eXtensible Markup Language (XML) for the 

description of functions and associated flows in computer-based 

design (Szykman, et al., 1999; 2002; Bohm, et al., 2008) 

introduced an extensive data schema to capture fundamental 

elements of design information. These representation schemes for 

products or parts are designed mostly from the viewpoint of the 

materials or energy flow, which is sequential or flow-oriented; 

however, research efforts towards EAM, demands a different 

viewpoint for representation. We assert that the new viewpoint 

should be interface-oriented because physical interfaces help 

bridge modularized components (sub-assemblies). Figure 3 shows 

a SOA-based Web Services for Energy Aware Manufacturing. 

Conceptually, in our proposed framework, a Universal 

Description Discovery and Integration (UDDI) would correspond 

to the digital Design Repository implemented by the Missouri 

University of Science & Technology and NIST (Bohm & Stone, 

2004; Szykman & Sriram, 2006) while a Web service would 

correspond to a production process. 

Component designers describe components in a standard language 

such as XML (eXtensible Markup Language) version of STEP 

(the Standard for the Exchange of Product). The description of 

each component includes input/output interfaces, dimensions, 

features, and required energy efficiency of it, etc. On the other 

hand, manufactures describe their manufacturing processes also in 

a standard language. The description of each manufacturing 

process in Web Service Description Language (WSDL) includes 

machines, tools, coolants, and energy rating, etc. Those 

standardized descriptions are stored in the SOA-based 

infrastructure for energy-aware manufacturing (Figure 3). SOA 

provides subscribe/publish mechanism for users, component 

designers and manufacturers. They can access those descriptions 

in an automated way. Three types of services can be provided by 

the proposed architecture, atomic, composite, and managerial 

services. Atomic services can respond to simple queries, such as 

getComponent, getMfgProcess, and getEnergyEfficiency; 

composite services can satisfy sophisticated queries, such as 

checkEnergyEfficiency (of a product) and designProduct; finally, 

managerial services provide administrative transactions for users. 

Those services must be open to public(or a networked 

manufacturing system) through a secured 

authentication/authorization protocol, and discoverable in a 

standardized way. 

As we are interested in studying the interaction between the 

physical facility and the production machines, it is important to 

represent the building related entities. EAM needs to consider the 

following services related to the physical facility (building): 

1. Lighting service: related to lighting aspects of the 

facility containing total lighting units, area, and energy 

requirement with respect to the season and time of the 

day 

2. Heating service: related to heating aspects of the facility 

containing total area, heating units, relationship to 

production machines, and energy requirement with 

respect to the season, time of the day and personnel on 

duty. 

3. Cooling service: related to cooling aspects of the facility 

containing total area, and energy requirement with 

respect to the season, time of the day and personnel on 

duty. 

Component Designers Manufacturers

•Publish   

Mfg Process

Descriptions

Negotiation

• Request manufacturing

• Provide components

getComponent

getMfgProcess

getEnergyEfficiency

negoManufacturing

subscribeManufacturer

publishMfgProcesses

updateMfgProcesses

designProduct

checkEnergyEfficiency

SOAP

Transportation

protocol

Atomic query

services

Managerial

services

Composite query services

•Publish

Component

Descriptions

Figure 3: SOA for Energy-Aware Manufacturing 

4. Airflow pumping service: Airflow requirements based 

on production, time of the day, season and personnel. 

5. Energy computation service: Aggregation of energy 

requirements calculations based on the component 

manufacturing plan (one can use AI planning algorithm 

and integrating with energy constraints of the physical 

facility to accomplish this). 

6. RESEARCH STREAM 3:SIMULATION 

MODELING 

The third research stream relevant to EAM is the estimation of 

energy consumption that minimizes the overall carbon foot print 

of the manufactured part. We would also like to ask what-if 

questions (study the dynamics) considering the stochastic nature 

of suppliers, machines, physical facilities and environment. We 

need to integrate the five levels from supply chain to component 

(discussed earlier). Though the information model we suggest is 

powerful to compose the processes and hence machines needed, 

and the suppliers to be selected, at this stage the SOA technology 

is not mature enough for building a simulator. In this section, we 

develop a conceptual framework for a multi agent based simulator 

for integrated energy analyses in manufacturing. 

Agent-based computation is a new paradigm of information and 

communication technology that largely shapes and, at the same 

time, provides supporting technology to the above trends (Luck et 

al., 2005; Wooldridge, 2000; Weiss, 1999). Agent theories and 

applications have appeared in many scientific and engineering 

disciplines. Agents address autonomy and complexity; they are 

adaptive to changes and disruptions, exhibit intelligence and are 

distributed in nature. In this setting computation is a kind of social 

activity. Agents can help in self-recovery, and react to real-time 

perturbations. 

The Core Manufacturing Simulation Data (CMSD) Information 

Model (CMSD, 2006) captures the essence of manufacturing 

43
simulation information. The UML based description can be used 

as a foundation to build the M-EAMS(Multi Agent Based Energy 

Aware Manufacturing Simulator). 

We identify the relevant agents from these information 

categories. Some of these are: Supplier Agents, Transportation 

Agents representing different transportation resources, Calendar 

Agent for storing all the relevant calendar information and 

pushing it as and when needed, Resource Agents each 

representing one of the resources, Employer Agent interfaced with 

skill database, Process Planning Agent, Component(Part) Agent, 

Sub-assembly Agent (containing all the components needed for 

Sub-assembly), Final Assembly Agent (containing all the 

components needed for Final Assembly), Inventory Agent, 

Maintenance Agent, Scheduling Agent, Energy Computation 

Agent (having decomposition and aggregation algorithms as a part 

of agent behaviors), Visualization agent and Metrics Calculator 

Agent. We also identify certain agents related to the physical 

facility representing Lighting, Heating, Cooling, Air-Flow and 

Building Energy Computation Agents. 

In certain cases, for example as in the case of Process Planning 

Agent, Maintenance Agent, and Scheduling Agent COTS is 

available that can be a part of the simulator. We can pass a 

message from the Part Agent to the Process Planner by wrapping 

the COTS Process Planner. In this case we need to focus on the 

ontological equivalences in communication primitives between 

the Part Agent and the Process Planner. Once we have a Process 

Plan, it can be communicated to a scheduling agent in a similar 

fashion. We undertake an explanation of the operational 

architecture of the EAM simulator (see Figure 4). We define four 

communities: Supplier Community: Composing of suppliers, 

Transportation Units-interfaced with carrier networks, and GIS 

Database 

Physical Facility Community: Consisting of heating, lighting, 

cooling other amenities represented as agents interfaced with 

energy providers and cost databases. 

Part Community: This contains components/parts/subassembly/Assemblies. All the geometric and material, process 

related information is stored in these agents. 

Production System Community: Composing of machines, other 

resources such as materials handling equipment, personnel, and 

monitoring devices and sensors. 

Machine 

Agents

Suppliers Community Physical Facility Community

Production Systems Community Parts Community

Electronic Market Place

Lighting 

Agents

Heating 

Agents

Cooling 

Agents

Other 

Facility 

Related

Agents

Energy 

Computation 

Agents

Monitoring 

Agents Visualization 

Agents

Scheduling 

Agents

Component 

Agents

Sub-Assembly 

Agents

Assembly 

Agents

Resources 

Agents

Monitoring 

Agents Personnel 

Agents

Energy Computation 

Agents

Transportation

Agents

Warehouse 

Agents

Coordinating

Agents

Policies

Figure 4: Operational Architecture for Multi Agent Based EAM 

Simulator 

This agent organization captures the five levels of a 

manufacturing system. Here we deliberately break away from the 

hierarchical relationship traditionally used to model 

manufacturing systems. We view the integrated manufacturing 

systems as a collection of loosely coupled communities. In each 

of these communities, we will have Monitoring (sensors and 

RFID devices) agents, Energy Computation agents and scheduling 

agents. We need to build the simulation platform using these four 

communities that capture the static information through agents. 

However, each of the communities will have their own monitoring 

agents that monitor the community and take control (adaptive) 

actions. These are local, guided by global metrics communicated 

from the electronic market place. 

The stochasticity of each of the communities and the relevant 

individual agents (for example machine breakdown, cost 

variation, Energy fluctuation etc) is injected externally and sensed 

by the monitoring agents. The electronic market (EM) place offers 

the means to carry out the overall dynamic optimization of EAM. 

The operation of the simulator can along the following lines. 1. 

Components/Sub-Assembly/Assembly request initiated by part 

community. 2. EM- Monitoring agent senses the information and 

invokes the process planning process through a Process Planning 

agent. 3. EM- Market Place Communicates information to 

Production Facility and supplier community (Bid Announcement). 

The utility function should be a composite of cost and energy 

requirements. EM will interface with the BOM or MRP to 

compute material requirement. 4. Machines and other resources 

bid to carry out the operations. When we have alternate process 

plans, we have different machines bidding with different energy 

budgets based upon their own ratings. 5. Similar process for 

materials will be initiated by the supplier community. 6. Physical 

facility agents send their bids on energy based upon their criteria 

of the time of the day, environmental conditions etc. 7. EMCoordinating agent will have all these information and will 

initiate through a market based control mechanism to arrive at a 

Nash equilibrium that will corresponds to a solution that can no 

longer be improved by individual communities. 8. Given the 

current solution, the EM- Coordinating agent will invoke different 

regulations (through the policies) to see their effect on the 

solution. 9. After performing policy impact analysis, EMcoordinating agent will communicate the detailed plans to 

individual communities for final acceptance. 10. The visualization 

agents can be invoked to see dynamic changes in each community 

and each agent. 

7. CONCLUSIONS 

In this paper, we propose a conceptual framework to undertake 

the development of the paradigm of EAM. We elaborated on three 

research streams that are critical: Energy Computation, 

Information Modeling and Simulation Development. The next 

step is to develop the details related to each of these streams. Our 

research so far on EAM shows that a well-designed simulator is 

essential to define and enumerate performance metrics for EAM. 

8. ACKNOWLEDGMENTS 

The author wishes to thank Dr. Arun Ayyagari, of Boeing; Dr. 

Xiang Zhao, Dr. Ninja Huang and Dr. Jeffrey Tew for their 

discussions on the concept of EAM. However, the writing in this 

paper expresses the author’s views only. 

44
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45
Discrete Event Simulation to generate Requirements 

Specification for Sustainable Manufacturing Systems

Design

Björn Johansson

Product and Production Development

Chalmers University of Technology

SE-412 96 Gothenburg, SWEDEN

100 Bureau Drive

20899 Gaithersburg, MD, USA

bjorn.johansson@chalmers.se

Mahesh Mani

National Institute of Standards and Technology 

mahesh.Mani@nist.gov

Anders Skoogh

Product and Production Development

Chalmers University of Technology

SE-412 96 Gothenburg, SWEDEN

100 Bureau Drive

20899 Gaithersburg, MD, USA

anders.Skoogh@chalmers.se

Swee Leong

National Institute of Standards and Technology 

swee.Leong@nist.gov

ABSTRACT

A sustainable manufacturing systems design using processes, 

methodologies, and technologies that are energy efficient and

environmental friendly is desirable and essential for sustainable 

development of products and services. Efforts must be made to

create and maintain such sustainable manufacturing systems. 

Discrete Event Simulation (DES) in combination with Life Cycle 

Assessment (LCA) system can be utilized to evaluate a 

manufacturing system performance taking into account

environmental measures before actual construction or use of the 

manufacturing system. In this paper, we present a case study to 

show how DES can be utilized to generate requirements 

specification for manufacturing systems in the early stages of the 

design phase. Requirement specification denotes the description 

of the behavior of the system to be developed. The case study 

incorporates use of LCA data in combination with DES. Data for 

the model in the case study is partly provided through the format 

supported by the Core Manufacturing Simulation Data (CMSD)

standardization effort. The case study develops a prototype paint 

shop model, and incorporates alternate decisions on energy use, 

choice of machines, and environmental bottleneck detection. The 

study results indicate the potential use of utilizing DES in 

combination with LCA data to generate requirements 

specification for designing sustainable manufacturing systems. 

Categories and Subject Descriptors

J.6 [Computer Applications]: COMPUTER-AIDED 

ENGINEERING – Computer-aided manufacturing (CAM)

General Terms

Management, Measurement, Performance, Design, Economics, 

Experimentation, Standardization.

Permission to make digital or hard copies of all or part of this 

work for personal or classroom use is granted without fee 

provided that copies are not made or distributed for profit or 

commercial advantage and that copies bare this notice and the full 

citation on the first page. To copy otherwise, to republish, to post 

on servers or to redistribute to lists, requires prior specific 

permission and/or a fee.

PerMIS’09, September 21-23, 2009, Gaithersburg, MD, USA.

Copyright 2009 ACM 978-1-60558-747-9/09/09...$10.00.

Keywords

Discrete Event Simulation, Life Cycle Assessment, Design for 

Sustainability, Manufacturing System Design, Standardization

1. INTRODUCTION

Requirements specification plays a vital part during design 

reviews when designing sustainable manufacturing systems. DES 

can be potentially used to generate requirements specification 

after considering what-if scenarios and analyzing alternative 

models to reflect how a system performs in implementation. This 

paper discusses how sustainability factors can be incorporated in 

defining requirements specification using DES to provide decision 

support for a more sustainable environment and society.

The paper is organized as follows: Section 2 presents a state of the 

art on DES. LCA as a measurement tool in the context of DES is 

described in Section 3. Section 4 presents a case study using an 

automotive paint shop facility example to demonstrate how DES

in combination with LCA can be used. Section 5 provides

discussions and conclusions as to how the presented case study 

can be generalized and used for decision support and requirements 

specification for a sustainable manufacturing systems design. 

2. DISCRETE EVENT SIMULATION

Simulation has been demonstrated to be a very effective approach 

for problem solving and optimizing manufacturing systems 

design. One of the primary application areas for modeling and 

simulation is manufacturing system, according to Law and 

McComas [1]. However, analysis and optimization of multiple 

objectives is not very common in manufacturing simulation. 

Detailed discussion of modeling and simulation can be found in 

numerous books, among the best known are Banks et al. [2], and 

Law and Kelton [3]. The technology of utilizing DES has been 

rapidly evolving, hundreds of academic publications and new 

software features are released every year. DES software and 

languages have been used for numerous purposes, such as patient 

flows in healthcare, military strategies, logistics, call centers, 

46
restaurants, etc. One of the most frequently stated objectives in 

DES is profit optimization, i.e., analyzing which of the alternative 

solutions is the most profitable over time. There are many other 

criteria, which one could measure with DES. In the past, the 

emphasis has been mainly on profitability. However, 

environmental considerations are becoming more relevant and 

require greater attention as long as humans continue to utilize 

natural resources. DES and LCA is one possible combination for 

analyzing the cause and effect of various scenarios where time, 

resources, place, and randomness of input variables affect the 

outcome in sustainable manufacturing design. This analysis is an 

unexplored area; only a few research publications exist. The few 

examples include: Solding and Petku [4] and Solding and 

Thollander [5] both describe how DES could be utilized to reduce

electricity consumption for foundries. Östergren et al. [6] and 

Johansson et al. [7] describe how DES could be utilized in 

combination with LCA for decreasing environmental impacts 

during food production.

3. LIFE CYCLE ASSESSMENT FOR 

DISCRETE EVENT SIMULATION

LCA is a methodology for evaluating the environmental impact 

associated with a product during its life cycle. LCA can be 

accomplished by identifying and quantitatively describing a 

product’s requirements for energy and materials, and the 

emissions and waste released to the environment. A product under 

study is followed from the initial extraction and processing of raw 

materials through manufacturing, distribution, and use, to final 

disposal, including the transports involved, i.e., its entire lifecycle. 

LCA is an ISO standardized tool [8-10]. 

Using LCA data in a DES model is a novel multidisciplinary 

technique, which enables environmental impact evaluations of the 

manufacturing system performance. To the best of our knowledge, 

only three models of real world systems have been built so far, 

which utilizes LCA data in a DES model. We discuss one such 

system in the paper. The other systems were developed for 

simulating a factory which produces sausages [6, 11, and a dairy, 

which produces cultured dairy products [12]. 

4. CASE STUDY

To demonstrate a manufacturing planning scenario with an 

emphasis on sustainability a simulation model has been built

based on the work flow schematic as shown in Figure 1. This 

scenario presents a paint shop with six painting steps to set the 

scene for requirements specification in an automotive paint shop. 

Figure 1. Example of paint shop processes [13]

Figure 1 shows six steps (Body Preparation, Tag Rag, Base Coat, 

Clear Coat, Oven and Polishing) incorporated in the simulation 

model. The model was created based on some earlier work [14-

17] as seen in Figure 2. 

Figure 2. 3D-representation of the paint shop test model

Table 1. Default settings for resources in the paint shop

47
4.1 Input data

Each production step has a setting for the resource to be down, 

idle, or busy. Down means disconnected from the power provider, 

i.e., no electricity is used. Idle means that the resource is on 

standby, i.e., some electricity is used. Busy means doing the work 

cycle as such, i.e., electricity is used. Table 1 shows the input data 

specifying the energy use from the default settings in the paint 

shop model, as well as other data needed for setting parameters at 

the resources of the model such as cycle times, MTTF (Mean 

Time To Failure), MTTR (Mean Time To Repair), etc. 

The data herein presented are for the purposes of demonstration of 

our scenario and do not necessarily imply an actual paint shop 

data.

4.2 Problem description

When designing a new manufacturing system certain production 

goals and economic measures need to be fulfilled. For example 

the production capacity is specified to be at least a certain level, 

the cost of the manufacturing system needs to be within the 

budget, and the environmental impact is expected to be below a 

certain guideline value.

4.3 Goal 

In this case study, the goals of the sustainable manufacturing 

system are assumed as follows: 1. to reach a production capacity 

of at least 50000 cars per year, 2. there will be no more than 500

metric tons of CO2 emission per year, and 3. no new investment

in equipment for the existing factory. The current factory is 

represented by the input data in Table 1, as well as the output data 

from Trial run 1 in Table 2. 

4.4 Experiments

In this case study, the number of input variables are simplified to 

only a few choices as shown in Table 2. In a real world 

application however, a variety of designed operating parameters 

are considered based on the required system throughput. In the 

experiments, the number of input data parameters can be varied 

more extensively and practically anything feasible for a real world 

change could be varied if necessary to bring forth sound 

requirements specification for the considered manufacturing 

system.

From the initial settings (Trial run 1 in Table 2), the oven had 

been identified to be the bottleneck in terms of utilization as well 

as energy consumption. Some trial runs were performed based on 

different parameter settings. The settings included the energy 

source, oven cycle time, and energy consumption as well as a 

single or two ovens in parallel. The energy sources in the 

parameter setting included wind, water, or a mix of energy 

sources depending on the country where the factory is located. 

The primary purpose of this simulation is to provide requirements 

specification support data, and hence also provide support towards 

designing a sustainable paint shop. In line with this effort, some 

examples of measures are provided in terms of energy, throughput 

and CO2 based on the simulation runs. In Table 2, from the twelve

trial runs one can identify the bottlenecks, energy consumption 

and CO2 emissions due to energy type used in the paint shop. The 

results presented in Table 2 are calculated by running the 

simulation model. The model incorporates lifecycle assessment 

data from an European Union LCA database as described in 

Heilala et al. [14]. 

4.5 Results

Following are examples of conclusions arrived from looking at

Table 2: 

• The initial setting gives the lowest energy consumption 

per produced car, as well as trial 3 and 5

• The Oven is the throughput bottleneck initially (trial 1)

• Decreasing cycle time for the oven with 60 seconds 

does increase output of cars; however Oven is still the 

bottleneck.

• By adding another parallel oven, the Base Coat will be

the bottleneck. 

• Wind powered paint shop gives the lowest CO2

emissions (from energy) per car produced.

Note that these conclusions are not the only items to consider, 

however they give more information needed and provide for a 

better decision space that a normal non-discrete event simulation

analysis does.

Table 2. An example result of twelve simulation runs 

48
The left side of Table 2 shows the input data which is varied for 

the twelve runs. Column one on “Input parameter changed” can 

be set to either 1 for normal conditions or 2 for 180 sec cycle time 

and 2400kWh. Column two shows which type of energy is used, 1 

for an average country energy (i.e. mixed sources), 2 for wind 

power, 3 for water power. Column three shows the number of 

parallel ovens used in the model.

4.6 Discussions

The study results and output data are shown in Table 2.

Constraints from the stated goals of the study have to be 

considered while analyzing the study results. To satisfy the goal 

to produce at least 50000 cars per year, Table 2 output data shows

that trial runs 7-12 are feasible, however an investment in another 

oven will need to be added to the process. The next goal is to 

decrease the CO2 emissions to less than 500 metric tons per year. 

To reach this goal, standard fossil fuel energy cannot be used.

Alternatively wind or water powered energy will need to be used. 

Table 2 shows trial runs 9-12 as feasible solutions with the use of 

“green” energy alternatives. In order to minimize the investment

goal, the cycle time and energy consumption of the oven does not 

need to be changed. This means trial run 9 or 11 will be the 

preferred choice, depending on the energy cost from the power 

provider. It may be worthwhile to notice that the wind power 

could be a better choice than the water powered energy alternative

in terms of CO2 emissions. 

5 CONCLUSION

The study demonstrated that using the environmental measures 

from a LCA database and traditional input data with cycle time, 

disturbance data, etc. for discrete event simulation, new output 

measures from the model can be used to identify and analyze 

sustainable manufacturing system design and measures such as 

energy consumption at the aggregated shop floor level, resource 

level, and production throughput. Such analysis can also be useful 

in identifying the bottlenecks on any environmental measure; in 

this case the energy consumption and carbon footprint in relation 

to energy source used. 

The software used for building and evaluating this model was 

developed under the SIMTER project as described in Heilala et al.

[14], Lind et al. [15], Lind et al. [16] and Johansson et al. [17]. To 

our knowledge, this software solution is the first effort on 

combining lifecycle assessment data directly into the discrete 

event simulation engine.

6 FUTURE RESEARCH

Based on the described case study it would be desirable to be able 

to represent sustainability related data in a neutral format. One 

possible solution is to store and use sustainability and other 

related data for discrete event simulation through the CMSD 

(Core Manufacturing Simulation Data) specification [19], 

developed under Simulation Interoperability Standards 

Organization (SISO) [18, 19]. This will allow us to maintain 

neutral and accessible measures for sustainability data. 

7 ACKNOWLEDGMENTS

We acknowledge the funding from VINNOVA (Swedish Agency 

for Innovation Systems, integrates research and development in 

technology, transport and working life.).

8 DISCLAIMER

No approval or endorsement of any commercial product by the 

National Institute of Standards and Technology is intended or 

implied.

9 REFERENCES

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[2] Banks, J., Carson, J. S., Nelson, B. L., Nicol, D. M. 2000. 

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River, New Jersey: Prentice-Hall, Inc.

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Orlando, FL, USA

[5] Solding, P., Thollander. P. 2006. Increased Energy 

Efficiency in a Swedish Iron Foundry Through Use of 

Discrete Event Simulation. In Proceedings of the 2006 

Winter Simulation Conference, ed. Perrone, L. F., Wieland, 

F. P., Liu J., Lawson, B. G., Nicol, D. M., Fujimoto, R. M. 

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[6] Östergren, K., Berlin, J., Johansson, B., Stahre, J., Tillman, 

A.M. 2007. A tool for productive and environmentally 

efficient food production management. European conference 

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2007.

[7] Johansson, B., Stahre, J., Berlin, J., Östergren, K., 

Sundström, B., Tillman, A.M. 2008. Discrete Event 

Simulation with Lifecycle Assessment data at a Juice 

Manufacturing System. In proceedings of the 5th FOODSIM 

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Assessment – Principles and Framework. ISO 14040:1997. 

European Committee for Standardization CEN, Brussels, 

Belgium.

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Analysis. ISO 14041:1998. European Committee for 

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Belgium.

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production; Ingemar Johansson i Sverige AB, MSc Thesis 

(In Swedish), Department of Product and Production 

Development, Chalmers University of Technology, 

Gothenburg, Sweden.

49
[12] Alvemark, O., Persson, F. 2007. Flow simulation of food 

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Swedish), Department of Product and Production 

Development, Chalmers University of Technology, 

Gothenburg, Sweden.

[13] Leng, C. F., Yingchao G. 2005. Production Performance 

Improvement Using DES of Low Volume Production in a 

Paint Shop, MSc Thesis, Department of Product and 

Production Development, Chalmers University of 

Technology, Gothenburg, Sweden.

[14] Heilala, J., Saija, V., Tonteri, H., Montonen, J., Johansson,

B., Stahre, J., Lind, S. 2008. Simulation-Based Sustainable 

Manufacturing System Design, In Proceedings of the 2008 

Winter Simulation Conference, eds. Mason, S. J., Hill, R. R., 

Mönch, L., Rose, O., Jefferson, T., Fowler, J. W. 1922–1930, 

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SISO-ADM-002-2008. 

50
Towards a New Geometric Metric for Sustainability 

Assessment 

Gaurav Ameta 

School of Mechanical and Materials 

Engineering 

Washington State University 

Pullman, WA, 99164 

+1-509-335-8218 

gameta@wsu.edu

ABSTRACT

This paper puts forth a novel geometric metric for assessing 

sustainability of a product. The purpose of the novel geometric 

metric is to present sustainability aspects of a product, for its 

entire life-cycle (material production, manufacturing, supplychain, use and disposal), to design engineers in a readily 

comprehensible way. Achieving sustainability is critical for 

minimizing the detrimental effects caused by global warming due 

to modern equipment manufacturing, use and disposal. Impact of 

various products on the environment, society and economy are 

primarily locked in the design stage of the product. Therefore 

engineers need a comprehensive metric to be used at the design 

stage for designing a sustainable product. The paper first reviews 

sustainability metrics and then focuses on the geometric metrics 

for evaluating sustainability aspects of a product. Based on the 

concept of areal coordinates, this paper constructs a preliminary 

geometric metric for sustainability assessment. 

Keywords

Sustainability, Geometric metric, Product life-cycle. 

1. INTRODUCTION 

Due to the current climate change scenario, the notion of 

sustainability has recently gained wide interest. According to the 

United Nations Environment Program, climate change is affected 

by various human activities such as land use changes and fossil 

fuel burning [1]. Although Sustainability is a common objective 

of all entities over the world, its realization is difficult as it is 

engulfed in myriad of political, societal, regional, technological, 

economical, legal and geological issues. It is also quite evident 

that sustainable development is a dynamic process by nature [2, 

3], as the biosphere and conditions around the world are ever 

changing and still quite unpredictable. Despite this 

unpredictability, scientist, governments, industry, consumers etc., 

have realized that increase in global temperatures is very likely 

due to the increase in anthropogenic (human) greenhouse gas 

concentrations. This increase in global temperatures, if not 

curbed, will have a debilitating effect on the viability of the 

biosphere to sustain life [4]. To impede and hopefully reverse the 

debilitating climate changes that have occurred, sustainable 

products should be designed. 

A sustainable product can be defined by understanding the 

meaning of sustainability or sustainable. The word “sustainable” 

was first used with respect to its current usage as sustainable 

development. Sustainable development is the development that 

“meets the needs of the present without compromising the ability 

of future generations to meet their own needs.” [5]. A definition 

of sustainability according to the US National Research Council is 

“the level of human consumption and activity, which can continue 

into the foreseeable future, so that the systems that provides goods 

and services to the humans, persists indefinitely” [6]. Other 

authors (e.g., Stavins et al. [7]) have argued that any definition of 

sustainability should include dynamic efficiency, should consist 

of total welfare (accounting for intergenerational equity) and 

should represent consumption of market and non-market goods 

and services. 

In this paper, sustainable product would imply that the product is 

sustainable in all aspects (society, economy and environment) 

throughout its entire life-cycle from material production to 

manufacturing, supply chain, use and disposal of the product. 

This paper puts forth preliminary work for a novel geometric 

metric for evaluating sustainability of a product. The next section 

discusses product life-cycle and interaction of sustainability 

aspects. Section 3 summarizes life cycle assessment method. 

Then, section 4 discusses various metrics and related efforts for 

sustainability assessment. Section 5 puts forth the notion of the 

novel geometric metric. 

2. LIFE CYCLE ASSESSMENT 

Life cycle assessment (LCA), have been developed by 

International Organization for Standardization (ISO) [8], for 

assessing the environmental impacts of products. By including the 

impacts throughout the product life cycle, LCA provides a 

comprehensive view of the environmental aspects of the product 

or process. LCA has been widely popular for identifying 

environmental impact of a product or process. LCA methodology 

has been incorporated into several commercial (SimaPro [9] and 

GaBi [10]), governmental (TRACI [11], BEES [12]) and 

academic environmental assessment tools (EioLCA [13], and 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies are 

not made or distributed for profit or commercial advantage and that 

copies bear this notice and the full citation on the first page. To copy 

otherwise, or republish, to post on servers or to redistribute to lists, 

requires prior specific permission and/or a fee. 

Permis’09, September 21–23, 2009, Gaithersburg, MD, USA. 

Copyright 2009 ACM 978-1-60558-747-9/09/09...$10.00. 

51
Figure 1: Different stages of a product life-cycle, including the material handling stages and the planning stage (design). 

EcologiCAD [14]) One of the important aspects of LCA is that it 

is able to present environmental and economic impacts in an 

aggregated manner. 

Despite the large application of LCA, it has been attributed with 

some drawbacks related to (a) System Boundaries (b) Data Issues 

and (c) Methodology Issues such as (Weighing methods, 

Aggregation methods and Comparison across indices)[2, 15-20]. 

The methodology issues are related to the selection of appropriate 

metric for comparing products. 

3. PRODUCT LIFE-CYCLE AND 

SUSTAINABILITY 

As shown in Figure 1, there are several stages in a product lifecycle. At the design and planning stage, function identification, 

geometry and material optimization, overall manufacturing, 

supply chain, and disposal planning is conducted. Then the design 

documentations are passed to the manufacturing stage. 

Manufacturing, supply chain, and disposal of the product are 

further planned (refined) before actual handling of material. 

Therefore, there is a need of sustainability related decision 

support at different levels; at the design stage and at the individual 

manufacturing, supply chain and disposal stages. The 

sustainability impact of the entire product life-cycle can be 

estimated with the following equation 

matprod manu supply use ++++= SSSSSS disp (1) 

where, Smatprod represents sustainability impacts from the material 

production, Smanu represents sustainability impacts for 

manufacturing, etc. 

Each of the sustainability terms in equation (1) can be further subdivided based on its influence one or a combination of aspects 

from society, economy or environment. 

4. METRICS FOR SUSTAINABILITY 

ASSESSMENT 

In this section we will discuss various issues related to 

sustainability and environmental metrics and some examples of 

metrics studied in the literature. 

4.1 Metrics Classification 

Scoping sustainability and defining clear system boundaries are 

critical for properly defining metrics for sustainability assessment 

[21]. Various metrics developed so far to measure the progress 

towards sustainability have been classified by Mayer [2] and Jain 

[22] into: a) indicators, b) indices and c) frameworks. In a recent 

article by Sikdar, indicators were identified as 1-D metric as they 

would quantify changes in only one of the bottom lines of 

sustainability [23]. Indices could be a 2-D metric or 3-D metric, in 

a sense that they could quantify changes in either two or three of 

the bottom lines of sustainability. 

4.1.1 Indicators 

Indicators basically measure a single parameter of a system, e.g., 

CO2 emission or energy use. A detailed survey of indicators has 

been presented in Patlitzianas et al. [24]. Keffer et al. propose a 

framework for developing a classification of indicators [25]. In 

the framework, indicators are classified based on aspects and 

categories. Categories are broad areas of influence related to 

environment, economy and society, referred to as the triple 

bottom line of sustainability. Aspects are defined as general type 

of data that is related to a specific category. 

4.1.2 Indices 

Indices are basically aggregates of several indicators, e.g., 

Ecological Footprint [26] (a ratio of the amount of land and water 

required to sustain a population to the available land and water for 

the population) or Environmental Vulnerability Index (consists of 

indicators of hazards, resistance and damage). Indices represent a 

single score by combining various indicators of different aspects 

of a system. 

52
 

(a) (b) 

Figure 2: (a) Spider Chart or Radar Graph, (b) Modified Spider Chart as demonstrated by the pharos project [39]. 

Key requirements and rigorous mathematical requirements for 

sustainability indices are proposed in Bohringer and Jochem [27] 

and Ebert and Welsch [28], respectively. The strengths and 

weakness of several sustainability indices are compared by Mayer 

[2]. 

4.1.3 Frameworks 

Frameworks present large numbers of indicators in qualitative 

ways, e.g., the vulnerability framework [29] or the CRITINC 

Framework [30]. Frameworks do not aggregate data in any 

manner. An advantage of frameworks is that the values of all 

indicators can be easily observed and are not hidden behind an 

aggregated index. The disadvantage of using frameworks is that 

they are hard to compare over time although this is possible by 

using Hasse diagrams [31]. A brief review of sustainability 

frameworks is provided by Mayer [2]. 

4.2 Examples 

In this section we will survey metrics and their categories that 

have been used to evaluate products from manufacturing 

enterprises. 

4.2.1 Sustainability Metric 

Sustainability metric should include impacts from not just 

environmental aspects but economic and societal aspects of the 

entire product life-cycle. In this regard, Datschefski proposed that 

the sustainability of a product should be measured using 

recyclability, safety, efficiency, use of renewable energy and 

social effects [32]. 

4.2.2 Environmental impacts Metric 

A review of eco-indicators used in product development is 

provided by [33]. Environmental impacts metric have been 

classified into quantitative (Material Intensity per Service Unit 

(MIPS), Cumulative Energy Demand (CED), and EcoIndicator 95 

(EI95)) and qualitative metrics in [34, 35]. As quantitative 

methods, were analyzed. 

4.2.3 Geometric Representations 

The sustainability metric can be represented as a single 

aggregated number or represented geometrically. The geometric 

representations can be in the form of (a) multidimensional 

geometric representation (b) spider chart [36] also known as radar 

graph [37], ternary plot, ternary graph, triangle plot, simplex plot, 

or de Finetti diagram (c) simplified graphs and tables and (d) 

annotated /colored CAD or PLM product design [38]. Figure 2 

demonstrates two different forms of spider chart. 

5. GEOMETRIC METRIC 

The purpose of creating the geometric metric is to provide the 

sustainability related information to engineers in a graphical 

manner. The geometric metric will be constructed from a basis–

simplex and described with areal coordinates, a method from 

affine geometry [40]. 

5.1 Areal Coordinates 

For a geometric metric of three-dimensions, four (3+1) basispoints (σ1..σ4 ) positioned arbitrarily will be utilized to create the 

three-dimensional space. (It is only necessary for affine geometry 

that the positions for basis-points σ1..σ4 be independent.) A 

geometric metric for sustainability of three-dimensions would 

consider three sustainability aspects that can be converted / 

normalized to have a single unit. 

At the four basis-points place four masses λ1, λ2, λ3, and λ4 that 

may be positive or negative. So long as λ1 + λ2 + λ3 + λ4 ≠ 0, the 

position of σ , the centroid of these masses, is uniquely 

determined by the linear combination 

( ) 1 432 σλσλσλσλσλλλλ 44332211 +++=+++ (2) 

and σ can assume any position in the space of σ1..σ4 by varying 

λ1..λ4; e.g. for λ1..λ4 all positive, σ identifies any point inside 

tetrahedron σ1σ2σ3σ4. The four masses λ1..λ4 are the barycentric 

coordinates of σ, yet, note that the position of σ depends only on 

three independent ratios of these magnitudes. Consequently, the 

four λi can be normalized by setting λ1 + λ2 + λ3 + λ4 = 1. Then, 

they are areal coordinates and 

σλσλσλσλσ 44332211 +++= (3) 

In three-dimensional space, the basis-simplex is a tetrahedron. In 

n-space the tetrahedron for dimension 3 generalizes to the nsimplex. The simplex occurs in spaces of all dimensions: in 

dimension zero, it is a point; in dimension 1, a line-segment; in 2, 

a triangle; in 3, a tetrahedron; in 4, a four-simplex; etc. Nonregular simplexes occur in all spaces of dimension 2 and higher; 

53
they are formed simply by spacing some or all of the vertices at 

different distances from each other. 

Figure 3: Basis-simplex used to create a three-dimensional 

geometric metric. 

5.2 Metric Use 

A sample basis-simplex and axes mapped on the simplex is shown 

in Figure 3 and Figure 4, respectively. The metric also shows 

emissions, carbon weight [41] and global warming potential [42] 

as axes mapped on the simplex. All of these impacts can be 

converted into the units of GWP (Global Warming Potential). 

Emissions

Carbon

Weight

Global 

Warming 

Potential

σ1

σ2

σ3

σ4

Figure 4: An example of a geometric metric constructed using 

the basis-simplex in Fig 3. The metric also shows Emissions, 

Carbon Weight and Global Warming Potential as axes 

mapped on the simplex. 

Based on the inherent inter-relations between emission, carbon 

weight and GWP and the effect of a particular product life-cycle, 

a designer may want to specify an allowable limit for the 

particular stages of the life-cycle of the product. These limits will 

take some shape geometrically in the hypothetical space of the 

geometric metric. One such sample shape (the shape is shown just 

as an example and does not represent the real inter-relation 

between these three axes) is shown in Figure 4 as a cylinder. 

Similar shapes from different stages of the product may be 

combined together (equation (1)) to obtain the possible total 

impact for the entire life-cycle of the product. 

The combination of these geometric shapes can be obtained 

through a process known as Minkowski Sum, which is defined as 

the vector sum of points on geometric shapes. Minkowski Sum 

has applications in image processing, robotics [44,45], CAD [46, 

47], spatial planning, graphic arts, animation [48,49] and 

tolerance analysis [50, 51]. 

Figure 5 (a) Minkowski sum of two shapes; 1-dimensional line 

and 2-dimensional circle and (b) Minkowski sum of four 3-

dimensional convex polyhedral (figure from [50]). 

5.3 Example Metric 

Let us consider a simplified case for constructing a simple metric. 

Consider the electricity consumption and carbon weight at the 

manufacturing stage. Data is available in [43] relating electricity 

consumption and carbon emission in different states across United 

States of America. If energy (electricity) and carbon weight are 

considered as two impacts from the manufacturing stage of a 

product, then the set of lines shown in Figure 6 can be obtained. Carbon Weight in Pounds 

Electricity in KWH 

Bounds

Figure 6: Set of lines as a geometric metric for representing 

electricity use and carbon weight. 

As is quite evident from the allowable bounds set by the designer 

that for the same amount of electricity consumption there will be 

higher amount of carbon weight associated. Currently, the 

shape/slope of the bounding lines is selected based on the average 

slope of the lines available for all the states. 

6. FUTURE WORK 

Although a very preliminary study is presented in this paper for 

developing the geometric metric for assessing sustainability, the 

geometric metric can be easily extended to include many more 

aspects of sustainability. In future the author would build different 

manifold geometries in n-dimensional space, will be obtained 

such as curves, open surfaces, closed surfaces, hyper-curves and 

hyper-surfaces based on n-impacts selected from a particular 

stage of the life-cycle. Since, not all sustainability aspects can be 

converted to have same units; a set of these geometric metrics 

would be created. More over each stage of the product life-cycle 

will have a complete set of these metrics. The aggregation of 

these sustainability impacts from different stages of the life-cycle 

54
would be accomplished using minkowski sum of the respective 

geometric metric. 

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Arizona State University. 

56
A Mission Taxonomy-Based Approach to Planetary Rover 

Cost-Reliability Tradeoffs 

David Asikin 

The Robotics Institute 

Carnegie Mellon University 

5000 Forbes Ave 

Pittsburgh, PA, USA 

dasikin@cs.cmu.edu 

John M. Dolan 

The Robotics Institute 

Carnegie Mellon University 

5000 Forbes Ave 

Pittsburgh, PA, USA 

jmd@cs.cmu.edu 

ABSTRACT

Our earlier work on robot mission reliability provides tradeoff 

analysis between input parameters such as mission success rate, 

robot team size, and robot component reliability, but only for 

specific tasks. Here we take a more comprehensive approach in 

order to draw more general conclusions about robot mission 

reliability. The approach is based on a mission taxonomy coupled 

with detailed reliability analysis of each of the resultant mission 

classes. This paper describes initial work towards that goal. 

In this paper we present the above-mentioned taxonomy, which 

divides planetary robotic missions into subgroups with common 

characteristics with respect to the time proportion of tasks 

involved in the missions. For a given mission class, we show how 

a mission designer can obtain the optimum robot configuration in 

terms of robot team size and component reliability that maximize 

mission success rate under a budget constraint. 

Categories and Subject Descriptors

B.8.2 [Performance and Reliability]: Performance Analysis and 

Design Aids; 

G.3 [Probability and Statistics]: reliability and life testing, 

stochastic processes, survival analysis 

General Terms

Performance, Design, Reliability. 

Keywords

Mission design, planetary robot, mission taxonomy, reliability, 

mission cost, failure, robot configuration optimization. 

1. INTRODUCTION 

Planetary robots built for NASA by the Jet Propulsion Laboratory 

are notable for their extremely high reliability. To achieve this 

magnitude of reliability, the robots make use of some of the most 

reliable components available and provide high redundancy in the 

design. This design paradigm comes at high financial cost, 

however, both in the development cost of the robots and in the 

ongoing operational costs. 

One good example of the high cost of NASA robots is the Mars 

Science Laboratory (MSL). The mission was given the highest 

scientific priority in NASA’s Mars Program of 2002, but then 

delayed in the 2006 plan as a result of cost constraints [1]. MSL 

was initially approved at a budget of approximately $1.5 billion 

[2], but the budget for the mission kept rising until it reached $2.3 

billion [3], of which $1.6 billion was development costs for the 

rover, its instruments and the spacecraft. The fiscal problem has 

led to the thought of continuing the over-budget MSL mission at 

the expense of delaying or even cancelling other projects [4]. 

MSL is one of many in-situ planetary missions NASA plans to 

launch in the future. If these future robots follow legacy designs, 

then the increasingly demanding future missions will require 

robots to be built using components of order-of-magnitude higher 

quality, rendering the mission eventually infeasible from a cost 

and availability standpoint. 

Reduction of robot development costs can be achieved if overly 

reliable components are exchanged for ones more in line with the 

mission requirement. For this, it is necessary to consider the 

impact of reduced component reliability on the overall mission 

risk. It is also necessary to regard risk not simply as something to 

be minimized to the greatest extent possible, but instead as a 

quantitative design factor to be traded off against other design 

factors in order to seek an optimal mission configuration. 

Therefore, tradeoff analysis between mission risk, component 

reliability and cost is crucial. 

A quantitative methodology for doing so has been proposed in our 

previous work [5] and [6], but has only been used on a limited 

number of specific examples. As a result, a mission taxonomy is 

desirable in order to characterize and examine the full range of 

planetary robotic missions. 

A number of taxonomies for robots, robot tasks, and robot teams 

have been proposed. For instance, [7] provides a taxonomy that 

classifies multirobot teams in terms of team size and composition, 

communications, and processing capability. Reference [8] 

provides a taxonomy which classifies multirobot tasks in terms of 

criteria such as time, energy and robot capabilities, and [9] & [10] 

present a taxonomy which categorizes robot tasks in terms of the 

amount and type of human-robot interaction involved. 

In order to have fundamentally different reliability characteristics 

and therefore tradeoff relationships among mission classes, we 

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57
hypothesize that it may be sufficient for the amount of time spent 

using various modules to differ significantly. 

Our taxonomy differs from the taxonomy mentioned above in that 

it classifies robot missions with respect to the time proportion of 

the tasks involved in the missions. The breakdown of the time 

proportion of the tasks in a mission is important for analyzing the 

nature/emphasis of a mission. 

Drawing from the NASA Roadmap for the exploration of the 

Solar System over the next 30 years [11] and Mars Exploration 

Program [1] & [12], we propose three mission classes formed 

from a set of tasks, which we call “Basic Activities”, defined in 

the next section. 

Using the method we introduced in a previous paper [6], we 

characterize each of the mission classes via state transition graphs 

and investigate the time proportions of basic activities for the 

various mission classes. Extending our work in [5], we explore 

the abovementioned tradeoffs by finding the global optimum 

robot configuration with respect to the cost and reliability for a 

specific mission class and setting. 

We expect significant differences in time allocation of the basic 

activities to be reflected in significant differences in the character 

of the tradeoffs. 

2. ROBOT MISSION TAXONOMY 

2.1 Taxonomy Criterion 

In generating a mission taxonomy, it is crucial to create a standard 

feature with respect to which different missions are compared and 

classified. It is also important that the feature be quantifiable so 

that the classification generated can be studied analytically. 

Identification of this key feature will allow its systematic 

variation in the step of determining mission reliability. In this 

light, we attempted to answer the following three questions: 

1. Can different types of missions be identified? 

2. If missions can be identified, can their features be 

isolated? 

3. If features of a mission can be isolated, can they also 

be tailored to form another type of mission? 

We comprehensively surveyed future in-situ planetary robotic 

missions from the NASA Solar System Exploration Roadmap 

(SSER) and Mars Exploration Program (MEP) and identified 

several fundamental tasks, independent of each other, that are 

present in all of the missions but exist in different proportions. 

The mix of these fundamental tasks, which we term “Basic 

Activities”, is the feature with which we measure and compare 

different missions. We quantify the proportion of a basic activity 

in a mission in terms of percentage by comparing the time spent 

in that particular task to the total mission time. 

Analyzing every mission instance we encountered in the roadmap, 

we concluded that a mission can be characterized using the 

following nine basic activities: 

1. Traverse (e.g., driving, flying) 

2. Subsurface Access (e.g., drilling, grinding, digging) 

3. Instrument Deployment (e.g., manipulator, camera) 

4. Sampling (e.g., image, soil) 

5. Assembly 

6. Communication 

7. Sample Analysis 

8. Recharging 

9. Idling 

We do not expect the percentage proportions of basic activities 

that constitute a specific mission class to be absolutely fixed. 

Rather, they will fall into a range such that the character of a 

mission significantly changes only when the proportions exceed 

that range. 

Initially, we performed qualitative separation between mission 

classes, thus determining the range mentioned above, based on 

our analysis of SSER and MEP. Afterwards, we will ground-truth 

the qualitative boundaries we made for each mission class 

quantitatively using the methodology outlined in subsection 4.1 

and use the resulting boundary for differentiating between 

missions. 

2.2 Mission Classes 

For the purpose of generating the taxonomy, we did a 

comprehensive study of the following NASA-proposed in-situ 

robotic operations: 

‐ Europa Explorer, Europa Astrobiology Lander, 

‐ Titan Explorer, 

‐ Venus In-Situ Explorer, Venus Mobile Explorer, Venus 

Sample Return, 

‐ Neptune-Triton Explorer, 

‐ Lunar South Pole-Aitken Basin Sample Return, 

‐ Mars Pathfinder-Sojourner, Mars Scout Phoenix, MER, 

MSL, Astrobiology Field Laboratory, and 

‐ Comet Cryogenic Nucleus Sample Return 

Based on the above study, we propose categorizing missions into 

three classes followed by their examples: 

1. Search and Exploration Mission: 

a. Search for biomarker signatures 

b. Search for water resources 

c. Surface mapping 

2. Sample Acquisition and Composition Analysis Mission: 

a. Surface rock sampling 

b. Organic materials sampling 

c. Analysis of chemical and isotopic 

composition of surface 

3. Construction Mission: 

a. Radiation shielded habitat construction 

(Lunar) 

b. Lunar outpost construction (Lunar) 

In order to validate the above classification scheme, we use the 

methodology introduced in our previous work [6] by 

stochastically simulating a mission class using a state transition 

diagram. Each state in the diagram corresponds to a basic activity. 

The process flow and the resulting time proportion of the basic 

activities are explained in subsections 3.1 and 4.1, respectively. 

58
3. CONSTRUCTION MISSION SCENARIO 

3.1 Mission and Environment 

Due to NASA’s strong interest in building permanent bases in 

planetary environments, we use the taxonomy above as a 

framework to consider a Construction Mission class in a planetary 

environment to install modules at several sites using a team of 

robots. The installation task consists of carrying modules from a 

module depot to the designated sites and then assembling them. 

We extend our previous work of simply carrying a module to a 

site and repeating the task [5] into a more mature scenario that 

better resembles a general planetary in-situ construction mission. 

This allows us to consider energy limitations on the robots and 

further elaborate the robot model and the working environment. 

Based on [13] and [14], we envision that in a construction mission 

at least three types of location would exist: a battery recharging 

station (i.e., solar panel plant, robot base), a module depository 

and the construction sites. As also stated in the literature, we 

expect that in a planetary environment there exist areas that 

receive steadier sunlight; thus, solar power generation on a 

stationary site would be more efficient than on the peripatetic 

robots, which would potentially work in a shadowed and dusty 

environment. However, our methodology also works in the case 

where a site co-locates with another or in the case where all of 

them co-locate in one place. 

This environment model is shown in Figure 1. The locations are 

represented as nodes with the distance between locations written 

as weights (in meters) on the edges. Connected nodes show the 

possible paths the robots can take. We selected the weights in the 

figure as our baseline model and varied them in the simulation. 

For the purpose of the reliability analysis, the mission is broken 

down into seven basic activities: 

1. Transit to the module depot 

2. Fetch modules 

3. Transit to the construction site 

4. Stack modules 

5. Assemble modules 

Repeat 1 – 5 until all sites are completed. 

6. Communicate with other robots after every subtask. 

7. When needed, return to the recharging site (via depot as 

checkpoint) and replenish battery. 

Figure 1. Environment model in graph diagram. 

Each robot works independently of the others and coordination to 

avoid overlapping tasks (e.g., carrying more modules than 

needed, assigning more robots than needed to install a module, 

etc.) is done by communication among the robots after the 

completion of each subtask. In the case of a failure of a robot, a 

new spare robot will be deployed from the headquarters to 

continue the mission. The mission is considered a success when 

all the necessary modules are installed at the sites. 

3.2 Robots and Components 

For this analysis we assume that the robots are identical. Making 

appropriate inference from various sources [14], [16], we assume 

each robot weighs 174 kg and uses two 7.15-kg lithium ion 

batteries (150 W-h/kg) for energy storage. The power 

consumption model used in the simulation is listed in Table 1. 

The robot velocity is assumed to be a constant 0.1 m/s throughout 

the mission. 

The robot subsystem reliabilities are listed in Table 2. The 

subsystem reliability data were derived from component 

reliability data provided by the Jet Propulsion Laboratory and are 

representative of components used in NASA’s planetary robots. 

The usage times of each subsystem for each basic activity are 

shown in Table 3. These usage times were assigned using 

reasonable assumptions about the relative durations of different 

activities and the relative usage of different modules. 

We used the above-described model and the methodology in our 

previous work [6] to calculate the probability of subsystem 

survival for a given mission activity. In this calculation, we 

assumed that the battery recharging task is always successful and 

hence excluded it from the reliability consideration. We also 

assumed that the recharging station is always capable of 

generating maximum energy and fully recharging batteries 

regardless of climate disturbances, e.g., dust storms, dust buildup, or poor sun exposure. 

Table 1. Power consumption model 

Basic Activity Power Consumption 

Traverse 150 W 

Instrument Deployment 52 W 

Communication 74 W 

Assembly 52 W 

Idling 15 W 

Table 2. Robot subsystems and reliabilities 

Subsystem MTTF (h) 

Power 4202 

Computation & Sensing 4769 

Mobility 19724 

Communications 11876 

Manipulator 13793 

Depot Solar 

Plant 

Site 

1 

Site 

2 

Site 

3 

20 25

25

25 

59
Table 3. Subsystem usage by task in minutes using baseline 

constant in Table 1 

Subsystem 

Transit 

(Solar Plant 

-Depot) 

Fetch / 

Stack 

Modules 

Transit 

(Depot-Site) 

Power 33.33 60 4.17 

Computation & 

Sensing 33.33 60 4.17 

Mobility 33.33 30 4.17 

Communications 0 0 0 

Manipulator 0 60 0 

Subsystem Module 

Assembling Communication 

Power 300 15 

Computation & 

Sensing 300 15 

Mobility 120 0 

Communications 0 15 

Manipulator 300 0 

4. APPROACH 

We generated a state-transition diagram for the Construction class 

mission based on the mission flow described in subsection 3.1. 

The state machines represented by the diagram are then 

implemented in simulation software. The simulation is repeated 

many times, with the average score of all trials giving the overall 

probability of mission completion (PoMC). 

For this mission scenario, once the mission flow, the basic 

activity durations and the baseline module reliabilities are fixed, 

then the input variables are: 

- Number of installation sites 

- Number of modules to be installed per site 

- Number of robots 

- Number of spare robots 

- Reliability of the components used 

- Maximum number of modules a robot can carry 

- Distance between recharging site and module depot 

- Distance between module depot and installation sites 

Thus, PoMC and the time proportion of the basic activities are 

functions of these input variables and varying these variables 

results in a change in the output PoMC and the time proportion. 

4.1 Time Proportion of Construction Class 

Given the hyper-dimensionality of the model, we simplify the 

analysis by varying only one variable at a time and fixing the rest, 

and looking at the relationship between the varied variable and 

the time proportion of the basic activities in the Construction 

mission class as well as PoMC. 

Graphically, this means that we are reducing the dimensionality 

of the model by only analyzing a slice of the hyper-plane at a 

time. Intuitively, the function of PoMC with respect to the 

variable being varied and the time proportion would only be valid 

at that particular slice and might not hold at different set of values 

of the remaining variables. In that light, here we would like to see 

how much the time proportion of the basic activities will vary for 

different slices of the hyper-plane. 

In every simulation with mission success rate greater than zero, 

we record the time spent on the basic activities. We set the 

baseline variables as shown in Table 4 and then increment the 

variable to be varied along the x-axis from the minimum to the 

maximum expected value. 

Our result shows that the time proportion of the basic activities in 

the Construction mission class does not vary greatly between 

different slices of the hyper-plane (see Table 5). 

Our sensitivity analysis shows that the number of installation sites 

and the number of modules to be installed per site have small 

influence on the time proportions. Intuitively, an increase in the 

amount of work (i.e., number of sites and/or modules) leads to an 

increase in the time share of module assembling. However, due to 

the limitation on the number of modules that a robot can carry, the 

robots are forced to return to Depot to fetch more modules, hence 

increasing the time proportion of other activities, as well (see 

Figure 2). 

The variables that have the most influence on the time proportions 

are the number of robots deployed and the distance (i.e., Solar 

plant to depot, depot to construction sites) to be travelled by them. 

For the latter, the reason is straightforward: an increase in either 

Table 4. Baseline constants used in the simulation 

Variable Value 

#Sites 10 

#Modules/site 5 

#Robots 2 

#Spare robots 0 

%MTTF 100% 

#Module capacity/robot 3 

d(Solar Plant - Depot) 200 

d(Depot – Site) 25 

Table 5. Time proportion in the Construction class mission 

Basic Activities % of Mission Time (±2%) 

Traverse 4 

Instrument Deployment 21 

Module Assembling 47 

Communication 17 

Recharging 11 

Idling < 1 

60
Figure 2. Varying number of installation sites. 

of these distances directly increases the time spent in Traverse, 

thus increasing its relative proportion to the remaining basic 

activities. For the former, increasing the number of robots 

proportionally increases the amount of coordination (hence, 

communication) among the robots. By fixing the amount of work 

(i.e., number of sites and/or modules), the coordination to avoid 

overlapping tasks between the robots causes deploying more 

robots to result in some of the robots’ being idle (see Figure 3). 

However, if the amount of work increases proportionally with the 

number of robots deployed, then the time proportion of the basic 

activities will follow the general proportion described in Table 5. 

Indeed, the resulting time proportion significantly depends on the 

robot work rate in performing an activity (see Table 3). However, 

we also have confirmed that the time proportion for each activity 

does not fluctuate drastically (still falls within a small range) and 

observed the same sensitivity pattern in different models. 

Figure 3. Varying number of robots. 

4.2 Designer Questions 

In the conceptual mission designing phase, a mission designer 

might ask questions such as: 

Given a fixed budget and a fixed number of sites to build, 

1. Which configurations of robots in terms of team size 

and robot component reliability (%MTTF) give the 

highest mission success rate? 

2. Which is better, a smaller number of robots with highly 

reliable components or a larger number of robots with 

less reliable components? 

4.3 Cost Function 

We adopt the general relationship of reliability and cost, where 

cost is an exponential function of component reliability. The 

exponential relation means that, the higher the reliability of a 

component, the smaller increase in reliability per unit 

expenditure. We assume that the robot component cost is a linear 

combination of two types of cost function: component material 

cost and production cost where each is represented as an 

increasing exponential function. The total cost function (robot 

component cost) is then given as follows: 

, (1) 

where 

 percentage of component reliability compared to 

 the baseline model 

 the weight of component material cost 

 the weight of component production cost 

parameters to adjust the initial cost 

 (when R=0%) and the cost when R=100% 

For analysis purposes, we assume that the component material 

cost and production cost contribute equally to the total component 

cost. We also assume that there is still a cost to be incurred even 

when producing a poor-quality component (i.e., reliability R=0). 

For this purpose, we set the parameters such that 

the initial component cost (when R=0) is 20% of the total budget. 

Note that . The details of the parameters are 

given in Equation 2 and a plot of the cost function for different 

robot team sizes is shown in Figure 4. 

, (2) 

Figure 4. Component cost (=material cost and production 

cost) as a function of component reliability (%MTTF). 

61
It is noteworthy that our methodology works with any cost 

function. The cost model described here serves as an alternative 

example to the cost model used in [5], which was taken from 

reference [15]. Both cost models are monotonically increasing 

functions of component reliability. However, the latter has a 

drastic gradient between low- and high-reliability components 

such that the cost of a high-reliability component is very high 

(i.e., an increase in 5% component reliability from 90% reliability 

to 95% reliability would result in a large price increase from 60% 

to 100% of the baseline cost) and the cost of a low-reliability 

component is very low (i.e., an increase in 40% component 

reliability from 40% reliability to 80% reliability results only in 

minor price increase from 40% to 47% of the baseline cost). It is 

possible to attenuate the extreme to some extent by lowering the 

feasibility parameter provided, but the maximum achievable 

reliability will also be greatly lowered. 

The cost model we propose here provides a more gradual increase 

in unit expenditure per increase in component reliability. In 

subsections 4.4 & 4.5, we will observe the outcome of the 

reliability tradeoff using both cost functions. 

4.4 Optimizing Robot Configuration 

Using the cost model from the previous subsection, we seek to 

optimize the robot configuration for the Construction mission 

class with respect to the criteria posed in subsection 4.2. 

A mission designer would presumably like to design as reliable a 

system as possible under budget constraints while achieving the 

highest possible mission success rate. This issue relates closely to 

our tradeoff model between component reliability (%MTTF), 

robot team size, cost and probability of mission completion 

(PoMC). The idea is to come up with a robot team size with a 

certain component reliability that can maximize PoMC under a 

fixed budget. 

In Figure 5, using the data listed in Table 4 as the input variables, 

we plot several tradeoff relations between component reliability 

(%MTTF) and mission success rate (PoMC) for different robot 

team sizes (from 2 to 5 in red, brown, green, yellow lines). We 

have also fitted a curve to these points, allowing this equalizing 

%MTTF to be estimated for intermediate points without running 

additional simulations. The black horizontal line shows the PoMC 

for the baseline configuration (1 robot with 100% component 

reliability). 

Based on the cost model for one robot (Equation 2), we calculate 

the respective budget needed for 2 to 5 robots. By doing so, we 

are able to compute the maximum achievable component 

reliability (%MTTF) under the budget constraint for each team 

size. This is shown as dashed vertical lines in Figure 5. 

The intersection between the dashed vertical lines and the PoMC 

curve (a function of %MTTF) then gives the maximum 

achievable PoMC for each team configuration (using the 

maximum achievable %MTTF) given the budget constraint. 

In this graph, we have 4 intersections (for 2 to 5 robots). Analysis 

of all the intersections shows that in this mission scenario of the 

Construction mission class, a configuration of 3 robots with 

31.9% MTTF of the baseline reliability (see Table 2) gives the 

highest probability of mission success under the budget 

constraint. 

Figure 5. Tradeoff between %MTTF, PoMC and Cost for 

various team sizes when #Sites = 10. 

Mission designers may replace the component reliability – cost 

model utilized here with their own. Obviously, using a different 

cost model would potentially result in a different optimum robot 

configuration. For example, with the same initial component cost 

(when R=0) of 20% from the total budget, using the cost model 

we utilized in our previous paper ( ) 

[5] would result in a robot team size of 4 with 52.7% MTTF of 

the baseline reliability being the optimum configuration under the 

budget constraint. 

4.5 Robot Team Size-Reliability Tradeoff 

Previously in [6], we answered this tradeoff question by providing 

an example of how a team of 4 robots with less reliable 

components has a higher mission success rate (PoMC) than a 

team of 2 robots with more reliable components. Here we would 

like to corroborate that statement and extend the analysis by 

observing it through the optimization methodology described 

above. 

Using the same scenario as in the previous subsection and the cost 

model from Equation 2, we increase the number of construction 

sites from 10 to 50 and plot the tradeoff graph in Figure 6. Note 

that increasing the number of construction sites also increases the 

mission duration. In the figure, we can see that the optimum robot 

configuration is no longer 3 robots with 31.9% MTTF of the 

baseline reliability, but 2 robots with 57.1% MTTF of the baseline 

reliability. In other words, increasing the mission duration causes 

the preferred configuration to move in the direction of fewer 

robots with higher reliability. 

Our analysis shows that there exists a turning point where a larger 

robot team with less reliable components will perform exactly the 

same (in terms of PoMC) as a smaller robot team with more 

reliable components when we increase the amount of work (i.e., 

number of sites and/or modules). Going beyond that turning point 

results in the smaller robot team with more reliable components 

producing a higher mission success rate. 

The turning point can be explained from the reliability 

engineering point of view. Reliability is a function of time where 

the reliability of a component with a constant hazard rate is equal 

62
Figure 6. Tradeoff between %MTTF, PoMC and Cost for 

various team sizes when #Sites = 50. 

to one at the beginning of the service life and decays 

exponentially towards zero. Thus, using components with reduced 

reliability compared to the baseline component will result in 

shorter service life and higher failure probability for prolonged 

usage. 

Since we assumed that a failure of any single component leads to 

a failure of the entire robot, the robot using the components with 

reduced reliability will likely have a smaller mission success rate 

for longer mission duration. Likelihood of mission failure can be 

compensated for by increasing the number of robots. However, 

this compensation may not be enough to cover the loss in 

component reliability, especially when the budget is constrained. 

Consider a simple example where a robot has a total system 

reliability expressed in MTTF (hour) of 4202. The budget 

constraint allows us to double the number of the robots while 

halving the component reliability of both robots. Now, we 

compare the mission success rate for both configurations of 1 

robot with 4202 MTTF(h) and 2 robots with 2101 MTTF(h). The 

mission success condition is such that one robot must stay alive in 

order to complete the mission. For 4000 hours of usage, the 

mission success rates (PoMC) for both configurations can be 

calculated as follows: 

1 robot, 4202 MTTF(h): 

2 robots, 2101 MTTF(h) each: 

For 1 robot,

 For 2 robots, 

 

Now, we do the same calculation for a mission of 1000 hours: 

1 robot, 4202 MTTF(h): 

2 robots, 2101 MTTF(h) each: 

For 1 robot,

 For 2 robots, 

Here we can see that for the prolonged 4000 hours mission, for 2 

robots 2101 MTTF(h), the loss in PoMC due to the halved 

component reliability is 0.237 (= 0.386 - 0.149). However, the 

compensation of doubling the robot number only increases PoMC 

by 0.127 (= 0.276 - 0.149), which is not enough to cover the loss 

in PoMC due to reduced component reliability. In the shorter 

mission of 1000 hours, the loss in PoMC due to halved 

component reliability is 0.167 (= 0.788 - 0.621) and safely 

covered by the increase in PoMC of 0.235 (= 0.856 - 0.621) due 

to the doubling of the number of robots. 

In this light, our analysis of various tradeoff cases suggests that, 

for relatively short missions, the PoMC gain per robot number 

increase has the likelihood to be larger than the loss of PoMC per 

component reliability decrease. For relatively long missions, the 

loss is typically greater than the gain. 

Because the maximum achievable number of robots is dependent 

on the component reliability – cost model, the location of the 

turning point is also dependent on the cost model. If the cost of 

mass-producing robots with low-reliability components is cheap 

enough that PoMC gain per robot number increase is always 

larger than PoMC loss per component reliability decrease, then 

the optimization methodology described in the previous 

subsection would have the general tendency to increase the robot 

team size and use lower-reliability components. 

5. CONCLUSIONS 

This paper presents a general framework to explore the tradeoffs 

among cost, component reliability, and robot team size in 

planetary robot missions. We comprehensively surveyed every 

mission instance proposed in NASA’s Solar System Exploration 

Roadmap over the next 30 years [11] and Mars Exploration 

Program [1] to generate a mission taxonomy. We propose that any 

mission instances can be characterized by a set of basic mission 

activities and categorize planetary robot missions based on the 

time proportion of the activities into three classes: Search and 

Exploration, Sample Acquisition and Composition Analysis, and 

Construction. We examined a general mission scenario in the 

Construction mission class and performed sensitivity analysis of 

the tradeoff parameters. Our results show the stability of the 

Construction mission class with respect to the activity time 

proportions. 

In this paper, we also propose a method allowing a mission 

designer to optimize robot configuration in terms of robot team 

size and component reliability with respect to probability of 

mission success (PoMC), given a cost model. The method is not 

limited to a particular cost model. Mission designers can use an 

arbitrary cost model and implement the methodology to obtain a 

specific optimum robot configuration that maximizes mission 

success rate under a budget constraint. 

For the Construction mission class, our analysis shows that for 

long-term missions, the PoMC loss per component reliability 

decrease is typically greater than the PoMC gain per robot 

number increase. Thus, it is more beneficial in terms of the 

mission success rate to increase the quality of the robot 

components as opposed to the number of robots, given the budget 

constraint. For short missions, however, the opposite trend can be 

63
observed, thus, configurations of more robots with less reliable 

components are preferable. 

In future work, we will investigate other mission classes and 

perform sensitivity analysis of the parameters on the mission 

classes. We intend to compare the time proportion and reliability 

tradeoff among the classes to see distinguishable characteristics 

among them as well as to validate the classification scheme 

proposed in this paper. In addition, we intend to extend the 

optimization problem to cost and mission duration. For instance, 

we seek to answer common mission designer questions like 

“Under a given mission duration, which configurations of robots 

result in the highest mission success rate?” or “Given a desirable 

mission reliability standard, which configurations of robots cost 

the least?” 

6. ACKNOWLEDGMENTS 

This research was funded by the National Aeronautics and Space 

Administration (NASA) under award NNX07AV71G S01 in the 

Advanced Intelligent Systems Research (AISR) program. 

7. REFERENCES 

[1] Mars Advanced Planning Group. 2006. Robotic Mars 

Exploration Strategy 2007-2016. Available from: 

http://mepag.jpl.nasa.gov/reports/3715_Mars_Expl_Strat_GP

O.pdf

[2] NASA. 2007. FY 2008 Budget Estimates. Available from: 

http://www.nasa.gov/pdf/168652main_NASA_FY08_Budget

_Request.pdf

[3] NASA. 2009. FY 2010 Budget Estimates. Available from: 

http://www.nasa.gov/pdf/345225main_FY_2010_UPDATED

_final_5-11-09_with_cover.pdf

[4] Green, J. L. 2008. MSL Cost Overrun Status and Plans. 

Available from: 

http://www.lpi.usra.edu/pss/presentations/200810/greenMSL

.pdf

[5] Stancliff, S. B., Dolan, J., and Trebi-Ollennu, A. 2007. 

Planning to Fail – Reliability as a Design Parameter for 

Planetary Rover Missions. In Proceedings of the 2007 

Workshop on Measuring Performance and Intelligence of 

Intelligent Systems. PerMIS '07. 

[6] Stancliff, S. B., Dolan, J., and Trebi-Ollennu, A. 2006. 

Mission Reliability Estimation for Multirobot Team Design. 

In Proceedings of the 2006 IEEE/RSJ International 

Conference on Intelligent Robots and Systems (Oct. 2006), 

pp. 2206 - 2211. 

[7] Dudek, G., Jenkin, M. R. M., Milios, E., and Wilkes, D. 

1996. A Taxonomy for Multi-Agent Robotics. Autonomous 

Robots, vol. 3, no. 4, pp. 375 - 397. 

[8] Balch, T. 2002. Taxonomies of Multirobot Task and Reward. 

In Robot Teams: From Diversity to Polymorphism, Balch, T. 

and Parker, L. E., eds. Natick, MA: A K Peters. 

[9] Yanco, H. A. and Drury, J. 2002. A Taxonomy for HumanRobot Interaction. In Proceedings of the AAAI Fall 

Symposium on Human-Robot Interaction, AAAI Technical 

Report FS-02-03, Falmouth, Massachusetts, November 2002, 

pp. 111 - 119. 

[10] Yanco, H. A. and Drury, J. 2004. Classifying Human-Robot 

Interaction: An Updated Taxonomy. In Proceedings of the 

IEEE Conference on Systems, Man and Cybernetics (Oct. 

2004). 

[11] NASA. 2006. Solar System Exploration. Available from: 

http://www.lpi.usra.edu/vexag/road_map_final.pdf

[12] Mars Advance Planning Group. 2006. 2006 Update to 

Robotic Mars Exploration Strategy 2007 - 2016. Available 

from: 

http://mepag.jpl.nasa.gov/reports/MAPG_2006_Update_Fina

l-1.pdf

[13] NASA. 2005. NASA’s Exploration Systems Architecture 

Study, part 4: Lunar Architecture, section 4.2.5.1.4 and 4.3. 

Available from: 

http://www.nasa.gov/pdf/140649main_ESAS_full.pdf

[14] Skonieczny, K., DiGioia, M. E., Barsa, R. L., Wettergreen, 

D. S., and Whittaker, W. L. 2009. Configuring Innovative 

Regolith Moving Techniques for Lunar Outposts. Aerospace 

conference, 2009 IEEE, pp. 1 - 11. 

[15] Mettas, A. 2000. Reliability Allocation and Optimization for 

Complex Systems. In Proceedings of the IEEE Annual 

Reliability and Maintainability Symposium, pp. 216 - 221. 

[16] JPL-PACC. 2000. Presentation on Power Management in 

Past and Present JPL/NASA Missions (Sept. 26, 2000). 

Available from: 

http://newport.eecs.uci.edu/impacct/d_research/d_presentatio

n/JPL-PACC092600.ppt

64
Towards a systematic assessment of the functions of 

unmanned autonomous systems

Robin JAULMES 

+33 1 42 31 95 33 

robin.jaulmes 

@dga.defense.gouv.fr 

Eric MOLINE 

+33 1 42 31 96 59 

eric.moline 

@dga.defense.gouv.fr 

Paris Expertise Center (CEP) 

16 bis avenue Prieur de la Cote d’Or 

Laurent VIELLE 

+33 1 42 31 95 84 

laurent.vielle 

@dga.defense.gouv.fr 

ABSTRACT

Being able to assess the performance of the algorithmic 

components of unmanned autonomous systems is a necessity. 

Defining repeatable and commonly shared test protocols to assess 

the performance of the algorithms involved in autonomy is the 

key to achieve standardization in the unmanned autonomous 

systems field (Unmanned Ground Vehicles (UGVs) and 

Unmanned Aerial Vehicles (UAVs)). 

This paper proposes a generic methodology to evaluate any 

function of an autonomous system and illustrates the 

methodology on two examples: for the evaluation of visual 

beacon tracking algorithm and for the evaluation of Simultaneous 

Localization And Mapping (SLAM) algorithms. The lessons 

learnt from these evaluations are then described. 

Categories and Subject Descriptors 

I.2.9 [Artificial Intelligence] Robotics and Autonomous vehicles 

– Performance measurement, methodology, standardization. 

General Terms

Algorithms, Measurement, Performance, Experimentation, 

Standardization, Verification. 

Keywords

Unmanned systems, Evaluation metrics, Databases, Evaluation 

protocols, Standardization, SLAM, perception algorithms. 

1. INTRODUCTION 

Unmanned systems ground and aerial, are more and more mature: 

their architecture can today involve complex data and image 

processing, data fusion, optimization, and learning algorithms, 

allowing them to be used in more and more complex 

environments. They are able to navigate through urban areas, with 

very little monitoring, and to execute very high level commands 

like “rally this goal while following the traffic rules and avoiding 

obstacles”, as was achieved recently in the Urban Challenge [23]. 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies are 

not made or distributed for profit or commercial advantage, and that copies 

bear this notice and the full citation on the first page. To copy otherwise, 

to republish, to post on servers or to redistribute to lists, requires prior 

specific permission and/or a fee. PerMIS’09, September 21-23, 2009, 

Gaithersburg, MD, USA. Copyright © 2009 ACM 978-1-60558-747-

9/09/09...$10.00 

However, for such complex systems, the performance and 

reliability is very important: the more reliable these systems are, 

the more they will be adapted to challenging environments and 

the more an operator will gain confidence using then. We believe 

that the precise assessment of reliability and performance 

becomes mandatory to assess the “level of autonomy”. Ideally, 

the more autonomous a system is, the less an operator needs to 

intervene. In a “fully autonomous” system, a human would only 

intervene when need to change the current goal arises, the system 

being able to overcome any difficulty. 

As is often done in systems engineering, the overall reliability of 

the system can be obtained from the assessment of the reliability 

of its different components; many of the components in 

unmanned autonomous systems are widely used for other 

applications, so evaluating their reliability and performance is a 

“solved” problem. However, the “innovating” algorithms used for 

perception, navigation, data fusion or planning are, for most of 

them, specific to the autonomous vehicles field, and evaluating 

their performance is still to be standardized. 

To address this issue, we have designed a generic methodology 

for the evaluation of elementary components and are applying it. 

After presenting briefly prior work in the domain, we present our 

methodology and illustrate it two examples, one taken from our 

work in the TAROT advanced study program (Technologies 

essentielles pour l’Autonomie des RObots Terrestres), the other 

addressing a classical algorithm of the literature: SLAM using a 

LIght Detection And Ranging (LIDAR) sensor. 

2. PRIOR WORK 

There are many algorithms involved in unmanned autonomous 

systems and some of them have already be evaluated for other 

applications : for instance, a set of metrics and benchmark data is 

available for visual vehicle tracking from air [4], for stereo depth 

estimation [16] and for vehicle detection [5]. 

Furthermore, the topic of the assessment of SLAM is more and 

more studied in the literature [2,3,18]. Among them, the more 

recent is the RAWSEEDS European project that has gathered a 

data set for the evaluation of SLAM algorithms and has defined a 

set of metrics to assess mapping and navigation quality (absolute 

trajectory error, mapping error, relative pose error, rough estimate 

of complexity, self localization error) [21]. Also note that 

algorithm [19] and data set repository [20] exist on this topic. 

65
Similar work concerns the definition of good experimental 

methodologies in robotics [1]. Also note that the Joint 

Architecture for Unmanned Systems (JAUS) aims at the 

normalization all the interfaces between components of unmanned 

systems [22]. 

An important work on the topic has been made by the National 

Institute of Standards and Technology (NIST) with the definition 

of the Autonomous Levels For Unmanned Systems (ALFUS) 

[11]: this work has defined standard terminology and principles to 

measure the autonomy of unmanned systems. 

The methodology we present here has matured through successive 

projects: first, we applied a similar methodology within the 

autonomy axis of the advanced study program “robotique”, 2002-

2005, to evaluate the performance of perception algorithms, and 

especially unstructured road detectors [7]. 

Real maturation has only been obtained within the “TAROT” in 

2006-2009 [14], that is the continuation of these projects, and that 

aims at developing elementary autonomous behaviors for 

unmanned ground systems: beacon rallying, vehicle following, 

vertical reference following, SLAM and obstacle avoidance. 

3. OUR METHODOLOGY 

Our methodology involves four phases: (1) a prior analysis, (2) 

the preparation of the evaluations, (3) the realization of the 

evaluations and (4) a posterior analysis. 

3.1 Prior analysis 

The first phase begins with an analysis of the documentation, of 

the literature concerning similar algorithms, and of the previous 

evaluations of the assessed algorithm. It is useful to have as much 

details as possible on the intrinsic performances of the algorithm 

and to understand the role of all its independent parameters. The 

knowledge of the role of the algorithm within the unmanned 

autonomous vehicle system and its operational use allow the 

definition of a list of evaluation criteria, which are ideally more or 

less the technical specifications of the algorithm. 

Furthermore, when an algorithm is composed of several 

elementary components, as it is illustrated on the case of a stereo 

obstacle avoidance solution in Figure 1, it is very useful to 

understand clearly the role of each component: only then can the 

evaluations of these components be used to refine the analysis. 

Each criterion is either associated to a wish on the “domain of 

validity” of the algorithm, or to a wish on its “level of 

performance”, which is linked to the executed function, to the 

processing time, to the failure rate, and to the computer resources 

needed. 

Each criterion needs to be associated with a set of metrics and 

with high level requirements of the experimentations. 

For instance, if we consider the criterion “performance of the 

ability of the algorithm to detect its own failures1

”, the set of 

metrics can be: 

 

1

 The ability of a given algorithm to detect its own failures allows 

the operator or other algorithms in the system to be able to 

correct these failures when they occur. 

Figure 1 : An analysis illustration on a stereo obstacle 

avoidance algorithm 

 (1) Nfu/Nf, where Nfu is the number of times a failure occurs and 

is undetected in the set of experiments and Nf is the number of 

times a failure occurs (=1 if no failure occur). 

(2) Nfd/Nok, where Nfd is the number of “false failure detection” 

and Nok is the number of test where no failure occurs. 

The same procedure is followed for every criterion, and each 

criterion is prioritized. We recommend to as much as possible 

find in the literature the usual criteria that are used to assess the 

performance of similar algorithms, so that the performance can 

easily be compared with previous work. 

3.2 Evaluation preparation phase 

The goal of the second phase is to define precisely the 

experiments that need to be done and to gather the data and 

simulation capabilities that are necessary to process the 

evaluations. 

Assessment uses “offline tests”, which are processed with 

technical simulation as well as with open-loop database replay, 

but not only: it is in most cases necessary to also proceed to 

“online testing”, in which the algorithm is integrated in the 

system. In order to give more flexibility in the integration and 

validation phases of projects, it is however absolutely necessary 

to be able to make a full evaluation according to every criterion 

without proceeding to online testing, which costs more. That is 

why we insist on offline testing in the scope of this paper. 

The preparation of the evaluations includes a data gathering 

phase. When public online databases exist [15] or database 

gathered through previous projects [7] it is always good to re-use 

them. But specific data gathering may need to be done: when it is 

the case, we proceed to “data gathering runs” for the robot, in 

which we record representative multi-sensory data for the 

algorithms. The data gathering can even be done using the sensors 

of the future system on a remote-controlled or manned vehicle. 

Data from multiple sensors (cameras, inertial sensors, wheel 

encoders, GPS, LIDAR, ...) need to be precisely timestamped in a 

common frame using a dedicated mean [6]. 

Stereo images

Raised img.

3D echoes

Camera pose

Loc. DTED

Robot pose

Calibration Data 

Disparity map

Robot model

Command

Modified command

66
Figure 2 : The evaluation with ground truth 

Even if evaluations can be processed simply with a qualitative 

analysis, it is necessary, for any quantitative measurement to 

proceed to evaluations with ground truth (GT). The principle of 

such evaluations is presented in Figure 2. 

The ground truth is what the algorithm should generate, to which 

the result of the algorithm on the data base needs to be compared. 

To evaluate, for instance, the performance of navigation and 

perception functions, the ground truth can be the reference 

positions of the robot and of elements of the environment. For 

image tracking and detection algorithms, it can be a reference GT 

bounding box, that represents the position of the element to detect 

or track The GT bounding box often needs to be designated by 

hand, using an interface like the one illustrated on Figure 3. 

The simulators we use, which are illustrated on Figure 4, are 

either well-documented open-source simulators like Gazebo [10] 

or proprietary software. They are needed when the evaluated 

algorithms are closed-loop controls and when it is difficult to 

have the perfect ground truth. We sometimes have to invest on 

specific simulators in order to evaluate algorithms on precise 

criteria. It is for instance the role of the Cadise simulator, that 

automatically produces the bounding box ground truth in order to 

proceed to visual tracking evaluations. 

Another useful feature is the ability to “post-process” the real data 

from the database. For instance, representative noise or additional 

vibrations2

 can be added to the sensor measurements and images. 

These post-processing abilities increase the amount of possible 

experiments (on Figure 5, is illustrated possibilities brought by 

the addition of noise (upper-left), the use of blur (upper-right), the 

diminution of contrast (lower-left) and the addition of vibration 

(lower-right). 

Figure 3 : A ground truth designation interface 

 

2

 Vibrations can be added with dynamical cropping and resizing. 

Figure 4 : simulator Cadise (up left), Gazebo (up right) and 

Marilou Robotics Studio (down) 

Figure 5 : Illustration of post-processing possibilities 

Once the database and the simulation possibilities are designed, 

the list of experiments is established, taking into account priorities 

when necessary. We then have to verify that every important 

criterion is evaluated with a sufficient quality. In order to achieve 

as most efficiency as possible, each experiment usually evaluates 

the algorithm according to several criteria, and each criterion is 

usually evaluated on several experiments. 

3.3 Evaluation processing phase 

Once the database, the simulations and the test protocols are 

prepared, the evaluation processing is simple. Specific tools [6], 

as illustrated on Figure 6, are used to automatically produce the 

test results. 

67
Figure 6 : Illustration of the evaluation software (RT MAPS) 

3.4 Posterior analysis phase 

The test results need to be analyzed in very much depth in order 

to make a good interpretation of the value of the metrics on each 

experiment. If inside parameters of the algorithms was accessible 

and has been measured, this data can be analyzed in order to 

identify the causes of failures and propose corrections. 

Sometimes, during the posterior analysis phase, complementary 

evaluations appear necessary, for instance to validate hypothesis 

of unexpected failures: because of this, we insist that the posterior 

analysis phase should begin before the evaluation processing 

phase is finished: therefore, the experiment schedule can be 

modified, when necessary, at the last minute. Furthermore, to 

guarantee the good quality of the overall analysis, the 

responsibility of the posterior analysis should be given to a 

different team than the team realizing the evaluation processing. 

We insist on the importance of the posterior analysis phase: this 

phase gathers all the information and brings a conclusion about 

the performance of the algorithm: the “resulting” specifications 

are detailed. During this phase, the experimental protocol that has 

been followed needs to be analyzed: the conclusions need to take 

into account the quality and the amount of the tests that have been 

made. The analysis determines if the algorithm is mature enough 

to answer an operational need, and identifies a list of possible 

evolutions. This list is usually discussed with the producer of the 

assessed algorithm. 

3.5 Results of the evaluation 

In the TAROT program, most of the implementation choices on 

the final robot have been made according to the conclusions of 

these posterior analyses that were made on the different 

algorithmic component we assessed. 

Outside programs, our team also evaluates the performance of 

open source algorithms and of published algorithms within the 

literature in order to sketch the possible performances of future 

systems. The developments are then capitalized within the HNG 

architecture [11], which allows the algorithms to be easily 

adapted from one system to another and to run hybrid simulations. 

4. APPLICATIONS 

We now illustrate some aspects of the methodology on two 

examples. The first one has been used within the TAROT 

program (evaluation of tracking algorithms). The other, more 

detailed, is an evaluation of classical LIDAR SLAM algorithms. 

4.1 Visual beacon tracking (VBT) 

The purpose of the VBT is to track a given beacon in a sequence 

of images. It is an elementary component that can be either used 

for the mission or for the navigation. It can also be at the core of 

“intelligent behaviours” like vehicle following and beacon 

rallying. 

4.1.1 Prior analysis 

The criteria we have identified for this algorithm are the 

following: 

(1) Reliability 

(2) Real-time ability 

(3) Ability to track varying targets (poorly textured targets, 

targets of varying apparent size, with a bad initial 

designation, etc...) 

(4) Precision of the tracking: we have used 2 different 

metrics, which are illustrated on Figure 7. One of them is the 

inter-barycentre distance, and the other is the following: 

S(AR) is the surface of the algorithm area, S(GT) the surface of 

the ground truth area, and S(AR^GT) is the surface of the 

intersection area (in blue on Figure 7). 

(5) Aptitude to detect its own failures and pertinence of the 

confidence indicator3

 : we compare the evolution of this 

indicator with the evolution of the precision of the tracking 

to measure the performance according to this criterion. 

(6) Robustness to a varying environment (low luminosity, 

fog, back light, shadows, distractions near target, occlusions, 

noise, blur, vibrations, etc...) 

Figure 7 : VBT performance metric illustration 

4.1.2 Evaluation preparation 

We have gathered and acquired a set of sequences with varying 

environments (various trajectories and types of roads in different 

weathers) , varying targets (vehicles, buildings, trees, fence, water 

 

3

 In the TAROT program, we have asked that every algorithm 

would give a “confidence indicator” representative of the 

( )

)()( ),(

2

GTSARS

GTARS GTARm ∧ =

68
tower, ...), with varying perturbations (occlusions, distracting 

vehicles, ...) and have obtained around 30 sequences, of which a 

subset is illustrated on Figure 8. 

Figure 8 : Extract of the gathered database 

4.1.3 Evaluation processing 

Table 1 gives a sample of summarized results for Algorithms 1 to 

4. For the sake of clarity, the metrics have here been translated, 

using thresholds, into the following qualitative formulations: OK 

means that the requirement is fully met; AVG means that the 

result is average, within the “tolerance” zone of the requirement. 

NOK means that the requirement is not met. “Q of Eval” 

represents the confidence in the evaluations realised and is equal 

to “Good” when 3 or more sequences have been used to assess the 

criterium; and “Average” otherwise. 

Table 1 : Extract of assessment results 

4.1.4 Posterior analysis 

The protocol has been used to proceed to the evaluation of, until 

now, around 10 different algorithm versions. The partners 

involved in the project have appreciated having a feedback on 

their developments and the average quality of the algorithms has 

increased with each successive version, beneficiating from the 

expertise. 

The algorithms which had the most successful evaluations at the 

end of developments have been associated with other algorithms 

to build two behaviours (vehicle following and beacon rallying). 

These behaviours have been demonstrated online on the robotic 

platform with the validated versions; the limitations observed 

were exactly the limitations identified through the evaluations. 

We are confident that our method and metrics captured efficiently 

the quality of the assessed algorithms. 

4.2 LIDAR SLAM component 

As we explain in [13], we have evaluated 2 popular LIDAR 

SLAM algorithms (DPSLAM [8] and FastSLAM [15]) using a 

similar methodology using our Hybrid Network-based Generic 

architecture framework [12]. 

4.2.1 Prior analysis 

The criteria we have investigated to evaluate SLAM techniques 

are the following: 

- Processing time ; 

- Allocated resources (processors, memory) ; 

- Precision of the localization (and drift) ; 

- Precision of the produced map ; 

- Robustness to noise in the wheel encoders ; 

- Robustness to noise in the LIDAR scans ; 

- Ability to work in cluttered environments ; 

- Ability to correctly map loops. 

Assessing the precision of the localization brought by SLAM is 

made like this: the error between the real position and the 

estimated position is measured. The drift is defined as this error 

divided by the distance travelled. 

Measuring the quality of the produced map is harder and no 

universally accepted quantitative approach exists yet. For this 

study we defined the utility metric Q to measure between 0 and 1 

the quality of a produced map: this metric was chosen after 

considering several utility metrics: to neutral persons represented 

relatively well the correctness of a given map (a map with Q=1 

being a perfect map, and a map with Q=0 being completely 

wrong). This metric uses the same information as the mapping 

error defined within the RAWSEEDS project [21], which is also 

based on “control points” but summarizes the quality of the map 

with a single number, which is independent of the mapped 

environment. 

To define Q, N control points are chosen from the “distinctive” 

points (e.g. corners) of the ground truth map. The produced map, 

result of the algorithm, is scaled and superposed to the ground 

truth map, and the error on the position of each ground truth point 

di is measured (as showed on Figure 9). Let W be the width of the 

ground truth map and H be its height; let k be a constant4

. Then, 

 

4

 We chose k=3. With this value, a map in which the mean error is 

equal the diagonal of the ground truth map has a value of 

approximately 0.001. 

69
⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

−= ∑=

N

i

di

HW N

k Q

1

22

1

exp

Figure 9 : SLAM performance metric 

Note that in order to measure Q, an operator has to designate, for 

each produced map, the position of each of the control point. 

When two or more produced points correspond to the same point 

on the ground truth map (which happens in poorly mapped loops), 

the farthest point is selected, and we also record the maximum 

distance between them, which is an indicator of the poorness of 

the algorithm to map loops. When no corresponding point can be 

found for one of the control points5

, Q=0 

4.2.2 Evaluation preparation 

We have defined a user interface to do a systematic assessment of 

the quality of the produced map, of which a screenshot is given on 

Figure 10. For more vast experiments, the process would need to 

be automatic. 

To assess the robustness to noise in the wheel encoders, we have 

added several times on one of the simulated log a controlled 

noise. We have added an additive Gaussian noise of standard 

deviation σ at each step for heading and movement, and from one 

experiment to the following we have increased the value of the 

variance. 

We have also acquired a dataset from our platform in our 

laboratory (with “real” noise). The ground truth was obtained by 

scanning the map of the building. 

We have built specific maps to assess the abilities to map in 

cluttered environment and to correctly map loops. We also used a 

data set (NSH_level_a) from the robotic data repository website 

[19]. Some of the ground truth maps of our data set are illustrated 

on Figure 11 

 

5

 This is usually the symptom of a serious failure in the algorithm. 

Figure 10 : the control point designation interface 

Figure 11 : sample maps from our dataset 

(Upper left: NSH_level_a, upper right: cluttered, bottom left: loop, 

bottom right: Arcueil laboratory

4.2.3 Evaluation processing 

We have measured the computing time, the memory resources we 

used, and the quality of the produced maps on the different maps 

of our dataset for different set of parameters : some of our results 

are shown on Figure 12. 

On the real world data we acquired, we have had varying 

performances depending on the experimental conditions. On the 

first data set we acquired, FastSLAM failed (Q=0) but DPSLAM 

managed to build something on which we can nearly recognize 

the different rooms: it is shown on Figure 13. 

We believe that this difference was due to the presence on this 

particular trajectory of abrupt curves. With other tests we made, 

without hard curves, the performance of both algorithms was 

correct. We believe that the dynamic model used by default by 

FastSLAM is not adapted to harsh turns made by our platforms 

(that are poorly measured by the wheel encoders). 

70
Figure 12 : Sample results on SLAM evaluation 

Figure 13 : map built by DPSLAM on real world acquisition 

“Arcueil laboratory n°1” 

4.2.4 Posterior analysis 

Our result with the “Arcueil laboratory n°1” sequence raises the 

issue of the dynamic models of the platform, which is a very 

important parameter in SLAM algorithms, and which is rarely 

easy to modify: we believe that one of the normalization actions 

that should be done about SLAM algorithms is to define precisely 

a standard format for the dynamic model of vehicles. 

As we can see on Figure 13, we saw that the metric we defined 

didn’t capture every aspect of the quality of a map. Actually, our 

metric don’t penalize artifacts properly: the surface of the area of 

the produced map that has no counterpart in the real world should 

be measured and have an impact on the measure of quality. 

Another issue brought by our experiments is the choice of the 

“control points”. We tried to pick as many of them as we could, to 

keep to corners and distinctive features of the environment, and to 

have them sufficiently spread. However, a more systematic way 

to define the position of the control points could be used, 

especially if we want to achieve a certain degree of normalization. 

This could be associated with the automation of the control point 

designing process. 

 

5. CONCLUSION 

We have only presented a small set of our recent works on the 

assessment of the algorithms of unmanned autonomous systems; 

in the TAROT program the method has been systematically used 

and among the techniques we have evaluated there have been 

vision-based SLAM, obstacle avoidance algorithms, and vertical 

reference servoing. 

The evaluation methods we formalize here already help us assess 

and compare robustness and performance of algorithms based on 

relevant quantitative information. The methodology we have 

briefly presented here was developed during several years and has 

evolved, taking incrementally into account the lessons learnt: 

using it systematically has allowed us to build databases of 

metrics, raw data, and evaluation results. We hope that soon we 

will be able to define testing protocols able to characterize the 

performance of most unmanned autonomous system algorithms, 

for both UGVs and UAVs. 

We believe that the kind of work we do here is absolutely 

necessary in order one day to have certified unmanned 

autonomous systems. Initiatives like JAUS [22] identify standard 

messages and services, yet they do not precisely define the level 

of performance of the services. Nor do they define ways to 

compare the performance of the defined components; we believe 

it is a step that will need to be taken at one point. 

Today, it is hard to evaluate whether or not two given components 

are compatible in a given architecture. Certification and clear 

definitions of level of autonomy will also become possible and 

with it an overall improvement of the performance of unmanned 

autonomous systems. 

Furthermore, the capabilities of unmanned autonomous systems 

have not yet reached their full potential. They are most certainly 

useful algorithms that could be applied to this domain and that 

have not been discovered yet: having a “closed” methodology that 

71
would not allow incremental definition of new metric would 

therefore not be appropriate : the “open” methodology we 

propose, which capitalizes experience and data as more and more 

evaluations are made, is, we think, a good way to follow the 

dynamic field of unmanned autonomous systems. 

6. ACKNOWLEDGMENTS 

Our thanks to Jonathan Obriet-Leclef for his assistance in the 

SLAM experiments described here, to the people from ETAS 

(technical expertise center of Angers) for their help in the 

gathering of evaluation data and ground truth for the TAROT 

program, and to our partners within the TAROT program, whose 

feedback helps us increasingly improve our methodology, metrics 

and testing procedures. Thanks also go to Nick Roy for providing 

some of the data we used for our experiments on SLAM and to 

the anonymous reviewers for their useful comments. 

7. REFERENCES 

[1] Amigoni, F., Gasparini, S., Gini, M., « Good Experimental 

methodologies for robotic mapping: a proposal. » 

GEMBENCH 2008 

[2] Balaguer, B., Carpin, S., Balakirsky, S., “Towards 

Quantitative Comparisons of Robot Algorithms: Experiences 

with SLAM in Simulation and Real World Systems” , IROS 

2008 

[3] Collins, T., Collins, J.J., Ryan, C. “Occupancy Grid 

Mapping: An Empirical Evaluation” MED 2007 

[4] Collins, R., Xuhui Zhou, Seng Keat The: «An Open Source 

Tracking Testbed and Evaluation Web Site », Robotics 

Institute, Carnegie Mellon University. 

[5] D’Angelo, E., Herbin, S., Ratiéville, M., « ROBIN 

Challenge » : evaluation principles and metrics. 

http://robin.inrialpes.fr (2006). 

[6] Dulac, N. “Real time, multisensor, advanced prototyping 

software. ” First National Workshop on Control 

Architectures of Robots. Montpellier (France), 2006. 

[7] Dufourd, D. and Dalgalarrondo, “Results and lessons learned 

from the quantitative evaluation of road detection and 

tracking algorithms.”, Performance Metrics for Intelligent 

Systems Workshop (PerMIS’03). Gaithersburg (MD, USA), 

2003. 

[8] Eliazar, A., Parr, R. “DP-SLAM: Fast, Robust Simultaneous 

Localization and Mapping Without Predetermined 

Landmarks”, IJCAI 2003. 

[9] Fontana, G., Matteucci, M., Neira, J., Sorrenti, D. 

« Proposals for benchmarking SLAM », GEMBENCH 2008. 

[10] Gerkey B.P., Vaughan, R.T., Howard, A., « The 

Player/Stage Project: Tools for Multi-Robot and Distributed 

Sensors Systems”, ICAR 2003. 

[11] Hui-Min H., Albus, J., Messina, E., « Toward a generic 

model for autonomy levels for unmanned systems 

(ALFUS) », PerMIS workshop, 2003. 

[12] Jaulmes, R., Moliné, E. « HNG: A Robust Architecture for 

Mobile Robots Systems », EUROS 2008. 

[13] Jaulmes R., Moliné, E., Obriet-Leclef, J. “Towards a 

quantitative evaluation of simultaneous localization and 

mapping methods”, Control Architecture of Robots national 

conference, 2009. 

[14] Lambert, M., Jaulmes, R., Godin, A., Moliné, E., Dufourd, 

D. “A methodology for assessing robot autonomous 

functionalities” , IAV 2007. 

[15] Montemerlo, M., Thrun, S. « FastSLAM: A Factored 

Solution to the Simultaneous Localization and Mapping 

Problem », AAAI 2002. 

[16] Scharstein, D., Szeliski, R. “A taxonomy and evaluation of 

dense two-frame stereo correspondence algorithms”, 

International Journal of Computer Vision, 47(1/2/3):7-42, 

April-June 2002. 

[17] Thrun, S. “Robotic mapping : a survey”, CMU-CS-02-111 

[18] Wulf, O., Nuchter, A., Hertzberg, J. and Wagner, B. 

“Ground Truth Evaluation of Large Urban 6D SLAM”, 

IROS 2007 

[19] http://openslam.org/ 

[20] http://radish.sourceforge.net/

[21] http://www.rawseeds/org/ Website of the RAWSEEDS 

project. 

[22] Http://www.jauswg.org/

[23] http://urban-challenge.com/

72
Performance Measures Framework for Unmanned Systems 

(PerMFUS) 

Initial Perspective 

Hui-Min Huang, Elena Messina, Adam Jacoff 

National Institute of Standards and Technology (NIST) 

100 Bureau Drive, MS 8230 Gaithersburg, Maryland 20899 USA 

+1.301.975. {3427, 3510, 4235} 

{hui-min.huang, elena.messina, adam.jacoff}@nist.gov

Abstract 

The performance of intelligent unmanned systems (UMSs) must 

be able to be measured to ensure that they can meet the 

operational requirements. A generic framework to enable 

capturing and organizing the performance metrics is highly 

desirable. In this framework, unmanned system (UMS) 

performance can be attributed to the missions that it is 

commanded to perform, the environments that the missions are 

to be performed in, and the capabilities of the system itself. 

These attributes constitute a “three-axis” UMS performance 

metrics model. This framework, once further populated, can 

benefit the UMS community by allowing capture of the UMS 

performance, from the technical and operational perspective. 

Categories and Subject Descriptors

J.2 [physical sciences and engineering] unmanned systems 

performance 

General Terms

Measurement, Performance, Design, Human Factors, 

Standardization, Verification 

Keywords

ALFUS, autonomy, communication, environment, goal, metrics, 

mission, mobility, robot, performance, sensor, terminology, test, 

unmanned system (UMS), urban search and rescue (US&R) 

1. INTRODUCTION 

UMSs have been deployed in many application domains, 

ranging from military, homeland security, manufacturing, 

medical, to general service applications. They either 

complement human operators to enhance mission performance 

or replace humans in dangerous or difficult situations (see 

Figure 1 and Figure 2). It is highly desirable that the UMS 

performance is able to be systematically and comprehensively 

measured to ensure that they can meet the operational 

requirements. The performance measures also facilitate 

understanding the UMS effectiveness, devising technological 

improvements, and inspiring innovations. A robot conducting 

an urban search and rescue (US&R) operation must be able to 

maneuver in confined space. An UMS conducting a perimeter 

surveillance operation of a large complex must have sufficient 

power and long-range communication capabilities. 

Figure 1: Robot performing mission in confined space 

Figure 2: UMS equipped for operating in unstructured 

environments 

The performance measurement is based, to a great extent, on the 

requirements. Requirements can be established with various 

kinds of documents such as Operational Requirements 

Documents (ORDs), Concepts of operation (CONOP), scenario 

73
descriptions, use case models, or operational vignettes [1]. 

Those documents can be generated with various degrees of 

formality and capture a range of levels of domain expert 

experience and observation. Operational requirements might 

cover UMS functional areas such as speed, stealthy 

maneuvering, explosive sensing, peak power, etc. Performance 

measures, on the other hand, deal with whether particular system 

design can meet the requirements, how to evaluate whether these 

requirements are met, and devising methods for the evaluation. 

Performance issues have been dealt with in other non-robotic 

areas. In the software engineering discipline, metrics and 

requirement analysis have been studied. Traditionally, simple 

metrics such as lines of code, functional points, or cyclomatic 

complexity are used to measure the sizes of programs or their 

complexities in a context independent manner [2][3][4][5][6]. 

Metrics for measuring software quality, productivity, and 

reliability also exist [7][8][9]. Requirement analysis is also an 

area of interest. Giorgini P., et al., described a goal-oriented 

technique for requirement analysis. Goals are identified with 

domain stakeholders before being modeled. Detailed analysis 

correlates the system functionality to the goals [10]. 

Though these techniques can serve as references or be applied to 

particular, local metric issues, they lack a comprehensive system 

level approach. 

It is extremely beneficial to have a general framework that 

establishes sets of metrics, describes an approach, and provides 

a set of guidelines to facilitate UMS performance measurement. 

The envisioned Performance Measure Framework for 

Unmanned Systems (PerMFUS) aims at serving these purposes. 

PerMFUS describes how one might organize and analyze the 

requirements, instantiate from the established generic metrics, 

generate additional program specific metrics, and devise 

methods to test and evaluate the UMS. 

2. PERMFUS: OVERALL CONCEPTS 

PerMFUS stems from the Autonomy Levels for Unmanned 

Systems (ALFUS) work [11][12]. As such, autonomy may be 

considered a specific metric under PerMFUS that has a wider 

scope. See Figure 3 and Figure 4 for a comparison. Earlier 

PerMFUS results involve metrics for mission goals [13]. 

Figure 3: ALFUS overall concept 

2.1 Three-Aspect Model 

An UMS executes missions to accomplish the goals. Therefore, 

the key is to measure the UMS performance via its execution 

behaviors, in other words, how the system interacts with the 

environment and with humans while aiming to achieve the 

mission goals. 

In this sense, the UMS performance can be attributed to the 

missions that it is commanded to perform, the environments that 

the missions are to be performed in, and the physical and logical 

capabilities of the system itself. These attributes constitute a 

“three- aspect” (or three- axis) UMS performance metrics model 

(Figure 4). 

Figure 4: PerMFUS Main Aspects 

Note that the three-axis graphic serves the purpose of 

highlighting the aspects that characterize the UMS performance. 

They do not represent that the aspects are independent from each 

other. Instead, they overlap. 

Later sections will further elaborate on these axes. 

2.2 Performance Characterization 

Parameters vs. Performance Metrics 

Metrics are the parameters identified for measuring the 

performance. A metric can correspond to one or multiple 

system or environmental parameters. In other words, those 

system or environmental parameters can affect particular 

performance areas to different extents. For example, the 

performance metric might be how steep a slope an UMS can 

climb. The corresponding performance characterization 

parameters include weight, the center of gravity (which, itself, 

can be affected by the onboard payload or other configuration 

parameters), and the traction on the wheels or tracks. 

The challenge is that the separation between the two aspects, 

system and environmental, might not always be clear. Particular 

system requirements can either specify the slope climbing 

performance or limit the weight. The requirements can either 

specify path planning performance or specify sensor ranges. For 

this reason, our current, first effort would be to identify all the 

performance characterization parameters. 

2.3 Increasing Complexity 

The axes convey increasing complexity starting from the origin. 

An example of primitive performance for an unmanned ground 

vehicle (UGV) (close to the origin) might be applying the 

mobility capability (wheels, for example) to a forward traverse 

task on a flat and paved surface. An example of higher level 

performance might be applying the mobility capability to an 

74
autonomous road following task. Yet an even higher 

performance might involve more complex missions such as 

emergency response, warfighting, or parts transfer on a 

manufacturing shop floor [32]. From the Framework 

development viewpoint, we take an bottom-up approach; the 

subsystem/component level is where the current focus is. 

2.4 Performance Areas 

Given that the performance is to be measured according to 

mission behaviors, PerMFUS decomposes a mission 

performance into a set of areas according to the UMS functions. 

Later sections will further elaborate these areas. 

2.5 Testing and Evaluation 

Besides the metrics and the performance characterization 

parameters, we envision that PerMFUS would also include 

testing and evaluation (T & E) methods. These methods would 

describe how to develop the T & E processes, how to set up the 

T & E environments, and how to conduct the T & E. The T& E 

methods would also guide how to instantiate these generic 

concepts and processes to specific program situations, including 

the metrics and the procedures. 

2.6 Technical and Operational Aspects 

We envision that PerMFUS will provide a comprehensive 

perspective of the UMS performance, technically and 

operationally. This framework, in its current scope, does not 

consider other aspects of the performance, such as program 

management or lifecycle costing. 

3. PERFORMANCE AREAS 

From the mission behavioral and execution perspective, the 

UMS performance can be divided into the following functional 

areas [26][27][28][29][30][31]: 

Figure 5: PerMFUS performance areas 

1. Mobility/locomotion/navigation: the performance of 

traversing space (ground, air, water) to achieve the 

spatial and temporal goal. This performance area 

includes various levels of path planning when 

necessary. A path plan that takes shorter amount of 

execution time, avoids required adversaries, and 

spends a lower level of energy is a higher level of 

performance than another plan that costs more. 

2. Energy/Power: to provide energy/power to enable all 

the other performance behaviors. Some tasks might 

require a very high level of peak power while some 

others might require long periods of steady levels of 

energy supplies. The performance involves whether 

and how the energy/power is supplied and managed. 

3. Sensing and perception: the performance of onboard 

sensing to support UMS mission goals. The sensing 

and perception performance should cover many 

aspects of situational awareness (SA) such as 

identifying mobility obstacles, navigation paths that 

are energy efficient, and areas that might have low 

communication coverage. The respective UMS 

functions should, then, plan to respond according to 

the SA gained. Human speech intelligibility is an 

important performance area in the situation of humanrobot interaction (HRI) [31]. Note that this 

performance area concerns sensing and perception for 

the basic UMS functions only. Mission types of 

sensing such as chemical, biological, radioactive, 

nuclear, and explosive (CBRNE) sensing is considered 

a separate performance area called Mission Package or 

Payload. 

4. Communication: the performance of collaboration 

and information sharing among UMS subsystems, 

with other UMSs, or with the remote OCU, including 

transmitting maps or other types of information. 

5. Human-Robot Interaction (HRI): A higher performing 

UMS may be when an operator is able to provide all 

the required interactions in a timely fashion. In 

another aspect, features such as easy to use, crash 

resistant, and responsive push buttons that support all 

required human-robot interactions may be indications 

of a higher performing OCU. See the ALFUS 

Framework for the established metrics [12]. 

6. Manipulation: UMSs often employ manipulator arm(s) 

for missions involving handling the environment, 

including objects and media (swimming). The types 

of manipulation include grasping, lifting, pushing, 

throwing, etc. The performance of the arm(s) should 

maximize working volume(s), strength, and dexterity, 

allow easy changing of grippers, etc. 

7. Coordination and collaboration: interacting 

harmoniously toward goals either among the 

subsystem or among the UMS team members. 

8. Mission Package or Payload: this performance area is 

application specific and, hence, is only identified here 

and will not be expanded until a later version of 

PerMFUS. 

Figure 5 illustrates the concept. These performance areas will 

be further elaborated in the following sections. 

An issue is whether the system internal processing capabilities 

(software and hardware) should be evaluated as part of the UMS 

performance. A system that is highly capable of processing the 

75
sensory data and generating information to support decision 

making should be likelier to have a higher level of performance. 

This argues for the world modeling and knowledge base as a 

part of performance evaluation. However, these capabilities 

should also be reflected in UMS actions. We must ensure the 

proper correspondences. 

4. PERFORMANCE 

CHARACTERIZATION 

PARAMETERS ALONG THE 

SYSTEMS AXIS 

The objective of this axis is to explore how the physical or 

logical properties of an UMS contribute or affect its 

performance. Both the hardware and software aspects are 

reviewed according to the aforementioned performance areas. 

The overall structure is shown in Figure 6 

[14][15][16][17][18][19][20][21][22]. 

Figure 6: system axis metrics attribute structure 

1. Common to software and hardware: 

a. Configuration scalability, enable complex 

structure, easy integration 

b. Seamless interoperability: standard interfaces vs. 

homogeneous system 

c. Life expectancy, deterioration resistance to 

environmental conditions (corrosion) or software 

modifications. 

d. Security 

2. Hardware: 

a. Subsystem/component level 

i. Mobility performance: Wheel/track 

sizes/widths can determine whether particular 

terrain is traversable for a ground UMS. 

ii. Energy/Power performance: The fuel tank or 

the equivalent can affect the system’s 

endurance and is a key factor for mission 

planning. 

iii. Communication: Signal strength, range, and 

bandwidth can determine whether particular 

mission-related areas are reachable. 

iv. Sensing and perception: Sensor types, 

resolution, and range can help understanding 

particular mission areas. Interface types or 

plug-and-play ability can affect whether a 

mission can succeed. 

v. HRI: Numbers and types of the human 

input/output (I/O) mechanisms and their 

responsiveness on an OCU can be important 

to the operator executing the mission. 

vi. Manipulation: Size/weight of the target that 

can be grasped are performance factors. 

vii. Coordination and collaboration: spacing 

among the subsystems allowing physical 

interactions. 

b. System level 

i. Mobility performance: System level physical 

characteristics including total weight, overall 

dimensions, turning radius, distances among 

wheels, speed ratings, etc., are all 

performance factors. 

ii. Energy/Power performance factors: replenish 

time, system maximum power output. 

iii. Communication: coverage area and the 

associated strength profile 

iv. Sensing and perception: coverage area and 

resolution profile 

v. HRI: usability of the OCU 

vi. Manipulation: profile of grasping dexterity 

throughout work volume 

vii. Coordination and collaboration: spacing 

among the systems allowing physical 

interactions 

c. Team level 

i. Mobility performance: a team of UMSs to 

march in an optimal formation 

ii. Energy/Power performance factors: team 

endurance time to achieve a common goal 

iii. Communication: team coverage area, strength 

profile 

iv. Sensing and perception: team coverage area, 

resolution profile 

v. Manipulation: inter-UMS coordinated 

reach/work volume; e.g., two UMSs grasping 

a large object together 

vi. HRI: team usability 

vii. Coordination and collaboration: spacing 

among the teams allowing physical 

interactions 

76
3. Software Architecture [25] 

a. Common to all performance areas: 

i. perception: multiple layers of abstraction for 

knowledge or intelligence 

ii. control: open vs. closed loop; on/off vs. 

continuous; central vs. distributed control; 

planning and coordination 

iii. communication: contextual or transport layers 

iv. integration of heterogeneous software 

elements 

v. enable goal adjustment and replanning in realtime 

vi. responsiveness, real-timeliness 

vii. enabling complexity: functional points can be 

a useful metric for generally measuring the 

size of computer software. It can also be used 

to reflect the scope. 

b. Subsystem, system, and team levels: 

i. Mobility and navigation performance: 

accommodate multiple, different scales. 

ii. Energy/Power performance: priority based 

management strategy 

iii. Communication: latency, dynamic/static 

iv. Sensing and perception: from raw data to 

multiple layers of information process 

enabling decision making; 

v. HRI: allowing for multiple modalities of 

human interactions 

vi. Manipulation: joint vs. coordinated control 

based on advanced sensing, singularity 

resistant 

vii. Coordination and collaboration: allowing 

for multiple types of UMS interactions; data 

sharing effectiveness and efficiency among 

subsystems/components or among UMSs. 

Note that, for an existing UMS, the characterization of its 

capability can facilitating understanding on how missions may 

be planned and executed; whether they can be done and how 

effectively and efficiently they can be accomplished. For new 

system acquisition, the system performance characteristics can 

help specifying the capability. 

5. PERFORMANCE 

CHARACTERIZATION 

PARAMETERS ALONG THE 

ENVIRONMENT AXIS 

In this axis, we seek to characterize UMS operating 

environments and determine how they might affect UMS 

performance. 

1. Media 

a. Type(s): air/ground/surface/underwater and the 

number of the domains that is/are involved; 

ground robots encountering streams, etc. 

b. Uniformity: paved/dirt/grass for ground systems 

c. Density: fresh water/sea with various levels of 

salinity; clear/misty sky; density of bushes 

d. Continuity: gap/pot hole/rock/man-made structure 

e. Dimensions: two, two and a half, and three: slope, 

steps, rolling field, confined space under collapsed 

structure for ground; no-flight zone for air 

f. Dynamicity: frequency and scope of changes ; 

wind direction and speed 

2. Anomaly/obstacle 

a. Discrete: rock, tree, river 

i. sizes 

ii. numbers 

iii. types 

iv. dynamicity: frequency and scope of changes 

v. adversity severity 

b. continuous: fog, rain, electro-magnetic 

interference, maze 

i. density 

ii. dynamicity; frequency and scope of changes 

iii. adversity severity 

The environmental characteristics can be applied to analyze the 

UMS performance areas, as described in the following: 

1. Mobility: traversability 

2. Fuel/Power: power requirements, energy consumption 

3. Communication: radio interference, multipath 

interference, scattering, and attenuation can all be 

caused by the environmental factors. 

4. Sensing and perception: 

a. Feature identification: correctness, able to 

track its dynamics 

b. quality of maps—coverage area size, 

resolution, completeness, coordinate 

accuracy throughout map, correctness or 

mis-identification of features, updateable/real-timeliness [23][24] 

5. HRI: the interaction time, levels of effort (workload), 

percentage of task execution, and initiation can all 

affected by the types of the environments. 

6. Manipulation: types of objects that can be handled to 

help goal achievement 

77
7. Coordination and collaboration: air and ground 

collaboration; ground team to search a commanded 

area 

Figure 7: overall illustration of PerMFUS perspective

6. PERFORMANCE 

CHARACTERIZATION 

PARAMETERS ALONG THE MISSION 

AXIS 

UMSs execute missions to achieve their goals. Therefore, the 

primary common metrics are whether and how well the goals are 

achieved. The following provides a list of metrics: 

1. Accuracy: goal state in time, space, and logically; 

tolerance 

2. Efficiency: time, costs 

3. Effectiveness: % completeness 

4. Reliability: % of trials accomplishing goal 

5. Autonomy 

6. Safety 

7. Handling complexity 

The mission metrics can be applied to the performance areas for 

detailed analysis by the particular programs. The metrics are 

applied to the particular UMS performance areas. 

1. Mobility: accuracy of own position; 

2. Energy/Power: power requirements, energy 

consumption 

3. Communication: allowed sizes of map to be 

transmitted 

4. Sensing and perception: correctness and resolution; 

able to perceive dynamic situations 

5. HRI: complexity of HRI 

rules 

6. Manipulation: reliably 

grasping required 

complex objects; pushing 

open a gate to enable 

navigation 

7. Coordination and 

collaboration: complex 

task structure: number of 

involving subtasks, 

numbers of involving 

subsystem, numbers of 

involving vehicles, 

numbers of involving 

teams. 

7. COMPREHENSIVE 

VIEW 

Figure 7 illustrates a 

comprehensive concept of the 

metric framework. Metrics are 

categorized along the three axes. 

Note that, UMSs may have additional performance areas. A 

wide variety of mission package(s) exists, such as CBRNE 

detection, lethality, RSTA (reconnaissance, surveillance, and 

target acquisition), etc. For these additional areas, the 

performance can be characterized using the same structure. 

8. TEST AND EVALUATION ISSUES 

To properly apply the metric sets, it is imperative to collect and 

organize the requirements. As stated in the earlier sections, the 

requirements can be in the formats of CONOPs, vignettes, 

scenarios, use cases, etc. The organization of the requirements 

may be the decomposition from high level requirements to series 

of low level but detailed sets. 

Test and evaluation methods may start from the low level 

requirements. The test methods include such elements as 

metrics, tasks or procedures, equipment or personnel involved, 

setups or apparatuses, safety guidelines, and accuracy statements. 

Beyond those elemental tests, high level, scenario based tests 

should also be designed to evaluate the UMS integrated 

performance. 

Certain considerations must be given during the design of the 

test and evaluation design. They include: 

 User requirements may or may not have one-to-one 

correspondence with testing and evaluation metrics. 

The former is described in the operational domain 

78
whereas the latter in the technical domain. The 

objective should be to identify a collection of metrics 

to sufficiently evaluate for a particular requirement. 

 Systems are developed to meet particular requirements, 

therefore, the metrics may be weighted. 

 The more details and the more specific the 

requirements can be decomposed into, the easier it is 

to develop the test and evaluation methods. It is 

desirable that an elemental test method can be 

executed with a single task, which, in turns, can 

minimize correlation with outside concerns. 

 The more critical the designed missions are, the higher 

degrees of rigor the test methods should have. This 

may entail more detailed metrics and testing tasks, 

higher levels of precision for the testing setups and 

apparatuses, or higher numbers of test runs for 

obtaining higher levels of confidence of the test results. 

These issues must be further examined and specified in the 

framework. 

9. A USE CASE 

Table 1 is a sample set of requirements for emergency response 

robots, particularly, applicable to a collapsed structure. The left 

column describes the operational requirements as stated by the 

would-be users of the robots, the emergency responders. The 

right column shows the corresponding performance areas. 

For an example, for the Requirement A, tumble recovery, a set 

of test methods must be devised that are representative of the 

terrain types in emergency situations. The testing terrains must 

possess different degrees of complexity so that robots with 

various mobility capabilities can be evaluated for the tumble 

recovery feature. 

For Requirement C, test methods must be designed to evaluate 

that the video link allows displaying the scenes of the confined 

space in sufficient resolutions and with sufficiently wide fieldof-view. These correspond to the metrics described in the 

earlier sections. 

In addition, to correspond to the Mobility-confined space 

performance area, the testing apparatuses must include confined 

space with various kinds of testing metrics such as gaps with 

various sizes, slopes with various degrees, different types of 

stairs, floor conditions of wetness, slipperiness, containing hardto-detect trip wires, etc. 

For Requirement E, test methods must be designed to evaluate 

the robot’s capability to traverse a long distance and to 

communicate with its onboard radio or tether-based 

communication system. 

Requirements Performance Areas 

A. Tumble recovery within Terrain Type Mobility 

B. Maintain operations beyond basic 

mobility requirements within a given 

terrain type. The system must have 

sufficient power to operate for the 

specified number of hours, assuming 

one power charge for one out and back 

mission. 

Energy/Power 

C. To project remote situational 

awareness into compromised or 

collapsed structures or to convey other 

types of information. To be able to 

ingress a specified number of meters 

into the worst case collapse, which was 

further defined as a reinforced steel 

structure. To operate around corners of 

buildings and other locations beyond 

line of sight. 

Sensing-video; 

Mobility-confined 

space; 

CommunicationNLOS; 

D. To enable use of video in confined 

spaces and for short-range object 

identification, which can wash out from 

excessive illumination of the scene; 

therefore, adjustability is required 

Mission packagevariable illumination; 

CommunicationNLOS; 

E. To project remote situational 

awareness or to convey other types of 

information down range within line of 

sight 

Sensing-video; 

Energy/Powerendurance 

CommunicationLOS, long range; 

Table 1: Requirement Statement Samples for Emergency 

Response Robots 

10. SUMMARY 

An initial perspective of the performance measures framework 

for unmanned systems, PerMFUS, is described. In such a 

framework, performance metrics are characterized from the 

three aspects, the system (software and hardware), the operating 

environment, and mission. They are further elaborated 

according to a set of performance areas. The combination aims 

at a comprehensive structure and sets of metrics. In addition, an 

approach is described for applying the metrics to the testing and 

evaluation process. 

A lot of the identified areas contain exemple metrics, which 

must be systematically expanded. Much more effort is planned 

to further develop PerMFUS. 

79
ACKNOWLEDGEMENT 

NIST Response robotics project team and the associated 

users/developers forum shared hands-on implementation 

experience. SAE AS4-D membership participated in the 

Framework development work. 

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2008 

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Set, 2009 

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Mobile Robot Navigation. Artificial Intelligence, pages 21–

71, February 1998 

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6910, 2002 

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and Rescue Robots on Rough Terrain,” Performance 

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Program,” Performance Metrics for Intelligent Systems 

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80
Optimum Combination of Full System and Subsystem

Tests for Estimating the Reliability of a System

Coire J. Maranzano

The Johns Hopkins University

Applied Physics Laboratory

11100 Johns Hopkins Rd

Laurel, Maryland, 20723-6099, USA

coire.maranzano@jhuapl.edu

James C. Spall

The Johns Hopkins University

Applied Physics Laboratory

11100 Johns Hopkins Rd

Laurel, Maryland, 20723-6099, USA

james.spall@jhuapl.edu

ABSTRACT

This paper develops a method for finding an optimum

test plan, which consists of a mixture of full system and

subsystem tests, to estimate the reliability of a system. An

optimum test plan is developed by trading off the number

of full system and subsystem tests to minimize the meansquared error (MSE) of the maximum likelihood estimate

(MLE) of system reliability and testing costs. The MSE is

decomposed into the variance of the MLE and a bias from

incorrectly specifying the function that relates the subsystem reliabilities to the full system reliability (series, parallel,

other). The variance of the MLE comes from Fisher theory.

The bias is due to the modeling error. Optimum test plans

involve trade offs between the MSE (estimation accuracy),

the degree of modeling error, and the cost of doing system

and subsystem tests. A Pareto frontier can be identified, as

illustrated in the paper.

Categories and Subject Descriptors

G.3 [Probability and Statistics]; J.2 [Physical sciences and engineering]

Keywords

Reliability, Test Sizing, Model Bias, Mean-Squared Error,

Maximum Likelihood Estimation

1. INTRODUCTION

System, subsystem, component, interface, and other1tests

are often carried out on complex systems to ensure that an

operational reliability requirement is satisfied. Fusing full

1To avoid the need to repeatedly refer to tests on subsystems, components, processes, and other aspects of the system as the key

source of information other than full system tests, we will usually

only refer to subsystem tests; subsystem tests in this context should

be considered a proxy for all possible test information short of full

system tests.

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies

bear this notice and the full citation on the first page. To copy otherwise, to

republish, to post on servers or to redistribute to lists, requires prior specific

permission and/or a fee.

PerMIS’09, September 21–23, 2009, Gaithersburg, MD, USA

Copyright 2009 ACM 978-1-60558-747-9/09/09 ...$10.00.

system and subsystem test data to evaluate the reliability

of a complex systems is desirable when full system testing

can be very costly, dangerous or requires the destruction of

the system itself. Additionally, it is desirable to include full

system testing in an overall reliability assessment to help

guard against possible mis-modeling of the relationship between the subsystems and full system in calculating overall

system reliability. One method of fusing full system and

subsystem reliability test data to form a full system estimate of reliability is the method of maximum likelihood [9].

This general maximum likelihood formulation for the fusion

of reliability test data applies across all system configurations (series, parallel, etc); only the optimization constraints

change, leading to an appropriate maximum likelihood estimate (MLE). The MLE method provides a characterization

of the estimation uncertainty—statistical uncertainty about

the model parameters—through the Fisher Information on

the parameters of the system reliability model. If the reliability of the system must be known within a specified confidence interval or if the test plan is limited by cost, there

exists an inherent trade off between performing full system

tests or subsystem tests. This paper develops a method for

finding an optimal test plan consisting of a mixture of full

system and subsystem tests, to estimate the full system reliability.

A necessary step in the process of developing an optimal

test plan is to quantify the uncertainty in the estimate of

full system reliability. Uncertainty arises from randomness

in the data and lack of knowledge of the model for the system

reliability. Randomness in the data results in uncertainty in

the parameter estimates. Lack of knowledge of the model

arises from incomplete understanding of the system and is

called model uncertainty. A formal theory for quantifying

estimation uncertainty exists within general statistical theory (specifically, within the framework of general maximum

likelihood estimation through the asymptotic distribution of

the parameter estimates [7]). In general, a formal theory for

quantifying model uncertainty does not exist. Significant

uncertainty results from how well the model for the system actually represents the system’s true behavior. Ref. [2]

identifies six major characteristics of modeling errors that

lead to model uncertainty: model topology, model parameters, model scope or focus, data, optimization technique,

and human subjectivity. Ref. [2] goes on to point out that a

modeling error exhibiting at least one of the aforementioned

characteristics can potentially contribute the most uncertainty to a quantitative prediction, and its reduction is not

81
straitforward or simple.

Theoretical frameworks to quantify the model uncertainty

typically rely on being able to observe and record the model

error [4, 5]. Such an approach can be applied to reliability estimation, though, for complex systems it is often not

practical because the system may be so unique that no prior

model error analysis is valid and/or there may be insufficient

tests to observe model error. Alternatively, a Bayesian approach can be taken to incorporate model uncertainty in a

reliability estimate by “us[ing] a weighted average of all possible models” [11]. This, too, is often not practical because

the analysis must specify all the possible reliability models

and provide a prior probability for each model. Also, the approach is dangerous because, although it is possible to state

that one model is better than another, it is not possible to

state that one model is more probable than another [3, 6].

The purpose of this paper is to identify an optimal test

plan by trading off between subsystem testing and full system testing, within a general maximum likelihood reliability

estimation paradigm, to minimize estimation uncertainty,

the effect of modeling error, and the cost of testing. The

general maximum likelihood method of reliability estimation described in [9], fuses data from subsystem tests and

full system tests via a model that reflects the constraints associated with the operation of the full system. The quality

of the MLE is assessed via the mean squared error (MSE)

of the estimate, this leads to a decomposition involving the

variance of the estimate and the bias of the estimate. In particular, the MLE of system reliability is decomposed into the

modeling bias from incorrectly specifying the function that

relates the subsystem reliabilities to the full system reliability (series, parallel, other) and the asymptotic variance

of the model parameters. Modeling error contributes to the

bias because an error in the model results in a biased full system reliability estimate even if the parameters of the model

are known perfectly. An optimal test plan is developed by

trading off the number of full system and subsystem tests

to minimize the MSE of the MLE of system reliability and

testing costs.

Section 2 of this paper presents the general MSE formula

for a system reliability estimate in terms of the system reliability model and parameter estimates. In Section 3, the

MSE of the maximum likelihood estimator of system reliability based on full system tests is compared with the maximum likelihood estimator based on subsystem tests for a

series system. Assuming no model uncertainty, the comparison reveals that, for a truly series system, performing a set

of subsystem tests (one for each subsystem) always reduces

the variance of the full system reliability estimate more than

performing a full system test. Section 4 establishes the MSE

for the general maximum likelihood estimator of system reliability due to [9], given a maximum modeling error. In

Section 5, the MSE for the general maximum likelihood estimator of system reliability is computed for a hypothetical

system and the results are used to determine optimal test

plans in terms of the MSE and testing costs.

2. GENERAL MEAN SQUARED ERROR FORMULATION

Consider a system composed of p subsystems, typically

a system process and/or components of subsystems. The

general estimation formulation involves a parameter vector

θ , representing the parameters to be estimated. Let ρ and

ρj represent the reliabilities (success probabilities) for the

full system and for subsystem j, respectively, j = 1,2,..., p.

The vector θ = [ρ1,ρ2,...,ρp]

T

. Let Θ represent the feasible region for the elements of θ . To ensure that relevant

logarithms are defined and that the appropriate derivatives

exist, it is assumed, at a minimum, that the feasible region

Θ includes the restriction that 0 < ρj < 1 for all j. The system reliability ρ is not included in θ because it is uniquely

determined (or bounded) by the subsystem reliabilities ρj

for j = 1,2,..., p and possibly other information via relevant

constraints. Herein, the relation is restricted such that ρ is

uniquely determined by a function h(θ ). The mapping, h,

between θ and ρ dictates the arrangement of the system,

which may be configured in series, parallel, combination series/parallel, or some other configuration, and it is analogous

to a model of system reliability in terms of its subsystems.

To mirror the commonly used lexicon in the literature, h will

be referred to as the model for the system reliability.

Thus, an estimate of the system reliability ρˆ is found by

evaluating h(·) at the estimate ˆθ . Further consider the test

data on the system and its subsystems. Let Y be the number

of successes in n independent identically distributed (i.i.d.)

tests of the system, and let Xj be the number of successes in

nji.i.d. tests of the j

th subsystem, for j = 1,..., p. And, let

ˆθ = ˆθ (Y ) be a function that produces an estimate of θ , where

Y is the set of test data on the system and its subsystems

{X,X1,...,Xp}.

A measure of the effectiveness of an estimator is the mean

squared error. The MSE for an estimate of system reliability,

given the model, h, and the data, Y , is E



h



ˆθ (Y )



−ρ

2



,

where the expectation is computed with respect to the random variable ˆθ . Ref. [8, Chapter 13] shows that the MSE

consists of two terms,

E



h



ˆθ (Y )



−ρ

2



=

E



h



ˆθ (Y )



−E

h

h



ˆθ (Y )

i2



| {z }

Variance of Estimate

+



E

h

h



ˆθ (Y )

i−ρ2

| {z }

Bias of Estimator2

,

(1)

that are the variance of the estimate (a measure of estimate uncertainty) and the bias of the estimate. The bias

term of the MSE is zero if the estimator is unbiased, that is

E

h

h



ˆθ (Y )

i = ρ.

Eqn. (1) forms the basis for evaluating estimators of the

system reliability given the model for the system and the

test data. As such, it can be used to determine the number

of system and subsystem tests needed to achieve a minimum

MSE estimate given the presumed system reliability (specifically, the presumed value of θ ). In other words, Eqn. (1)

is used as a criterion for determining optimal test plans. In

Section 5, optimal test plans for maximum likelihood reliability estimation are found by selecting the combination of

system and subsystem tests that minimize the MSE (variance and bias) and testing costs.

82
3. MSE FOR A SERIES SYSTEM WITHOUT

MODEL UNCERTAINTY

Consider estimating the reliability for a fully series system comprised of stochastically independent subsystems by

performing only full system tests or only tests of the subsystems using maximum likelihood estimation (and assuming

that the model for the system is perfect). In this case, the

system reliability model is h(θ ) = ∏

p

i=1

ρi. An optimal test

plan can be found by selecting the combination of tests—

tests of the full system or tests of each subsystem—that

minimizes the MSE of the estimate of system reliability.

3.1 Variance of System Reliability Estimates

Given only tests of the full system, the MSE of the maximum likelihood estimator of full system reliability, ˆρ ̃ ≡ X/n,

is the variance of the estimate,

var 

ˆρ ̃



=



∏

p

i=1

ρi





1− ∏

p

j=1

ρi



n

, (2)

since the estimator is unbiased. Given only tests of the subsystems, the maximum likelihood estimate of system reliability for a series system is the product of the subsystem

reliability estimates. Let ρˆi = Xi/nifor i = 1,..., p be the the

maximum likelihood estimates of the subsystem reliabilities.

Then, the maximum likelihood estimate of the system reliability ρ is ρˆ = h(

ˆθ ) = ∏

p

i=1

ρˆi. This estimator is unbiased,

and so its variance, var 

∏

p

i=1

ρˆi



, is the only contributor to

the MSE.

The variance of ∏

p

i=1

ρˆiis found by computing a related

variance: the variance of the product of binomial success.

Let Xifor i = 1,..., p be a binomial random variable that

is the number of successes in niindependent Bernoulli trials with probability ρi of success on each trial. Then, the

variance of the product of binomial random variables is

var p

∏

i=1

Xi

!

=

p

∏

i=1

E



X

2

i



−

p

∏

i=1

[E (Xi)]2,

=

 p

∏

i=1

niρi[(ni −1)ρi +1]

!

−

 p

∏

i=1

niρi

!2

,

=

 p

∏

i=1

niρi

!" p

∏

i=1

[(ni −1)ρi +1]

!

−

p

∏

i=1

niρi

#

,

(3)

and the variance of the product of maximum likelihood estimates is,

var p

∏

i=1

ρˆi

!

= var p

∏

i=1

Xi

ni

!

,

=

p

∏

i=1

1

n

2

i

 

var p

∏

i=1

Xi

!!,

=

 p

∏

i=1

ρi

ni

!" p

∏

i=1

[(ni −1)ρi +1]

!

−

p

∏

i=1

niρi

#

.

(4)

Given Eqn. (2) and Eqn. (4), the variance of the maximum likelihood reliability estimates can be compared to

determine the type of test, a full system test or a set of

subsystems tests, that produces the largest reduction in the

variance of the full system reliability MLE (equivalently the

MSE). The comparison reveals that, when the model for system reliability is a set of p subsystems in series and there is

no modeling error, performing a set of subsystem tests (one

for each subsystem) always reduces the variance (equivalently, MSE) more than a single full system test. The next

section is devoted to a formal proof. And so, for a series

system, assuming no modeling error, choosing to perform

sets of subsystem tests instead of full system tests yields

the minimum MSE test plan. Also, if the cost of a set of

subsystem tests is less than a full system test, it is also the

minimum cost test plan. As shown in the next subsection,

these two conclusions, which prescribe an optimum test plan

for a series system, are weakest when the true reliability of

the system is very close to 1 or 0.

3.2 Comparison the Variance of Series System Reliability Estimates

Theorem 1 presents the conditions that relate var 

ˆρ ̃



and

var 

∏

p

i=1

ρˆi



.

Theorem 1. Let ρ be the probability of full system success where ρ = ∏

p

i=1

ρi. Assuming that the number of trials

for the full system, n, is the same as for each subsystem,

that is n = n1 = ··· = np, then for n ≥ 2, p ≥ 2, and 0 < ρi < 1

for i = 1,... p,

var 

ˆρ ̃



> var p

∏

i=1

ρˆi

!

. (5)

Substituting Eqn. (2) and Eqn. (4) with n = n1 = ··· = np

into Eqn. (5) yields an equivalent inequality to consider:

∏

p

i=1

ρi



1− ∏

p

i=1

ρi



n

>

p

∏

i=1

ρi

n

"

p

∏

i=1

[(n−1)ρi +1]−

p

∏

i=1

nρi

#

.

(6)

This statement can be simplified algebraically to yield a

statement equivalent to Eqn. (5). First, divide each side

of Eqn. (6) by ∏

p

i=1

ρi/n to obtain,

1−

p

∏

i=1

ρi >

1

n

p−1

"

p

∏

i=1

[(n−1)ρi +1]−

p

∏

i=1

nρi

#

, (7)

and then, multiply each side by n

p−1 and add to each side

∏

p

i=1

nρi to obtain,

n

p−1

 

(n−1)

p

∏

i=1

ρi +1

!

>

p

∏

i=1

[(n−1)ρi +1]. (8)

And so, proving that the inequality in Eqn. (8) is true for

n ≥ 2, p ≥ 2, and 0 < ρi < 1 for i = 1,... p is equivalent to

proving that Theorem 1 is true. Proof proceeds by induction

on p.

Proof. The base case is p = 2. For probabilities 0 < ρ1 <

1 and 0 < ρ2 < 1,

ρ1ρ2 +1 > ρ1 +ρ2, (9)

because (ρ1 − 1)(ρ2 − 1) > 0. Manipulating Eqn. (9) algebraically yields an inequality, which is then also true when

n ≥ 2,

(n−1)

2

ρ1ρ2 + (n−1) (ρ1ρ2 +1) +1 >

(n−1)

2

ρ1ρ2 + (n−1) (ρ1 +ρ2) +1. (10)

83
After factoring the left side of Eqn. (10) and recognizing

that the right side of Eqn. (10) is a product, it is easy to see

that the following inequality is true,

n((n−1)ρ1ρ2 +1) > [(n−1)ρ1 +1][(n−1)ρ2 +1], (11)

and so, for p = 2, the inequality in Eqn. (8) is true, and

Theorem 1 holds.

For the inductive step, assume that Theorem 1 is true for

some p ≥ 2. This is equivalent to assuming that,

n

p−1

 

(n−1)

p

∏

i=1

ρi +1

!

>

p

∏

i=1

[(n−1)ρi +1], (12)

is true for n ≥ 2 and 0 < ρi < 1 for i = 1,... p. The next step in

the inductive proof is to show that Theorem 1 holds for p+1.

Proceeding from Eqn. (12), for n ≥ 2 and for 0 < ρp+1 < 1,

it follows that,

nρp+1n

(p−1)

 

(n−1)

p

∏

i=1

ρi +1

!

> nρp+1

p

∏

i=1

[(n−1)ρi +1].

(13)

Given that, for n ≥ 2 and 0 < pi < 1 for i = 1,... p,

n

p >

p

∏

i=1

[(n−1) pi +1], (14)

and, given the inequality in Eqn. (13), the following holds,

nρp+1n

(p−1)

 

(n−1)

p

∏

i=1

ρi +1

!

+n

p



1−ρp+1



>

nρp+1

p

∏

i=1

[(n−1)ρi +1] +

p

∏

i=1

[(n−1)ρi +1]



1−ρp+1



. (15)

Factoring out n

pρp+1 from the left side of Eqn. (15) and

multiplying through the second term on the right side of

Eqn. (15) gives the following inequality,

n

p

ρp+1

 

(n−1)

p

∏

i=1

ρi +

1

ρp+1

+1−1

!

>

(n−1)ρp+1

p

∏

i=1

[(n−1)ρi +1] +

p

∏

i=1

[(n−1)ρi +1]. (16)

Multiplying through by ρp+1 on the left side of Eqn. (16)

and multiplying the through the first term on the right side

of Eqn. (16) gives the following inequality,

n

p

 

(n−1)ρp+1

p

∏

i=1

ρi +1

!

>



(n−1)ρp+1 +1



p

∏

i=1

[(n−1)ρi +1], (17)

and finally, replacing p with p+1 gives,

n

(p+1)−1

 

(n−1)

p+1

∏

i=1

ρi +1

!

>

p+1

∏

i=1

[(n−1)ρi +1]. (18)

Thus, the condition in Eqn. (8) for p+1 subsystems follows

from the assumption that it is true for some p ≥ 2 subsystems, and thus, it follows that Theorem 1 holds for p + 1

subsystems given the assumption that it is true for some

p ≥ 2 subsystems. And since Theorem 1 is true for p = 2

subsystems, it follows that it is true for all p ≥ 2 subsystems.

Theorem 1 indicates that for series systems a set of subsystem tests reduces the variance of the full system reliability

estimate (and, hence, reduces the MSE and the confidence

bound on the estimate) more than a single full system test.

Of interest is the fact that as ρ → 1 or ρ → 0, the variances

var 

ˆρ ̃



and var 

∏

p

i=1

ρˆi



converge to zero. In the limit of a

very reliable or a very unreliable system there is little difference in the reduction of the variance of the maximum

likelihood reliability estimate (equivalently, reduction in the

MSE and the confidence bound on the estimate) from performing a set of subsystem tests instead of a full system

test.

4. MSE FOR THE GENERAL MLE OF SYSTEM RELIABILITY

Consider the following general maximum likelihood estimator of the parameter vector θ ,

ˆθ = ˆθ (Y ) ≡ argmax

θ∈Θ

L(θ )

subject to ρ = h(θ ), (19)

where L(θ ) ≡ log(p(Y |θ ,ρ)) [9]. Given both system and subsystem test data, Y , the estimate of ρ is derived from the

MLE for θ through the model for the system, h. The model

dictates how subsystems are arranged in the full system (i.e.,

L(θ ) is the same regardless of whether, the subsystems are in

series or parallel). For a given parameter vector θ , the definition of L(θ ) does not depend on the model for the system.

However, the MLE does change as a function of the model

for the system. This is a consequence of the system model

being used as a constraint in the optimization problem that

is solved to produce the MLE. From the assumption of independence of all test data, the probability mass function

is:

p(Y |θ ,ρ) = 

n

Y



ρ

Y

(1−ρ)

(n−Y)

| {z }

system

×



n1

X1



ρ

X1

1

(1−ρ1)

(n1−X1)

···

np

Xp



ρ

Xp

p



1−ρp

(np−Xp)

| {z }

p subsystems

, (20)

leading to the log-likelihood function L(θ ) ≡ log p(Y |θ ,ρ):

L(θ ) = Y logρ + (n−Y)log(1−ρ)+

p

∑

j=1



Xjlogρj + (nj −Xj)log(1−ρj)



+ constant, (21)

where the constant is not dependent on θ . The MLE is determined by finding a root of the score equation ∂L(θ )/∂ θ =

0 (or a normalized form of this equation in the asymptotic

sample size case), where

∂L

∂ θ =

Y

ρ

∂h

∂ θ +

n−Y

1−ρ

∂h

∂ θ +









X1

ρ1

−

n1−X1

1−ρ1

.

.

.

Xp

ρp

−

np−Xp

1−ρp









. (22)

The solution to ∂L(θ )/∂ θ = 0 must generally be found by

numerical search methods.

84
Except in trivial cases, the analytic expression for the variance of the general MLE for system reliability is not easily

found. The likelihood function for the general MLE is twice

differentiable with respect to θ [9]. Assuming that the general MLE of system reliability is asymptotically unbiased

(and assuming no modeling error), the Fisher Information,

F(θ ), is given by the negative of the expectation of the second derivative of the likelihood function with respect to θ ,

F(θ ) = −E



∂

2L

∂ θ∂ θ T



, (23)

and, by the Cramer-Rao inequality, the inverse of the Fisher

information is a lower bound on the variance the MLE.

When h(θ ) is differentiable in θ and if the asymptotic normality holds, then

p

sample sizehh(

ˆθ )−h(θ

∗

)

i

dist −−→ N



0,h

0

(θ

∗

)

T F

−1

h

0

(θ

∗

)



,

(24)

where θ

∗

is the vector of true reliabilities for the subsystems

and F is the limit of the mean information matrix. Hence,

h(

ˆθ ) ∼ N



h(θ ),h

0

(θ )

T F(θ )−1

h

0

(θ )



, (25)

for θ close to θ

∗ when the sample size is reasonably large

[8]. In practice, θ is often set to ˆθ in the mean and variance expressions of Eqn. (25). Thus, ρˆ = h(

ˆθ ) has a normal

distribution with an approximate variance given by the variance in Eqn. (25) evaluated at ˆθ . And so, the “variance of

estimate” term of the MSE for the general MLE of system

reliability is approximated with the asymptotic variance of

the MLE. Further, assuming that the estimator is asymptotically unbiased and that there is no modeling error, the

“bias of estimate” term is zero.

4.1 MSE With Model Error

If the system reliability model is incorrect or a model is

chosen that does not adequately represent the system’s behavior, then the true system reliability ρ is not uniquely determined by the model for the system, h(·). Given a model

error, the estimate of system reliability from only subsystem

test data is biased by the model error ε; that is

ρ = h(θ

∗

) +ε. (26)

where, in general, −ρ ≤ ε ≤ (1−ρ) to ensure 0 < ρ < 1 (recall

that θ

∗

is the vector of true reliabilities for the subsystems).

If the subsystem reliabilities are estimated individually and

the model, h, is used to compute the system reliability, then

the model error contributes a pure bias, ε to the estimate of

full system reliability and to the MSE (as an example consider the estimator ρˆi = Xi/ni

for i = 1,..., p). However, if the

estimate of the subsystem reliabilities depends on the model

h, (as in the general MLE) then the estimate, ˆθ , is influenced

by the modeling error and the model error contribution to

the bias is not straightforward.

To determine the bias in the general MLE for system reliability, ˆθ , due to a small error in the model for the system reliability, assume that the model, h, is in error as described in the preceding paragraph. Thus, the constraint

in Eqn. (19) becomes ρ ≡ hε (θ , ε) ≡ h(θ ) +ε, where ε is an

error in the model (a true bias), which does not depend on

θ. The addition of the modeling error will not change the

log likelihood function. However, the relationship between

ρ and the ρj differs, and so, the MLE of θ differs. Let

the function gMLE(θ , ε) be the value of the score function in

Eqn. (22), where the function h is replaced with hε . Given

the model error, ε, the MLE is determined by finding a root

of gMLE(θ , ε).

Since the model error is unknown before estimation and

its presumed value may change after the vector ˆθ has been

estimated, the new MLE of θ is formulated as first-order

function of ε via Taylor series expansion. The function that

produces the MLE, Eqn. (19), is written:

ˆθ ε = ˆθ (Y ) + ∂

ˆθ

∂ ε











Y ,ε=0

ε +O(ε

2

), (27)

where ˆθ ε is the MLE given that the maximization of the

likelihood function is subject to the constraint ρ = hε (θ , ε).

Assuming that the model error, ε is small, the O(ε

2

) and

higher-order terms of the Taylor Series expansion can be

dropped to form a good approximation of ˆθ ε .

To obtain the derivative, ∂

ˆθ /∂ ε, the conditions necessary

to obtain a unique function ˆθ (ε) relating ε to θ must be

established.

Lemma 1. Suppose that the constraint in Eqn. (19) is

rewritten ρ = h(θ ) +ε and the solution to the maximization

problem in the presence of the model error ε is determined

by finding the root of gMLE(θ , ε) with gMLE being a continuously differentiable function in θ ∈ Θ. For ε = 0, suppose

∂gMLE(θ , ε)/∂ θ is invertible and det[∂gMLE(θ , ε)/∂ θ ] 6= 0 at

ˆθ such that gMLE(ˆθ , ε) = 0. Then there exists an open neighborhood about ε = 0 and a unique continuous differentiable

function ˆθ (·) such that for all ε in this neighborhood, θ =

ˆθ (ε) and

∂

ˆθ (ε)

∂ ε = −



∂gMLE(θ , ε)

∂ θ −1

∂gMLE(θ , ε)

∂ ε . (28)

Proof. By definition, the MLE satisfies, gMLE(θ , ε) = 0.

Also, the derivative of the score vector is the Hessian,

∂gMLE(θ , ε)

∂ θ =

∂

2L(θ , ε)

∂ θ∂ θ T

,

which is a negative definite matrix when evaluated at the

solution to the maximum likelihood problem. By the Implicit Function Theorem [see 1, Section 13.4], there exists

an open neighborhood about ε = 0 and one, and only one,

continuously differentiable function ˆθ (·) such that for all ε

in this neighborhood, θ = ˆθ (ε). Further, ∂

ˆθ /∂ ε is as shown

in Eqn. (28) in this neighborhood [see 10, p. 483].

Lemma 1 establishes the existence of the derivative, ∂

ˆθ /∂ ε,

which is found explicitly via Eqn. (28). However, the derivative in Eqn. (27) is evaluated with ε = 0, and so, the terms in

Eqn. (28) must also be evaluated with ε = 0. The first term

in Eqn. (28) is found by taking the second derivative of the

score function, Eqn. (22), where the function h is replaced

with hε . When evaluated with ε = 0, the term simplifies to

the inverse of the Hessian for the log likelihood, as follows,

 ∂gMLE(θ , ε)

∂ θ 

ε=0

−1

=



∂

2L

∂ θ∂ θ T

−1

. (29)

The second term in Eqn. (28) is found by taking the second derivative of the score function, Eqn. (22), where the

function h is replaced with hε . The elements of the vector,

85
∂gMLE(θ , ε)/∂ ε, are,

∂

2L(θ , ε)

∂ θj∂ ε =

Y

hε (θ , ε)

∂

2hε (θ , ε)

∂ θj∂ ε −

n−Y

1−hε (θ , ε)

∂

2hε (θ , ε)

∂ θj∂ ε

−

(

∂hε (θ , ε)

∂ θj

∂hε (θ , ε)

∂ ε  

Y

hε (θ , ε)

2

+

n−Y

(1−hε (θ , ε))2

!),

= −



∂hε (θ , ε)

∂ θj

∂hε (θ , ε)

∂ ε  

Y

hε (θ , ε)

2

+

n−Y

(1−hε (θ , ε))2

!

,

(30)

for j = 1,..., p, because, given the definition of hε , the second derivative ∂

2hε (θ , ε)/∂ θj∂ ε = 0. The terms in Eqn. (30)

simplify further when used in Eqn. (27) because the derivative is evaluated with ε = 0.

Eqns. (27)–(30) comprise the mathematical machinery

necessary for determining the bias in the general MLE of θ

due to a small error in the model. The difference, ˆθ ε − ˆθ , is

the sensitivity of the MLE to the model error ε. To determine the bias in the full system estimate, ρˆ, a Taylor series

expansion is used to find the MLE of ρ, given a deterministic

error, ε, in the model:

ρˆε = hε



ˆθ



+

∂hε

∂ θ









ˆθ



ˆθ ε − ˆθ



+O



ˆθ ε − ˆθ

2



. (31)

For series, parallel, and combination series/parallel systems

the second order and greater terms are zero. The remaining

terms of Eqn. (31) are supplied by Eqns. (27)–(30) to give

an explicit formula for the MLE of ρ, given a deterministic

error, ε:

ρˆε ≈ h



ˆθ



+ε +

∂hε

∂ θ









ˆθ

·

∂

ˆθ

∂ ε











y,ε=0

ε,

≈ ρˆ +ε −

∂hε

∂ θ









ˆθ

 

∂

2L(

ˆθ )

∂ θ∂ θ T

!−1 "

∂gMLE(

ˆθ , ε)

∂ ε #

ε=0

ε.

(32)

The quantity, |ρˆε −ρˆ|, is the magnitude of the change in the

estimate of full system reliability due to the modeling error,

ε. It is a measure of the sensitivity of the general MLE of

full system reliability to a given modeling error, and it is the

bias in the MLE due to the modeling error, ε.

Practically, the outcome of the tests are unknown before a

testing regime must be developed. Thus, the expectation of

the quantitiy |ρˆε −ρˆ| is useful for test sizing and evaluating

estimator accuracy. The expression for the expectation is,

E [ρˆε −ρˆ] ≈ ε +

∂hε

∂ θ









ˆθ



F(

ˆθ )

−1

E

"

gMLE(

ˆθ , ε)

∂ ε #

ε=0

ε.

(33)

Note that the Hessian is replaced with its expectation, which

is the negative of the Fisher Information, F(θ ), for the general maximum likelihood estimator. The expectation is a

bias in the MLE estimate due to model error.

Given the model error, ε, the MSE of the general maximum likelihood estimator is composed of the asymptotic

variance of the estimate from and the approximate expected

bias of the estimate from Eqn. (33). From Eqn. (1), the expression for the MSE is,

E



h



ˆθ



−ρ

2



≈ h

0

(

ˆθ )

T F(

ˆθ )

−1

h

0

(

ˆθ ) + (E [ρˆε −ρˆ])2

.

(34)

Note that E [ρˆε −ρˆ] = ε when full system tests are not performed and that the quantity ε

2

is the penalty to the MSE of

the estimator for not performing any full system tests. The

bias, ε, is a subjective input into any analysis performed

using the MSE in Eqn. (34) (e.g. performing a test sizing

study to minimize MSE); it is interpreted at the maximum

error in the model for the system reliability h.

4.2 Special Case: Fully Series System

From Eqn. (19), the MLE in the series-subsystem case is

found according to

ˆθ = argmax

θ∈Θ

L(θ )

subject to ρ =

p

∏

j=1

ρj, (35)

It is straightforward to determine the score vector taking

partial derivatives of Eqn. (21). Making the substitution

ρ = ∏

p

j=1

ρjin Eqn. (21) and taking the derivative, the j =

1,2,..., p elements of the score vector for the series case are:

∂L

∂ ρj

=

Y +Xj

ρj

−

(n−Y)ρ

(1−ρ)ρj

−



nj −Xj



1−ρj

, (36)

[9]. Because Θ = 0 < ρj < 1 for all j, it is known that the

Hessian matrix is continuous and, consequently, symmetric.

From Eqn. (36), the elements of the Hessian for the series

case are:

∂

2L

∂ ρj∂ ρk

=







−

Y+Xj

ρ

2

j

−

(n−Y)ρ

2

(1−ρ)

2

ρ

2

j

−

(nj−Xj)

(1−ρj)

2 when j = k,

−

(n−Y)ρ

(1−ρ)

2

ρjρk

when j 6= k,

(37)

[9].The Fisher information matrix, F(θ ), for the series case is

the negative expectation of the Hessian. The corresponding

elements of the information matrix F(θ ) = Fjk(θ)



for the

series case are:

Fjk(θ) =







nρ+njρj

ρ

2

j

+

nρ

2

(1−ρ)ρ

2

j

+

nj

(1−ρj)

when j = k,

nρ

(1−ρ)ρjρk

when j 6= k,

(38)

[9]. The elements of the derivative ∂hε/∂ θ for the series case

are

∂hε

∂ ρi

=

p

∏

j=1, j6=i

ρj. (39)

Finally, the derivative, ∂gMLE(ρi, ε)/∂ ε, is found by evaluating Eqn. (30) with the series model, this gives,

∂gMLE(ρi, ε)

∂ ε =

Y

ρi ∏

p

i=1

ρi

−

(n−Y)∏

p

i=1

ρi



1− ∏

p

i=1

ρi

2

ρi

, (40)

and, the expected value of the derivative is

E



∂gMLE(ρi, ε)

∂ ε 

=

n

ρi

−

n∏

p

i=1

ρi



1− ∏

p

i=1

ρi



ρi

. (41)

Eqns. (35), (38), (39), and (41) are the terms necessary for

evaluating the MLE with the MSE (the asymptotic lower

86
bound variance and bias) for a series system, given the modeling error ε.

5. OPTIMAL TEST PLANS

In this section, the optimal combination of system and

subsystem tests, in terms of MSE and total test plan cost,

is determined for a hypothetical series system using the

methodology described in the previous section. The hypothetical system is an analogue for a system composed of five

independent subsystems that are testable. Let the reliabilities of the five subsystems be as defined in Table 1. Given no

modeling error, the full system reliability is 0.904, and performing a set of subsystem tests (a set of subsystem tests

consists of one test for each subsystem) reduces the MSE

(estimation uncertainty) more than a full system test (see

Section 3).

Table 1: Subsystem reliabilities for a hypothetical

system with five subsystems in series.

Reliability

Subsystem 1 0.990

Subsystem 2 0.985

Subsystem 3 0.980

Subsystem 4 0.975

Subsystem 5 0.970

The methodology described in the previous section allows

a test planner to assume that the system reliability model

may be incorrect (the function that relates the subsystem

reliabilities to the full system reliability is incorrect). Among

other reasons, model error may arise because some of the

subsystems are dependent or because a component is left out

of the subsystem definitions or test plan. The methodology

allows the model error to contribute a bias to the MSE of the

general maximum likelihood estimator based on the number

of full system/subsystems tests. Loosely, full system tests

contribute unbiased information to the general maximum

likelihood estimator. Thus, as the number of full system

tests increases relative to the number of sets of subsystem

tests, the model error contributes less to the bias term of

the MSE.

The contours of the MSE for the general maximum likelihood estimator of system reliability are computed for the

hypothetical system from Eqn. (34) given no model error

and given three different non-zero model errors: ε = −0.025,

ε = −0.050, and ε = −0.075. (Hence, the maximum errors

range from approximately 2.8 to 8.3 percent of the true reliability.) The contours are plotted in Figure 1. The X-axes

of the plots is the number of sets of subsystem tests, and

the Y-axes of the plots is the number of full system tests.

Each contour is a Pareto frontier for achieving the specified MSE, given the modeling error, in terms of the number

of full system tests and sets of subsystem tests. The subfigure for the case with no model error indicates that a set of

subsystem tests reduces the MSE of the general MLE more

than a full system test (although the difference in the reduction is small indicating that a set of subsystem test is worth

about the same as a full system test in terms of reducing

MSE). The sub-figures for the nonzero negative model errors indicate that the model for system reliability (a series

0.003

0.003

0.003

0.004

0.004

0.004

0.005

0.005

0.005

0.005

0.006

0.006

0.006

5 10 15 20 25 30

5

10

15

20

25

30

Number of Sets of Subsystem Tests 

Number of Full System Tests 

MSE for  = -0.050 

0.003

0.003

0.003

0.003

0.004

0.004

0.004

0.005

0.005

0.006

0.006

5 10 15 20 25 30

5

10

15

20

25

30

0.003

0.003

0.003

0.003

0.004

0.004

0.004

0.005

0.005

0.006

0.006

5 10 15 20 25 30

5

10

15

20

25

30

Number of Full System Tests 

Number of Sets of Subsystem Tests 

MSE for  = 0.000 

(No Model Error) 

Number of Sets of Subsystem Tests 

Number of Full System Tests 

MSE for  = -0.025 

0.003

0.003

0.003

0.004

0.004 0.004

0.005

0.005 0.005 0.006

0.006 0.006

5 10 15 20 25 30

5

10

15

20

25

30

Number of Full System Tests 

Number of Sets of Subsystem Tests 

MSE for  = -0.075 

Figure 1: The contours of the MSE are computed

for the hypothetical system, given no model error

and given three different model errors: ε = −0.025,

ε = −0.050, and ε = −0.075.

model) produces overly confident estimates of system reliability when subsystem tests are predominant. The MSE

87
values are penalized by the square of the assumed modeling

error when full system tests are not performed and less when

some full system tests are performed. Assuming the model

error ε = −0.025, ε = −0.050, or ε = −0.075, indicates that

performing a full system test reduces the MSE more than a

set of subsystem tests.

The design of a test plan should also account for the cost

of the tests. To achieve an MSE of 0.005 or less (root mean

squared error 0.07 or less) many different test plans can be

devised. The total cost of the test plan depends on the

number of each type of test: subsystem and full system. To

illustrate the effect of cost on the test plan design, assume

that a full system test of the hypothetical system (described

in the previous paragraph) costs twice as much as a set of

subsystem tests. If the modeling error is ε = −0.025, then

the optimal test plan, in terms of cost, is to always perform

sets of subsystem tests. However, if the modeling error is

ε = −0.050 or ε = −0.075, then the optimal test plan is a

mix of full system and sets of subsystem tests.

The cost benefit of performing a mixture of full system

and subsystem test is is depicted in Figure 2. Four test

plans are listed, each provide a MSE of 0.005, given a model

error of ε = −0.050. The maximum cost test plan consists

of performing only full system tests. The other three test

plans, which consist of a mixture of full system and subsystem tests, are less costly (given that a set of subsystem

tests is half the cost of a full system test). The potential

cost reduction from performing one of these three test plans

instead of performing only full system tests is plotted as a

percentage in Figure 2. For ε = −0.050, the minimum cost 	
 

"


	


$

'/

*%,/

()%+/

((%,/






 


!

0.005 


 = 0.050 

Figure 2: The potential cost reduction from performing a mixture of full system and subsystem tests

instead of performing only full system tests.

test plan consists of nine sets of subsystem tests and ten full

system tests. Several other test plans have the same total

cost, for example, nine full system and eleven sets of subsystem tests or eight full system tests and thirteen sets of

subsystem tests. However, if modeling error is a concern,

then it is optimal to perform the maximum number of full

system tests that can be performed while achieving the desired MSE for the least cost. The contours of the MSE for

ε = −0.075 betray the optimal test plan in terms of cost for

achieving a MSE of 0.005 (see Figure 1). The contour of

0.005 is almost level after a few sets of subsystem tests, and

so, the optimal test plan in terms of cost is to perform fifteen

system level tests and two sets of subsystem tests.

6. CONCLUDING REMARKS

The main purpose of this paper was to develop a method

for including the effect of modeling error in the MSE of a

general MLE for system reliability. This was accomplished

by decomposing the MSE of the MLE into the variance of

the MLE and a bias from incorrectly specifying the model

for the system reliability. The variance of the MLE was

approximated with the Fisher Information. The bias was

approximated by computing the sensitively of the system

reliability MLE to a maximum modeling error, given a proposed test plan consisting of a mixture of full system and

subsystem tests. The method showed that the bias penalty

in the MSE diminished as the number of full system tests

increased relative to the number of sets of subsystem tests

and that the square of the model error was the bias term in

the MSE when full system tests were not performed. The

method enables optimum test plans to be developed for system reliability estimation involving trade-offs between the

MSE (estimation accuracy), the degree of modeling error,

and the cost of doing system and subsystem tests.

References

[1] T. M. Apostol. Mathematical Analysis. AddisonWesley, Reading, MA, second edition, 1974.

[2] Y. Y. Haimes. Risk Modeling, Assessment, and Management. Wiley Interscience, New York, 1998.

[3] M. Henrion and M. G. Morgan. Uncertainty: A Guide

to Dealing with Uncertianty in Quantitative Risk and

Policy Analysis. Cambridge University Press, New

York, 1990.

[4] R. Krzysztofowicz and K. S. Kelly. Hydrologic uncertainty processor for probabilistic river stage forecasting.

Water Resources Research, 36(11):3265–3277, 2000.

[5] L. Ljung. System Identification Theory for the User.

Prentice Hall, Uper Saddle River, NJ, second edition,

1999.

[6] C. J. Maranzano and R. Krzysztofowicz. Bayesian reanalysis of the Challenger O-ring data. Risk Analysis,

28(4):1053–1067, 2008.

[7] C. R. Rao. Linear Statistical Inference and Its Applications. Wiley Interscience, New York, second edition,

1973.

[8] J. C. Spall. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. Wiley

Interscience, Hoboken, New Jersey, 2003.

[9] J. C. Spall. System reliability estimation and confidence

regions from subsystem and full system tests. In Proceedings of the American Control Conference, number

FrB14.1, St. Louis, MO, June 2009.

[10] W. F. Trench and B. Kolman. Multivariable Calculus

with Linear Algebra and Series. Academic, New York,

1972.

[11] R. Zhang and S. Mahadevan. Model uncertainty

and Bayesian updating in reliability-based inspection.

Structural Safety, 22:145–160, 2000.

88
Unmanned and Autonomous Systems Mission 

Based Test and Evaluation 

Philipp A. Djang, Ph.D. 

Army Research Labs 

Survivability/Lethality Analysis Directorate 

Information & Electronic Protection Division 

Attn: AMSRL-ARL-SL-EA 

White Sands Missile Range, NM 88002 

575-678-1550, philipp.djang@us.army.mil

Frank Lopez 

Army Research Labs 

Survivability/Lethality Analysis Directorate 

Information & Electronic Protection Division 

Attn: AMSRL-ARL-SL-EA 

White Sands Missile Range, NM 88002 

575-678-8316, frank.lopez@us.army.mil 

ABSTRACT 

We propose to apply principles from the Army 

Evaluation Center’s Mission Based Test and 

Evaluation (MBT&E) to Unmanned and Autonomous 

Systems (UAS) Test and Evaluation (T&E) in order to 

conduct rigorous, real-world testing based on 

anticipated military missions. In order to understand 

MBT&E, we introduce and describe its parent, the 

Mission and Means Framework. Finally, we describe a 

vignette that incorporates autonomous systems in the 

context of a mission to illustrate these principles. 

Categories and Subject Descriptors 

General Terms

Management, Performance, Design, Experimentation, 

Standardization, Languages, Verification 

Key Words 

Mission and Means Framework, Mission Based Test 

and Evaluation, Measures of Performance, Measures 

of Effectiveness, Simulation Based Test and 

Evaluation, Unmanned and Autonomous System Test 

and Evaluation, Capability Based Evaluation 

1. Mission and Means Framework 

The two – sided missions and means 

framework provides a structured way to describe key 

elements of military operations that are essential to 

understand in order to successfully model and 

simulate those operations. The framework provides 

the necessary structure to support a disciplined, 

repeatable procedure to explicitly specify the mission 

and assess mission accomplishment [1]. 

The framework consists of seven levels and 

four operators that describe a military mission. Cast 

within a context and environment, each side executes 

a mission to achieve a specific purpose. A mission is 

decomposed into tasks which are the building blocks 

and based on authoritative sources 

such as the Uniform Joint Task List (UJTL) which are 

commonly accepted terminology and definitions. task 

lists such as the UJTL, etc. are deliberately designed 

to facilitate the ability to associate mission specific 

conditions and standards[2]. 

Figure 1: Mission Means Framework 

Conditions and standards for specific tasks 

are established based war gaming and course of action 

(COA) mission planning. The same task may be 

iterated several times with different sets of conditions 

and standards based on when and where the task 

iteration is to occur within the concept of operations. 

The execution of the task (operations) may be 

structured to provide quantitative metrics in the form 

of Measures of Performance (MoP) which describe 

minimum acceptable levels of performance in terms of 

time, distance, accuracy, etc. Standards may also be 

structured to provide more qualitative metrics in the 

form of Measures of Effectiveness (MoE) which 

describe the desired end state or purpose of the task as 

specified by the commander in his intent statement. 

Functions and capabilities are based on physical 

systems (and systems of systems) and their 

components. When a physical system is employed in 

the context of a task, the degree to which the task can 

be successfully executed depends upon the capability 

and functionality afforded by the system. Components 

(the building blocks of systems) and forces (human 

and robotic) are directly affected by interactions and 

effects (kinetic, electromagnetic, etc.) of conflict. 

This paper is authored by employees of the United 

States Government and is in the public domain. 

PerMIS’09, September 21-23, Gaithersburg, MD, 

USA, ACM 978-60558-747-9/09/09 

89
The framework employs four operators to 

describe the dynamics of military planning and 

decision making as both the functions and capabilities 

of components and forces are affected by conflict. 

The blue operators (arrows) represent military 

planning and re-planning based on available 

components and forces. The red operators describe 

feedback dynamics of the conflict. 

2. Mission Based Test & Evaluation 

(MBT&E) 

In 2008, the Army Evaluation Center 

adopted and customized the Mission Means 

Framework as a touchstone for conducting testing. 

MBT&E [3] is a methodology that focuses T&E on 

the capabilities provided to the war fighter. MBT&E 

links the mission and associated tactics, tasks and 

performance standards with the capabilities of the 

system under test. The Center shifted their focus from 

component testing (meets a standard) to answer a 

more challenging and relevant question: “Does the 

system provide what the war fighter needs to 

accomplish the mission?” with respect to the test 

domain (performance, quality, reliability, etc). 

MBT&E provides a framework, procedure and 

complexity constraint strategies to 1) link capabilities 

to the attributes of the materiel system-of-systems, 2) 

develop evaluation measures that assess capabilities 

and attributes and 3) and link the evaluation measures 

to all available data sources. 

MBT&E is focuses on a key concept: 

capability – which is defined as the ability to achieve a 

desired effect or result, outcome, or consequence of a 

task under specified standards and conditions, through 

a combination of means and ways to perform a set of 

tasks. Capabilities are the building blocks for this 

evaluation strategy. 

Figure 2: MBT&W Capability 

System performance is measured by systems 

engineering metrics are integrated to determine system 

performance functionality in the context of doctrine, 

tactics, leadership and policies. They are integrated 

into system-of-system task performance and compared 

against MOPs and MOEs. 

MBT&E specifies five major purpose areas 

with a total of 19 steps. The five major purpose areas 

are: 1) understand the mission, 2) understand the 

system, 3) design the test & evaluation, 4) determine 

the results and 5) report the results. MBT&E is an 

evolving process and a number of innovative proof-ofprinciple pilot studies are underway. 

3. Application of MBT&E to UAS 

For the purpose of modeling mission – task 

– function – component decomposition, we present the 

following vignette which was a developed as part of a 

larger scenario. This vignette was created with a 

minimalist intent to allow researchers to isolate the 

impact of component failure upon functions, task 

performance and mission accomplishment. 

Per the MBT&E guidelines, we specify the 

following environmental context: The area of 

operation is Southwest Asia (desert environment); the 

context for the scenario is that the national 

government has been overthrown and a combination 

of terrorists and militants will take possession of a 

nuclear weapons facility. The mission for blue forces 

are to overcome local security forces if required, 

emplace failsafe devices on the nuclear weapons and 

secure the facility for follow-on forces. For the 

vignette, the mission of the blue force is to conduct a 

reconnaissance by fire. The mission of the red force is 

to set up a hasty defense and prevent blue forces from 

traversing the road. The force composition of this 

vignette is simple: two red tanks versus a blue 

autonomous aerial surveillance drone (UAV) and an 

autonomous armed ground robot (UGV). 

Our mission-task decomposition follows 

standard doctrine until autonomous system tasks are 

assigned. Autonomous system tasks do not currently 

exist and were adapted from currently existing tasks. 

In particular, our UAS tasks incorporate surveillance, 

the ability to infer enemy intent and re-plan a 

navigation course that will take advantage of terrain 

features to provide tactical surprise. Our mission to 

task relationship: 

• “SN 3 Employ Forces”: represents the 

decision and action taken at the national 

level to use the military element of national 

power in response to a crisis caused by the 

actions of an external opposing faction. 

• “ST 1.3.4 Integrate Direct Action in 

Theater”: represents the planning and 

coordination actions taken by the geographic 

combatant commander to secure the nuclear 

warheads. 

• “OP 1.2.4.7 Conduct Direct Action in the 

Joint Operations Area (JOA)”: represents 

the planning and execution actions being 

taken by the Joint Task Force Commander 

responsible for the JOA, to neutralize 

opposing forces in order to secure the 

nuclear warheads. 

• “ART 8.1.2 Conduct an Attack”: represents 

the mission given to the blue forces 

(humans, traditional systems and robotic 

systems) to support the Joint Task Force’s 

action to neutralize opposing forces. The 

purpose of the attack is to destroy opposing 

forces and occupy positions on key terrain in 

Conditions Standards

Capability 

Task Desired 

Means 

Organization (forces, units), Training, 

Materiel (equipment functions &

Ways 

Doctrine (tactics, techniques and 

procedures), Leadership and 

Education concepts and policies

Higher Level Task/Action 

or

Enable

90
order to prevent those forces from capturing 

the nuclear warheads. 

• “UAS 2.3 Perform Intelligence, Surveillance 

and Reconnaissance”; represents the activity 

performed by unmanned sensors (i.e. 

UAV’s) to monitor, detect, identify and 

report enemy activity in areas of interest. 

• “UAS 2.3.1 Provide aerial tactical 

intelligence overwatch”: represents activity 

performed by a UAV to detect, 

communicate enemy location and movement 

in areas of interest. 

• “UAS 3.3 Employ Fires”: represents the 

means by which the robotic equipped 

company intends to engage opposing faction 

forces who might interfere with their 

maneuver to and occupation of the key 

terrain. 

• “UAS 3.3.3.1 Conduct route 

reconnaissance”: by robotic assets based on 

terrain database movement and tactical 

intelligence 

• “UAS 3.3.5.1 Exploit terrain to expedite 

tactical movements”: – based on mobility 

constraints, enemy location and terrain 

features, compute optimal path 

• “UAS 3.3.8 Conduct Lethal Direct Fire 

Attack”: – apply direct fire to neutralize 

identified enemy. 

We adapted the UAS specific tasks from FM 7-

15 Army Universal Task List for both the UAV and 

the UGV. The UAV is responsible for UAS 2.3 

Perform Intelligence, Surveillance and 

Reconnaissance and it’s subordinate task: UAS 2.3.1 

Provide tactical intelligence over watch. The tasks 

assigned to the UGV are UAS 3.3.3.1 Conduct route 

reconnaissance, UAS 3.3.5.1 Exploit terrain to 

expedite tactical movements and UAS 3.3.8 Conduct 

Lethal Direct Fire Attack. Inferring enemy intent is a 

joint intelligence task UAS 2.1 Collaborative 

Situational Decision Making. 

During the initial phase of the operation, the 

UAV performs tasks UAS 2.3 Perform Intelligence, 

Surveillance and Reconnaissance and subordinate task 

UAS 2.3.1 Provide aerial tactical intelligence over 

watch. During the execution of this task, the UAV 

identifies two enemy tanks that are located on the 

road. This information is communicated to the UGV. 

The UAV determines that the enemy tanks are in a 

fortified position and is blocking the route. The UAV 

infers that the mission of the enemy is to prevent 

friendly vehicles from passing their fortified position. 

Figure 3: Vignette – UAV performs aerial tactical 

intelligence over watch

Once the enemy’s intent is determined and 

passed to the UGV, the UGV performs UAS 3.3.3.1 

conduct route reconnaissance and determines that the 

original course of action – to follow the road – is no 

longer viable. The UGV switches to task UAS 3.3.5.1 

Exploit terrain to expedite tactical movements and recomputes a new course of action based on the terrain 

elevation and mobility. The UGV computes an 

alternative route dynamically by evaluating off-road 

mobility and selecting terrain features to mask 

movement in order to maximize the element of 

surprise. 

Figure 4: Vignette – UGV exploits terrain to 

expedite tactical movement 

 The new route exploits the hilly nature of 

the terrain. The UGV computes an off road route that 

requires more fuel expenditure because of decreased 

mobility associated with sand but provides a tactical 

advantage. The enemy forces expect the main attack 

from the direction of the road. Once the UGV crests 

the hill, task UAS 3.3.8 Conduct Lethal Direct Fire 

Attack is executed and the enemy tanks are destroyed. 

91
Figure 5: UGV Conducts Lethal Direct Fire Attack 

4. Component Failure and Loss of 

Functionality 

While the outcome of the vignette favors the blue 

side, the value of mission based testing is 

illustrated when a component or components fail 

due to stress or are destroyed during ballistic 

interactions. 

 We examine a key subsystem of the weapon 

system that the UGV will use to destroy the 

opposing enemy tanks. This subsystem is critical 

to accomplishing UAS task 3.3.8 Conduct Lethal 

Direct Fire. 

 The projectile tracking sub-system of the 

UGV weapon system is an electromechanical 

system that is composed of multiple components 

and subordinate subsystems. The purpose of the 

projectile tracking sub-system is to measure the 

ballistic trajectory of the projectile (bullet) and 

determine if the projectile reached a desired aim 

point. The components of the projectile tracking 

system (PTS) shown in figure 6 are: 

Figure 6: Projectile tracking system (PTS)

There are three parallel sensors connected to four 

components. The integrated circuit unit (ICU 1) 

computes the ballistic trajectory and determines if the 

system has achieved the desired aim point. While one 

sensor may fail, the other two sensors can provide the 

required information. But suppose one of the 

components in the serial chain fails before the ICU 

can process this information? Clearly, the system will 

not be able to accurately track the ballistic trajectory 

of the projectile. 

 Failure of one of the components in the PTS 

is critical because for the UGV, the PTS provides 

critical feedback to the UGV primary computing 

system. The relationship between the PTS and other 

subsystems is shown in Figure 7. 

The PTS is linked to the vehicle mobility 

subsystems: drive sprocket, tracks, left and right 

traction motors and backup braking. Because the PTS 

is linked to the mobility subsystems, it failure will 

prevent the UGV from correctly adjusting it position 

so that future projectiles can be accurately aimed 

Figure 7: PTS and Mobility Subsystems 

 . 

 If the PTS failed, the UGV would be unable 

to accomplish UAS task 3.3.8 Conduct Lethal Direct 

Fire. In turn, the enemy tanks in their reinforced 

blocking position on the only road would prevent blue 

force access. Denying access to the road would 

degrade or delay the ability of the blue forces to 

accomplish a higher order task: ART 8.1.2 Conduct an 

Attack. 

 From a graph theoretic viewpoint, the 

relationship between the mission, tasks, functions or 

capabilities and components or forces can be viewed 

as a rooted tree. The mission is of course the root of 

the tree with branches for each task, as well as for 

each function or capability and with terminal leaves as 

components or forces. The role of Mission Based Test 

and Evaluation is to determine the impact of 

component, subsystem and subsystem failures and 

map these failures to this directed graph in order to 

find a minimum cut set that prevents the successful 

accomplish of the mission. Identifying the maximum 

likelihood of such cut sets could lead to more robust 

mission based metrics. 

5. Conclusion 

The Mission and Means Framework is 

responsible for stimulating innovative concepts and 

PTS transmitter

Rcvr 1 

PTS 

Rcvr 2 Rcvr 3

Low-voltage power

ICU 1 

Turret wire 2 

PTS exciter

Projectile tracking 

Either drive sprocket 

Either track

Left traction motor 

Right traction motor

Backup braking 

92
applications across the Department of Defense. One of 

these applications has been MBT&E. 

MBT&E is a new philosophy developed by the 

Army Evaluation Center for testing the efficacy of 

new systems within a mission context. The purpose of 

MBT&E is to answer the question: “Does the system 

provide what the war fighter needs to accomplish the 

mission?” By refocusing the purpose of test and 

evaluation on the needs of the war fighter, test and 

evaluation gains relevance to real world conflict and 

increases confidence that newly fielded systems can 

perform in operational environments. 

We propose to apply MBT&E concepts to testing 

unmanned and autonomous systems in order to 

demonstrate their relevance to the warfighter. By 

integrating postulated tasks to notional components, 

critical mission essential tasks, functions and 

components can be identified for hardening and 

reinforcement. 

6. REFERENCES 

[1] Sheehan, J., Deitz, P., Bray, B., Harris, B., Wong, 

A. The Military Missions and Means Framework, 

Interservice/Industry Training, Simulation and 

Education Conference, 2003. 

[2] Cavaleri, Zehr,Minchew, Kearley, and Smits, 

Testing in a Joint Environment: A Mission and 

Means Framework Application Case Study, 

presentation to Office of Secretary of Defense, 

2003. 

[3] Wilcox, Christopher, Mission Based Test and 

Evaluation Tutorial, 25th Annual National Defense 

Industrial Association, Test and Evaluation 

Conference, March 2009. 

93
Modeling and Simulation for Unmanned and Autonomous

System Test and Evaluation

Mauricio Castillo-Effen

General Electric Global

Research

1 Research Circle

Niskayuna, NY 12309, USA

Castillo-Effen@GE.com

Nikita Visnevski

General Electric Global

Research

1 Research Circle

Niskayuna, NY 12309, USA

Nikita.Visnevski@GE.com

Raj Subbu

General Electric Global

Research

1 Research Circle

Niskayuna, NY 12309, USA

Subbu@GE.com

ABSTRACT

Test and evaluation may be viewed as a technology enabler for the successful deployment of unmanned vehicles

and robots in all their envisioned applications. It is however

a challenging endeavor, considering that roboticists and developers are not used to thinking of test and evaluation as

an integral component of robot development. Moreover, the

community who has conducted test and evaluation up to

this date does not possess the tools to cope with the growing complexity of unmanned and autonomous systems. This

paper proposes a solution to one of the hardest problems in

testing and evaluation of robots: test planning. The approach put forward relies on constructive simulation tools

and on new techniques for searching in high dimensional

spaces. The goal of the test planner is to generate a set

of tests that make highly efficient use of resources to unveil

weaknesses of the system under test. A secondary objective

of the paper is to create reciprocal awareness between test

and evaluation and robotics communities, who could benefit

significantly from each other.

Keywords

Robotics, Unmanned and Autonomous Systems, Modeling

and Simulation, Test and Evaluation

1. INTRODUCTION

Several technology roadmaps that have been published lately,

e.g.: [8], [14], [15], etc. foresee a more predominant role of

robotics1in several aspects of human society in the coming

years. Furthermore, most of these reports agree on the fact

that robotics represents a significant and growing commercial market. The main goal of these roadmaps is to guide

policy makers and to focus research efforts. However, it is

1The term “robots” in the context of this paper refers to

a variety of autonomous agents with the ability to interact

with their environment, with humans, and eventually with

other robots.

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage, and that copies

bear this notice and the full citation on the first page. To copy otherwise, to

republish, to post on servers or to redistribute to lists, requires prior specific

permission and/or a fee.

PerMIS’09, September 21-23, 2009, Gaithersburg, MD, USA.

Copyright 2009 ACM 978-1-60558-747-9/09/09...$10.00

almost perplexing, how key technologies and technology enablers are easily overlooked. The “challenges”in autonomous

driving sponsored by DARPA have been regarded as major

successes. However, it is unclear when and how these technologies will be used in common day driving. According to

the Computing Community Conssortium’s (CCC) robotics

roadmap [8], within the next 5-year time frame, autonomous

vehicles will demonstrate driving safety “comparable to a human driver.” The authors cautiously add: “with clearly lit

and marked roads.” This exemplifies the gaps and ambiguities that plague robotics. First, to the best of knowledge of

the authors, a well-defined methodology that will allow for

assessing “driving safety comparable to a human driver” in

autonomous systems is nonexistent today. Furthermore, the

disclaimer specifying the conditions under which the system

will work, creates ambiguity that confounds someone who

may want to acquire a vehicle with autonomous driving capability.

The example above illustrates well current challenges that

stand before acquisition organizations procuring Unmanned

and Autonomous Systems (UAS) for the government, particularly in the military domain. On the one hand, UAS are

becoming key assets in modern military operations. There

is significant pull from the warfighter requiring technology

that provides relief from “dangerous, dirty, and dull” tasks.

On the other hand, developers are trying to satisfy those

needs with unmanned vehicles that are increasingly capable and sophisticated. Between these two parties stands the

Defense Acquisition System with the Test and Evaluation

(T&E) community supporting it. They have the mission

of expediting the transfer of those technologies to the field,

while making sure that the acquired assets fulfill the needs

of the end-user. Higher levels of autonomy, emergent behavior, heterogeneous forms and evolving levels of cognition are

some of the new challenges that need to be tackled by the

T&E community when dealing with UAS. These are all new

challenges for a community who had established procedures,

facilities and methodologies for testing against well-defined

and very specific requirements. The paper by Macias expounds the problem of UAS T&E (UAST) in full length [9].

Limitations of current T&E practices do not nullify its validity. T&E represents the feedback element that provides

knowledge for timely risk mitigation during the development

cycle of any system [6]. Hence, the development of robots

could benefit from such knowledge. The authors of this pa94
per argue that T&E represents a key, highly underrated, and

neglected technology enabler. Particularly, in robotics, there

is the need for metrics to quantify robot capabilities and effectiveness. Then, complementary to metrics, there is the

need for principled approaches to the generation of procedures altogether with facilities and personnel requirements

for Test and Evaluation of robotic capabilities. Improved

T&E capability will be followed by a smoother transition

of robots from the developers’ laboratories to the field. As

a consequence, society will start benefitting from robotics

technology much sooner, rather than waiting for its maturation, or worse, being afraid of it.

Recently, a growing tendency has emerged promoting the use

of modeling and simulation (M&S) to support the development of robot software. Tools such as Microsoft Robotics

Developer Studio, Player/Stage/Gazebo, Webots, USARSim, etc. provide simulated environments where developers

test their control algorithms as they are developed. The advantages of this approach to robot software development are

evident, among them:

• There is no risk that the robot will cause damage to

people, to its environment, or to itself.

• This approach is highly efficient in terms of time and

resources.

• Minimal requirements in terms of facilities and test

equipment.

• Code can be used with almost no modifications in the

real platform.

Simulations consist of experiments created by the developers

to verify proper function of their code. However, a visible

deficiency of these tools is the lack of a connection to concepts from dependable computing, verification and validation, formal methods, etc. These are all key aspects to consider when robots are viewed as safety critical systems. The

military community has gathered significant experience and

knowledge in these areas, which could be assimilated by the

robotics community in order to generate more systematical

approaches to the development of robots. Conversely, the

military community may benefit from the knowledge stemming from the robotics community, because roboticists are

the ones who advance technology underlying unmanned and

autonomous vehicles used in the battlefield.

Test planning is one of the key challenges that need to be addressed by the UAST and robotics communities. More “intelligent” test planning capability may lead to a more agile

T&E process and to a more efficient utilization of resources.

The main goal of this paper is to introduce an architecture

that leverages upon M&S for automated test planning. This

confers its users the ability to use M&S tools to fully understand the main factors that affect the measures of effectiveness and to identify the most relevant tests which may be

used in different phases of the T&E process.

Section 2 introduces important terminology, concepts and

principles related to M&S and T&E. Section 3 addresses the

need for metrics, and it presents the mission-based capabilitydriven T&E and the mission and means framework as useful

tools for finding metrics and measures of effectiveness. Section 4 elaborates on the process of test planning in general.

Section 3 presents a concrete instance of an architecture for

automated test planning with its major components. Section 6 explains the main rationale guiding the decisions to

select an appropriate modeling and simulation tool. Finally,

Section 7 summarizes the main ideas introduced in this paper, and it lists a series of conclusions.

2. MODELING AND SIMULATION IN UAST

One of the objectives of this section is to establish a clear

distinction between the concepts of modeling and simulation and test and evaluation, as well as their relationships.

A number of definitions are introduced in this section to establish proper terminology usage and the conceptual background underlying the work presented in this paper. These

definitions have been extracted textually or adapted from [6], [5],

and [4].

Test and Evaluation (T&E). “Process by which a system or components are exercised and results analyzed

to provide performance-related information. T&E enables an assessment of the attainment of technical performance, specifications, and system maturity to determine whether systems are operationally effective,

suitable and survivable for intended use, and/or lethal.”

Developmental T&E (DT&E). Encompasses all T&E activities that take place while the system is still being

developed.

Operational T&E (OT&E). It “is conducted to evaluate

system operational effectiveness, suitability and survivability in support of the full-rate production decision review.”

Modeling and Simulation (M&S). The processes by which

simplified representations of reality (models) are used

to predict how systems might perform or survive under

various conditions or environments.

Live Simulation. “A simulation involving real people operating real systems.” This definition assumes that

“people operate systems,” which is not general enough

to accommodate autonomous systems. This category

could consider mock operations where real autonomous

systems are used. This type of simulation is the most

demanding in terms of resources, range safety, instrumentation, etc. Although the simulation results may

be considered realistic because they involve the actual

physical system, the simulated operations are designed

with many constraints. As stated by a test pilot: “in

live exercises we simulate; in simulation, we actually

do things.”

Virtual Simulation. “A simulation involving real people

operating simulated systems.” For instance, this category applies to different forms of flight simulators operated by real pilots for training purposes. Extending

this definition to the autonomous domain, it could be

interpreted as a mode of operation where autonomous

systems operate in a virtual world, that is, a form

95
of hardware-in-the-loop simulation. Here, the control

computer of a robot could be interfaced to a virtual

world. The robot gets synthetic stimuli from the simulated environment. Conversely, signals generated by

the robot controller affect the simulated environment.

Constructive Simulation. “Models and simulations that

involve simulated people operating simulated systems.

Real people stimulate (make inputs) to such simulations, but are not involved in determining the outcomes.” In the autonomous case, complete systems

are simulated in the virtual environment. This is the

main simulation type addressed in this paper.

Accreditation. “The official certification that a model, simulation, or federation of models and simulations and

its associated data are acceptable for use for a specific

purpose.”

Verification. “The process of determining that a model implementation and its associated data accurately represents the developer’s conceptual description and specifications.”

Validation. “The process of determining the degree to which

a model and its associated data are an accurate representation of the real world from the perspective of the

intended uses of the model.”

VV&A. Verification, validation and accreditation. All three

are necessary for models to have relevance if used in

the context of T&E.

The Defense Acquisition Guidebook (DAG) [6] acknowledges

the value of M&S in T&E recognizing it as an “essential

tool in achieving both: an effective and efficient T&E program.” The DAG also points to the pitfalls, particularly

to the limitations of M&S when dealing with systems that

are not understood well. The DAG recommends a philosophy of interaction between T&E and M&S. According to

this philosophy, M&S provides predictions of system performance, effectiveness, suitability, and survivability. On the

other hand, T&E provides empirical data to confirm those

predictions. Empirical data is used to refine the models.

Then, the cycle is repeated, as depicted in Figure 1. The

DAG also highlights the need for accreditation of all all M&S

by the intended user. Accreditation can only be achieved

through a robust verification, validation, and accreditation

(VV&A) process.

The main conclusion that may be drawn from previous analysis is that M&S and T&E are two different complementary concepts. Pure physical T&E does not lead to efficient

use of resources. On the other hand, pure M&S lacks relevance. Models can be validated and accredited through

T&E. Hence, there is a continuum of simulation categories

between purely physical (live simulation) and purely virtual

(constructive simulation), with different “shades” of mixed

categories (virtual simulation) in between. T&E is the enabler that allows shifting focus from live simulations which

are most demanding in terms of resources towards constructive simulations. The most complex tests at the “Systems of

Systems” (SoS) level are basically impossible to be executed

Models

Simulation

New Tests T&E

Emprical Data

Predictions

M&S

+

-

Figure 1: Model-test-fix-model philosophy [6]

as live simulations. However, complex constructive simulations can only be validated if smaller subsystems have been

validated and accredited through T&E.

While useful, the DAG recommendations and concepts are

not specific and practical enough to answer key questions

that arise in UAST and robotics in general. Some of them

are: how to design tests?, how to analyze empirical data?,

finding appropriate metrics, etc. This paper proposes practical alternatives to fill some of those gaps.

3. MISSION-BASED CAPABILITY-DRIVEN

UAST

The main question addressed in this section is the one pertaining to metrics. Real-life cases have demonstrated that

the traditional approach to T&E based on the verification of

performance requirements do not work properly for complex

systems and that a paradigm shift is necessary. An example that is often cited to support this notion is the Predator

MQ-1 UAS, which failed operational T&E but proved extremely useful on the battlefield [9]. The case of the Predator proves that metrics for complex systems need to be tied

to measures of effectiveness (MoE) and not necessarily to

measures of performance (MoP). Frameworks such as the

Mission and Means framework (M&M) can help in establishing a hierarchical relationship between mission effectiveness,

tasks, capabilities and system components [7]. By using this

framework it is also possible to trace back mission success or

failure to specific tasks, to capabilities, and to components.

The RoboCup Virtual Rescue competition represents a good

test-case, where mission-based capability driven UAST could

be applied [1]. Although the scenario is simulated, metrics such as number of victims found within certain time or

energy constraints are related directly to measures of effectiveness. The mission is composed of a number of tasks such

as: exploring, searching for victims, reporting victims, etc.

Similarly, tasks may be performed only if certain capabilities

are present, for instance: an appropriate locomotion system,

collaborative search capability, localization, navigation, etc.

Another specific example illustrating the application of the

M&M framework is presented in [7].

4. TEST PLANNING

It was mentioned before that the block “New Tests” in Figure 1 is not elucidated fully in [6]. The process of devis96
ing tests, called test planning, is one of the main challenges

of UAST and robotics in general. Traditional approaches

based on knowledge and experience are riddled with limitations, particularly, considering the degree complexity of

UAS. To guarantee that tests are truly relevant and efficient,

a formal approach to test planning is necessary. Statistical

techniques such as Design of Experiment (DoE) have been

already proposed as viable tools [3]. The authors adhere

to that notion and to the view of T&E of robots as a DoE

problem. The main objective is to obtain a minimal set

of experiments which yields the maximum information with

respect to the hypothesis that need to be tested. To maximize the efficiency of physical tests in terms of time and

resources, test planning is crucial. The only way to avoid

the curse of dimensionality in designing experiments with

a large number of independent variables is by including as

much knowledge of the process as possible. This is where

modeling and simulation is most valuable. M&S can be seen

as the main vehicle for incorporating knowledge about the

system.

M&S shall be used as a tool for the generation of a set of experiments. Each experiment corresponds to a specific selection of independent variables Xi. Two kinds of independent

variables may be distinguished:

Intrinsic variables This refers to parameters of the UAS

or team of UAS. Some examples of this type of variables are:

• Physical properties of the UAS or UAS team,

such as: sensor accuracy, turning radius, actuator power, number of team members, maximum

speed, etc.

• Behavioral properties and their parameters, such

as: localization algorithm, software architecture

(reactive, deliberative, hierarchical, etc.), navigation algorithm, world model representation, etc.

Extrinsic variables These are all external factors that affect UAS behavior including initial conditions. Some

examples of external factors are temperature, humidity, wind, lighting conditions, initial state (e.g.: position), terrain type, obstacles, etc.

As seen from the examples, independent variables may be

continuous or discrete. The outcome of an experiment may

be measured through metrics Mj (E), which correspond to

measures of effectiveness, as explained previously. Given

the stochastic nature of the problem, dependent variables

are actually probabilities of the form P[Mj (E) > MTh],

where MTh is a threshold value used to measure mission

failure (or success). An experiment Ek may be considered

an n-tuple, which is a combination of independent variables

Xi

Ek ≡ hX1, X2, . . . , XN i; Ek ∈ R

N (1)

Hence, the goal of the test planner is to search in the Ndimensional space of experiments for those experiments that

E∗

k

Search Space: Ek ∈ RN

E∗

k

(i)

P[M(E∗

k

(i)) > MTh]

Figure 2: Search space and sought set of experiments

have a specific probability with respect to a metric M(E).

The search could be also extended to consider multiple simultaneous metrics. The search is conducted with the help

of a simulation engine, which has encoded a number of rules

of interaction among entities and between entities and the

environment, both defined by models. Thus, the simulation

engine is used to determine the specific outcomes M(Ek).

The actual determination of the sought experiments E

∗

k is

done through a search technique S, which determines the set

of experiments fulfilling the conditions corresponding to the

probability of a specific metric having a concrete threshold

value.

E

∗

k = {Ek : P[M(Ek) > MTh]} (2)

The search processes may be better understood through Figure 2. The rectangle represents the whole search space. Gray

zones surrounded by dotted lines represent the sought set

of experiments E

∗

k. The other continuous lines represent

boundaries of zones where the probability for a particular

value of the metric M(Ek) has a specific value. In practice,

an analytical solution describing E

∗

k cannot be obtained.

Therefore, the final E

∗

k will be a sample of this set.

5. AUTOMATED TEST PLANNER

This section proposes a concrete instance of an architecture

for test planning according to the principles presented in previous section. The main elements of the planning process are

depicted in Figure 3. The prime objective of this system is to

find the set of experiments E

∗

k in an iterative process viewed

as an automated test planning process or, also as an automated DoE generator. Currently, concrete implementations

of each of the functions shown in Figure 3 are being developed and integrated in the Cognitive Autonomous Systems

Testing (CAST) project sponsored by the DoD Test Resource Management Center’s Unmanned and Autonomous

System Test (UAST) focus group, in collaboration with the

White Sands Missile Range and the Army Research Lab.

Two main groups of blocks may be distinguished: group (1),

consisting of simulation engine with model libraries (white

blocks), and group (2) with what could be viewed as the

planner itself (gray blocks). Details about group (1) are

97
UAS

Model

Library

Environm.

Model

Library

Scenario

Generator

Simulation

Engine

Search

Engine

Effectiveness

Evaluation

Scenario sk

New Experiment Ek

Previous Experiment Ek

Initial Experiment Ek

Sought Experiments E∗

k

P[M(Ek) > MTh]

Metrics M

(1)

(1)

(2) (1) (2)

(2)

Figure 3: Automated planner architecture

provided in the following section. The planner, i.e.: group

(2), consists of three main elements:

Scenario generator. This component accesses libraries of

models, which should be verified, validated, and accredited. There are two libraries of such models: one

library with models of UAS and models of other entities relevant to the missions being simulated, and another library with models of the environment. Experiments generated by the search engine altogether with

models are used to generate so-called scenarios. Scenarios are understood by the simulation engine, which

is able to execute them.

Effectiveness evaluator. The effectiveness evaluator uses

metrics defined by the testers to evaluate the probability of mission success/failure. Since the simulation

engine is stochastic, the outcomes for a certain experiment may vary for different iterations. Thus, the effectiveness evaluator needs to run one experiment several

times to obtain probabilistic measures. The number

of iterations depends on the computational capability

available to the automated planner.

Search engine. The search engine may be considered the

core of the automated test planner. The search may

start from a set of randomized experiments. The main

function of the search engine is to generate a new set

of experiments using previous experiments and their

outcomes.

The overall function of the three components is to generate scenarios with increasing difficulty for the systems under test. Hence, only extrinsic independent variables may

be manipulated. The resulting experiments E

∗

k correspond

to scenarios yielding a high probability of mission failure.

In CAST, evolutionary computation techniques are used to

implement the search engine. They are explained in further

detail in [16]. All three functions are implemented in MATLAB, which interfaces with the simulation engine through

an Application Programming Interface (API). This API is

a customization of the API provided with the simulation

engine by the vendor.

6. SIMULATION ENGINE

In this section, the rationale are introduced for selecting

a simulation engine suitable for test planning according to

the general principles explained in Section 4, and to the

architecture illustrated in Section 5. What are the main

requirements that need to be considered when selecting a

simulation engine suitable for designing robot tests?. Here

is a non-exhaustive list of key features to consider:

Modeling flexibility. This refers to the ability to incorporate models with varying levels of fidelity. This is useful for covering all phases of the T&E process (DT&E,

OT&E, etc.). When the system is under development,

some subsystems are still being developed; therefore,

they do not have corresponding models. This is the

case when abstract generic surrogates need to be used.

For example, in case SLAM algorithms to allow for

localization and mapping have not yet been implemented, knowledge of position may be supplied directly from the actual state of the entity by the simulation engine. At a later point, when SLAM algorithms

need to be tested, the surrogate localization capability

may be replaced with the actual SLAM solution. The

engine should also allow for instantiating several forms

of the same functionality.

98
Built-in functionality. M&S-based test planning may quickly

become a major software development effort. To avoid

this, it is important that the engine already incorporates simple models of interaction and perception,

which could be used as surrogates in the initial phases

of DT&E. This also applies to robot and behavior of

other entities. For instance, if a robot algorithm is

tested for roaming in crowded spaces, it is important

that the simulation engine has default realistic behaviors for the people who act as obstacles to the robot.

The effort of developing test scenarios for robot navigation should not become a major effort in developing

the dynamic obstacle behavior.

Handling large-scale simulations. Major challenges in

T&E stem form the fact that it is envisioned that

UAS may be more effective when operating as cooperative teams. Hence, simulations may involve significant

numbers of UAS. Consider for instance the current

vision of employing swarms of micro aerial vehicles.

Simulations can quickly become a serious computational challenge to any centralized simulation engine.

Therefore, the ability to perform distributed simulation through the use of distributed computing techniques should be considered.

Fast-time simulation support. Many simulators are designed only for real-time simulation. They are not very

useful for search techniques that need to run hundreds

or thousands of these simulations in the shortest time

possible. Hence, the ability to run scenarios in fasterthan-real-time is essential for automated T&E planning.

Visualization. When performing search, the simulator should

be used in fast-time mode and only a few status messages may be displayed to the user. However, when the

search engine converges to a specific set of scenarios,

the tester may want to perform a qualitative assessment of their validity. This is done best using some

form of realistic visualization, where the scenarios are

“played” in a 3-D visualization environment with interactive viewing control. Sometimes, to have a proper

overview of the test scenario, 2-D visualizations are

also extremely helpful.

Extensibility. Since simulation engines have been developed for different uses, they lack functionality needed

specifically for T&E. Therefore, the engine needs to be

extendable by the user with elements that are needed

in specific domains. For instance, aerial vehicles may

need high-fidelity aerodynamic behavior, which is not

part of the basic engine functionality. Similarly, simulation of RF-propagation is another feature, which

may not be part of the simulation engine but which

should be addable by the user.

Interfacing to other applications. Since the user may

already have tools developed in other environments,

it is also important that the simulation engine has the

capability to interface easily with other applications.

For example, in the architecture presented in previous section, the search engine, performance evaluation

and scenario generation modules are implemented in

Figure 4: 3-D interface to simulation engine

MATLAB. Seamless interfacing between the simulation engine and MATLAB is necessary. Interfacing to

other applications may be simplified when the simulation engine is provided with a well-documented API.

Reliability. Although stochastic variability is a desired feature, it needs to be under control of the user. For example, simulations may be triggered to exhibit stochasticity by changing seeds of random generators in the

different scenario executions. However, if seeds are

kept the same, the simulation engine should always

yield the same results.

The simulation engine selected for the CAST project is VRForces from MAK technologies [13]. VR-Forces complied

with most of the requirements listed above. VR-Forces itself is the simulation engine (“back-end”), which has been

developed with the main objective of enabling distributed

simulation by making use of standard distributed simulation

protocols such as DIS and HLA. This feature is ideal for implementing large scenarios with many participating entities

distributed across several networked computers. For visualization (“front-end”), there are two types of GUIs which

could be selected, one with 2-D representation and one with

3-D representation, as seen in Figure 4.

Out-of-the-box functionality of VR-Forces is relatively sophisticated, and it includes a number of features, such as:

models of interaction between entities, basic models of perception, rules of engagement, ability to incorporate plans,

path planning capability and sophisticated individual and

group behaviors enabled through the add-on B-Have [11].

Currently, the authors are engaging with researchers from

the Army Research Lab (ARL) with the purpose of obtaining validated models of real UAS. Meanwhile, the automated

test planning capability is developed using models that are

well-known in the robotics community [10].

The VR-Forces API has been used extensively to allow for

seamless connectivity between MATLAB and the simulation

back-end. As mentioned before, MATLAB is used as the

main prototyping tool for algorithms related to test planning capability. The effort of interfacing these two elements

99
has two main work areas: enabling functionality for configuring scenarios, and enabling functionality for metric evaluation by obtaining simulation information from the so-called

“state repository” [12].

7. CONCLUSIONS

The robotics field stands at a crossroads. The exploratory

phase with some of its successes has created great expectations. But it seems that it has also reached a critical point.

It could be even said that, to a certain extent, the robotics

field has outpaced itself. The time has arrived when it is necessary to assess what has been accomplished and the path

forward. Sound scientific principles need to be brought back

to the practice of the robotics discipline to solidify its foundations. This fact has been acknowledged in several circles

in the robotics community. It has motivated the organization of special interest groups and workshops such as “Good

Experimental Methodology in Robotics” [2], “Performance

Metrics of Intelligent Systems,” etc.

Society needs robotics technology, but it cannot take the risk

of accepting systems that do not offer any guarantees with

respect to their regular operation and safety. Testing and

evaluation offers an opportunity to think about effectiveness,

safety, reliability , etc. of robotic systems. Furthermore, it

also bring a wealth of knowledge and experience accumulated over the years in the practice of T&E of military systems. It is in the hand of roboticists to use this experience

and to contribute to its further development.

This paper has presented some concepts on using M&S as

an efficient way to plan and generate tests of UAS. Beyond

that, it has also put forward specific alternatives on how to

put those concepts into practice. While there is still a long

path to travel before reaching a seamless synergy between

M&S and T&E, this work could be viewed as an initial effort

towards that goal. It is expected that the concepts and

tools proposed here will be refined and expanded gradually

with the demands of the users of robotics technologies, and

with the efforts of robot developers from all backgrounds:

academic, industrial, military, etc.

8. ACKNOWLEDGMENTS

The authors would like to thank the Test Resource Management Center (TRMC) Test and Evaluation/Science and

Technology (T&E/S&T) Program for their support. This

work is funded by the T&E/S&T Program through the White

Sands Missile Range, NM, contract W9124Q-08-C-0562.

9. REFERENCES

[1] S. Balakirsky, C. Scrapper, S. Carpin, and M. Lewis.

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the requisites of a replicable robotics experiment. In

Workshop on Good Experimental Methodology In

Robotics, at RSS 2009, Seattle, WA, June 2009.

[3] M. L. Cohen, J. E. Rolph, , and D. L. Steffey, editors.

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[4] Defense Acquisition University. Glossary of Defense

Acquisition Acronyms & Terms. Defense Acquisition

University Press, 12th edition, July 2005.

[5] Department of Defense. Modeling and Simulation

(M&S) Verification, Validation, and Accreditation

(VV&A). DoD Instruction 5000.61, May 2003.

[6] Department of Defense. Interim Defense Acquisition

Guidebook. https://acc.dau.mil/dag, June 2009.

[7] P. A. Djang and F. Lopez. Unmanned and

Autonomous Systems Mission Based Test and

Evaluation. In Performance Metrics of Intelligent

Systems PerMIS’09, Gaithersburg, MD, September

2009. NIST.

[8] Henrik I. Christensen et al. A Roadmap for US

Robotics – From Internet to Robotics. Technical

report, The Computing Community Consortium –

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http://www.us-robotics.us, May 2009.

[9] F. Macias. The Test and Evaluation of Unmanned and

Autonomous Systems. ITEA Journal, 29(4):388–395,

December 2008.

[10] R. Madhavan, E. R. Messina, and J. S. Albus, editors.

Intelligent Vehicle Systems: A 4D/RCS Approach.

Nova Science Publishers, 2007.

[11] MAK Technologies – VT Systems, Cambridge, MA

02138 USA. B-HAVE Module for VR-Forces, User’s

Guide, revision bhv-1.2-1-080214 edition, 2008.

[12] MAK Technologies – VT Systems, Cambridge, MA

02138 USA. VR-Forces – Developer’s Guide, revision

vrf-3.11-2-080103 edition, 2008.

[13] MAK Technologies – VT Systems, Cambridge, MA

02138 USA. VR-Forces User’s Guide, revision

vrf-3.11-1-080125 edition, 2008.

[14] Maurits Butter et al. Robotics for Healthcare – Final

Report. Technical report, European Commission, DG

Information Society, October 2008.

[15] Office of the Secretary of Defense. FY2009-2034

Unmanned Systems Integrated Roadmap. Technical

report, Department of Defense, United States of

America, April 2009.

[16] R. Subbu, N. A. Visnevski, and P. A. Djang.

Evolutionary Framework for Test of Autonomous

Systems. In Performance Metrics of Intelligent

Systems PerMIS’09, Gaithersburg, MD, September

2009. NIST.

100
Evolutionary Framework for Test of Autonomous Systems

Raj Subbu 

General Electric Global Research 

1 Research Circle 

Niskayuna, NY 12309 

(518) 387-6457 

subbu@research.ge.com 

Nikita Visnevski 

General Electric Global Research 

1 Research Circle 

Niskayuna, NY 12309 

(518) 387-4385 

visnevsk@research.ge.com 

Philipp Djang 

Army Research Lab 

ARL SLAD IEPD 

White Sands MR, NM 88002 

(575) 678-1550 

djang@arl.army.mil 

ABSTRACT

A DoD mission and challenge is to enable a high percentage of 

autonomous vehicles in the warfighter fleet by 2015. These 

systems will need to display a high degree of autonomous 

capabilities. The capabilities of these autonomous systems must 

be acceptable to the warfighter and his/her logistical support 

structure. Autonomous systems of the future will need to be tested 

so their mission capabilities and robustness are predictable to the 

warfighter. The principal challenge therefore is the set of test 

strategies for these future autonomous systems. The goal of the 

test community is that these autonomous systems be broadly 

accepted to seamlessly operate either independently or as part of a 

human-in-the-loop system. Our goal is to develop an efficient 

intelligent test process that will enable the rapid introduction of 

autonomous systems on the battlefield. We propose a novel war 

game simulation-based multi-objective evolutionary test 

framework that combines the elements of testing an autonomous 

system’s mission execution capabilities as a function of its innate 

capabilities and evolutionary computation. 

Categories and Subject Descriptors

I.2.8 [Artificial Intelligence]: Problem Solving, Control 

Methods, and Search — Heuristic methods; I.2.9 [Artificial 

Intelligence]: Robotics — Autonomous vehicles; I.6.7 

[Simulation and Modeling]: Simulation Support Systems — 

Environments; J.7 [Computers in Other Systems]: Military. 

General Terms

Algorithms, Measurement, Performance, Experimentation, 

Verification. 

Keywords

Autonomous Systems, Test and Evaluation, Evolutionary 

Algorithms, Multi-objective Optimization, Tradeoff frontier, Wargame Simulation. 

1. INTRODUCTION 

A key DoD mission is to enable a high percentage of autonomous 

vehicles in the warfighter fleet by 2015. This is being driven 

principally to reduce risk exposure to combat troops in hazardous 

conditions, and for advance reconnaissance and threat 

neutralization. These vehicles will need to display a high degree 

of autonomous capabilities. Further, their capabilities must be 

acceptable to the warfighter and his/her logistical support 

structure. However, current DoD test and evaluation capabilities 

and methodologies while sufficient for tightly tethered human-inthe-loop systems are insufficient for the mission certification of 

complex autonomous systems operating in non-deterministic 

environments [1]. Autonomous systems of the future will need to 

be tested so their mission capabilities, robustness, and failure 

modes are predictable to the warfighter. The principal challenge 

therefore is the set of scalable test strategies for these future 

autonomous systems. The goal of the test community is that these 

autonomous systems be broadly accepted to seamlessly operate 

either independently or as part of a human-in-the-loop system and 

scale from small to large deployments. Our goal is to develop an 

efficient intelligent test process that will enable the rapid 

introduction of autonomous systems on the battlefield. 

We propose a novel war game simulation-based test 

framework that utilizes evolutionary algorithms for identifying 

the mission failure modes. While the traditional application of 

evolutionary methods is for efficient synthesis or design, we 

propose the use of these methods for the efficient identification of 

failure scenarios from a mission satisfaction perspective. This 

approach combines the elements of testing an autonomous 

system’s mission execution capabilities as a function of its innate 

capabilities and evolutionary computation. In this paper, we 

present the evolutionary test framework, preliminary experimental 

results based on a limited scale war game, and ideas for 

developing this work into a deployable mission based test and 

evaluation framework [2]. 

This research effort is being conducted under the auspices of 

the DoD Test Resource Management Center’s Unmanned and 

Autonomous System Test (UAST) focus group, and in 

collaboration with the White Sands Missile Range and the Army 

Research Lab. 

2. RELATED WORK 

In this section, we present background and briefly review the 

literature in complex adaptive systems, evolutionary multiobjective optimization, and search-based systems test. 

2.1 Complex Adaptive Systems 

Complex adaptive systems constitute a dynamic network of 

diverse and adaptive systems. The paradigm originally coined at 

the Santa Fe Institute has been used to model disease propagation, 

financial markets, and economic networks [3]. 

© 2009 Association for Computing Machinery. ACM acknowledges that 

this contribution was authored or co-authored by a contractor or affiliate 

of the U.S. Government. As such, the Government retains a 

nonexclusive, royalty-free right to publish or reproduce this article, or to 

allow others to do so, for Government purposes only. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

Copyright © 2009 ACM 978-1-60558-747-9/09/09...$10.00 

101
The complex adaptive systems paradigm has been 

implemented in a number of military simulations in the form of 

agent based models. Structurally, a set of combat agents interact 

with one another in a simulated battlefield. A combat agent is an 

autonomous computational entity with an internal state and 

associated decision-making process implemented as a set of rules 

governing tactical behavior [4]. The agent’s state is usually 

represented as a dynamic vector describing metrics such as agent 

position, identity, current functionality, and so on. Combat agents 

can interact with one another by passing messages between 

themselves, which can represent communication, cooperative 

actions, or conflict. Given these elements, a military-domain 

agent-based model is then a collection of interacting combat 

agents instantiated within a virtual “artificial world” that contains 

a terrain-based environment within which the agents function as 

well as contend with other hostile combat agents. Existing agentbased models of land warfare such as Irreducible SemiAutonomous Adaptive Combat (ISAAC) [5], Simulation of 

Information in Battlefield Decisions (SinBaD) [6], and AgentKit 

[7] all address the emergent behavior of combat units of 

interacting Blue and Red agents. 

2.2 Evolutionary Multi-objective 

Optimization 

Evolutionary Algorithms (EAs) have received a lot of attention 

for use in optimization and learning applications, and have been 

applied to various practical problems [8], [9]. In recent years, the 

area of evolutionary multi-objective optimization has grown 

considerably [10], starting with the pioneering work of Schaffer 

[11]. 

Most real-world optimization problems have several, often 

conflicting objectives. Therefore, the optimum for a multiobjective problem is typically not a single solution—it is a set of 

solutions that trade-off between objectives. The Italian economist 

Vilfredo Pareto first generally formulated this concept in 1896 

[12], and it bears his name today. A solution is Pareto optimal if 

(for a maximization problem) no increase in any criterion can be 

made without a simultaneous decrease in any other criterion. The 

set of all Pareto optimal points is known as the Pareto frontier or 

alternatively as the efficient frontier. In the absence of further 

information, each such solution is as good as the others are when 

all objectives are jointly considered. Each solution on the Pareto 

frontier is not dominated by any other solution. Formally, given 

an n-dimensional measurable space whose elements can be 

partially ordered, a vector in this space x = (x1, x2, ..., xn) is 

considered non-dominated if there exists no other vector z such 

that xi ≤ zi for all i, and xk < zk for at least one 1 ≤ k ≤n. The 

symbol ≤ may be interpreted as “the right-hand-side of it is as 

good as or better than its left-hand-side” without loss of 

generality. 

A review of mathematical programming-based optimization 

methods for multi-objective problems is presented in [13]. These 

techniques generally require multiple executions to identify the 

Pareto frontier, and may in several cases be highly susceptible to 

the shape or continuity of the Pareto frontier, restricting their 

wide practical applicability. An evolutionary multi-objective 

optimizer works by systematically searching, memorizing, and 

improving populations of vectors (solutions), and performs multiobjective search via the evolution of populations of test solutions 

in an effort to attain the true Pareto frontier. This characteristic 

allows finding an entire set of Pareto optimal solutions in a single 

execution of the algorithm. Traditionally, multi-objective 

optimization has been pursued via the application of singleobjective optimizers to linearly (or nonlinearly) weighted and 

aggregated objectives, and repeating the optimization for multiple 

weight combinations. While this traditional approach appears 

satisfactory in practice, the method is unable to identify nonconvex regions of the Pareto frontier [14]. This problem is more 

pronounced when the underlying models that represent mappings 

to multiple mutually competing output objectives are nonlinear. 

Practical evolutionary search schemes do not guarantee 

convergence to the global optimum in a predetermined finite time, 

but they are often capable of finding very good and consistent 

approximate solutions. However, they are shown (theoretically 

and practically) to asymptotically converge under mild conditions 

[15]. 

2.3 Search-based Systems Test 

Mission based testing involves the automated generation of a 

large number of realistic missions within a high-fidelity 

simulation environment to help identify scenarios that induce 

system failure. This concept is not only being adopted by the DoD 

and presented in this paper, but also by NASA for test and 

evaluation of autonomous deep space systems [16], [17]. We 

briefly but chronologically review the literature on the use of 

evolutionary algorithms for systems test. 

Schultz et al. [18], [19] use a genetic algorithm for testing 

the controller of an autonomous system. Their approach subjects a 

controller coupled to a vehicle simulator to a combination of fault 

scenarios generated by a genetic algorithm. The genetic algorithm 

searches for those combinations of faults that produce degraded 

system performance. Corno et al. [20] use a genetic algorithm to 

generate test patterns for sequential digital circuits. Their genetic 

algorithm generates a sequence of values to be applied to the 

input pins such that the outputs of a fault-free circuit will be 

different from the corresponding ones of a faulty circuit. A 

generally similar approach to dynamic test pattern generation for 

software programs is presented by Michael et al. [21]. Harman et 

al. present a multi-objective genetic algorithm that can identify a 

“branch-adequate” test set for software programs. A branchadequate test set includes at least one test case that can trigger the 

execution of at least one branch, and covers every possible branch 

in the program flow. Windisch et al. [23] present a particle swarm 

optimization approach that generates test cases for software 

programs. Nguyen et al. [24] present an evolutionary test method 

for autonomous distributed software systems such as web 

crawlers. Terrile and Guillaume [25] use evolutionary algorithms 

to search a space of possible behaviors to identify emergent 

behaviors that are unexpected or detrimental in spacecraft 

systems. 

3. EVOLUTIONARY MISSION BASED 

TEST AND EVALUATION 

The evolutionary mission based test and evaluation framework is 

based on the use of a multi-objective evolutionary algorithm 

(NSGA-II [26]) and MÄK VR-Forces [27], a simulation toolkit 

for generating and executing battlefield scenarios. In this 

simulation environment, various types of Blue (friendly) and Red 

102
(enemy) force compositions can be created and allowed to engage 

according to high-level directions. The engaging entities (such as 

tanks, infantry, and UAVs etc.) have configurable behaviors, and 

they behave as distributed autonomous agents in the battlefield 

scenario. A vignette of a battlefield and forces engagement 

scenario involving tanks is shown in Figure 1. 

Figure 1: A vignette of a battlefield and forces engagement 

scenario involving tanks. 

Figure 2: Evolutionary framework for test of autonomous 

systems. 

The architecture of the test and evaluation framework is 

shown in Figure 2. For a given mission, the multi-objective 

evolutionary algorithm intelligently generates a series of test 

cases that will drive the mission to fail. This approach of driving 

mission failure rather than mission success allows the 

identification of an autonomous system’s failure modes. Each test 

case that the evolutionary algorithm generates varies the Blue and 

Red force asset distributions, initial conditions, opposing force 

capabilities, terrain, and theater of engagement, constituting a 

configuration. This configuration is executed to completion in the 

simulation environment. At the conclusion of each execution the 

system states and outcomes are observed to generate metrics 

feedback to the evolutionary algorithm. The simulation execution 

and outcome are influenced by factors such as dynamic obstacles, 

and behavior of opposing forces. The metrics of interest computed 

at the conclusion of each execution are Loss Exchange Ratio

(LER)1

 and Percentage of Healthy Enemy Forces (PHEF)2. The 

evolutionary algorithm seeks to minimize both these metrics. 

Minimization of the LER metric rewards friendly losses, and 

minimization of the PHEF metric rewards enemy losses. Both 

these metrics oppose one another, and an optimal tradeoff frontier 

is identified. Such a tradeoff frontier allows the decision-maker to 

evaluate the conditions and outcomes associated with each of the 

tradeoff scenarios optimal from a mission dissatisfaction 

perspective. 

4. EXPERIMENTAL RESULTS 

In this section, we present preliminary experimental results based 

on a limited scale war game involving a set of M1A1 Blue tanks 

and T80 Red tanks. While the M1A1 tanks are more superior and 

powerful to the T80 tanks, the evolutionary algorithm drives the 

search to identify weak engagement scenarios for the M1A1 

tanks. 

Figure 3: The simulated 2000 meter x 2000 meter battlefield 

terrain. 

The simulated 2000 meter x 2000 meter terrain where the 

battle ensues is shown in Figure 3. The highest point on this 

undulating terrain with significant foliage (elevated green areas) 

is 66.5 meters with respect to a zero altitude (brown areas) 

baseline. A three dimensional view of a portion of this terrain is 

shown in Figure 4 for perspective. The layout of the forces 

engagement theater is shown in Figure 5. In this layout, the Blue 

and Red forces may originate from one of the four corners, but the 

Blue and Red forces may not originate from the same corner. The 

task for each opposing force is to reach and take a target point 

within the Blue-Red forces engagement zone. For these 

 

1

 Ratio of number of damaged enemy assets to number of 

damaged friendly assets. 

2

 Ratio of number of healthy enemy assets to total number of 

enemy assets. 

103
preliminary experiments we lower-left biased the engagement 

theater due to the dense foliage in the upper right of the terrain 

and further due to the two lakes present. This way, all engagement 

takes places only on solid ground. 

Figure 4: A 3D view of the battlefield terrain. 

Figure 5: Layout of the forces engagement theater. 

The multi-objective evolutionary algorithm varies the 

following parameters: Blue force size from 50% to 200% of the 

Red force size, which is fixed at ten T80 tanks; X-Y coordinates 

of the target point within the Blue-Red forces engagement zone; 

and the start locations of the Blue and Red forces with the 

constraint that Blue and Red forces cannot start from the same 

location. We use a population size of 20, and a total of 30 

generations, for a total of 600 simulations. Each simulation 

executes for 3 minutes, resulting in a total execution time of 30 

hours. The best tradeoff frontier at the conclusion of the 

evolutionary algorithm execution is shown in Figure 6. Each of 

the scenarios on the tradeoff frontier is shown superimposed on 

the terrain in Figure 7. The green circles correspond to the target 

points; the red squares show the starting location of the Red force; 

the blue square shows the starting location of the Blue force. 

Further, the Blue force size is at least 80% and up to 200% of the 

Red force size for each of the tradeoff scenarios. 

The interesting observation is that as the overall goal is 

mission dissatisfaction, the Blue force is always selected to 

originate from the undulating upper right of the terrain, while the 

Red force is predisposed to originating from the more even lower 

left or right of the terrain. Further, there is a cluster of targets at 

the transition point from the higher to low altitudes which allows 

the Red force to take advantage due to the line of sight visibility 

constraints for the Blue force as they descend to the lower altitude 

terrain. 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Loss Exchange Ratio

% Healthy Enemy Forces

Figure 6: Best tradeoff frontier identified at the conclusion of 

the evolutionary algorithm execution. 

Figure 7: Best tradeoff frontier points shown superimposed on 

the terrain. 

5. CONCLUSIONS AND FUTURE WORK 

In this paper, we have developed and presented a basic framework 

for the intelligent test and evaluation of autonomous systems. 

This framework is based on the use of autonomous systems 

simulation tools and multi-objective evolutionary algorithms. The 

evolutionary algorithm identifies failure modes through intelligent 

search. We have presented preliminary experimental results based 

on a limited scale war game involving a set of M1A1 Blue tanks 

and T80 Red tanks. 

Our goal is to develop an efficient and scalable test process 

that will enable the rapid introduction of autonomous systems on 

104
the battlefield. The basic framework presented in this paper in an 

advanced deployable form is expected to support the DoD 

mission and challenge to enable a high percentage of mission 

certified autonomous vehicles in the warfighter fleet by 2015. To 

achieve this goal several objectives need to be met. A first 

objective is experimentation with more complex battlefield 

scenarios involving force mixtures (tanks, infantry, aircraft etc.) 

and terrain. The next objective is speeding up the overall 

simulation execution time through high-performance hardware. 

The next objective is distributing the computation and leveraging 

a coevolutionary computational framework [28] as shown in 

Figure 8 to enable a scalable network-efficient search over largescale battlefield scenarios. The long-term vision is to empower a 

decision-maker tasked with test of complex autonomous systems 

with a fast virtual system so these autonomous systems can be 

robustly mission certified to be broadly accepted and seamlessly 

operate either independently or as part of a human-in-the-loop 

system. 

Figure 8: Distributed coevolutionary computation. 

6. ACKNOWLEDGMENTS 

The authors would like to thank the Test Resource Management 

Center (TRMC) Test and Evaluation/Science and Technology 

(T&E/S&T) Program for their support. This work is funded by the 

T&E/S&T Program through the White Sands Missile Range, NM, 

contract W9124Q-08-C-0562. 

This research effort is being conducted in collaboration with 

the Army Research Lab. 

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106
Metrics for Co-evolving Autonomous Systems 

Jack Ring 

Educe LLC 

442 N Sage Ln. 

Gilbert, AZ 85234 

1+480-488-4615 

jring@amug.org 

1. ABSTRACT 

Autonomous system innovations have overrun the test and 

evaluation capability to find problems before they become 

expensive to fix --- or lethal. The autonomy paradigm demands 

that an equivalent test and evaluation system be conceived, 

architected and engineered, operated and evolved. This in turn 

demands an autonomous test and evaluation enterprise, staffed 

with competent systemists, as the enabling agent. This paper 

outlines the metrics and key capabilities for realizing such an 

enterprise. It features a game-theoretic basis, a model-based 

systems engineering approach and a four part strategic framework. 

This paper focuses on the unclassified situation in the U.S. Dept. 

of Defense. However, these ideas will apply to other domains of 

autonomy in both the public and private sectors. 

Categories and Subject Descriptors

C.3 [Computer Systems Organization]: Special Purpose and 

Application-based Systems – process control. 

General Terms

Management, Measurement, Design, Economics, 

Experimentation, Human Factors, Languages, Verification. 

Keywords

Ontology, Agility, Reconfigurable, Repurposed, Systemics, 

Persistent Integrity Assurance 

2. INTRODUCTION 

Independent, objective test and evaluation, T&E, is a crucial part 

of all projects, especially for systems of higher complexity and 

autonomy. DoD-sponsored autonomous systems, both manned and 

unmanned, are not benefitting from T&E. T&E is equivalent to 

C4ISR where the ‘enemy’ is the extent, variety and ambiguity of 

the Unmanned Autonomous Systems, UAS’s, to be tested. DoD 

plans to produce more than 1,000 new kinds of UAS’s costing 

billions of dollars in the next few years. However, a necessary, 

sufficient and efficient T&E capability is not being pursued with 

equivalent alacrity and vigor. 

The T&E community must learn to create an autonomous 

T&E enterprise that can design and generate an agile 

infrastructure and plug-in modules thereby enabling the rapid 

instantiation of situation-specific T&E systems whenever and 

wherever needed. 

Failure to do this will result either in bypassing independent and 

objective testing by taking UAS's from development directly to 

the warfighter, or in over-reliance on modeling and simulation that 

does not indicate the possible error in its results. Neither of these 

alternatives are prudent. 

This is about T&E of more than an autonomous vehicle. This is 

about T&E of whole systems composed of multiple, 

heterogeneous vehicles, networked to C4ISR enabling joint 

military missions. Such systems are configured in the field in a 

matter of hours, supported by world-wide production and supply 

chains and operated by humans, often without benefit of specific 

training on the system just created. Also, this is about the design, 

engineering and construction of collateral T&E systems in less 

than one tenth the cycle time typical of past practices in order to 

keep pace with the evolutionary acquisition of UAS’s. In fact, this 

challenge reaches clear back into the way practitioners think, 

formulate, experience and learn about systems[14]. Current 

standards, guides, and handbooks, regarding the processes and 

practices of systems engineering, system of systems engineering 

and family of systems engineering under conceptualize the praxis 

required to formulate necessary, sufficient and efficient 

autonomous T&E systems. 

Metrics are key. Metrics define the problem. Metrics drive system 

design/architecting, engineering/construction/ and 

deployment/evaluation. Hypothesizing solutions sans metrics is 

malpractice. Accordingly, this paper focuses on whole system 

metrics. A subsequent paper will focus on whole systems 

realization. 

The balance of this paper presents a description of the metrics that 

will quantify the problematic situation then a summary of key 

capabilities that can respond at the rate UAS’s evolve and 

warfighter situations demand. 

In addition to military value the advancements described herein 

are expected to benefit government, industrial and commercial 

domains ranging from global-scale intelligent transportation to 

endo-human nanomachines and neurons interfaced to silicon. 

3. RELEVANT SEMIOTICS 

3.1 Systemics 

The concepts that will be used throughout this paper are shown in 

Figure 1. On the left side, reading from top to bottom, ‘e’ signifies 

entity. ‘─’ signifies a relation and ‘e─e’ signifies a system. Most 

systems consist of more than two entities and one relationship. 

This notion is illustrated as three ‘e’ and three r’s. When all are 

observable this is called an explicit system. Importantly, [18] adds 

that there can be relationships among relationships. This is called 

an implicit system. Also, [3] and others point out that if any 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies 

are not made or distributed for profit or commercial advantage, and 

that copies bear this notice and the full citation on the first page. To 

copy otherwise, to republish, to post on servers or to redistribute to 

lists, requires prior specific permission and/or a fee. PerMIS'09, 

September 21-23, 2009, Gaithersburg, MD, USA. Copyright © 2009 

ACM 978-1-60558-747-9/09/09...$10.00. 

107
entities or relationships are not observable or predictable then the 

notion of soft system applies. 

e = entity

= relation

e e = system

e

e e

= system (explicit)

e

e e

= system (implicit)

= behavior = 

e

e e

S R 

S = Stimulus, R = Response

e

e e

= system

(soft)

e =

e

e e Entity can contain 

a system

e

e e

e

e

e

e

e e

System of Systems

(is just another system)

Figure 1. Systemics Concepts 

Figure 1, upper right, shows a whole system of entities and 

relations exhibiting a behavior labeled ∏. This notion is also 

called Stimulus<>Response by some and Transfer Function by 

others. Figure 1, center right, shows the notion that any entity can 

be a system, containing other entities and relationships. Bottom 

right shows that one or more entities in one system may also 

participate as entities in another system. Some people label this 

situation System of Systems. Note, however, that the bottom 

relationship in the middle system is different when the e’s are 

participating in the larger system than if the middle system is not 

interoperating with the other systems. Relevant, here, is the [7] 

notion of holon, a system that simultaneously can be a component 

of another system. 

All systems are man made. Some by descriptive modeling of 

“natural’ systems, e.g., the human body, and others by prescriptive 

modeling of systems intended to successfully intervene in 

problematic situations. The essence of a systemics praxis adapted 

from [15]. Is shown in Figure 2. Key concepts are Context, 

Content Structure. Prudent praxis involves descriptive modeling 

of the Context, designated as [1] in Figure 2, especially the 

underlying problem system [2], then nominating System Content 

[3], the capabilities that are intended to moderate the problem 

system. The several hypotheses are then systemized by nominating 

structure, the pattern of interrelations among them [5] that is 

expected to produce the intended responses when encountering the 

various stimuli. The arrangement of content and relationships 

comprise the system architecture. In modern systems engineering 

parlance this is called the Effects and Capabilities approach to 

systems. 

Jack Ring, jack@jackring.com 2

System Context

System 1.

Adapted from 

Science of Generic Design, 

John Warfield

Problem Suppression 3. System: Content

Problem Suppression 

System: Content, 

Process, Behavior

4. t1

t

2

t3

Problem

Suppression

System

2.

Problem System;

Content, Process,

Behavior

Figure 2. Systems Praxis 

When this prescriptive model is implemented the behavior of the 

system becomes undeniably evident and often surprising. This 

spawned the notion of POSIWID, the purpose of a system is what 

it does regardless of designer intent [2] which leads to the notion 

that T&E must measure not only system viability in specified 

usages (contexts) but also system limits of stability and integrity. 

Figure 3 shows the notion of a system (in the center ellipse) 

exhibiting its behavior labeled ∏. A system exists in a Situation 

Space, is influenced by the Problem Space and influences the 

Value Space. System behavior results from several influences. 

35

 = f(k) = ballistic

 = f(O) = governor

 = f(I) = anticipatory

 = f(Sit, O) = homeostatic

 = f(Val) = goal-seeking

 = f(Pr) = self-organizing

 = f(Pr, Val) = autopoietic

 = f(all) = autocatalytic

Problem

Space

Class

Type f(t)

In Out

Situation Value

Space

Class

Type

f(t)

Pr = Problem Space

Val = Value Space

S = Stimulus

R = Response

Sit = Situation

 = System Transfer 

Function

Value

Class Type



Figure 3. Categories of System Behavior 

The Problem Space and Value Space artifacts can be categorized 

by Class, Type and temporal existence. The Situation Space 

attributes are the  Value that the ∏ must contribute as well as 

Class and Type. Labels for the various kinds of system behaviors 

are shown at the lower left of Figure 3. These indicate the 

spectrum of system behaviors that will be encountered in T&E of 

autonomous systems, manned or unmanned.

System behavior ordains system worth. In all but trivial cases and 

especially in autonomous situations system behavior manifests in 

multiple modes, each with perhaps multiple states within each 

mode. We use ‘coverage’ to signify the degree to which T&E 

reveals all of these. 

3.2 Field of Discourse 

Figure 4 presents a concept map of our Field of Discourse 

regarding unmanned autonomous systems test and evaluation. 

UAST(i) Modeling

Requisite Variety C>B>A Requisite Variety B>A Requisite Variety A

Autonomous

T&E

Enterprise

Autonomous Autonomous

T&E System T&E System

UAST(1...n) UAST(i) exercises

&

produces observes

&

conveys

designs

&

generates

Know ledge

UAS(1)

UAS(2)

UAS(n)

UAS(1.m)

(n.m)

5000.02 

Programs

Quick Reaction 

Programs

Missions

(1 ... k) Warfighters Oversight

//4// [4] [3] //3// [2] //2//

[1] //1//

Descriptive Models

//5a//

adjust

adapt

co-align

Descriptive 

Models

T&E

Assets

//5b//

[6] //6//

//7//

Figure 4. UAST Field of Discourse. 

Knowledge //1// serves the needs of various stakeholders 

regarding respective UAS’s //2//. Members of a set of autonomous 

T&E Systems, UAST(1...n) //3// produce and convey relevant 

108
knowledge as each exercises and observes selected UAS’s. An 

Autonomous T&E Enterprise, ATEE //4// designs and generates 

instances, UAST(i), of the generic Autonomous T&E System. 

Requisite Variety, RV, [1] ramifications are noted across the top 

of Figure 4. A UAS must exhibit a certain degree of RV to 

accomplish its mission. This, in turn requires a higher degree of 

RV by the UAST(i) for testing the UAS. This, in turn, requires a 

yet higher degree of RV by the ATEE, that produces UAST’s. 

Three more concepts are shown in Figure 4. One concerns 

descriptive models of UAS’s //5a// and Stakeholders //5b//. A 

second concerns descriptive models of T&E Assets //7// that are 

re-used to formulate UAST’s. Descriptive models are executable 

formal ontologies that reveal expected system behaviors. The third 

recognizes that the Autonomous T&E Enterprise continuously 

adjusts its own gradients, adapts its pattern of relationships and 

co-aligns its content relative to its context [8]. 

3.3 Whole System Viewpoint 

Figure 5 depicts a whole system anatomy signifying the several 

aspects of a whole system that must be measured across a range of 

contextual situations. The cloud on the left represents the 

problematic situation, containing the problem system from which 

stimuli emanate. The primary mission system is called the 

Problem Suppression System. It is enabled by the Operational 

Availability System (the logistics, maintenance, etc., that keep the 

Problem Suppression System running). The Operator Preparation 

System prepares the war fighters. The Production System 

produces multiple copies. The Test System(s) report on the 

readiness of all the others and of ‘itself.” 

Figure 5. Whole System Anatomy

It is important to note and remember our use of UAS signifies the 

whole system, including the personnel, as do UAST and ATEE, 

respectively. 

4. METRICS 

4.1 General System Metrics 

Useful metrics for a general system are Quality, Parsimony and 

Beauty. Quality in the [4] sense of Conformance to Requirements, 

a binary Yes or No, not the fuzzy ‘high’ or ‘low’ quality. 

Parsimony in the sense that no other system exhibits required 

quality at less cost of ownership. Beauty in the machine sense as 

articulated by [6] and in the human sense as articulated by R. 

Buckminster Fuller, "When I am working on a problem, I never 

think about beauty but when I have finished, if the solution is not 

beautiful, I know it is wrong." 

4.2 Knowledge Metrics 

Figure 6, adapted from [10], clarifies the knowledge metric. Kinds 

of knowledge ranging from concepts to theory are listed on the left 

side of Figure 6. 

Theory

Principles

Propositions

Situation Invariant

Relationships form

Concepts

Salient discontinuities 

in a semantic space

Relationships form

Instance

Transform

Interpret

Knowledge 

Claims

Observe

Figure 6. Sources and Kinds of Knowledge Claims 

A theory consists of a set of interrelated Principles which, in turn 

are a set of propositions that transcend specific situations. 

Propositions are interrelated Concepts. Concepts are the 

fundamental building blocks and are simply meaningful 

discontinuities in a semantic space. A T&E activity fits on the 

right side of Figure 6. T&E Acknowledges UAS events, Observes 

the characteristics, Interprets the findings and Produces 

knowledge claims (as indicated by the dashed lines). Knowledge 

claims can pertain to any of the four constructs on the left side of 

the Vee. 

Quality of knowledge claims is measured by adequacy, accuracy 

and timeliness of the claims. Adequacy connotes the spectrum of 

knowledge that occurs across the several kinds of interested 

parties. Accuracy (of knowledge claims) connotes not only the 

quality of observations but also the assessment of the likelihood of 

error in evaluations. Timeliness connotes whether the latency from 

time of observation to time of conveyance of the knowledge claim 

is/was consistent with stakeholder intended usage. 

Parsimony concerns not only the cost of producing the claims but 

also how well new claims are conveyed. In light of Ausabel’s 

theory of meaningful learning [10] this entails relating new 

knowledge claims to stakeholders’ existing knowledge. Of course 

this implies that the Evaluation side of T&E be familiar with the 

‘audience’ of stakeholders and their knowledge states even though 

neither the UAST nor Autonomous T&E Enterprise gets to select 

the stakeholders. 

4.3 UAS Metrics 

The U.S. Dept. of Defense planning horizon anticipates more than 

1,000 kinds of UA vehicles spanning Space, Air, Ground, Marine 

and Undersea. Current examples range from 40 ton ground-based 

monsters to 2.3 gram airborne platforms that carry video cameras. 

In addition to these in physical space we can expect many other 

kinds of UAS’s in cyberspace. 

Stakeholders want to know about UAS Safety, Suitability, 

Effectiveness and Survivability. These describe UAS 

characteristics and properties (see Appendix 1) not only in a 

specified, nominal scenario but also across a various operating 

modes such as degraded, diagnostic, training, maintenance and repurposing. Further, UAS dynamic and integrity limits must be 

determined by actual test or by estimation techniques. 

109
UAS Testability is a key metric. Inevitably, stakeholders want to 

know ‘why’ a UAS system did what it did (and want to predict 

what it may or will do in a future scenario). This is a challenge for 

the Evaluation side of T&E. It gives rise to a metric regarding the 

degree to which a UAS Whole System as shown in Figure 5 selfidentifies and reports its current configuration self assessment of 

its readiness. For example, no testing should begin if the UAS is 

rife with bugs. If the UAS does not have these capabilities then the 

UAST must have the capability to inspect and assess the UAS. 

4.4 UAST Metrics 

The Autonomous T&E System, shown at [3] in Figure 4, covers 

all members of the set, UAS, as well as relevant members of the 

sets, Missions, and Stakeholder Interests. The Autonomous T&E 

System is a set of UAST(I), each member being sufficient yet 

parsimonious with respect to specific UAS-Mission-Stakeholder 

triples. A framework for UAST’s is described in (8). 

The knowledge needs of the several diverse stakeholders suggest 

that tens of test episodes will be needed for each kind of system. 

Parsimony factors such as budgets constrain the T&E community 

to far less than thousands of unique UAST installations. However 

one UAST would be far too large and complex to build, let alone 

schedule and operate. The answer lies somewhere in between. We 

can be reasonably confident of the concept of a set of UAST(i), 

each using a mix of multi-use and situation-specific components 

specifically configured to stimulate and observe a part of or a 

whole UA system then produce and convey knowledge claims. 

However, range of ‘i’ remains to be discovered because it depends 

on the maximization of the whole system shown in Figure 4. 

Considering the degree of autonomy that can be exhibited by a 

UAS or a mission-oriented group of UAS’s a UAST(i) must 

exhibit even greater autonomy (essentially Ashby’s Requisite 

Variety, RV). This metric has been obvious in Range Safety 

systems at large test ranges. Now, a UAST(i) must have the 

requisite variety to morph its operations and even its configuration 

during UAS operation not only for Range Safety and fratricide 

avoidance but also for contriving stimulations and observations as 

well as producing and conveying unforeseen knowledge claims. 

In essence, the UAST(i) must be able to prevail as the Angel in a 

formal game with UAS(s) as Demons while the UAS(s) are 

prevailing as Angels in a formal game with their Mission context 

as Demons. [Pizzarello, A., OntoPilot LLC, private 

communication]. 

Because of its autonomy each UAST(i) must include the capability 

of persistent viability assurance. In Joint testing episodes a 

UAST(i) may interoperate with other operational and test systems. 

Persistent viability of the associations (as Demons) must be 

measured. 

Meanwhile parsimony demands that UAST’s must be able to 

determine readiness for test of both UAS and UAST systems. 

Relevant metrics are incidence of test aborts (ideally zero). 

Other metrics reveal cost of UAST generation (including 

proactive, facilitated reuse of assets), operation and recovery. 

Underneath, asset turnover is a key metric along with metrics for 

characterizing each asset as reuseful, reusable and reused. 

4.5 ATEE Metrics 

The Autonomous T&E Enterprise, ATEE, designs and generates 

the Autonomous T&E System as UAST(1...n) instances in 

response to UAS–mission-stakeholder triples. The extent, variety 

and ambiguity presented by the UAS set and the desired UAST set 

demands that the ATEE operate as an Intelligent Enterprise. 

Figure 7 indicates the key context, content, structure and behavior 

of an intelligent enterprise [12]. p gp

Enterprise Intelligent

throughout all change.

Two or more persons 

applying resources 

through actions to 

achieve mutual purpose.

Ability to Maximize 

Stakeholder Value while 

conforming to principles

Limited

Resources

People

Actions

Mutual

Purpose

Stakeholder 

Value

Systems &

Societal

Principles

Figure 7. Concept of Intelligent Enterprise

Two or more persons applying resources through actions to 

achieve mutual purpose regarding both stakeholder value and 

systems and societal principles, all in a context of 

unpredictable change. 

Key capabilities are [8] a Goal, Triggers, Energy, Competence, 

Situation Assessment and Gap Closure. Intelligent system quality 

metrics include accuracy in achieving mutual purpose, response 

time to external and internal changes, and dynamic limits with 

respect to rate of change. Parsimony metrics are cost to achieve 

goal and the cost of disergy, having more responsiveness than is 

needed. Beauty metrics are [5] Market Standing, Productivity, 

Innovation and Liquidity. 

Unfortunately, the Drucker metrics are lagging indicators. Leading 

indicators involve the orchestration of change because an 

enterprise evolves in many steps across many aspects. Because a 

system can be viewed as content and structure (relationships) and 

because its behavior depends on the interrelationship gradients, an 

intelligent enterprise has three modalities of change, notably, 

adjusting gradients, adapting the pattern of interrelationships and 

co-evolving its content. All three must be measured so that the 

enterprise changes gracefully rather than chaotically. Gracefully 

recognizes well known thermodynamics constraints, notably, 

conservation of mass, momentum and energy. In the intelligent 

enterprise graceful change also acknowledges other constraints 

stemming from other factors comprising an enterprise, notably, 

Informatics (data, information and knowledge), Teleonomics 

(skills mix, rate of learning, and rate of invention), Human social 

dynamics (trust, enthusiasm, co-evolution), Economic 

(investment, ROI, liquidity), and Ecology (tbd). 

Graceful evolution of the ATEE is highly important. Once change 

appears incoherent to the incumbents a variety of depressing and 

destructive behaviors can arise. Enterprises exhibit an etiology, 

exemplified in Figure 8. The quality of information available to 

the participants is shown on the left. Important metrics regarding 

personnel attitudes are shown on the right, aligned with each kind 

of information. 

110
8/6/2009 59

Lack Of:

Mission&Vision

Strategy

Intent

Goals

Plans

Commitments

Competencies

Energy and Automation

Teambuilding

Collaboration

Tenacity

Achievement

Recognition

Co-celebration

Causes:

Ambiguity

Disorientation

Ambivalence

Alienation

Dissonance

Distrust

Futility

Malaise

Isolation

Dread

Apathy

Depression

Negative Rumors

Sabotage

Figure 8. Enterprise Etiology 

The lack of each item on the left causes the personnel situation 

on the right. 

As in the UAS and UAST cases, ATEE Testability is a key metric. 

Inevitably, stakeholders want to know ‘why’ the ATEE did what it 

did (and want to predict what it may or will do in a future 

scenario). An autonomous ATEE must contain a model of ‘itself.’ 

It gives rise to a metric regarding the fidelity of the model and 

challenges the Evaluation side of T&E to quantify it. 

5. DISCUSSION 

The myriad metrics identified in the foregoing (undoubtedly 

readers can nominate many more) indicates the mind-numbing 

extent, variety and ambiguity that the UAS T&E community must 

master. As [17] cautions, such situations of cognitive overload 

lead to underconceptualization of solutions. Fortunately, ways of 

coping with these problematic situations have been demonstrated 

recently though perhaps not within the Department of Defense, 

DoD. The page limit in this paper does not allow more than 

pointers to the key ideas that merit further consideration. Four are 

summarized in this section. However, brevity does not imply 

arbitrary choice. All four must be pursued in order to formulate a 

sufficient cascade of requisite variety. 

a) Autonomous T&E Enterprise: Commit to creating an 

Autonomous (intelligent) T&E Enterprise staffed with 

qualified DoD personnel. The DoD T&E capability must 

be distributed geographically and across Services but 

unified in strategy, objectivity and systemics. The new 

enterprise should be acknowledged as an intelligent system 

and conduct their practice reflectively (14). This may entail 

formulation of a Concept of Operations (usage), an 

intervention strategy, agile system design and evolutionary 

acquisition of personnel and assets. The Interactive 

Management process [16] is appropriate. Architectureframeworks are not. 

b) Enhance Systemics Praxis: Practitioner productivity and 

innovation must be increased approximately ten fold over 

current practices [13]. Evolve the Effects-Capabilities 

approach into model-based systems engineering [19] of 

whole systems. 

Evolve the DoD guidelines for systems engineering of 

system of systems. The current version adds seven new 

processes to sixteen processes from traditional systems 

engineering as described in ISO #15288. All ignore 

metrics. Further, improving traditional systems praxis will 

not suffice for the autonomy paradigm shift. 

Revise the JTEM approach to separate the systems 

engineering of the operational capability from the systems 

engineering of the T&E capability. 

c) UAST Generator: Develop an ontology-based 

configuration generation and behavior estimation tool 

encompassing thermodynamics, informatics, biomatics, 

teleonomics, human social dynamics, economics and 

ecologics. 

Generate prescriptive models and UAST(i) build scripts 

that enable engineering, construction and readiness 

verification within the UAS development cycle times and 

Field Command cycle times. 

Leverage new technologies to assess UAS and UAST 

correctness in seconds instead of hours of test and retest 

[11]. 

d) Proactive Interchange of Models: Demand that UAS 

development projects provide an executable model of the 

mission profile for which the UAS was designed. Likewise 

from field commanders who use JTEM and net-centric 

configurators. 

6. CONCLUSIONS 

Current practices for exercising and observing UAS’s in various 

stages of development are inadequate, partly because of the 

complexity (actually the extent, variety and ambiguity) of the 

situation but also because most participants under conceptualize 

the systemics involved [17]. 

Adequate, accurate and timely knowledge about UAS’s will not be 

garnered by simply understanding and categorizing UAS concepts. 

UAS’s must be exercised and observed by simulation, emulation 

and actual operation. In Live, Virtual and Constructive approaches 

the fidelity of contrived context is a key metric. Expansion of 

evaluation techniques and tools for in situ testing should be 

pursued. 

A necessary and sufficient response to the problematic situation 

requires that the whole field of discourse be treated as one system, 

that the cascade of requisite variety be necessary, sufficient and 

efficient and that the whole system be treated as an implicit, soft 

system. 

Metrics define the problem. Hypothesizing solutions sans metrics 

that describe the intended effect on the problem is malpractice. 

Presuming the T&E community converges on a set of metrics, 

then evolutionary acquisition of the ATEE will be prudent and 

urgent. A concept of operations (usage) of the whole system 

depicted in Figure 5 must be prepared and vetted along with an 

Intervention Strategy, and a framework of ATEE capabilities. 

Proven practices exist for accomplishing these objectives. They 

should be identified, adopted and systematized. 

Because an autonomous system includes a model of ‘itself’ two 

kinds of metrics are key. One set of metrics is concerned with the 

state of the system while the other set of metrics is concerned with 

the state of the model, especially the viability of a proposed 

scenario of change. 

Because any system ‘as is’ rarely matches the system presumed 

then establishing the identity of a system (the capability of a 

111
[9] Macias, F. 2008 Test and Evaluation of Unmanned and 

Autonomous Systems, ITEA Journal, Vol 29 No 4, pg 392. 

UAST to ‘know’ the UAS and of a ATEE to ‘know’ a UAST) is 

fundamental [11]. 

7. ACKNOWLEDGMENTS [10] Novak, J., 1998 Learning, Creating, & Using knowledge, 

Lawrence Erlbaum Assoc. 

Fil Macias, Ph.D., Executive for the UAST Focus Group, TRMC, 

DoD, and members Thomas Tenorio, Ph.D., Frank Lopez, Ph.D., 

and Philipp Djang, Ph.D. stimulated ideas on this subject. Thanks 

to PreMIS track organizers, Nikita Visnevski, Ph.D. and Mauricio 

Castillo-Effen, Ph.D., both of GE. 

[11] Ring, J. and Pizzarello, A., System of Systems Viability 

Assessment Capability, Presentation at 3rd Annual System of 

Systems Conference, NIST, Gaithersburg, MD, December, 

2008, available from author, jring@amug.org 

[12] Ring, J. ed. 2007 About Intelligent Enterprises. A Collection 

of Knowledge Claims. INCOSE 2007 8. REFERENCES 

[1] Ashby, W.R., 1956 Introduction to Cybernetics, Chapman 

and Hall. [13] Ring, J. 2009 Discovering a Strategy for Evolving to Whole 

System Modeling, IEEE Systems Conference. [2] Beer, S. 2002 "What is cybernetics?" Kybernetes (MCB UP 

Ltd) 31 (2): 209–219. [14] Schon, D.,1987 Educating the Reflective Practitioner, 

Jossey-Bass. [3] Checkland, P, and Scholes, J. 1990 Soft Systems 

Methodology in Action. (Wiley) ISBN 0-471-92768-6 [15] Warfield, J. 1990 Science of Generic Design, Intersystem 

Publications. [4] Crosby, P., 1995 Quality Without Tears: The Art of HassleFree Management, McGraw Hill [16] Warfield, J. 1994 Handbook for Interactive Management, 

Iowa State University Press. [5] Drucker, P. 1954 The Practice of Management, Harper & 

Brothers [17] Warfield, J. 2002 Understanding Complexity: Thought and 

Behavior, Ajar Publishing. [6] Gelertner, D., 1998 Machine Beauty, Basic Books 

[18] Weinberg, G. 2001 An introduction to general systems 

thinking, Dorset House Publishing Co., Inc. [7] Koestler, A. 1967. The Ghost in the Machine Penguin Group 

(1990 reprint edition).. 

[19] Wymore, A. 1993 Model-based Systems Engineering, CRC 

Press. [8] Livingston III, W. 1990 Friends In High Places, F.E.S. 

Limited Publishing 

Appendix 1. Example UAS Characteristics and Properties 

112
The Role of Competitions in Advancing Intelligent 

Systems: A Practitioner’s Perspective

Elena Messina

National Institute of Standards and 

Technology

Gaithersburg, MD 20899-8230

+1 (301) 975-3510

elena.messina@nist.gov

Raj Madhavan

National Institute of Standards and 

Technology

Gaithersburg, MD 20899-8230

+1 (301) 975-2865

raj.madhavan@nist.gov

Stephen Balakirsky

National Institute of Standards and 

Technology

Gaithersburg, MD 20899-8230

+1 (301) 975-4791

stephen.balakirsky@nist.gov

ABSTRACT

In recent years, the number of competitions in the robotic domain 

has increased tremendously. This growth is spurred by the 

appealing nature of robots, the flexibility that they afford in 

competition themes due to the practically unlimited applications, 

and by the recognition that competitions can yield advances in a 

technological domain that is immature. The National Institute of 

Standards and Technology has supported a variety of competitions 

to help stimulate innovation in certain critical technologies and 

capabilities needed in robotics. In this paper we present some 

general views of competitions and discuss the experiences NIST 

has had with robotics competitions as catalysts for advancing the 

state of intelligent systems. This paper is a lead-in to others in a 

special session organized at the 2010 Performance Metrics for 

Intelligent Systems (PerMIS) workshop that describe in more 

detail how competitions are used to advance intelligent systems. 

Categories and Subject Descriptors

F.2.3 [Theory of Computation]: Analysis of Algorithms and 

Problem Features – Tradeoffs among complexity measures.

General Terms

Measurement, Performance Evaluation, Performance Metrics, 

Intelligent Systems, Robotics

Keywords

Robotics, Competitions

1. INTRODUCTION

Robot competitions are becoming increasingly popular, serving a 

number of goals that range from primary education to stimulating 

technological advancements for real-world applications. 

Competitions can be categorized in many ways. In this paper, we 

view them through a prism of three dimensions: motivation, 

objectives, and evaluation techniques. Note that these usually 

have some form of interdependence.

The spectrum of motivations for robot competitions ranges 

widely. On one end, robot competitions are pure entertainment. 

There have been several popular television shows featuring robot 

combat, for instance, such as Robot Wars1

 

1 Certain commercial equipment, instruments, or materials are 

identified in this document. Such identification does not imply 

and BattleBots [1] [2]. 

These types of competitions exercise the creativity of the 

contestants, but are not designed to specifically advance the state 

of the art. Education is another popular motivation for 

competition. The For Inspiration and Recognitions of Science and 

Technology (FIRST) competition was specifically created to 

inspire young peoples’ interest and participation in science and 

technology [3]. A third major motivator for competitions is to 

stimulate progress in the technology itself. One-time “grand 

challenges” are an example of this. Some competitions blend 

education and technological advancement. One instance of this is 

the RoboCup array of competitions. In this paper, we briefly 

present an overview of the spectrum of objectives and 

methodologies employed in robotics competitions. Certainly, all 

forms of competitions involve performance measurements, 

making them useful to examine in the context of performance 

metrics for intelligent systems. 

2. THE BEGINNINGS: MICRO-MOUSE

It has been over three decades since the first mobile robot 

competition made its debut. The Micro-mouse maze contest, 

announced in 1977, was first conducted in 1979 and is considered 

the first such competition [4]. The initial task was simple: a robot 

mouse was to drive from start to goal through a maze in the least 

time. Over the years, the rules evolved to create greater challenges 

to the intelligence of the “mice.” In the first contest, the robots 

were simple wall-huggers. An early rule change was to have the 

mouse start in a corner of the maze and end up in the center. This 

change forced more intelligence in the path planning. Another 

later rule change required the mouse to explore the entire maze 

and then compute the shortest path. Even this earliest competition 

illustrates the basic premise of this paper: that carefully crafted 

competitions (and their rules, which should evolve) can steer 

research advancements. According to Braünl [5], by 1999 the 

electronics, sensors, and software problems of the micromouse 

were solved, with only mechanical improvements still possible. 

 

recommendation or endorsement by the National Institute of 

Standards and Technology nor does it imply that the products 

identified are necessarily the best available for the purpose.

113
3. SPECTRUM OF COMPETITIONS

Since the micro-mouse beginnings, robotics competitions have 

flourished, for a variety of purposes and reasons. Robots are 

inherently appealing to youngsters. Therefore robot-centered 

competitions are useful for attracting students to science, 

technology, engineering, and math (STEM). Competitions can 

expose students to many aspects of STEM and encourage them to 

pursue studies in these disciplines. Notable examples of STEMoriented competitions are FIRST and BotBall [6]. 

At the college level, a number of competitions are designed to 

bolster engineering education by providing a systems design and 

integration challenge. For example, the Association for 

Unmanned Vehicle Systems International (AUVSI) has a wide 

range of robotics competitions spanning ground, aerial, and 

aquatic domains [7]. The challenges posed are representative of 

missions that robots currently cannot complete in the military or 

commercial world, but the emphasis is on education. A recent 

AUVSI International Aerial Robotics Competition (IARC) had 

fairly detailed scoring, allotting points for effectiveness measures, 

such as avoiding all obstacles without collision, and for specific 

mission tasks such as retrieving a specific object, as well as 

subjective measures, such as elegance of design and safety of 

design to bystanders. There are even points allotted for the 

quality of a journal paper submitted by the team and for best tee 

shirt design. The mission design for each competition builds on 

the prior ones, increasing in difficulty with each year.

High risk, high payoff competitions have been staged to advance 

the state of the art in targeted applications. This “grand challenge” 

model is used to introduce a community to a compelling and 

major technological goal that can only be attained by concentrated 

and often collaborative efforts. A recent example is that of the 

United States’ (U.S.) Defense Advanced Research Projects 

Agency (DARPA) competitions for driverless vehicles. These 

competitions have offered prizes of a million dollars and above to 

teams that successfully complete a course autonomously. The 

impetus for this competition is the U.S. military’s stated goal of 

having one third of ground military vehicles autonomous by 2015. 

The first challenge was off-road, and none of the robots 

completed more than 11.78 km out of the 20 km length course in 

the first year. By the second year, all but one of the contestants 

went beyond 11.78 km and five teams completed the entire course 

[8]. For the third year, the competition turned its focus to urban 

driving environments and required the vehicles to follow traffic 

laws. The competition featured multiple vehicles on the course 

simultaneously. Six teams completed this challenge [9]. Another 

dimension of competitions is the set of objectives for victory. 

Yanco proposed a taxonomy for determining competition 

outcomes that includes ranked competition with subjective 

scoring, ranked competition with objective scoring, and nonranked competition with technical awards [10]. In this paper, we 

augment Yanco’s perspective. Some robotic competitions may 

require the contestants to complete a successful task. The 

Association for the Advancement of Artificial Intelligence 

(AAAI) has held several competitions at its annual conference, 

many of which have been credited with fostering advancements in 

robotics. For the “Hors d’ Oeuvres Anyone?” competition, robots 

served food to attendees at the conference’s banquet. The scoring 

for this competition included an audience vote component, along 

with the successful completion of the task of serving food 

(including restocking the serving tray). The Hors d’ Oeuvres event 

drove research in manipulation, navigation through dynamic 

worlds, and human-robot interaction [11]. 

Some competitions are based on team contests. Various leagues 

within the RoboCup Soccer organization pit two teams against 

each other on a range of soccer fields. The RoboCup initiative’s 

goal is to “foster artificial intelligence and robotics research by 

providing a standard problem where a wide range of technologies 

can be examined and integrated” [12]. The soccer competitions 

were begun in 1997, with several leagues designed to challenge 

different aspects of the overall robotics problem. For example, 

the small-size (below 18 cm diameter) robot league focuses on the 

issues of multi-agent cooperation with a hybrid 

centralized/distributed system, whereas the humanoid league 

encourages mechanical and electronic advances in physical 

bipedal robots, as well as in the planning and perception software. 

The abilities of the robots have steadily – even dramatically – 

improved over the years. Robots can detect the ball, goal, and 

opponents as well as teammates much more quickly than in the 

early years and can apply strategy and adaptive techniques. The 

sensing and planning have evolved tremendously [13]. On the 

hardware front, there has been significant progress for humanoid 

robots. They have increased bipedal stability and can move with 

greater agility each year. The progress is evidence that an 

ongoing, well-defined set of challenges can inspire innovation. 

Other competitions use performance-based models, pitting the 

robots against a baseline measure. Such is the case with RoboCup 

Rescue. In 2001, the RoboCup organizers expanded their 

competitions to include disaster rescue [14]. Viewed as an 

important challenge in robotics, wherein large numbers of 

heterogeneous agents collaborate within hostile environments, 

there are multiple competitions and leagues in this application 

area [15]. The multi-faceted goals of RoboCup Rescue are “to 

promote research and development in this significant domain by 

involving multi-agent team work coordination, physical robotic 

agents for search and rescue, information infrastructures, personal 

digital assistants, standard simulator and decision support 

systems, evaluation benchmarks for rescue strategies and robotic 

systems that are all integrated into a comprehensive system in the 

future” [16]. The competitions in the physical robot league pit a 

robot or team of robots against a disaster environment, called the 

arena. The robot is to explore the space, map it, and identify 

victims within a fixed time period. There are many mobility 

challenges, and areas where fully autonomous operation is 

required (teleoperation is allowed in many parts of the arena). 

Robotic hardware designs, as well as software algorithms and 

sensors have shown tremendous progress in the past decade. 

Innovations introduced by teams that prove successful are quickly 

replicated by others, disseminating good designs and accelerating 

progress. Robots can tackle terrains that were deemed 

impossible a few years ago. They produce maps of better quality 

with each passing year. The competition’s rules and scoring 

metrics are revised each year in order to ensure that the challenges 

grow increasingly complex and more reflective of reality. For 

instance, there are areas of the arenas where robots must operate 

exclusively autonomously and new manipulation tasks (e.g., 

opening doors) are being introduced.

A virtual competition for rescue offers larger, more complex 

environments and stresses collaborative planning of teams of 

robots [17]. Teams are required to address elemental tasks that 

114
include the autonomous distribution of up to eight robots to form 

communication repeater networks, autonomous multi-vehicle 

mapping, and multi-vehicle tele-operation. These skills are then 

brought together in a simulated full rescue scenario. As in the 

physical rescue league, the tasks and rules are modified as teams 

become more capable. This competition also provides an open 

source coding environment and the requirement that a team’s 

source code becomes open source at the conclusion of the event. 

This assures that new teams are able to quickly become 

competitive and that good ideas propagate throughout the 

community. 

RoboCup Rescue has evolved over the years to directly tie the 

competition to performance standards being developed for 

response robots. In a NIST-led project funded by the Department 

of Homeland Security, individual test methods are being 

developed to measure how well a robot meets certain 

requirements, which have been defined by end users [18]. 

Examples of requirements entail mobility over terrains of varying 

difficulty and the ability to aim or direct cameras and other 

sensors in a purposeful way to identify victims or relevant items 

in the environment. Individual test method elements are 

incorporated within the physical competition arenas. Thus, the 

research community is presented with real-world challenges 

against which they can pit their ingenuity and thereby advance the 

state of the art in robotics. 

The success in stimulating innovation in the rescue robotics 

community via the RoboCup competitions led NIST to establish 

competitions in other domains. A virtual manufacturing 

automation competition (VMAC) strives to promote 

advancements in robotic algorithms, especially in sensing and 

planning, for factory operations [19]. Recent advances in 

microelectromechanical systems have enabled the development of 

mobile microrobots that can autonomously navigate and 

manipulate. This technology is expected to be critical to numerous 

applications, including sensor networks, medical diagnosis and 

treatment, and micro-assembly. Since there are many challenges, 

such as in locomotion, NIST has organized performance-based 

competitions for mobile microrobots to help coalesce the research 

community. Both the VMAC and the microrobotics competitions 

have been adopted by the Institute of Electrical and Electronics 

Engineers’ annual International Conference on Robotics and 

Automation. The RoboCup organization hosted microrobotics 

demonstrations for the first few years.

Recent advances in the design and fabrication of 

microelectromechanical systems (MEMS) have enabled the 

development of mobile microrobots that can autonomously 

navigate and manipulate in controlled environments. It is expected 

that this technology will be critical in applications as varied as 

intelligent sensor networks, in vivo medical diagnosis and 

treatment, and adaptive microelectronics.

However, many challenges remain, particularly with respect to 

locomotion, power storage, embedded intelligence, and motion 

measurement. As a result, NIST has organized performance-based 

competitions for mobile microrobots that are designed to: 1)

motivate researchers to accelerate microrobot development, 2) 

reveal the most pressing technical challenges, and 3) evaluate the 

most successful methods for locomotion and manipulation at the 

microscale (e.g., actuation techniques for crawling).

4. CONCLUSIONS

We have discussed a sampling of robotics competitions and the 

various objectives possible. This is not meant to be an exhaustive 

list, as there are too many competitions (and the number is 

growing annually). For example, [20] gives several examples of 

how mobile robot competitions can foster advances on many 

fronts. Clearly, robotics competitions are useful mechanisms to 

serve many purposes, ranging from entertainment to education to 

stimulating innovations. According to Yanco, “Competitions 

often influence the direction of research in robotics, which can be 

used to great advantage” [10]. Incorporating ways of measuring 

performance in particular tasks or missions has proven to be a 

useful means of helping the research community better understand 

the problems to be solved. Having annual competitions with 

evolving challenges as technologies mature is an effective way of 

motivating the creativity of the international robotics community 

towards useful, real-world solutions and advancements in the 

technologies for robots.

 

5. REFERENCES

[1] http://www.marcthorpe.com/robot.html

[2] http://www.battlebots.com/BattleBots.com/Home/Home.html

[3] http://www.usfirst.org/

[4] http://micromouse.cannock.ac.uk/history.htm

[5] Braünl. T., Research Relevance of Mobile Robot 

Competitions, in IEEE Robotics and Automation Magazine, 

Vol. 6, no. 4, Dec 1999, pp 32-76.Balch T. and Yanco, H., 

Ten Years of the AAAI Mobile Robot Competition and 

Exhibition, AI Magazine, Vol. 23, No. 1 (Spring 2002).

[6] Miller, D., Using robotics to teach computer programming 

and AI concepts to engineering students. In Accessible 

Hands-on Artificial Intelligence and Robotics Education, 

AAAI Spring Symposium, 2005.

[7] http://www.auvsi.org/AUVSI/AUVSI/Events/AUVSIStudent

Competitions/

[8] Seetharaman, G. Lakhotia, A. Blasch, E.P., Unmanned 

Vehicles Come of Age: The DARPA Grand Challenge, 

Computer, Volume: 39, Issue: 12, December, 2006.

[9] Buehler, M., Iagnemma, K., and Singh, S., Editorial, Journal 

of Field Robotics, Special Issue on the 2007 DARPA Urban 

Challenge, Part I, Volume 25 Issue 8. 

[10] Yanco. H., “Designing Metrics for Comparing the 

Performance of Robotic Systems in Robot Competitions, “ In 

Proceedings of the Workshop on Measuring Performance 

and Intelligence of Intelligent Systems (PerMIS), IEEE 

Conference on Control Applications, Mexico City, Mexico, 

September 2001.

[11] Balch T. and Yanco, H., “Ten Years of the AAAI Mobile 

Robot Competition and Exhibition,” AI Magazine, Vol. 23, 

No. 1 (Spring 2002)

[12] Kitano, H., Kuniyoshi, Y., Noda, I., Asada, M., Matsubara, 

H., and Osawa, E., RoboCup: A challenge problem for AI. 

AI Magazine, 18(1):73–85, Spring 1997.

115
[13] E. Pagello, E. Menegatti, A. Bredenfel, P. Costa, T. 

Christaller, A. Jacoff, D. Polani, M. Riedmiller, A. Saffiotti, 

E, Sklar, and T. Tomoichi, “RoboCup-2003 New Scientific 

and Technical Advances”, AI Magazine, Volume 25, No. 2, 

2004.

[14] Jacoff, A., Messina, E., Evans, J., “A Standard Test Course 

for Urban Search and Rescue Robots,” Proceedings of the 

Performance Metrics for Intelligent Systems (PerMIS) 

Workshop, NIST SP 970, August 2000.

[15] Kitano, H. and Tadokoro, S., “RoboCup Rescue: A Grand 

Challenge for Multiagent and Intelligent Systems,” AI 

Magazine, Volume 22, No. 1, 2001.

[16] http://www.robocuprescue.org/

[17] S. Balakirsky, S. Carpin, and A. Visser , “Evaluating The 

RoboCup 2009 Virtual Robot Rescue Competition,” 

Proceedings of the Performance Metrics for Intelligent 

Systems (PerMIS) Workshop, Gaithersburg, MD, September 

2009.

[18] Messina, E., “Performance Standards for Urban Search & 

Rescue Robots: Enabling Deployment of New Tools for 

Responders,” Defense Standardization Program Office 

Journal, July/December 2007, pp. 43-48.

[19] Balakirsky, S. and Madhavan, R., “Advancing 

Manufacturing Research Through Competitions,” 

Proceedings of the SPIE Defense Security and Sensing, 

Orlando, FL, April 13-17, 2009.

[20] L. Almeida, J. Azevedo, C. Cardeira, P. Costa, P. Fonseca, P. 

Lima, F. Ribeiro, V. Santos, “Mobile Robot Competitions: 

Fostering Advances in Research, Development, and 

Education in Robotics,” in Proceedings of 

CONTROLO’2000, the 4th Portuguese Conference on 

Automatic Control, Guimarães, 2000

116
Evaluating The RoboCup 2009 

Virtual Robot Rescue Competition 

Stephen Balakirsky

NIST

100 Bureau Drive

Gaithersburg, MD, USA

+1 (301) 975-4791

stephen@nist.gov

Stefano Carpin

University of California, Merced

5200 N Lake Rd

Merced, CA, USA

+1 (209) 228-4152

scarpin@ucmerced.edu

Arnoud Visser

Universiteit van Amsterdam

Science Park 107

1098 XG Amsterdam, NL

+31 (20) 525-7532

A.Visser@uva.nll

ABSTRACT

The 2009 RoboCup Competitions took place in Graz, Austria in 

July of 2009. The Virtual Robot Rescue Competition included 11 

competitors from 10 different countries. The main objective of 

this competition is to utilize teams of robots to perform an urban 

search and rescue (USAR) mission over both indoor and outdoor 

terrains. For the first time, elemental tests were performed in 

autonomously generated map quality, multi-vehicle teleoperation, and communication’s system deployment. In addition, 

a comprehensive USAR scenario was performed. This year’s 

competition featured new performance metrics and automatic 

scoring programs. This paper presents an overview of the metrics 

for the competitions and lessons learned from their application 

during a high-intensity international competition.

Categories and Subject Descriptors

F.2.3 [Theory of Computation]: Analysis of Algorithms and 

Problem Features – Tradeoffs among complexity measures.

General Terms

Measurement, Performance, Experimentation, Human Factors, 

Standardization.

Keywords

Robotics, Evaluation, Competition, Simulation, Performance 

Metrics, RoboCup

1. INTRODUCTION

July 2009 saw the fourth annual running of the RoboCup Rescue 

Virtual Robot Competition in Graz, Austria. The RoboCup 

competition [1] provided an international forum where 

approximately 400 teams, with 2000 participants from 35 

countries came together to compete in the areas of robot soccer, 

rescue, service robotics, and junior leagues. For the Virtual Robot 

Competition (VRC), 11 teams from 10 countries (Austria, Brazil, 

China, England, Germany, Iran, Italy, the Netherlands, Spain and 

the USA) participated.

Figure 1: Example of the bridge accident scene from the 

outdoor environment used in the RoboCup07 competition.

In the past, the VRC was run as several rounds of Urban Search 

and Rescue (USAR) scenarios. Each scenario consisted of teams 

of robots striving to find as many victims as possible in an indoor 

or outdoor accident scene such as the one depicted in Figure 1. 

The scoring performance metrics were specifically designed to 

award research advances in the general areas of multi-agent 

cooperation, human-computer interfaces (HCI), and map building. 

Specific emphasis was placed on the formation of multi-agent 

communication networks, complex terrain navigation, and victim 

search and identification strategies. While certain aspects of the 

scoring were computed automatically, a significant part of the 

scoring metric was computed by hand by the technical committee 

of the competition. This scoring procedure was very time 

consuming and placed a large burden on the committee, thus 

limiting the number of teams that would be able to participate in 

the event. More information on the scoring metrics utilized in past 

competitions may be found in Balakirsky et al. [2]. 

While these performance metrics proved useful in determining the 

overall winner of the competition, it was not possible to get deep 

insight into why a team won. The individual components that 

constitute a team’s capabilities were not evaluated; only the 

composite results. Since strength in several different areas is 

required to successfully carry out the mission, it may be stated 

that the team with the strongest weakest link would win the 

competition. The goal of this year’s event was to change that. We 

wished to be able to determine which team had the strongest 

mapping, which had the best communications strategy, which had 

(c) 2009 Association for Computing Machinery. ACM acknowledges 

that this contribution was authored or co-authored by a contractor 

or affiliate of the U.S. Government. As such, the Government 

retains a nonexclusive, royalty-free right to publish or reproduce 

this article, or to allow others to do so, for Government purposes 

only.

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA.

Copyright © 2009 ACM 978-1-60558-747-9/09/09...$10.00

117
the best human-computer interface, AND which had the best 

overall system. To this end, we implemented the SCORE 

framework of evaluation [3] and evaluated three elemental tests as 

well as the overall USAR scenario. In addition, an effort was 

made to automate as much of the scoring procedure as possible. 

This allowed our three person technical committee to 

simultaneously work with four teams (two teams competing in an 

actual event and two teams setting up to compete in the next 

event). 

The remainder of this paper is organized as follows: Section 2 

provides an overview of the elemental tests and the final scenario. 

Section 3 describes the scoring metrics and the automated scoring 

tools that were utilized during the competition. Section 4 presents 

a summary of the results and lessons learned about the scoring 

metrics and Section 5 presents conclusions and future work.

2. COMPETITION OVERVIEW

The competition consisted of three preliminary events that were 

designed to test the individual team’s capabilities in elemental 

skills followed by four comprehensive events that presented an 

overall search and rescue scenario. The points gained from the 

preliminary events (0 to 50 for each elemental test) were totaled to 

determine who would proceed to the semi-final round. The semifinal round presented two disaster scenarios (one indoor and one 

outdoor) to the remaining teams. Points were again summed to 

determine who would proceed to the final round. The final round 

was run in the same manner as the semi-finals. All of the 

environments used in the competition have been released to the 

public via our sourceforge website.1

 

An enhancement in the 2009 worlds included the use of elements 

that were directly borrowed from the RoboCup Rescue Physical 

Robot League. This league features real robots competing in 

physical arenas to provide maps of the environment and locate 

victims. For this year’s competition the physical league’s maze 

area was virtually constructed and replicated in order to fill the 

upper right room of the mapping challenge world (see Figure 2). 

Additional elements, such as step fields, appeared in all of the 

virtual competition worlds.

Figure 2: View on the physical league’s maze inside the VRC 

mapping challenge environment.

 

1 http://sourceforge.net/projects/usarsim/files/Maps/3.31/RoboCup09.zip

2.1 Mapping Challenge

The first elemental skill test consisted of a mixed-autonomy (both 

tele-operation and autonomous operation) mapping challenge. The 

idea behind this event is that robots are given 20 minutes to map 

out the environment before the emergency responders enter the 

building. Once they enter, the emergency responders need to 

know the best routes to take to various newly discovered points of 

interest. No a priori data was provided to the teams for this event. 

Teams were allowed to use up to four robots to explore this indoor 

environment. During this exploration, the robots must 

communicate with each other through the use of a 

communications simulator and with the outside world through a 

communications station. The simulated sensors included a 

realistic noise model for both external (laser range finder and 

sonar) and internal (wheel encoders and IMU) sensors. The world 

was designed to stress the robot’s sensors and algorithms by 

featuring both flat floored and sloped floor mazes, large 

featureless spaces, and various lighting conditions. The overall 

size of this world was 45m x 55m. No team was able to explore 

the entire environment with 4 robots during the 20 minute period.

Figure 3: Team 1’s mapping challenge map.

A sample of “Team 1’s” map is shown in Figure 3. The green 

circles in the figure represent the starting locations of the four 

robots. Yellow circles are utilized for scoring and will be 

discussed in 3.1. The team’s map (white) has been overlaid on the 

ground truth for the event. The upper-left and lower-right rooms 

consisted of flat floored mazes. The upper right room was an 

enlarged model of one of the physical robot’s mapping mazes. 

The room on the lower-left was a large featureless and dark space. 

The robots were able to maintain a good connection with the 

118
communication’s station (shown as the red dot) from anywhere in 

the world. Teams from this event also participated in the interleague challenge which featured real data collected from the 

physical leagues maze. For the interleague challenge, the teams 

ran their identical code from the simulation events on real data. 

2.2 Deployment Challenge 

The second elemental skill test consisted of a deployment 

challenge. Teams were allowed to use combinations of up to eight 

robots in this event. For this event, a radio propagation model was 

utilized to determine if robots were able to communicate with 

each other. This model computes radio signal attenuation based on 

a combination of the distance between the two robots and the 

number of objects (walls and obstacles) that the signal needs to 

pass through. A uniform signal loss was applied for each object 

penetration.

The idea behind this event was that emergency responders needed 

to enter and work in a building that was too large to have 

continuous communications coverage without repeaters. Each of 

the team’s robots had a repeater mounted on it, and the teams 

were required to establish a communications network that covered 

as much of the building’s interior as possible. Scoring for this 

event was based on the number of square meters of the building 

that had network connectivity with a communications base 

station. This measure was automatically generated. 

Figure 4: Section of Deployment map from Team 2.

Approaches to accomplish this task ranged from pre-planning the 

locations for the robots based on an estimate of the 

communication strength, to driving a robot until it reached the end 

of its communication range and then extending this range by 

driving a new robot into the frontier. No operator involvement 

was allowed during this test, so all robots had to navigate 

autonomously through the environment.

Figure 4 shows Team 2’s map for this challenge. Team 2 precomputed expected coverage and then autonomously navigated 

their robots to the computed locations. The blue dots in the figure 

represent the robot’s starting locations and the white dots 

represent the robot’s final locations. The green dot is where the 

communication station was located. The red areas are obstacles or 

outside of the building structures, while the green area represents 

the radio coverage. Teams were provided with a priori data for 

this event, but the data had several intentional errors with some 

rooms being blocked by collapses. Some teams miscomputed the 

radio coverage and drove their robots too far thus disconnecting 

them from the overall network. Team 2 was one of those teams. 

As may be seen in Figure 4, the two top robots are not connected 

to the overall network (i.e. there is no path from them to the 

communication’s station) and thus did not generate any points for 

the team. In addition, not all of the robots were able to reach their 

desired ending locations. This was due to blockades and 

navigational challenges present in the terrain, which the robots 

had to overcome autonomously.

2.3 Tele-Operation Challenge 

The final elemental skill test performed was a tele-operation 

challenge. For this challenge, teams were permitted to use 8 

robots in order to reach 8 predetermined goal points. Each goal 

point was selected such that a particular class of robot was best 

suited to reach it. For example, there were elevated goal points, as 

well as goal points in small spaces. The teams did not have a 

priori data on which robot should attempt which goal location. 

The idea behind this challenge is that emergency responders have 

knowledge of interesting locations that must be remotely 

examined. The teams need to reach these locations and provide 

feedback. In addition, past experience has shown that few teams 

were experimenting with novel robots and teaming arrangements. 

Therefore, another idea behind the tele-operation challenge was to 

introduce teams to a number of different platforms and to stress 

their human computer interfaces. Teams could use up to 8 robots, 

but could only use 2 of each robot class. This rule was put into 

effect in order to encourage teams to experiment with multiple 

types of robots and to form heterogeneous teaming arrangements.

An additional challenge for the teams was the lighting condition. 

All ground robots had to navigate through a small maze before 

they could reach the target points. In that maze it was quite dark, 

forcing teams which rely on visual feedback to fall back to the 

other sensors present on the robots.

119
Figure 5: Example of GUI from the Tele-operation challenge.

This challenge was automatically scored by a metric that 

evaluated the number of goals reached and the distance from the 

goal that the robot was able to achieve. A screen shot from a 

sample GUI is shown in Figure 5. This figure shows the map that 

the robots are generating as well as unreached goal locations (the 

red dots).

2.4 Semi-Finals

The semi-finals took the competition back to its roots of 

performing a tele-assisted multi-robot rescue mission. The top 

five teams ran through both an indoor and outdoor disaster 

environment. The environments were challenging with large areas 

of uneven terrain. The semi-finals incorporated a complex scoring 

metric that included automatic and hand-generated scores. The 

metric included the amount of area cleared by the robots 

(guaranteed to be victim free), victim scoring that included the 

number of victims found and various attributes of the victims 

(location of injuries, physical description, ...), and map quality 

points.

Figure 6: Example of the outdoor disaster environment.

The original idea behind the semi-finals was to award the 3rd place 

prize and have only the top two teams proceed to the finals. This 

idea was abandoned, because after the semi-finals there was only 

a one point difference between 3rd and 4th places Therefore, a 

decision was made to have a 3rd place runoff the next day.

Figure 7: Vector components from final's indoor map.

2.5 Finals and Run-off

Another set of worlds was utilized for the finals and run-offs. 

These were the most complex worlds that have ever used since the 

start of the competition in 2006. The top two teams were able to 

provide both pixel maps (in our standard color scheme) and 

MapInfo Data Interchange Format (MIF) formatted vector 

skeletons. One such skeleton (with black arcs and purple nodes) is 

shown in Figure 7. This was a new feature of this year’s 

competition and shows one way that we are encouraging 

innovation amongst the teams. Figure 7 also displays other 

innovative vector information such as responder paths to located 

victims (red lines) and detailed victim information (each red dot is 

tagged with information on victim location and vital signs). Raster 

map components included geo-referenced victim pictures that can 

be displayed on the map as well as a raster obstacle map. 

3. AUTOMATIC SCORING

As previously stated, it was desired to provide automatic scoring 

programs whenever possible. During this competition, several 

new scoring techniques were examined with various degrees of 

success. These techniques are evaluated in the following sections.

3.1 Mapping Challenge

The evaluation of autonomously generated maps is an open 

question in the current literature. Many approaches tend to treat 

the map as an image and apply various image processing 

techniques to the map in order to judge its quality. One such 

technique is presented by Varsadan et al. in [5], where an image 

similarity metric is used to compare robot-produced maps against 

their ground truth equivalents. Past VRC events utilized a 

combined metric that had such a comparison at its heart. More 

information on previous year’s map evaluation metrics may be 

120
found in [5]. A problem with such a metric is that it is likely to 

negatively affect maps with a single misalignment that propagates 

through the rest of the map. Collins et al. [6] augment a purely 

image-based evaluation approach by adding a measure that assess 

if a path generated on the robot-generated map would be valid on 

the ground truth map. This approach is useful if another robot will 

be utilizing the autonomously generated map as ground truth for 

planning its own routes.

It is said in [8] that any map assessment method should be 

intimately tied to the practical task for which the map will 

eventually be used. In the case of the VRC, this would be for a 

emergency responder to utilize the map to find a path to a point of 

interest. These points would be specified in terms of features (e.g. 

fourth office on the left) instead of geodetic coordinates. It was 

hoped that this evaluation technique would reduce the problem of 

misalignments propagating through the map and distorting the 

map’s score. The actual approach that was implemented was to 

choose several random destinations from the environment as 

points of interest (POI). Paths to these POIs would then be 

computed on the team’s maps and evaluated for their topological 

correctness. 

The problem with this approach is that the team’s maps are 

delivered as grid-based images and not as topological structures. 

Therefore, a technique for extracting the topological information 

needed to be developed. To solve this task, the POIs were 

manually mapped to the team’s map and a standard path 

generating algorithm [9] was run to compute a path solution. The 

topological properties of this path were then evaluated and used to 

determine the map’s score.

Unfortunately, this automatic procedure was not finished in time 

for the competition and topological paths were generated by hand 

on the competitors map. While not the ideal solution of having an 

automated scoring tool, this procedure was easy and quick to 

implement and provided valuable insight to the value of this 

scoring metric.

3.2 Deployment Challenge 

The deployment challenge saw the first application of a fully 

automatic scoring technique applied to the competition. The Java 

scoring application may be found at sourceforge2

. 

The program faithfully replicates the equations used by the 

Wireless Communication Server (WSS) server in order to 

determine the signal attenuation between a transmitter and a 

receiver located at arbitrary positions in the world. The 

attenuation considers both degradation due to increasing distance, 

and the presence of obstacles. The overall score is computed as 

follows. Once the challenge is over, final positions of all robots 

are retrieved from the log files. These log files are automatically 

generated by the simulation system and contain full ground truth 

of the simulation run. The position of the communication base 

station, instead, is fixed and known to all participants. A 

connectivity graph is then created. The connectivity graph is a 

graph whose vertices are the robots and the communication base 

 

2 http://usarsim.cvs.sourceforge.net/viewvc/usarsim/usarsim/Tools/ScoreRadio/

station. An edge is added between two vertices if the 

corresponding elements can communicate with each other. In 

order to determine if two elements (robots or base station) can 

exchange data, the formerly mentioned equations are used to 

compute the signal attenuation between the two. If the signal 

strength is above a given threshold (-93 dB during the 

competition) an edge is added between the vertices. Once the 

graph is available, a breadth-first graph search is computed having 

the base station as the source vertex. All vertices reachable from 

the source are considered in communication with the base station. 

The rationale is that they can send information to the base station 

either directly or indirectly. Once the set of robots connected to 

the base station is known, the whole environment is sampled on a 

grid with a given resolution (specified by the user). A point in the 

grid is considered in communication range if it is connected to 

one or more robots reachable (directly or indirectly) from the base 

station. The overall score is the number of sampled points that are 

connected.

3.3 Tele-operation Challenge 

The tele-operation challenge was also scored automatically by a 

program located at sourceforge3 using the automatically generated 

log files. The goal of the challenge was to bring at least one robot 

to each target location in the allocated time.

A target location was considered reached if at least one robot was

within T meters of location where T was a constant determined by 

the judges and known to the teams before the competition. For the 

2009 event, this constant was set to 2 m. 50 points were awarded 

for each point reached. In addition, the program automatically

summed the distance of the target location from the deployment 

site. This distance was an indication of the difficulty to reach this 

point. To be able to rank teams which reached exactly the same 

targets, the distance of the robots to those target locations was 

subtracted from the score. 

The scores of all three challenges were normalized relative to the 

score of the best team, which gives each challenge an equal 

weight.

3.4 Semi-finals and Finals

The scoring of the full scenarios involved a combination of

automatic scoring and hand scoring. Points for world exploration 

and victims were computed with automatic programs4 while map 

quality assessments were performed by hand. For world 

exploration, a team’s map was first trimmed to remove any outof-bounds areas or poorly covered areas that were claimed to be 

explored. An example of a poorly covered area may be seen in the 

upper right corner of the lower left room of Figure 3. Here a team 

presents stripes of explored area mixed with stripes of unknown 

areas. An automatic program was run on the resulting image that 

computed the area of map that was explored (seen by the robot) 

and the area that was cleared (guaranteed to be victim free). 

Exploration points were normalized to a maximum of 50 points.

Victim points included points for correct victim localization and 

attribution, and subtractions for incorrect localizations and victims 

 

3 http://usarsim.cvs.sourceforge.net/viewvc/usarsim/usarsim/Tools/ScoreTeleOp/

4 http://usarsim.cvs.sourceforge.net/viewvc/usarsim/usarsim/Tools/ScoreVictims/

121
that resided in “cleared” areas and were not detected. Victim 

localization was computed automatically while attribution needed 

to be hand computed. Victim points were again normalized to a 

maximum of 50 points.

The final area of scoring was in the computation of skeleton 

quality, metric quality, and attribution of team provided maps. 

These scores were computed entirely by hand following the 

procedure outlined in [2]. 

Figure 8: Image of two different team's maps for the same 

area of the mapping challenge world.

4. SUMMARY OF RESULTS

Overall, we were very happy with the results of the automatic 

scoring. However, issues did arise with the proposed automatic 

scoring of the mapping challenge. The intent of the scoring metric 

was to select several pseudorandom points in the environment and 

to then compute routes to these points. However, mapping errors 

made is difficult to place these points on some of the competitor’s 

maps (even by hand). For example, Figure 8 shows the maps from 

two of the teams. While the map on the left shows slight 

misalignments, the map on the right presents several rotational 

errors and scan mis-matches that have caused extra walls to be 

added. 

The problem becomes one of determining where to place our 

pseudorandom points in the right hand map. This determination 

must be made before any topological map calculations may be 

made. One possible solution for this problem is to allow the teams 

to know the locations of the points before the run. The teams will 

then need to mark the point locations in their maps and routes will 

then be generated from the starting location to their marked 

points. The topological properties of these routes may then be 

judged against routes created on the ground truth map.

 

5. FUTURE WORK

While the current automation performed well, there are still 

several areas that need automating. Techniques need to be 

developed (or the metrics modified) that will allow for the 

automatic generation of scores for the semi-final and final rounds. 

In addition, the mapping challenge scoring program needs to be 

created and validated.

6. REFERENCE LIST

[1] H. Kitano, RoboCup-97: Robot Soccer World Cup I, Berlin: 

Springer-Verlag, 1998.

[2] S. Balakirsky, C. Scrapper, and S. Carpin, "The Evolution of 

Performance Metrics in the RoboCup Rescue Virtual Robot 

Competition," Proceedings of the Performance Metrics for 

Intelligent Systems (PerMIS) Workshop, . Aug. 2007.

[3] C. Schlenoff, M. Steves, B. Weiss et al., "Applying SCORE 

to Field-based Performance Evaluations of Soldier Worn 

Sensor Technologies," Journal of Field Robotics, vol. 24, no. 

8-9. pp.671-698, 2006.

[4] I. Varsadan, A. Birk, and M. Pfingsthorn, "Determining Map 

Quality Through An Image Similarity metric." In RoboCup 

2008: Robot Soccer World Cup XII. Springer 

Berlin/Heidelberg. Lecture Notes in Artificial Intelligence, 

volume 5339, pp. 355-365. 2009.

[5] B. Balaguer, S. Balakirsky, S. Carpin et al., "Evaluating 

Maps Produced by Urban Search and Rescue Robots: 

Lesssons Learned from RoboCup," Autonomous Robots 27, 

2009.

[6] T. Collins, J. Collins, and C. Ryan, "Occupancy Grid 

Mapping and Empirical Evaluation," Proceedings of the 

Mediterranean Conference on Control and Automation, pp. 

1-6, 2007.

[7] G. Fontana, M. Matteucci, and D. G. Sorrenti, "The 

RAWSEED proposal for representation-independent 

benchmarking of SLAM," Workshop on Experimental 

Methodology and Benchmarking in Robotics Research (RSS 

2008), 2008.

[8] J.C. Latombe, "Robot Motion Planning", Kluwer Academic 

Publishers, Boston, MA, 1991.

122
RoboCupRescue Interleague Challenge 2009:

Bridging the Gap between Simulation and Reality

Alexander Kleiner

University of Freiburg

Freiburg, Germany

kleiner@informatik.unifreiburg.de

Chris Scrapper

The MITRE Corporation

McLean, VA, USA

cscrapper@mitre.org

Adam Jacoff

National Institute of Standards

and Technology

Gaithersburg, MD, USA

adam.jacoff@nist.gov

ABSTRACT

The RoboCupRescue initiative, represented by real-robot

and simulation league, is designed to foster the research

and development of innovative technologies and assistive

capabilities to help responders mitigate an emergency response situation. This competition model employed by the

RoboCupRescue community has proven to be a propitious

model, not only for fostering the development of innovative

technologies but in the development of test methods used

to quantitatively evaluate the performance of these technologies. The Interleague Challenge has been initiated to

evaluate real-world performance of algorithms developed in

simulation, as well as to drive the development of a common

interface to simplify the entry of newcomer teams to the

robot league. This paper will discuss 1) the development

of emerging test methods used to evaluate robotic-mapping,

2) the development of a common robotic platform, and 3)

the development of a novel map evaluation methodology deployed during the RoboCupRescue competition 2009.

Keywords

SLAM, Performance Metric, Simulation, Mapping, Rescue

1. INTRODUCTION

Response robots refer to a broad class of mobile robots intended to assist emergency response personnel in a variety

of application domains; such as Urban Search and Rescue

(USAR), Explosive Ordnance Disposal (EOD), and Intelligence, Surveillance, and Reconnaissance (ISR). These platforms serve as an extension of the operator to improve remote situational awareness and to provide assistive capabilities that minimizes the risk to responders and maximize

the effectiveness and efficiency of a response in a tactical

environment. Although recent advancements in the technical capabilities of these robots have improved the flexibility,

utility, and survivability of overall system, in large these

systems have failed to achieve a technology readiness level

(c) 2009 Association for Computing Machinery. ACM acknowledges that

this contribution was authored or co-authored by a contractor or affiliate

of the U.S. Government. As such, the Government retains a nonexclusive,

royalty-free right to publish or reproduce this article, or to allow others

to do so, for Government purposes only. PerMIS’09, September 21-23,

2009, Gaithersburg, MD, USA. Copyright 2009 ACM 978-1-60558-747-

9/09/09...10.00

suitable for fielded systems deployed in their respective operational domains.

(a)

(b)

(c)

Figure 1: (a) Common robotic platform. (b) 2009

RoboCupRescue Maze in Graz. (c) Virtual maze in

USARSim.

Test methods establish a confident connection between developers and consumers regarding the expectations and performance objectives of robotic technologies. This is a cardinal step in fostering innovation and assessing the maturity

of evolving technologies. They consist of well-defined testing

apparatuses, procedures, and objective evaluation methodologies that isolate particular aspects of a system in known

testing conditions [ASTM, 2007]. The development of test

methods start with a comprehensive analysis of the application domain to identify requirements with associated metrics and the range of performance, starting from a baseline

threshold to the objective “best-case” performance. This

analysis provides the basis for developing test methods and

testing scenarios that are intentionally abstract so as to be

repeatable across a statistically significant set of trials and

reproducible by other interest parties. This also provides

developers with a basis for understanding the objective performance of a system and allows consumers to confidently

select systems that will meet their requirements.

Robotic competitions have also proven to be a propitious

model for fostering innovation and assess the maturity emerging robotic technologies. Commonly, test methods provide

123
the basis for evaluating the performance of robots in the

competitions; setup as either elemental tests or embedded

in operational environments to produce testing scenarios.

For instance, Defense Advanced Research Project Agency

(DARPA) and the European Space Agency have employed

this model to assess autonomous ground vehicle in urban

environments [Darpa, 2007] and in extraterrestrial environments, such as lunar landscapes or Mars exploration [ESA,

2008]. Not only do these competitions provide a means to

assess the performance of emerging technologies, they also

provide feedback as validity of the tests themselves.

The RoboCupRescue initiative also leverages the competition model to foster the research and development of the

key capabilities to assist in the mitigation of an emergency

response situation. This initiative partitions the emergency

management problem into three competitions; the RoboCupRescue Robot, the RoboCupRescue Virtual Robot Simulation, and RoboCupRescue Agent competitions. Each of

the competitions explore the partitioned problem space at

different levels of abstraction, ranging from search and rescue of a single building to the development of an Incident

Command System. The relevance of this initiative can be

gauged according to two aspects: 1) the ability of the competitions to communally develop comprehensive systems capable of achieving an appropriate technology readiness level,

and 2) the development of quantitative benchmarks and test

methods capable to assess emerging technologies and assisting responders making deployment and purchasing decisions.

Therefore, the 2009 RoboCupRescue competitions sponsored

an Interleague Challenge between the Robot competition

and the Virtual Robot Simulation competition to demonstrate how well robotic algorithms developed in simulation

can perform on a real robot. The challenge utilizes a common robotic platform and emerging standard test methods

to explore the stability and accuracy of online mapping technologies, emphasizing the impact on an operator’s ability to

efficiently and completely map an unknown environment.

This paper will discuss the development of emerging test

methods used to evaluate robotic-mapping, the development

of a common robotic platform, and the development of a

novel map evaluation methodology deployed during the 2009

RoboCupRescue Interleague Challenge.

The remainder of this paper provides an overview of the Interleague Challenge and is structured as follows. Section 2

provides an overview of the test methods and robotic platform employed at the challenge. Section 3 will detail the

2D map evaluation framework used to evaluate the competing maps at the challenge. This map evaluation framework

consists of a ground truth generation process and a map assessment tool. The evaluation results from the challenge are

presented in Section 4, followed by the conclusion in Section 5.

2. THE INTERLEAGUE CHALLENGE

The evaluation of robot-generated maps is often based on

qualitative approaches that do not take into account how

specific environmental conditions, differing sensor configurations, and in situ decisions impact the performance of the

system. While this type of analysis provides some indication

(a)

(c)

(b)

(d)

Figure 2: (a) The Maze is a testing apparatus

that limits complexities in the environment in order to evaluate the objective performance of particular mapping systems. (b) The configuration of

the maze, whose dimension is 15 meters by 10 meters, with black lines representing the maze layout

and the gray arrows showing the configuration of

15◦pitch and roll ramp flooring, shown in (c). (d)

The maze utilizes a variety of materials and shapes

to produce additional mapping features, such as concave and convex surfaces.

of the overall performance, it does not allow researchers to

identify problematic situations, analyze how errors propagate throughout the system, or compare the performance of

one system with competing approaches.

To address these issues the Interleague Challenge leverages

emerging test methods for robotic mapping and the development of a common robotic platform to enable repeatable

and reproducible testing scenarios. This constrains the variability in the test to abet in the evaluation of mapping algorithms as they transition from a purely virtual world to

the real world. The remainder of this section is dedicated to

describing the testing method and common robotic platform

used for this challenge.

2.1 A Test Method for Robotic Mapping

Associated Metrics

Arguably, the most common mapping paradigm employed

for robotic navigation is the metric mapping paradigm. This

intuitive mapping paradigm provides a representation where

the spatial relationship between any two objects in the map

is proportional to the spatial relationship of the corresponding objects in the actual environment. Therefore, assessing

the quality of metric maps is based on the spatial consistency

of features, such as walls and hallways, between the map produced by the robot and the ground truth map of the actual

environment. Error propagation and sensitivity to performance singularities [Scrapper et al., 2008] idiosyncratic to

most robotic mapping systems suggests the associated metric needs to quantify the local (or regional) consistency of

124
areas within the map, as well as the global consistency of

the overall map.

Apparatus

The Maze testing apparatus, seen in Figure 2, is part of an

emerging suite of test methods for characterizing the performance of the robotic mapping for response robots [ASTM

E54.08, 2009]. The apparatus is essentially a random maze,

whose overall dimension is 15 meters by 10 meters, and is

constructed from oriented strand board (OSB) to form a series of hallways that are approximately 1.2 meters wide. The

non-flat flooring, comprising of 15◦ramps, makes the vehicle

pitch and roll as it traverses the maze. The modularity of

the apparatus enables the randomization of the maze configurations for repetitive testing. The use of OSB to construct

this apparatus provides a surface friction similar to dust covered concrete and a cost-effective testing apparatus that is

easy to replicate.

This apparatus was chosen for the Interleague Challenge because it provides a feature-rich scenario that limits environmental complexities. This should provide the best-case

scenario, where mapping systems should perform optimally.

The configuration of this apparatus generates a closed set of

distinct mapping features and vertical walls that provide a

mapping system with distinct observations throughout the

apparatus. The presence of distinct features and the lack

of occlusions found in this apparatus reduce the uncertainty

in the mapping system when associating features, which increases the likelihood of determining valid correspondences.

Additionally, surfaces perpendicular to the motion of the

vehicle are present in almost every scan. This increases the

ability of the system to make accurate measurements of the

surrounding environment and accentuates the displacement

of features between observations of the external world. Limiting the environmental complexities allows developers to

tune their systems and establishes a baseline for comparison.

2.2 A Common Robotic Platform

A common robot with a fixed sensor configuration is used

to challenge researchers and ensure compatibility. As mentioned above, this platform also support the entry to robotics

of newcomer teams.

Robotic Platform

The base robotic platform used for the Interleague is designed to provide a cost-effective robotic platform capable

maneuvering in the complex terrain. The dimensions of the

common robotic platform is .533m x .304m x .762m (21 x

12 x 30 inches) , it weighs approximately 18 kg (40 lb), and

it is equipped with 4 rechargeable 12 volt batteries allowing

it to operate in for up to 10 hours. The platform is capable of operating in all weather conditions and a high degree

of mobility allows it to navigate on rough terrain and to

climb stairs up to 50◦inclination. The motors are equipped

with encoders for controlling set-velocities and computing

the wheel odometry.

Sensor Configuration

The sensor configuration is defined by two statically mounted

laser scanners (one horizontal scan and one vertical scan),

and an inertial measurement unit (IMU). Both laser scanners deliver range readings with a field of view of 240◦ with

an angular resolution of 0.36◦and a range of approximately

4 meters. The IMU provides measurements of the three Euler angles; yaw, roll, and pitch. Although the IMU supports

compass-based yaw measurements, i.e., orientation measurements relative to magnetic North, the current implementation does not utilize this information. Therefore, the orientation angles obtained from the IMU are based solely on

measurements take from the gyroscopes and accelerometers.

This is motivated by the fact that, particularly in harsh

environments, magnetometer readings can strongly be perturbed.

Robotic Interface

The platform provides two robotic interfaces: a low-level

serial interface and a high-level robotic interface. The serial interface provides developers with direct access to the

on-board controller, giving them direct access to motor commands and supporting the integration of both laser scanners

and the IMU via an USB interface implementing a vendor

specific protocol. In order to create a more user-friendly

interface, the common robotic platform has implemented a

high-level robotic interface. This interface is built on a wrapper application that automatically collects data from all the

sensors and provides them via a TCP/IP server executed

on-board the robot. The messaging protocol used by this

process server is chosen according to the protocol of the USARSim simulation application [Wang and Balakirsky, 2009].

Thereby users can control the robot and access sensor data

in the same way as if they would connect to the USARSim

simulator used in the RoboCupRescue Virtual Robot Simulation competition. Hence, the migration of software developed in simulation towards execution on the real platform

is simplified.

3. A 2D MAP EVALUATION FRAMEWORK

As mentioned in Section 2.1, the development of an evaluation framework for quantifying the performance of robotic

mapping algorithms in the metric paradigm requires two

key components. First is the ability to obtain or generate

an accurate ground truth of the test method. Second is the

ability to quantify the local consistency, incremental errors

arising in bounded regions within the map, as well as the

globally consistency, accumulation of errors throughout the

entire map. The remainder of this section will describe a

2D map evaluation framework consisting of a generalized

ground truth generation methodology (Section 3.1), and a

methodology for quantifying the performance of the mapping algorithms. (Section 3.2).

3.1 A Ground Truth Generation Tool

The process developed for generating ground truth of the

test methods at the Interleague Challenge, as described in

this section, is an attempt to automate the production of

ground truth with minimal a prior information about the

environment. However, this ground truth generation process

requires human intervention. Depending on the length of the

runs and sampling frequencies employed, this process can

be laborious. It is the belief of the authors that some level

of human intervention is necessary when developing ground

125
Figure 3: Manual verification of automated laser

scan alignment for ground truth generation.

truth of test environments, especially environments where a

priori information is not readily available.

The process used for generating ground truth during the

Interleague Challenge is a two-step process. The first step

uses a Simultaneous Localization and Mapping (SLAM) algorithm, supervised by a subject-matter expert (SME), that

directly processes raw sensor streams to estimate a globally

consistent trajectory and to derive initial displacement candidates δi,j , referred to in this paper as constraints. During the second step the SME is required to inspect every

constraint and verify the displacement. For example, the

SME inspects two consecutive observations obtained from

the laser range finder and the corresponding pose estimates

at time step i and j belonging to the two poses of the constraint, respectively (see Figure 3). During the verification

process, the SME can either accept or reject the constraints

previously estimated by the SLAM algorithm. In the case

of an accept, the SME verifies the final constraint, δ

∗

i,j , and

adjusts the displacement as necessary.

3.2 A Map Assessment Tool

Many of the methodologies being developed for quantifying

metric maps employ feature extraction and image registration techniques on an occupancy grid (or image) of the global

map. Commonly, these approaches do not take into account

the resolution and scale of the grid and do not consider the

local consistency within the global map. Therefore, the approach employed at the Interleague Challenge attempt to

quantify the errors in a map using the relative displacements

between poses, i.e. relative motion, as the evaluation criteria. This motivation for using this evaluation criterion is

based on two anecdotal factors. First, to consider the pose

instead of the resulting occupancy grid based map is advantageous because features in grid-based maps can become

obscured beyond recognition, especially after closing loops,

although the actual pose error is within centimeters. Second, using the relative displacements of the poses minimizes

error propagation during the evaluation, providing a more

accurate metric that isolates regional errors when considering the absolute pose error. For example, pose estimates

towards the end of the run are not impacted by the pose

errors that occurred at the beginning of the run.

Essentially this map assessment tool quantifies errors in a

(a)

(b)

Figure 4: An example illustrating the utilized metric for map assessment. (a) A visualization of the

ground truth constraints (red lines) superimposed

on the map generated by the SLAM algorithm. (b)

The error plot showing the relative displacement error at each constraint plotted over time. As depicted

by the blue rectangle the error increases drastically

in the last third where the robot returned after driving a long hallway without features.

robot-generated map by comparing the constraints from the

ground truth generation process and the pose estimates being produced by a given mapping system being considered.

An associative relationship is built between the constraints

and the pose estimates based on time 1. It then uses this

associative relationship to compute the error in the relative

displacements for each of the sets of corresponding data.

The map evaluation process can be expressed formally as

follows: Given the estimated trajectory of a robot, x1:T ,

consisting of a series of the pose estimates, xi, produced by

the mapping system at timestep i from 1 to T. Let x

∗

1:T be

1While correlating data between two systems based on time

is suitable in some situations, it is not a good assumption

and it will be addressed in later versions of the tool.

126
the set of the corresponding reference poses generated during the ground truth process. The relative displacement can

then be defined as δi,j = xi 	 xj , where ⊕ is the standard

motion composition operator and 	 its inverse. Instead of

comparing x to x

∗

(in the global reference frame), the operation is based on δ and δ

∗

as

ε(δ) = X

i,j

(δi,j 	 δ

∗

i,j )

2

. (1)

The major advantage of this tool is the ability to adjust

the resolution of the evaluation criteria, which is defined by

the constraints produced during the ground truth generation process. This suggests that this tool can be dynamically adjusted to evaluate the global or local consistency of

a map. It can also be used to identify and test performance

singularities in mapping systems by evaluating how different environmental conditions impact a particular mapping

system.

A demonstration of the map assessment used to evaluate a

SLAM algorithm in a difficult environment with long hallways and glass walls is shown by Figure 4(a). Ground truth

constraints are depicted by red lines in the map. Figure 4(b)

shows the temporal analysis of errors in the map for each

constraint over the course of the entire run. As can be seen,

the error increases drastically in the last third (depicted by a

rectangle), which corresponds to the real situation where the

robot returned after driving a long hallway without features.

This cluster clearly identifies a weak point in the estimated

algorithm, which did not manage to close the loop in this

particular situation, shown by the shearing effect in the corridor. A more detailed description can be found in previous

work [Burgard et al., 2009].

4. THE EVALUATION AND RESULTS

At the RoboCup competition in Graz 2009 the Interleague

Challenge provided a testing scenario to facilitate the intercomparison of the robotic mapping algorithms developed in

simulation and the applicability of these algorithms to real

data captured from a real robot while operating complex terrain. This section will provide an overview of the challenge

and present the results of the three teams demonstrating

the most proficient performance. In the remainder of this

section, these teams will be referred to as Team 1, Team 2,

and Team 5.

Prior to actual the challenge the common robotic platform

(described in Section 2.2) and a variation of the The Maze

test method (described in Section 2.1) was modeled in USARSim (see Figure 1c) and provided to the teams. These

models allowed teams to tune their mapping systems in this

particular environment.

For the actual challenge, the evaluation was based on a sensor data set logged while teleoperating the real common

robotic platform through the maze built in support of the

RoboCupRescue Robot competition, shown in see Figure 1b.

The sensor data set logged the data streams of the two laser

range finders, mounted vertically and horizontally, and the

(a)

(b)

(c)

Figure 5: (a-c) Visual mapping results from team 1,

team 2, and team 5, respectively.

IMU sensor using the USARSim message protocol. A simple

server application, simulating the USARSim interface to the

actual robot, publishes the contents of the sensor data set

over a TCP/IP socket at the same data rates found on the

actual robot. Simulating a USARSim connection to a real

robot not only reduces the integration required for teams

to deploy their mapping algorithms but provides a repeatable testing scenario that eliminates the impact of in situ

decisions on the mapping process.

Teams participating in the mapping challenge ran their respective mapping algorithms against the data from the real

robot using the server application mimicking the USARSim

127
(a)

(b)

(c)

Figure 6: (a-c) Visualization of position errors at

each constraint for team 1, team 2, and team 5,

respectively. As can be seen, the resulting metric

corresponds to the visual impression given by the

according maps.

interface to the real robot. At the end of the run, each of

the teams were required to submit a log file reporting their

results as a list of pose estimates, xt, yt, θt, computed for

each observation at time t. Each of the tuples in the log file,

denoted as a hx, y, θ, ti, corresponds to observations used to

construct ground truth constraints used in the map assessment tool, described in Section 3.2.

Table 1: Quantitative results (avg. position error)

Team Abs. Err. [m] Sqr. Err. [m

2

] Max. Err. [m]

1 1.08 ± 1.81 4.44 ± 14.84 9.46

2 1.40 ± 1.77 5.08 ± 11.74 9.11

5 0.48 ± 1.04 1.32 ± 4.00 4.46

The resulting metric maps from each of the teams are shown

in Figure 5. Qualitative analysis suggests that divergent

behavior has occurred in each of the approaches but does

not provide any empirical evidence on which team was able

to more accurately map the environment. For instance, the

map produced by Team 5 appears to be more consistent but

this does not provide a basis for distinguishing differences in

the maps produced by Team 1 and Team 2. Additionally,

the convolution of features in the maps makes it hard to

assess if the divergent behavior seen in the map is due to

regional errors that have propagated through the system or

a catastrophic error.

The application of the map assessment tool quantifies the

errors as they arise during the run and provides the means

to temporally assess the errors that occurred in each of the

respective maps. The error plots shown in Figure 6 show the

relative displacement as compared to the constraints formulated during the ground truth generation process. This temporal analysis shows the stability of the mapping system and

helps classify the impact of the errors that have occurred.

This shows the map produced by Team 5 appears to be more

stable then the competing approaches but is plagued with

some regional errors arising during the course of the run,

probably due to problems arising from loop closure at the

end of the run. It also indicates that the mapping system

deployed by Team 1 is more stable then Team 2 and could be

suffering from as configuration or data association problem.

This information could be used by developers to improve

their approach.

The overall performance of the competing mapping systems

is summarized in Table 1 by averaging the relative displacements measured by the map assessment tool over the whole

run. Again this shows that the approach deployed by Team

5 outperformed the other two approaches.

5. DISCUSSION

The Interleague Challenge made its debut at the 2009 RoboCup Competition in Graz, in the first attempt to literally

bridge the gap between simulated and real robot platforms.

The challenge, built on a common robotic platform and a

common testing apparatus, laid the foundation for not only

assessing the applicability of algorithms developed in simulation on real data but also the need to develop quantitative

metrics and test methods capable of evaluating the local and

global consistency of robot generated maps. This challenge

also exemplifies there is still work to be done.

The undulating terrain found in the maze presented a particularly problematic environment for the fixed sensor configuration. As the robot traversed the pitch and roll ramps, the

horizontally aligned laser range finder periodically scanned

the ceiling and floor of the maze. This produces artifacts in

128
the data that further complicate the data association problem inherent to many techniques used for mapping, such

as incremental scan matching. While there are mechanical

techniques used to address this issue, i.e. actuating laser

scanners to continuously level the orientation of the sensor

with respect to world reference frame, the sensor configuration of the common robotic platform was intended to realize a survivable mapping solution that limits the number of

moving parts. The surface friction of the OSB exacerbates

the non-systematic errors in skid-steered vehicles rendering

the encoder-based odometry almost useless.

In order to facilitate the transfer of technologies from simulated world to actual implementations on real robotic platforms, there needs to be a tighter coupling between simulated systems and their real world counterparts. For example, the simulated IMU or INS sensor in USARSim reports

location and orientation of the robot assuming a Gaussian

noise model, which is not consistent with available inertial

measurement systems.

The evaluation of robot-generated maps is often based on

qualitative approaches that do not take into account how

specific environmental conditions or in situ decisions impact

the performance of the system. While this type of analysis provides some indication of the overall performance, it

does not allow researchers to understand what errors a specific system is prone to, how these errors impact the overall

performance of that system, and how performance of that

system compares with competing approaches. Developing

testing scenarios for evaluating robot-generated maps can

greatly benefit from the development repeatable and reproducible testing scenarios that isolate potential failure conditions in a controlled environment.

The development of a common robotic interface that enables developers to seamlessly transition robotic algorithms

from simulation to the real world can help foster innovation

and expedite the transfer of the technologies from the lab to

fielded systems. The continuing development of this interface will lower the entrance barrier to robotics for newcomers

and help improve the development cycle.

In the future, we plan to extend the Interleague Challenge

to address other performance benchmarks for robotics; for

example, an autonomous behavior challenge that will focus

on the robot’s ability to autonomously navigate a vaguely

defined testing apparatus in a known way. In this challenge,

teams will have to directly control the real robot via the

USARSim interface according to a given task description.

6. ACKNOWLEDGMENTS

We would like to thank the teams during RoboCup 2009 in

Graz, Austria for contributing their mapping results.

7. DISCLAIMER

Commercial equipment and materials are identified in this

paper in order to adequately specify certain procedures. Such

identification does not imply recommendation or endorsement by affiliated institutions, nor does it imply that the

materials or equipment identified are necessarily the best

available for the purpose.

8. REFERENCES

[ASTM E54.08, 2009] ASTM E54.08. ASTM Committee

E54.08.01 on Homeland Security Operations;

Operational Equippment; Robots, 2009. http:

//www.astm.org/COMMIT/SUBCOMMIT/E5408.htm.

[ASTM, 2007] ASTM. In Form and style for ASTM

Standards. ASTM International, March 2007.

[Burgard et al., 2009] W. Burgard, C. Stachniss,

G. Grisetti, B. Steder, R. Kummerle, C. Dornhege,  ̈

M. Ruhnke, A. Kleiner, and J. D. Tard ́os.

Trajectory-based comparison of slam algorithms. In

Proc. of the Int. Conf. on Intelligent Robots and Systems

(IROS), 2009. To appear.

[Darpa, 2007] Darpa. Darpa Urban Challenge, 2007.

http://www.darpa.mil/grandchallenge/.

[ESA, 2008] ESA. Lunar robotics challenge, 2008.

http://www.esa.int/esaCP/SEM4GKRTKMF index 0.html.

[Scrapper et al., 2008] C. Scrapper, R. Madhavan, and

S. Balakirsky. Performance analysis for stable mobile

robot navigation solutions. volume 6962, page 696206.

SPIE, 2008.

[Wang and Balakirsky, 2009] J. Wang and S. Balakirsky.

USARSim V3.1.3 - A Game-based Simulation of mobile

robots.

http://sourceforge.net/projects/usarsim/,

2009.

129
Mobile Microrobot Characterization 

through Performance-Based Competitions 

Jason J. Gormana, Craig D. McGrayb, and Richard A. Allenb 

a

Manufacturing Engineering Laboratory b

Electronics and Electrical Engineering Laboratory 

National Institute of Standards and Technology 

100 Bureau Drive, Gaithersburg, MD 20899 

jason.gorman@nist.gov, craig.mcgray@nist.gov, richard.allen@nist.gov 

ABSTRACT

Recent advances in the design and fabrication of 

microelectromechanical systems (MEMS) have enabled the 

development of mobile microrobots that can autonomously 

navigate and manipulate in controlled environments. It is 

expected that this technology will be critical in applications as 

varied as intelligent sensor networks, in vivo medical diagnosis 

and treatment, and adaptive microelectronics. However, many 

challenges remain, particularly with respect to locomotion, power 

storage, embedded intelligence, and motion measurement. As a 

result, the National Institute of Standards and Technology has 

organized performance-based competitions for mobile 

microrobots that are designed to: 1) accelerate microrobot 

development by providing researchers a venue to demonstrate and 

observe novel technologies, 2) reveal the most pressing technical 

challenges, and 3) evaluate the most successful methods for 

locomotion and manipulation at the microscale (e.g., actuation 

techniques for crawling). This paper will discuss the goals and 

structure of the competition, results from past competitions, and 

plans to make performance characterization methods an integral 

component of future competitions. 

Keywords

Microrobotics, microrobots, robot competition, performance 

characterization 

1. INTRODUCTION 

Microscale robotics, or microrobotics, has emerged over the last 

decade as the next wave in intelligent systems. As a result of 

scaling effects, microrobots have functionalities that will open up 

application paths that would otherwise have been impossible. 

Their small size and low unit cost allows them to be embedded 

into subsystems such as consumer electronics, and their small 

mass results in extremely high accelerations. Additionally, their 

size facilitates new modes of operation. As in many systems in 

nature, microscale robots can form large collaborative networks 

that can work either together to complete tasks faster or 

independently to cover more ground. This massive parallelism 

will result in complex system behaviors that have yet to be 

explored for macroscale robots. Microrobots are likely to have a 

major impact on advanced manufacturing, the health care 

industry, and the continued miniaturization of consumer products 

over the next two decades. However, this technology faces many 

new challenges with respect to fabrication, integration, control, 

power delivery, and embedded intelligence, among many others, 

which must be addressed for this field to find widespread 

acceptance. 

This paper is authored by employees of the United States Government and 

is in the public domain. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

ACM 978-1-60558-747-9/09/09 

This paper focuses on a mobile microrobot competition organized 

by the National Institute of Standards and Technology (NIST) that 

is designed to accelerate the adoption of this technology by U.S. 

industries. First, we give an overview of the field with an 

emphasis on the most successful methods for robot locomotion at 

the microscale. Next, the goals and structure of the competition 

are described along with the results from these competitions over 

the last three years. Difficulties in measuring a number of 

parameters for microrobots have been identified as a major 

limiting factor in the development of microrobots. Therefore, a 

list of measurement needs identified through the framework of the 

competitions is presented. Finally, plans for future competitions 

are presented, particularly with respect to the integration of 

microrobot metrology within the competition so that the 

performance of different systems can be directly characterized 

and compared. 

2. MOBILE MICROROBOTS 

The field of microrobotics is extremely broad and brings together 

a number of disciplines including microelectromechanical 

systems (MEMS), precision machine design, biology, materials 

science, and of course, robotics. Examples of common research 

thrusts include the manipulation and assembly of MEMS 

components, insect-inspired flying microrobots, the manipulation 

of particles and cells in solution, and the mobile microrobots 

discussed here (see [1] and [2] for an overview.) In all of these 

cases, either the robot or the manipulated object has microscale 

dimensions (i.e. between 1 mm and 1 μm). The term microrobot

has also been used by some to simply mean a small robot (e.g., 

robots having dimensions on the order of centimeters) but this 

definition is not utilized here. 

The dimensional scaling of robots and manipulated parts down to 

the microscale presents many challenges in microrobotics, 

including difficulties in precision fabrication, sensor and actuator 

integration, power delivery, and the interfacing between the 

micro- and macroscale domains. Most importantly, though, is the 

130
way in which the role of various forces changes between the 

macro- and microscales. Due to scaling effects [3], electrostatic, 

van der Waals, and capillary forces – among others – are 

significantly larger than inertial forces at the microscale. As a 

result, adhesion between robots, parts, and the surfaces that they 

interact with can limit dexterity and mobility. This is particularly 

true for mobile microrobots, which can easily become stuck while 

moving on a surface. Therefore, methods of locomotion that can 

overcome these adhesive forces, or even exploit them, are needed. 

One of the first and most common methods of locomotion is 

based on the electrostatic scratch drive actuator. The untethered 

scratch drive actuator, developed by Donald et al. [4], consists of 

a conductive plate that has a bushing positioned on the bottom 

side of the plate near one side. When placed on a surface the 

scratch drive actuator sits at a slight angle to the surface due to 

the placement of this bushing. Motion in the plane is generated by 

applying an electrostatic force between the plate and surface that 

is large enough to make the plate snap down to the surface. 

Consequently, the edge of the plate that contains the bushing 

moves forward by a small increment (10 nm to 50 nm). When the 

electrostatic force is removed, the plate straightens but remains in 

the newly obtained planar position. The repetition of this 

sequence has been shown to yield repeatable motion with 

velocities approaching 2 mm/s. The electrostatic force is 

generated using an engineered surface composed of an 

interdigitated electrode array and a dielectric coating. By applying 

a voltage across the electrodes, the electrostatic force can be 

cycled at high rates (100 kHz) to yield high speed motion. 

However, this approach only provides unidirectional straight-line 

motion. Therefore, a turning arm has been added to the scratch 

drive actuator so that the microrobot can turn, leading to global 

controllability of the robot in the plane [5]. 

Electromagnetic forces have also been shown to be effective in 

actuating microrobots. Floyd, Pawashe, and Sitti [6] have 

demonstrated microrobots fabricated from a hard magnetic 

material, which can be as simple as a solid magnet block. Forces 

are exerted on the microrobot by uniform magnetic fields 

generated by macroscale multi-axis electromagnetic coils. By 

adjusting the control currents applied to the coils, the microrobot 

can be moved on a planar surface. The most repeatable motion 

has been achieved by applying a pulsed current signal along the 

desired motion direction, which results in a stick-slip motion 

caused by balancing the friction forces and electromagnetic 

forces. 

Another electromagnetic actuation approach developed by 

Vollmers et al. [7] utilizes a resonant drive mechanism. Similar to 

[6], a set of electromagnetic coils is used to generate a 

controllable uniform magnetic field in the workspace of the 

microrobot. However, the microrobot’s mechanical design is 

significantly different. The microrobot consists of two nickel 

blocks of different size that are connected by a metal spring. The 

magnetic field is modulated at the first resonant frequency of the 

mass-spring system to cause the two ferromagnetic blocks to 

vibrate relative to one another. When the vibration amplitude is 

large enough to cause the blocks to collide, the resulting impact 

force moves the microrobot in the plane. In addition to the 

electromagnetic forces, an electrostatic force is applied normal to 

the surface by operating the robot on an interdigitated electrode 

array as described above for scratch drive actuators. The 

electrostatic force is used to clamp the microrobot to the surface 

when the two masses are separated. Just before the two masses 

collide, the clamp is removed and the microrobot moves forward 

after the collision in a controllable increment. 

Although other methods of locomotion have been demonstrated, 

including thermal impact drives [8] and piezoelectric crawlers [9], 

electrostatic and electromagnetic locomotion have been the 

dominant methods for microrobots. Each of the methods 

discussed above has also been extended to multi-robot control, 

which is essential in realizing the parallelism that makes 

microrobotic systems so powerful. Donald, Levey, and Paprotny 

[10] have shown that multiple electrostatic microrobots can 

operate on a single electrode array by designing each robot to 

have independent snap-in voltages for their scratch drive and 

turning arm. The electromagnetic robot in [6] has been shown to 

be extendable to parallel operation using a grid of independent 

electrode arrays for electrostatic clamping [11]. Finally, 

Kratochvil et al. [12] have demonstrated multi-robot operation 

using the resonant drive mechanism described in [7] by designing 

robots to have unique resonant frequencies, which can all be 

addressed through a single control signal for each degree of 

freedom of motion. Continued development of multi-microrobot 

systems is needed to fully utilize the nascent capabilities of 

microrobots working together collaboratively. 

3. PAST COMPETITIONS 

The mobile microrobot technologies discussed in the previous 

section have all been developed over the past decade and with the 

greatest momentum in the past three years. Although this field is 

in its infancy, it is a clear extension of the MEMS and robotics 

technologies that have become integral to many consumer 

products, manufacturing capabilities, biomedical tools, and 

military systems. However, mobile microrobotics is also a 

disruptive technology because microrobot designs are often not 

compatible with existing MEMS fabrication methods and the 

complexity of microrobot control presents new challenges in 

communications and power transmission at the microscale. 

Therefore, this field will require considerable investment to 

transition the technology to the marketplace. As a result, NIST 

has organized competitions over the past three years that are 

designed to accelerate development in this field while mitigating 

the risks in adoption of this technology by U.S. industries. The 

main goals of the competitions are to: 

Assess the State of the Art - The competitions bring together a 

number of experts in the field along with their latest 

developments in mobile microrobotics. This provides the best 

vantage point to assess what is currently feasible and where this 

technology is going. 

Accelerate Development - Competing teams must focus their 

technologies toward specific microrobot tasks in the competition 

and must meet hard deadlines to participate. This pressure 

provides considerable motivation to accelerate their research. 

Provide Head-to-Head Comparisons - The competitions provide a 

unique opportunity to compare disparate approaches for realizing 

controllable microrobots that would not be possible by studying 

the technical literature alone. This has been particularly useful in 

evaluating the controllability and repeatability for different 

methods of locomotion. 

131
Identify Measurement Needs - There are many measurements 

routinely performed on macroscale robots that cannot be 

performed on mobile microrobots because of their small size and 

high speed. NIST gains considerable insight into the shortcomings 

of existing measurement methods, which motivates the 

development of new measurement techniques that will aid in the 

adoption of this technology by U.S. industries. 

NIST has organized the annual series of microrobotic 

performance competitions, beginning in 2007, in association with 

the RoboCup Federation. The competition events, while presented 

to fit the soccer theme of the RoboCup organization, are 

structured to test microrobotic systems in the key performance 

areas of mobility, maneuverability, and manipulation capability. 

Robots in these competitions are required to be no bigger than 

300 micrometers in their largest dimension, and to have no wires 

or physical tethers extending outside of a 300 micrometer cube. 

Participating teams had to complete the following three tasks on a 

field of play based on a soccer pitch that measures 2 mm long and 

2 mm wide and has a goal at each end: 

The Two-Millimeter Dash 

The microrobot must traverse a straight-line distance of two 

millimeters in as little time as possible, beginning from, and 

ending at, a complete stop within a defined area. Most obviously, 

this event measures the speed of a microrobot, but in practice the 

responsiveness of the robot to start and stop signals is often the 

critical capability. 

The Slalom Drill 

The microrobot must navigate the same two-millimeter course as 

before, this time avoiding a set of obstacles placed in its path. The 

number of obstacles is increased as necessary to force more 

complex paths and to differentiate higher levels of 

maneuverability. 

The Shootout 

The microrobot must maneuver through an obstacle course, 

collecting and delivering microscale silicon discs (soccer balls) to 

a goal location (see Fig. 1). This performance metric tests the 

ability of the robot to perform planar pushing manipulation tasks. 

Participating teams have the option to attempt these tasks 

autonomously, using image feedback from a microscope and 

digital camera, or by teleoperation. However, since teleoperation 

is easier to implement, a penalty is assessed for this mode of 

operation. Figure 2 shows several of the microrobots that have 

been entered in the competition, each of which to date has been 

based exclusively on the electrostatic and electromagnetic 

locomotion methods described in the previous section. 

So far, only one team has been able to complete The TwoMillimeter Dash and The Slalom Drill autonomously (ETH 

Zürich), while two teams have completed them by teleoperation. 

No team has completed The Shootout using the soccer ball shown 

in Fig. 1, but ETH Zürich has demonstrated goal-scoring with 

soccer balls developed specifically for their robot. These 

shortcomings point to the high level of difficulty of the tasks in 

this competition. 

Over the three years that the NIST microrobotics competition 

program has been operating, a broad variety of microrobotic 

systems has been evaluated. Tested robots include those operating 

based on electrostatic attraction, soft magnetic resonant actuators, 

Figure 1 A sequence of two images showing an electrostatic 

microrobot moving a silicon disc (soccer ball) from point A to 

point B (elapsed time ≈ 3.5 s) 

and hard magnetic actuators. Masses of the competing robots have 

ranged from 10s of nanograms up to 10 micrograms.Material 

combinations used for the microrobots have included silicon and 

chromium; metal thin films and thermoset polymers; nickel and 

gold; and rare-earth magnetic materials. The microfabrication 

protocols used to manufacture the evaluated microrobots have 

included surface micromachining processes, high-aspect-ratio 

electroplating, and laser micromachining. Despite this tremendous 

diversity of microrobotic technologies, all of the evaluated 

systems have converged on the same class of gaits. 

In contrast to the legged or wheeled modes of locomotion typical 

of macro-scale robotic systems, which seek to minimize friction, 

the class of gaits that has become most prevalent in microrobotics 

consists of a slip-stick motion in which frictional anisotropies are 

exploited. Typically, the microrobot slides against friction in one 

part of the motion cycle, then is held fast by friction forces during 

the recovery portion of the motion cycle. 

The success of the slip-stick class of gaits leads to many more 

questions than answers about the future of microrobotic 

technologies. Friction at such small size scales typically exhibits 

non-Amontonian behavior, in which the friction force is not 

linearly proportional to the normal force. Non-Amontonian 

friction regimes remain poorly understood and can be difficult to 

model or predict. In addition, the normal contact forces can 

change by orders of magnitude in response to variations in the 

environment or operating surface and in response to wear and 

electrostatic charging of the contacting surfaces. 

Reliability is a significant challenge for microrobotic systems, 

with performance variations from robot to robot of the same 

design and for individual robots over time. Operable lifetimes 

range from minutes to hours, and failure modes are poorly 

understood. 

The operating mechanisms for microrobotic devices are 

understood primarily in abstract terms, so that optimization of 

microrobot performance is accomplished mostly on a trial and 

error basis. For example, robot motions corresponding to new 

resonant modes were discovered in the midst of the 2009 

competition by changing the electrical input parameters. 

More detailed models of microrobot operation are required, along 

with the experimental means to validate them. Validating models 

of operation is made difficult by the fact that discrete microrobot 

motions are often much smaller than the microrobots themselves 

and can be difficult to observe. For example, single steps of the 

scratch drive actuator are thought to be as small as 10 nm. 

132
Figure 2 Various microrobots that have been demonstrated in 

previous competitions: a) hard magnet microrobot (Carnegie 

Mellon University), b) polymer-based electrostatic microrobot 

(Simon Fraser University), c) resonant electromagnetic 

microrobot (ETH Zürich), and d) electrostatic microrobot 

(U.S. Naval Academy). 

4. MEASUREMENT NEEDS 

The locomotion mechanics for most microrobots are not 

understood in detail due to complex force interactions at the 

micro- and nanoscales. As a result, precision measurement of the 

physical behavior of microrobots will play a significant role in 

modeling locomotion mechanics, developing new microrobot 

designs, and pushing their performance limits. The list below 

highlights the most pressing measurement needs, which has 

considerable overlap with those needed for current and 

prospective commercial MEMS devices. In many cases, suitable 

instrumentation and methods for these measurements are 

currently not available. 

• Coarse Motion. Characterizing the motion of the device on 

the field of operation. The most successful mobile 

microrobots utilize a slip-stick gait. Tools are needed to 

understand the dynamics of this motion as well as the 

interaction between the microrobot and the surface as the 

microrobot makes nominally identical steps in a constant 

direction, as well as how the robot behaves as its direction of 

motion changes. 

• Fine Motion. Characterizing the motion of subsystems of the 

microrobots at the nanoscale; in particular, the motion of the 

actuators that determine the direction and rate of movement. 

• Actuation Force and Stiffness. Characterizing the output 

force of actuators and the stiffness of microrobot components 

will further understanding of modes of locomotion. 

Instrumentation that can measure multi-axis forces on the 

order of micronewtons with measurement bandwidth greater 

than 100 kHz must be developed. 

• Electromagnetic Properties. Although the applied magnetic 

and/or electrostatic fields can be estimated for free space 

conditions, the presence of the microrobot and the region of 

operation means that the actual magnetic and/or electrostatic 

fields applied to the microrobot may differ substantially from 

the free space estimate. Therefore, tools are needed for local 

measurement of the magnetic flux density and capacitance. 

• Materials Properties. Materials at the microscale are 

dominated by surface, rather than bulk, properties. Novel 

measurement methods to determine surface properties of 

microscale elements are needed to characterize the elements 

that compose a microrobot. 

• Friction and Adhesion. At the microscale, friction and 

adhesion forces dwarf inertial forces due to the high surface 

area-to-mass ratio of microrobots. Methods for measuring 

non-Amontonian friction, adhesion forces (van der Waals 

forces, capillary forces, etc.), and quantum mechanical 

effects are needed. 

• Reliability. The original promise of MEMS devices in the 

1980s was that of small-scale machines incorporating gears 

and complex motion. This promise has yet to be realized 

primarily due to the poor reliability of MEMS devices with 

contact motions. Microrobots provide a platform for 

evaluating the reliability of a range of microscale contact 

modes and observing how their performance evolves over 

time. 

• Environmental Sensitivity. For microrobots to meet many of 

the challenges elucidated previously, they must be able to 

function in a wide variety of environments (temperature, 

humidity, air, water, etc.). Performance metrics for 

microrobots to operate under varied environmental 

conditions will assist in overcoming existing requirements 

for tightly controlled operational environments. 

5. PLANS FOR FUTURE COMPETITIONS 

Although significant qualitative data has been captured in 

previous competitions, quantitative measurements have not been 

made while the microrobots performed competition tasks. As 

discussed in the previous section, new measurement methods and 

extensive data sets are critical for improved understanding of 

microrobot operation. The competition presents an excellent 

opportunity to measure the performance of a number of different 

technologies that would generally not be available in a single 

research laboratory. Therefore, we intend to incorporate 

microrobot metrology into the competition, which will be used to 

evaluate technologies and provide new insights into the 

mechanics of microrobots. 

Unfortunately, many of the measurement technologies required to 

meet the needs listed in the previous section are complex, 

expensive, and not portable. However, as a first step in building 

performance characterization methods into the competition, a 

high-speed digital video system will be integrated into the 

competition microscope and an automated image processing 

application will be developed to provide coarse motion 

measurements with high motion bandwidth (> 500 Hz). The 

software will be capable of providing the planar coordinates (x, y, 

θ) of multiple microrobots and other objects (obstacles, 

manipulated parts) as a function of time, as well as other variables 

that can be extrapolated from this data. These include microrobot 

velocity, acceleration, trajectory tracking precision, turn radius, 

and motion repeatability, as well as an analysis of their kinematic 

constraints. Although it is expected that the image processing will 

be performed off-line for high-speed video, this tool will also be 

used for visual feedback when operating at slower frame rates (< 

60 fps). 

133
6. REFERENCES 

[1] Sitti, M. 2007. Microscale and nanoscale robotic systems. 

IEEE Robotics and Automation Magazine 14 (1), 53-60. 

[2] Abbott, J. J., Nagy, Z., Beyeler, F., and Nelson, B. J. 2007. 

Robotics in the small, part I: microrobotics. IEEE Robotics 

and Automation Magazine 14 (2), 92-103. 

[3] Fearing, R.S. 1995. Survey of sticking effects for micro parts 

handling. Proceedings of the IEEE/RSJ International 

Conference on Intelligent Robots and Systems, Pittsburgh, 

PA, 212-217. 

[4] Donald, B. R., Levey, C. G., McGray, C. D., Rus, D., and 

Sinclair, M. 2003. Power delivery and locomotion of 

untethered microactuators. Journal of 

Microelectromechanical Systems 12 (2), 947-959. 

[5] Donald, B. R., Levey, C. G., McGray, C. D., Paprotny, I., 

and Rus, D. 2003. An untethered, electrostatic, globally 

controllable MEMS micro-robot. Journal of 

Microelectromechanical Systems 15 (1), 1-15. 

[6] Floyd, S., Pawashe, C., and Sitti, M. 2008. An untethered 

magnetically actuated micro-robot capable of motion on 

arbitrary surfaces. Proceedings of the IEEE International 

Conference on Robotics and Automation, Pasadena, CA, 

419-424. 

[7] Vollmers, K, Frutiger, D. R., Kratochvil, B. E., and Nelson, 

B. J. 2008. Wireless resonant magnetic microactuator for 

untethered mobile microrobots. Applied Physics Letters 92, 

144103. 

[8] Sul, O. J., Falvo, M. R., Taylor, R. M. II, Washburn, S., and 

Superfine, R. 2006. Thermally actuated untethered impactdriven locomotive microdevices. Applied Physics Letters 89, 

203512. 

[9] Oldham, K., Rhee, Choong-Ho, Ryou, Jeong-Hoon, 

Polcawich, R., and Pulskamp, J. 2009. Lateral thin-film 

piezoelectric actuators for bio-inspired micro-robotic 

locomotion. Proceedings of the ASME IDETC, San Diego, 

CA, DETC2009-86427. 

[10] Donald, B. R., Levey, C. G., and Paprotny, I. 2008. Planar 

microassembly by parallel actuation of MEMS microrobots. 

Journal of Microelectromechanical Systems 17 (4), 789-808. 

[11] Pawashe, C., Floyd, S., and Sitti, M. 2009. Multiple 

magnetic microrobot control using electrostatic anchoring. 

Applied Physics Letters 94, 164108. 

[12] Kratochvil, B. E., Frutiger, D., Vollmers, K., and Nelson, B. 

J. 2009. Visual servoing and characterization of resonant 

magnetic actuators for decoupled locomotion of multiple 

untethered mobile microrobots. Proceedings of the IEEE 

International Conference on Robotics and Automation, 

Kobe, Japan, 1010-1015. 

134
Is an Agent Theory of Mind (ToM) Valuable for Adaptive, 

Intelligent Systems? 

Gary Berg-Cross 

 Knowledge Strategies 

Potomac MD 20854 

gbergcross@gmail.com 

 

ABSTRACT

This paper serves as a short introduction for the special 

PerMIS session on Theories of Mind (ToM). The session 

intends to explore the viability of the ToM concept for 

R&D and if the ToM hypothesis is mature & relevant to the 

goal of highly competent systems able to achieve goals in a 

relatively autonomous way. The question can be considered 

from philosophical, research and robotic implementations 

as well as critiques central to the topic.The introduction is 

organized into 4 parts. Part 1 briefly reviews some of the 

history of the ToM idea and its recent reformulations. Part 

2 discusses the widening use of the concept as an 

explanatory device within a few areas using developmental 

studies as a focus. Part 3 introduces the idea that particular 

types of robotics offer a new kind of tool to investigate 

cognitive development and the validity of some theories 

such as a ToM. The paper concludes with an outline of 

some issues that remain to be explored and advanced to 

show the value of a ToM theory in general and especially 

within the domain of intelligent systems. 

Categories and Subject Descriptors

Primary Classification: I. Computing Methodologies

I.2 ARTIFICIAL INTELLIGENCE, I.2.6 Learning 

General Terms

Measurement, Experimentation, Design, 

Keywords

Theory of Mind, Developmental Robotics, adaptation. 

1. INTRODUCTION and History 

A Theory of Mind (ToM) refers to reasoning about the 

mental states of self and others. Empathy, the concept of 

putting yourself in another person’s shoes and relating to 

his situation, is a good example of theory of mind at work. 

This idea fits our everyday understanding of others, or 

what is called a folk psychology explaining why some 

things happen in the world. The practice of folk 

psychology has been a recurrent topic in philosophical and 

psychological discussions for a long time. In philosophy 

conceptualizing intentional states such as beliefs and 

desires has often been seen as dependent upon our 

linguistic abilities. That is, language ability seems to 

provide a representational medium for describing our own 

and others people’s actions in an intentional way. 

Recently, a new perspective on folk psychology has 

emerged in philosophy of mind and psychology. Such 

conceptualizations achieved a new purchase when Premack 

& Woodruff (1978) asked, ‘Does the chimpanzee have a 

theory of human intentions?’ or as they put it do they have 

a “theory of mind”. How would we know? The resulting 

research on this concept included its use as an explanation 

for autistic children’s cognitive deficits building on 

evidence suggesting that autistic individuals lack an ability 

to maintain a theory of other minds and to reflect on their 

own thought processes (Baron-Cohen, 1995). Since this 

early work the concept of a ToM has come to be broadly 

used in a developmental perspective of how children come 

to understand the social and psychological world. 

Formalized as a ToM theory these propose alternative 

inherited or acquired paths by which a particular cognitive 

capacity may arise in a cognitive agent (children) so they 

understand and predict external behavior of others by 

attributing unobservable mental states, such as beliefs, 

desires and intentions. Such a theory fits the observation 

that one human can predict how another human will behave 

in familiar surroundings because they are maintaining a 

theory of other people’s beliefs. Indeed everyday behavior 

seems largely based on what an adult person thinks others 

know, believe or want. But this idea might apply to highly 

social animals, such as chimpanzees and young children, in 

general since they need to compete and cooperate 

effectively with others in their family or group. It would be 

advantageous to not only to react to what others are doing 

but also to anticipate what they will do. A practical way of 

accomplishing this is to act like a junior scientist - observe 

what others do in particular situations and construct a set of 

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135
‘behavioral rules’ that fit the pattern. The reward is that 

behavioral prediction in similar situation. But a more 

flexible way to anticipate others behaviors is posit what the 

goals of others might be and what state of affairs they are 

trying to bring about. This goes beyond behavioral 

prediction in similar situations but may also apply to novel 

situations. 

Scientific evidence for a ToM comes from several sources 

with a particular favorite being the false belief task 

described by Wimmer and Perner (1983) which seemed to 

show that a full-fledged TOM doesn’t develop before the 

age of 3/4. They set up a series of experimental tests in 

order to check whether children between 3 and 5 years of 

age were able to attribute a false belief to someone else. In 

one experiment a child (Maxi), puts chocolate in a blue 

cupboard and leaves to play. Following this an adult uses 

part of the chocolate placing the remaining part into a 

different color (green) cupboard. Wimmer and Perner 

reported that when asked where ”Maxi” would look for the 

chocolate after returning most children under four years of 

age attributed to Maxi what they understood and had seen 

themselves, that the chocolate is now in the green 

cupboard. This suggests that a full-fledged TOM doesn’t 

develop before the age of 3/4. Younger children believe 

what it is the case, while older kids show a capacity of 

distinguishing what another mind might believe that is in 

reality false. 

2. USES OF THE ToM CONCEPT 

In the last 20 years ToM has been used to investigate many 

cognitive issues and is a broad explanatory device. For 

example studies of autistic children show a significant 

lower performance on false belief task compared to other 

cognitive tasks for testing intelligence and language 

capacities. This has lead some cognitive researchers of a 

nativist bent to see ToM as a mental mechanism and 

hypothesis that autism is the consequence of a specific 

deficit of the Theory of Mind Module (TOMM). A less 

daring view, called the Theory Theory, is to consider ToM 

as a naively constructed theory which is one of many 

conceptual "revolutions” that are achieved by cognitive 

agents. In this formulation concepts of mental states arise 

in a way similar to many other heuristics that we generate 

to deal with the world. Karmiloff-Smith’s (1992), for 

example proposed a series of such cognitive revolutions 

based in change of children’s internal representations. 

These internal changes are called representational 

redescription (RR). Initially external influences 

predominate and are represented by semsorimotor 

procedures. Knowledge as such is implicit and dominated 

by the concrete appearance of things such as seen in 

younger children in the false belief test. Over time a need 

to have this knowledge in a more 

useful form leads to re-coding it into progressively more 

explicit forms. Explicitness increases the flexibility and 

manipulability of the “knowledge” stored in the developing 

system. As knowledge and a child’s theories become more 

encapsulated internal modules are formed. This means that 

input and output processing become more stable and 

progressively and less accessible to and influenced by other 

modules and processing. Each higher form/level emerges 

from a redescription that re-encodes the prior lower level. 

For example perception of a zebra may be redescribed 

using the category of animal adding an attribute of stripes. 

In this redescription some of the perceptual detail and 

precision has been lost, but some connection to other things 

has been added and communication with others is enhanced 

because the child can be conscious of the concepts when 

procedures are not active. At each successive level, these 

representations become more explicit and hence more 

available to linguistic expression. Thus, development 

proceeds from implicit representations of basic behavioral 

procedures to successively more abstract, explicit, and 

flexible structures. This position is essentially consistent 

with a constructive epistemology, since one need not 

attribute any special, modular initial ability to children in 

order to explain how they know what they know. In this 

view language and a ToM is more like other domains of 

knowledge and may capitalize on innate perceptual biases 

and constraints. 

 Karmiloff-Smith’s model provided a speculative 

mechanism to explain representational within a 

constructionist approach emphasizing domain-specific 

constraints on development that are not realized by prefixed modules. Instead children develop their language 

skills as a repository of language-relevant representations 

that is tuned by focused/constrained experience within a 

language community. If this community uses mentalist 

terms to explain why things happen then this affords 

development of a ToM as a naïve model which can easily 

be used to communicate. Evidence for this is that around 

24 months (Hirsh-Pasek and Golinkoff, 1996) we see a 

predominant reliance on a coalition of external cues 

diminishe as a grammatical system becomes more robust. 

This is also a time when a child grows more aware of the 

complex relationships among people and which could 

develop into a Theory of Mind. This combination is an 

opportunity to go from early language to later language 

skills with more abstract meanings communicated about 

past or future events and feelings. As proposed by HirshPasek and Golinkoff (1998), paraphrasing Bloom's (1993) 

Principle of Elaboration a child is driven to formulate (or 

discover) ways of communicating these ideas in a later 

phase using language as the preferred mode. A child’s 

development of a ToM would fit this general path of 

development, but of course that remains a hotly debated 

topic in cognitive development. 

136
3. DEVELOPMENTAL ROTOBICS 

If it proves viable a ToM could be relevant to the eventual 

goal of highly competent systems able to achieve goals in a 

relatively autonomous way. However, theories around a 

ToM mind need to be fleshed out and critiqued. The 

empirical base also needs to be expanded since the bulk of 

early research was a mix of child development studies 

along with evidence from brain function (e.g. viewing 

specific brain regions as a component of ToM) or clinical 

studies or autism, schizophrenia, Asperger and Williams 

syndrome and some on non-human primates. A 

broadening of discussion of all these is sponsored in the 

session and diverse disciplines that study intelligent 

systems including computer science, AI, cognitive science, 

psychology, behavioral and social science, and philosophy 

to provide improvements to our understanding of ToMs. A 

new way of investigating the theory comes from the field 

of Developmental/Epigenetic robotics. These combine 

developmental psychology and robotics in the effort to 

mimic cognitive development and show emergent behavior

in these intelligent systems. Given a focus on emergent of 

behavior ToM represents an important topic in the field 

since it takes an accumulation of cognitive skills and 

knowledge as discussed by Brian Scassellati in his 

“Foundations for a Theory of Mind for a Humanoid Robot 

“(2001). Such work uses a robot with high-level cognitive 

skills coupled to the low-level perceptual abilities of a 

humanoid robot draws from a ToM and while interested in 

advancing and testing the theory is also interested in the 

practical question of how to build machines that interact 

naturally with people. This means building intelligent 

systems that can both interpret the behavior of others using 

social rules as well as display the social cues that will allow 

people to naturally interpret a system’s behavior. 

4. ISSUES REMAINING TO BE 

EXPLORED 

Robotic tests of a ToM ask several fundamental questions 

which have only partly been addressed: 

o Can robots employ and exhibit a ToM 

like humans including beliefs? 

o How might a ToM `be represented in a 

robot implementation? 

o How can we use developmental robots to 

test how a ToM might be learned through 

their interactions with a surrounding 

environment? 

o What is the role of social involvement, 

language and understanding? 

 including: Human-robot and 

robot-robot interaction, 

o How important is embodiment to 

developing a ToM? 

Only a small part of these have been explored and much 

needs to be considered. In additional other important topic 

areas can be directed at the ToM topic. These include: 

• Critical philosophical analysis of the hypothesis 

that beliefs and desires that are the central mental 

states required to make sense of behavior and 

resulting questions about what heuristics/lower 

cognitive abilities are needed for a ToM to 

develop and to make a ToM computationally 

practical. 

• Developmental issues to better understand the 

consistent path of ToM 

o For example, the interactions and mix of 

higher-order, executive functions (e.g. 

self-regulatory cognitive processes, 

working memory and control of attention 

along with resistance to interference) 

o Observational methods (e.g. false belief 

task, imitation) to test a ToM including 

work with children and non-human 

primates 

• The relation of self reflection, joint attention, 

communication, imitation, or episodic memory to 

a ToM, 

• Modular vs. explanatory theory formulations of a 

ToM. 

• How important is ToM to levels of autonomy? 

• What cognitive architectures can support a ToM? 

Such understanding may support computational theory of mind as 

an emerging phenomena with the practical possibility that such 

intelligent machines could "help produce wealth i.e., goods and 

services that people want and need.”.

5. REFERENCES 

[1] Baron-Cohen, S. (1995). Mindblindness: An essay on autism 

and theory of mind. Cambridge, MA: The MIT Press. 

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Grammar: Evidence from Early Language Comprehension. 

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have a theory of mind? 

[8] Behavioral and Brain Sciences 4:515–26. 

137
[9]

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a Humanoid Robot, Autonomous Robots, 12, 13-24. 

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13:103–28. 

138
Towards a simple Robotic Theory of Mind

Kyung-Joong Kim

Mechanical & Aerospace Engineering 

Cornell University, Ithaca, NY 14853, USA 

Department of Computer Engineering

Sejong University, Seoul 143-747, Korea

kimkj@sejong.ac.kr

Hod Lipson

Mechanical & Aerospace Engineering 

Computing & Information Science 

Cornell University 

Ithaca, NY 14853, USA

hod.lipson@cornell.edu

ABSTRACT

Theory of mind (ToM) is a cognitive function in which an agent 

can infer another agent’s internal state and intention based on 

their behaviors. Can robots realize ToM like humans? There are 

many issues to be tackled to address this challenging problem, 

such as the representation, discovery and exploitation of an 

actor’s self models. In this paper we study how robots can 

represent other’s self with artificial neural networks and an 

evolutionary learning mechanism. This framework was tested with 

simulated and physical robots and a novel prey-predator scenario 

was introduced to measure the performance of ToM learning. 

Experimental results showed that the proposed ToM approach can 

recover other’s self models successfully.

Categories and Subject Descriptors

I.2.0 [Artificial Intelligence – General]: Cognitive Simulation 

General Terms

Algorithms, Performance, Design, Reliability, Experimentation,

and Verification

Keywords

Robotics, Evolutionary Computation, Estimation-Exploration 

Algorithm, Theory of Mind, Neural Network, Robot Test

1. INTRODUCTION

Theory of Mind (ToM) is a cognitive capability that allows us to 

understand another’s internal states (intention, goal, and belief) 

and predict future behaviors of others [1]. From the observation 

of other’s behavior, facial expression, and speech, we can infer 

the person’s internal state (emotions, thought, decision making, 

and plans). It was known that this function is supported by widely 

distributed areas of human brain [2][3]. For Chimpanzees, they 

have ToM but it is a bit different with human’s one [4]. 

ToM has gained great interest from an engineering society. 

Scassellati built “finding faces and eyes and distinguishing 

animate from inanimate stimuli” functions for humanoid robots 

[5]. Buchsbaum et al. developed an anthropomorphic animated 

mouse character that uses his own behavior repositories to 

interpret other’s behavior [6]. Hegel et al. studied human’s theory 

of minds for different shapes of robots [7]. Ono et al. used theory 

of mind mechanism to improve human’s understanding on robot’s 

intention [8]. 

Implementing ToM has great difficulty because it is a kind of 

reverse inference based on observation. Other’s self model is 

hidden and it exists inside of objects. It is not possible to see the 

internal model directly and it is only indirectly observable. The 

only thing that we can observe is that the reaction of the object to

the inputs from environments. The model with continuous inputoutput signals is more difficult to be discovered than discrete one.

In this paper, each robot has its own self and the problem of 

ToM is to discover other robot’s self as close as possible. In case 

of human, the self is located inside of human brain and 

represented with biological neural networks. The problem of ToM 

for human is to build models inside of my brain that approximate 

the behaviors originated from other’s internal self. Like other’s 

original self, the inferred other’s self is also represented as 

biological neural networks. The problem can be reformulated as 

finding another biological neural network that shows close 

behavior with the original one. Robot uses the similar mechanism

to do the ToM. 

Figure 1. Theory of Mind in robots

In this paper, robots do theory of minds by inferring the 

neural networks inside other robots based on their movement

(Figure 1). An artificial neural network controls the movement of 

robot’s wheels based on sensory inputs. The inference is based on 

the exploration-estimation algorithm (EEA) used in reverse 

engineering of nonlinear-dynamical systems [9] and robot’s self 

modeling [10]. After building other’s self models, the robot 

exploits them to predict other robot’s behavior. Figure 2 shows a 

neural network and virtual/physical robots used. 

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139
(a) A neural controller

(b) A real robot

Figure 2. An artificial neural network and robots

2. RELATED WORK

Premack and Woordruff asked “does the chimpanzee have a 

theory of mind?” in 1978 [1]. Heyes surveyed experimental 

evidences of non-human theory of mind in 1998 [11]. After 30 

years research from the initial question, it was revealed that 

chimpanzee do have a theory of mind but do not understand 

others like humans do [12]. Series of experiments were conducted 

with chimpanzees to know what they know about others 

[13][14][15]. Two chimpanzees compete for food when only one 

of them has complete information about the location of food. 

They concluded that chimpanzees know what other can see and 

exploit it for food competition. 

Childhood autism is related to the lack of theory of mind 

[16][17]. Baron-Cohen et al. compared normal, autistic, and 

Down’s syndrome subjects using a belief question to test theory of 

mind. The results for Down’s syndrome and normal subjects were 

similar (85% and 86% success ratio). On the other hand, 80% of 

autistic children failed the belief question. Ozonoff et al. tested 

the relationships between autism and first-order, second-order 

theory of mind [18]. 

Based on [19], theories for “theory of mind” are classified 

into four categories: Modular theory, simulation theory, theorytheory, and executive function theory. In modular approach, the 

theory of mind is functionally dissociable from other cognitive 

functions [17][20]. They assume that there are one or more neural 

structures specifically dedicated to theory of mind. In simulation 

theory [21], there is no general theory guiding the theory of mind. 

Instead, human’s brain mentally simulates other person ’ s 

situation by placing himself to the other person’s place. This 

perspective-taking view of theory of mind does not support 

specialized, distinct neural structure for this cognitive skill. In 

theory theory view, child has a theory about how other minds 

operate and it evolves over time [22]. Some theorists argue that a 

distinct theory of mind does not exist and executive functions are 

sufficient for the cognitive skills [18]. 

Recently, there are new finding about theory of mind of 

humans. Herrmann et al. compared theory of mind ability among 

human, chimpanzee, and orangutan with gaze following and 

intention understanding tasks [23]. They concluded that human 

outperforms other species in theory of mind. Falck-Ytter et al. 

investigated proactive goal-directed eye movements in 12-month 

old and 6-month old infants using a specialized system for action 

perception [24]. They concluded that 12-month old infants do the 

proactive goal-directed eye movements and this is evidence on the 

action understanding of infants. False-belief test is a 

representative method to know whether infants have theory of 

mind. Onishi et al. proposed a novel nonverbal task to examine 

15-month old infant’s ToM ability [25]. Rosenbaum et al. 

conducted theory of minds tests for someone with severe 

impairment of episodic memory and autonoetic consciousness 

[26]. They reported that there is no difference of the ToM ability 

between normal and impaired persons. Bloom’s research 

suggested that theory of mind is important to learn meanings of 

words [27]. 

There are works on verifying theories of “theory of mind” 

with neuroscience knowledge. Gallese et al. [28] related to the 

theory of “theory of mind” and the discovery of mirror neurons in 

human and monkey’s brain. They argued that the finding supports 

“simulation theory” but not “theory-theory.” Blakemore et al. 

supports the simulation theory based on the psychophysical and 

neurophysiological studies [29]. Ramnani et al. tested “simulation 

theory” by comparing human brain’s activation for preparing 

one’s own actions with one for predicting the future actions of 

others [30]. The conclusion was that both of them use action 

control system of the human brain but activate different action 

sub-systems. This result suggests that a simple form of simulation 

cannot be the only mechanism involved in ToM [31]. Siegal et al. 

reviewed recent findings on the relationships between brain 

regions and theory of mind [32]. Some functional components 

found were not solely dedicated to the theory of mind. However, 

domain-specific component (centered on the amygdale circuitry) 

was included in the region. This result supports modularity view. 

Saxe et al. related developmental psychology and functional 

neuroimaging research and supported the modular approach by 

arguing the existence of a specialized neural system for ToM [33]. 

Brain-imaging technology has been widely used to pinpoint 

region of brain for theory of mind [2]. Frith et al. used “story 

comprehension task” to invoke theory of mind and revealed 

several active regions (medial prefrontal cortex and posterior 

superior temporal sulcus) of human brain by ToM [34]. McCabe 

et al. reported that prefrontal cortex is highly activated to the 

cooperator in “trust and reciprocity” games for cash rewards 

against human [35]. Gallagher et al. reviewed several functional 

imaging works for theory of mind [36]. Krach et al. tested 

human’s ToM with human-robot game and the activation of brain 

regions related to ToM is related to the human-likeness 

(computer<functional robot<anthropomorphic robot<human) 

[37]. Hampton et al. investigated the activation of human brain 

using fMRI when they play simple two-player strategy game [38]. 

In their game, players use three different strategies (reinforcement 

140
learning, fictitious play based on history of other players, and 

sophisticated ToM). They investigated brain activation regarding 

to the choice of the strategy. 

The works that implemented ToM are categorized into two 

groups based on the level of implementations. Some of them 

focused on the demonstration only with simulation. A few 

demonstrated their works in real physical robots. The complexity 

increases when the work is realized in physical robots. 

Christopher developed synthetic vision, memory, and theory 

of mind module for embodied conversational agents [39]. In his 

work, agent has three theories to do ToM: “Have they seen me”, 

“Have they seen me looking”, and “interest level.” Robinson et al. 

invented a mind-reading machine recognizes human’s mental 

states (discrete six states) from video input of human’s facial 

expression [40]. Breazeal et al. developed synthetic mouse 

characters that recognize other mouse’s behavior based on their 

own repositories [6]. Treur et al. proposed a two-level BDI 

(Belief, Desire and Intention) model for ToM [41]. The first level 

was used to model self’s BDI and the other was for reasoning 

about other agent. Marsella et al. developed a social simulation 

tool, PsychSim whose agents have beliefs about other agents [42]. 

Arita et al. [43] and Zanlungo [44] applied ToM to complex 

agent-based simulations and discussed about the effect of the level 

of ToM. Kondo et al. used the ToM in “carrying a stick task” for 

the cooperation of two computer programs [45]. Bringsjord et al. 

created a virtual character with a reasoning engine and they 

demonstrated that the character can pass the false-belief task by 

inserting “If someone sees something, they know it and if they 

don’t see it, they don’t” statement [46]. 

Kelley et al. developed a physical robot that uses own 

learned experience to detect the intentions of the humans [47]. 

The experience of robot was encoded into Hidden Markov 

Models. Breazeal et al. created animated robot LEONARDO that 

infers other person’s goals based on the simulation theory [48]. It 

passes a basic false-belief task. Scassellati developed ToM for a 

humanoid robot COG based on two representative ToM theories 

[5]. Yokoya et al. used a recurrent neural network to model the 

relationships between robot’s movement and actual object’s 

reaction [49]. After building its own model, it observes human’s 

behavior of rotating objects (blocks) and expanded the original 

self model to model human’s one. Demiris et al. followed 

“simulation theory of mind” and used robot’s own motor system 

to understand other robot’s behavior [50]. Takanashi et al. 

inferred other robot’s behavior based on its own behavior 

repositories in the game of robot soccer [51]. 

 There are several works targeted to theory of mind. Kuniyoshi 

et al. developed several skills of simulated and embodied robots 

for theory of minds: “learning by watching,” and “imitation” [52]. 

Kozima et al. proposed a framework to implement and exploit 

theory of mind from indirect experience of infant humanoid robot 

[53]. Ono et al. assumed that human’s theory of mind model is 

organized as Baron-Cohen’s modular view and implemented an 

interface system to help humans understand robot’s intention [54]. 

Agents migrate from physical robots to user’s computer for shared 

attention. Ito et al. also focused on factors related to human’s 

ToM in the interaction of artifacts [55][56]. Scassellati et al. built 

a self model from the relationships between visual input and 

actual motor movement of robot and used it to discriminate others 

from self [57]. This is an important skill to do theory of mind. 

Kramer provided with an overview of the theory of mind in 

communication with virtual humans [58]. McCabe et al. 

introduced the concept of theory of mind to interpret the results of 

theoretical games played by humans [59]. They mentioned that 

the form of games is related to the human’s theory of mind 

execution and produce different outcomes. Boella et al. stressed 

the importance of theory of mind in the construction of social 

reality with multi-agent systems [60]. Akiwa et al. recognized that 

just imitating human’s behavior is not interesting to human 

demonstrator and proposed a system to predict subject’s next 

action based on past experience [61]. The prediction was done 

based on the difference between current behavior and past one. 

Flax modeled Leslie’s modular view on the theory of mind using 

first-order modal logic with an example of a scenario [62]. Hall et 

al. used theory of mind assessment of children to evaluate a 

virtual character system [63]. 

3. METHODS

In [64], authors tested ToM learning in simulated robots. In this

real robot testing, simulation and a real robot was used together to 

do the ToM. In actor learning, simulation is used. In observer 

learning, the trajectories were collected from real robots and 

simulation was used in EEA. In actor exploitation, the position of 

new light source to seduce actor’s robot was determined with 

simulation and tested in real robots. 

Figure 3. Overview of ToM learning

3.1 Actor Learning

In the first stage, the neural network controller is evolved for the 

actor robot. The architecture of neural network is fixed and only 

the weights are evolved. The sensory inputs (light level) are 

inputted to the neural network and the output is the movement of 

wheels. Figure 4 explains the details of the evolutionary algorithm

used. Each controller is represented with a vector of weights and 

each entry has an associated self-adaptive parameter. The 

mutation operator updates the weights based on the self-adaptive 

parameter’s value. A task is to follow light source and a fitness 

function is defined based on the distance to the light source.

141
Figure 4. The evolutionary procedure to evolve actor robot’s 

controller

3.2 Observer Learning 

The goal of this stage is to discover actor robot’s self (the neural 

network evolved) based on their real trajectories. It uses EEA 

(Estimation-Exploration Algorithm) to learn other’s self models

[9]. Initially, one trajectory is observed from the actor robot. In 

Estimation step, it runs learning other’s self models multiple times 

with different random seed and produces multiple candidates 

(neural networks). In Exploration step, using the candidates, a 

number of starting points are tested and the EEA chooses the one 

with the maximum disagreement of the candidates as a next 

observing point. The next trajectory is observed from the new 

starting point chosen and the two trajectories are used for the next 

estimation step. A new population of the estimation step is 

initialized with the best candidates of the previous estimation step. 

Evolutionary algorithm is used to learn the other’s self model in 

the estimation step. It is a kind of active incremental learning 

algorithm.

Figure 5 explains the fitness function in the evolutionary 

algorithm. The trajectory of the robot is a time-series sequence of 

the X-Y coordinates. At time t, the robot is placed in (X(t), Y(t)) 

in the environment and the next position is estimated by a 

candidate neural network. The fitness was calculated based on the 

difference between the original position and the estimated one. 

Figure 5. Fitness measuring of a candidate neural network

3.3 Actor Exploitation 

Once actor’s self models are discovered, they can be used to 

predict the robot’s trajectory and observer robot can catch it with 

a trap based on the estimation. A trap is placed in the middle of 

the light source and a starting position of the other robot. With the

actor’s self models discovered, a new light position can be 

estimated to seduce the other robot to the trap. This is called 

“ToM estimation.” The easiest way to predict the other robot’s 

movement is “straight line estimation” assuming that the robot 

will go straightly to the light source. However, the movement of 

robot evolved is not straight line and shows several interesting 

patterns. The two approaches are compared to measure the 

goodness of our method.

4. EXPERIMENTAL RESULTS

The proposed method was tested in various settings from 

simulations to real physical robots. In a simulation side, PhysX 

(A simulator with physics engine) and EnKi (for E-Puck robot) 

are used. In a physical side, E-Puck robots are used to get results. 

The robot has two light sensors (left and right) and controls the 

robot by adjusting the wheels. In PhysX simulation, the neural 

network outputs are “the rotational angle” and “speed” of wheel. 

For E-Puck robot, the speed of left and right wheels is outputs of 

the network. In case of visible trap, the robot can detect the trap 

located, and left and right sensors digitize the strength of signals 

from the trap. Each neuron in a neural network has a bias 

parameter and the arc tangent function is used as a transfer 

function. 

Based on the success of the virtual experiments [64], our 

experiments were expanded to the real physical robots. In our 

experiment, E-Puck mobile robots were used. It has two wheels 

and eight infra-red sensors. Like the virtual cases, only two 

sensors were used. As a light source, infra-red LED light was used. 

The trajectory of robot was recorded using Vicon motion capture 

system. Reflexive balls were attached to robot’s custom-built 

mounting base and the Vicon system recognized the position and 

angles of the robot based on the balls detected. Our simulator was 

implemented based on EnKi simulator. In our simulator, a sensor 

model was built based on sampling data (129 positions ×15 

different angles × 8 sensors). Additionally, wheel speed level was 

readjusted based on real sampled data.

The actor’s neural network was evolved at each setting. 

Figure 6 shows trajectories of the evolved controllers at various 

starting positions. Their trajectories are not straight line and have 

a lot of curves. Also, they are very complex and have a lot of 

rotations to reach the goal position (light source). Although the 

controllers are evaluated at one starting position in the evolution, 

they can generalize well for different starting positions. 

Figure 6. Trajectories of evolved neural controller (Black 

circle = Initial position, Black cross = Light)

Figure 7, Figure 8 and Figure 10 shows the progress of EEA 

learning. Figure 9 shows successful exploitation results for real 

physical robots. In EEA learning, real trajectories were collected 

from actor’s robot. In the exploitation scenario, the new light 

142
position was estimated with simulation and tested with real robots. 

It shows that the reconstructed controllers can be used 

successfully to seduce the actor robot to the trap. 

Figure 7. The progress of the observer learning in various 

environments

Figure 8. The trajectories actively chosen by the observer 

learning

 Table 1 summarizes errors of all experimental environments. 

The ToM was compared with straight line estimation (assume that 

the robot will go straightly to the light source). For all cases, the 

ToM method can beat the straight line estimation method. In 

PhysX case, the ensemble of five candidate neural networks was 

successful and outperforms the single best neural network 

candidate and the straight line estimation. However, it is not true 

for the EnKi case and the ensemble method was not used for real 

robots.

Figure 9. An example of exploitation for real robots. 

Table 1. Statistical summary

Straight Line 

Estimation 

ToM

(Single 

neural 

network)

ToM

(Ensemble of 

5 neural 

networks)

PhysX1 5.93 ± 0.54 3.87 ± 0.69 1.10 ± 0.28

PhysX with a 

Visible Trap1 18.21 ± 2.60 18.29 ± 2.72 11.97 ± 2.24

EnKi1 10.75 ± 1.26 0.89 ± 0.30 29.08 ± 5.75

Real Robots1

(Simulation) 54.28 ± 2.84 35.37 ± 3.35 - 

Real Robots2 34.80 ± 7.66 26.59 ± 9.33 -

1: Average of 100 points

2: Average of 10 points 

5. CONCLUSIONS

In this paper, a variety of experiments were conducted to 

show the possibility of theory of mind implementation for robots. 

Each robot can model other robot’s internal self model (neural 

network) based on their observation using EEA learning 

algorithm. Once the model was built, they can be used to predict 

other robot’s future behavior. In these experiments, several virtual 

experiments and real physical robot testing successfully show the 

benefit of the other’s self modeling. 

143
 In this paper, it is assumed that the neural structure of an actor 

robot is the same with the one of observer and there is no process 

to identify the fundamental structure. The number of the inputoutput neurons has to be analyzed to determine the structure of 

neural networks. After then, there are also many structural 

considerations: The number of layers, the number of hidden nodes 

for each layer, and the existence of recurrent links. The solution 

might be evolving topology and weights of neural networks 

simultaneously.

6. ACKNOWLEDGMENTS

This work has been supported in part by the U.S. National 

Science Foundation (NSF) Creative-IT program, grant #0757478. 

K.-J.K. was supported by the Korea Research Foundation Grant 

(KRF-2007-357-D00220) funded by Korean Government 

(MOEHRD) and Korea Health 21 R&D Project, Ministry for 

Health, Welfare and Family Affairs (A040163)

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(50.8, 41.90) (85.8, 41.90) (120.80, 41.90) (50.8, 121.91)

0 

1 

2 

Figure 10. Progress of observer learning for real robots (Red line = Real Trajectory, Yellow line = Predicted trajectory)

146
Resilient Behavior through Controller Self-Diagnosis, 

Adaptation and Recovery

Juan Cristobal Zagal Hod Lipson

Computational Synthesis Laboratory Computational Synthesis Laboratory

Mechanical & Aerospace Engineering Mechanical & Aerospace Engineering

Cornell University Cornell University

Ithaca, NY 14853, USA Ithaca, NY 14853, USA

jcz35@cornell.edu hod.lipson@cornell.edu

ABSTRACT

We explore robot behavior recovery through a process akin to 

self-reflection. A robot contains two controllers: A primary

“innate” reactive controller, and a secondary “reflective”

controller that can observe, model and control the primary

controller. The reflective controller adapts the innate controller 

without access to the innate controller’s internal state or 

architecture. Instead, the reflective controller models the innate 

controller and then synthesizes input/output filters that adapt the 

innate controller’s existing capabilities to new situations. The 

innate controller is subjected to a variety of sensory, motor, and 

internal control damage scenarios. The reflective controller 

diagnoses the level of failure using a self-model and the observed 

sensorimotor time-series data and is able to recover performance. 

Categories and Subject Descriptors

I.2.6 [Artificial Intelligence]: Learning; I.2.9 [Artificial 

Intelligence]: Robotics---Autonomous vehicles

General Terms

Algorithms 

Keywords

Self-reflection, damage recovery, machine learning, evolutionary 

robotics, self-modeling.

1. INTRODUCTION

Living systems are able to maintain their performance while 

compensating for changes in their environment and in their own 

structure [16]. Here we explore processes that allow machines to 

model their own behavior and use those models to detect 

controller failure and identify paths to recovery. Such ability is 

known to exist in the fish and reptiles [6][21] and it has been 

recently discovered in some structures of the adult mammalian 

brain [1][9][10][7] at the scale of changing and reorganizing 

neurons and their function by experience.

(a)

(b)

Figure 1. (a) Six-legged robot used for the experiments. The 

robot perceives light intensity by means of color specific 

sensors (red and blue) located at either side of its front legs. 

Each leg is independently controlled by three motors, two for 

the 2-DOF base joint and one for the 1-DOF mid joint. The 

robot is driven by 18 motors. A total of 15 rigid bodies 

complete the robot architecture. (b) The function recovery is 

achieved by the synthesis of modifier networks. A self-model

(right) of the innate controller (left) is used to synthesize these 

modifiers. 

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Copyright © 2009 ACM 978-1-60558-747-9/09/09...$4.99

Light sensor (red)

Light sensor (blue)

2 DOF joint

1 DOF joint

3-DOF leg

base joint

mid joint

Innate-NN Self-model-NN

Ze

t

Ue,c

t Ue,c’

t

Input modifier NN

Output modifier NN

0 1 2 3

6 7

4 5

0 1 2 3

8 9

4 5 6 7

147
In recent studies, we showed that a robot’s resiliency increases 

when self-modeling its own morphology [3][4][18]. We explored 

how self-models of a robot body can be used for damage recovery

[2]. Here we wish to take this concept a step further, by having a 

robot model its own controller as well. Just as a robot benefits 

from modeling its own morphology and then using that model to 

determine how to best compensate for a new situation, can a robot 

benefit from modeling its own controller, then use it to 

compensate for a new situations?

The first question to answer is why a robot would need to model 

its own controller at all, instead of directly accessing and 

manipulating it. The reasons for this are many fold: First, there are 

many aspects of a control system that cannot be explicitly 

described, even if its architecture is perfectly known: Sensor and 

actuation lag time, noise, and computational errors and delays, for 

example. Second, the controller may change in unanticipated 

ways due to failure or change in the environment. Manual 

modeling of an existing controller also takes time and effort, and 

direct manipulation of a controller could require an unwarranted 

increase in software and hardware complexity. Finally, in some 

cases the controller of a robot is simply inaccessible – either 

locked by design, or obfuscated by legacy code. The ability to 

modify performance of an existing controller without directly 

accessing it also serves as a safe adaption strategy, since the 

original controller is never modified and therefore its behavior can

be restored at any time. This process may also shed light on the 

evolution of more opaque controllers such as biological nervous 

systems.

(a)

(b)

Figure 2. (a) A proposal of nested brains architecture. (b) 

Minsky’s brain chain from [12].

The approach we use here is based on the assumption that there 

are two co-resident controllers; one reflecting on the other (Figure 

2a). This architecture is a form of metacognition: the ability to 

reflect upon one’s own mental processes and to self-regulate 

them. Such metacognitive processes are recognized to be present 

in humans, non-human primates, and a few other mammals

[8][15]. It has been recently demonstrated to exist in the rat as 

well [5], suggesting that metacognition concepts might be 

applicable to simpler systems such as robots.

Minsky [11] points out that a brain can be better understood as 

a “society of minds” interacting with each other. He proposed a

thought experiment consisting on dividing an artificial brain in 

two parts. While the input-outputs of the first part (A-brain) are

connected to the external world, the second part (B-brain) is only 

connected to the A-brain; thus A is the only world seen by B

(Figure 2b). As proposed by Minsky, the B-brain might help to 

the A-brain even without having access to the real world, and by 

just looking at the activity of the A-brain. Simple questions such 

as Are you repeating? Are you feeling better? And How do you 

think? might help to produce a better brain state in the world.

As pointed out by Minsky in recent work [13] there must be 

some brains that critique the performance of other brains or sets of 

brains. They might also be able to identify certain ways of 

thinking and to reconfigure thinking states by activating or 

reconnecting certain brain areas. If we are about to explore how to 

implement such a system using current robots the first question 

that arises is how a critic system might identify and manipulate a 

certain way of thinking? We hypothesize that minds should be 

able to perform some sort of self-modeling of other minds as a 

way to compare and reason about patterns of activation.

The remainder of this paper is organized as follows (Figure 8

presents a description of the entire process): In section 2 we 

describe the simulated robot and experimental environment used 

for our study. We also describe the generation of an innate robot 

behavior. In section 3, we present experiments showing the first 

stage of self-reflection by reverse engineering of the robot innate 

controller. In section 4, we describe different types of controller 

damage used for the experiments. In section 5 we describe how 

the self-model is used for damage diagnosis. In section 6 we 

describe the process of damage recovery through the synthesis of 

input/output modifiers. Finally in section 7 we present the 

conclusions of this work.

2. ROBOT AND ENVIRONMENT

We used a simulated six-legged robot for our experiments. The 

robot is free to walk on its environment, comprising a plane 

surface covered with moving emitters of red and blue light (see

Figure 4); these light sources are initially randomly distributed 

along the surface and follow different patterns of motion. The 

robot architecture is illustrated in Figure 1a. The central portion of 

the robot is composed by three solid cylinders. The central 

cylinder is connected to six legs by means of 2-DOF motors. The 

forward walking behavior is the result of the rhythmic oscillation 

of the limbs. Each motor i={1,...,18} follows a reference signal r

i

=i + aisin(t +i),where i is a pre-defined central angle of 

oscillation for motor i. PID dynamic compensators are in charge 

of ensuring successful reference following for each motor. These 

pattern generators were obtained by evolving the amplitude ai and 

relative phase i of each oscillator such that the robot maximizes 

frontal displacement. A similar strategy was reported in [17].

Figure 3 shows comparisons of the reference signals given by the 

oscillators versus the actual angle which is achieved by each 

motor during a normal walking of the robot along an observation 

period of duration T=1500 time steps. 

Two pairs of color-specific light sensors are located at the front 

legs of the robot, generating the measurement signals z

0 (blue) and

z

1 (red) from sensors located at the left leg, and signals z2 (red) 

and z

3 (blue) from sensors located at the right leg. At each

simulation step, the read-out of a light sensor z

k is computed as 

the instantaneous light intensity at the sensor due to contributions 

of all the environment light sources li of corresponding color

using and inverse-square law.

148
(a) (b) (c)

Figure 3. Angular reference signals for each motor r

i : i = {1,...,18} (dash line) versus the instantaneous angle achieved while the

robot walks (gray solid line). The central angle of oscillation is presented in radians at the left of each plot. In (a,b) the reference 

following for motors attached to the robot torso are illustrated. In (c) the reference following for the femur-tibia joints are shown.

Once the robot is provided with the forward-moving behavior, it 

learns to follows the moving sources of blue light while avoiding

the sources of red light. This behavior was obtained by evolving 

the weights of an “innate” recurrent neural network (RNN) 

controller (Innate-NN) shown on Figure 1b (left). Four input 

neurons {0, 1, 2, 3} are fed by the four light-measurement signals. 

The network contains two hidden nodes {4, 5} and two output 

nodes {6, 7} that generate the left u0 and right u1 motor 

modulation signals. The signal u0 modulates the amplitude of the 

left legs oscillators {1,2,3,7,8,9,13,14,15} and u1 the amplitude 

the remaining right leg oscillators {4,5,6,10,11,12,16,17,18}. The 

output yk of neuron k is computed as















    j

k jkj k y  xw  (1)

where () is the sigmoid activation function, xj are the input 

signals, wkj are the connection weights and k is the threshold of 

neuron k. The controller is represented by a genome c of Nc = 34

scalar parameters (in the range [−1, 1]): 26 connection weights, 

and 8 activation thresholds. The reward perceived by a robot is 

defined in equation (2) by assigning a positive (negative) reward 

to the amount of blue (red) light intensity that is collected during 

the evaluation of controller c under environment e after a period 

of T time steps.

F dtzzzz ce

t

ce

t

ce

t

ce

t

t ce

t (= ) ,1,,0, ,2, ,3,

0

,   (2)

To avoid exploiting the peculiarities of a unique environment, 

we used a set of Ne = 3 randomly generated environments and we 

defined the fitness of a candidate controller c as follows:

ce

T

E N

e

c F f ,

1=

=  (3)

Due to perceptual aliasing (where different locations trigger the 

same sensor state, albeit requiring different control actions) an 

optimal motor action Ut = {ut

0

, ut

1

} cannot be purely determined 

by the sensor state Zt = {zt

0

,..., zt

3

} at a single time t; however, we 

hypothesize that a causal controller (such as a RNN) might have 

good overall performance over the evaluation period T.

The algorithm runs with a probability of mutation pm = 1/Nc

using Cauchy mutation and a probability of crossover pc = 0.9. 

The population size was set to 30 individuals per generation. 

Figure 4 shows the innate behavior that result after about 1000 

evaluations. 

Figure 4. Illustration of the innate behavior. The robot moves 

toward areas of higher intensity of blue light while avoiding 

red lights (the discs are emitters of colored light). The figure 

shows four different scenarios. Motion traces are represented 

with the same color of the source.

149
3. SELF-MODELING CONTROLLER

In this section we describe the process by which the reflective 

controller acquires a self-model of the innate controller. Going 

back to Minsky’s formulation, this is equivalent to the B-brain 

asking the A-brain about its way of thinking. The self-model is

represented as a recurrent neural network controller (Self-modelNN) that has the same number of input and output nodes as the 

Innate-NN, though not necessarily the same number of hidden 

units. Figure 1 (right) presents the architecture of the self-model 

that we use for our experiments (contains 4 hidden nodes). It is 

represented with a genome c’ of Nc’ = 62 scalar parameters (in the 

range [−1, 1]): 52 connection weights and 10 activation 

thresholds.

We also considered the special case of a self-model (Selfmodel-NNT) that has the same architecture of the Innate-NN. This 

is for the sake of testing some hypotheses described in the next 

section. We note corresponding test genome as cT’. 

We allowed the robot to operate freely under an environment e

while executing its Innate-NN controller and we recorded vector 

time series of sensor data Ze = {Zt

e : t  T} and motor data Ue = 

{Ut

e : t  T}. We fed the inputs of each candidate Self-model-NN 

controller c’ with the recorded time series of sensor data Ze

, 

resulting in predicted motor actuation data Uc’,e = {Ut

c’,e : t  T}. 

We then measured the quality of each candidate self-model 

controller c’ by its ability to reproduce the same input-output 

patterns as those observed during the operation of the Innate-NN 

controller in different environments. To find the best self-model, 

we minimized the signal distance D(c) described by equation (4).

Figure 5 shows results from optimizing the self-model, using a 

genetic algorithm, with the settings described in previous section. 

The search is steered toward minimizing the distance D(c) over 

the space of candidate self-model controllers. The figure 

corresponds to an average of eight runs of the minimization 

procedure. 

The standard error is depicted with vertical error bars. In order 

to avoid overfitting, a 30% of the data was used for validation. 

The minimization stops when the error in the validation data starts 

to increase (early stopping). The convergence of candidate selfmodels is illustrated on Figure 7. The figure shows how the 

similarity of 3D trajectories increases when minimizing the 

distance D(c) over the training environment (a) and over different 

test environments (b,c). As it can be seen from the figure, 

resulting self-models can predict the robot performance under 

unseen environments to a large extend.

We note as c’* the optimal self-model solution obtained during 

this stage.

4. CONTROLLER DAMAGE

In this section we describe the different scenarios of damage 

introduced to the innate controller for testing the here explored 

recovery method. Figure 6 shows the Innate-NN under six 

different types of controller damage.

The scenarios are summarized on Table 1. They consists on 

disconnecting different inputs to the network, disconnecting 

synaptic links inside the network, swapping synaptic links and 

also introducing constant perturbations on specific neurons. 

Figure 5. Results of minimizing the distance D(c) using genetic 

search over the space of self-models of the robot controller. 

Blue continuous line corresponds to the minimum distance 

achieved at corresponding evaluation. Burlywood line shows 

results obtained over validation data set. Standard error is 

depicted with error bars.

1 2 3

4 5 6

Figure 6. Different damage scenarios introduced to the InnateNN controller for testing. Red or blue colors are used to 

indicate the location of the change introduced to the network.

Link disconnections are presented in {1,2,3,4}, an exchange of 

neuron connections is introduced in {5} and a constant 

perturbation is introduced in {6}.

cD dtUU e

t

ec

t

T

e

   ,

0 =)( (4)

0 1 2 3

6 7

4 5

0 1 2 3

6 7

4 5

0 1 2 3

6 7

4 5

0 1 2 3

6 7

4 5

0 1 2 3

6 7

4 5

0 1 2 3

6 7

4 5

150
(a) (b) (c)

Figure 7. Convergence of robot trajectories induced by different candidate self-models (varying shades of blue). As it can be seen, a 

minimization of D(c) increases the similarity of resulting robot trajectories versus the innate target (red). Trajectory curves were 

obtained from environments of varying initial configuration of lights. Curves shown in (a) correspond to the environment used as 

training, (b) and (c) correspond to environments of increasing variation from the innate environment. As figures (b) and (c) show, 

resulting self-models are able to predict the robot behavior under unseen situations to a great extend. The axis scaling was adjusted 

for the data to fit in a cube.

Table 1. Damage Scenarios Tested

Scenario Description

1 The left blue sensor input is damaged (input to node 0 

is disconnected).

2 The left red sensor input is damaged (input to node 1

is disconnected).

3 The link from neuron 4 to neuron 7 is disconnected 

(weight changed to take the value of 0).

4 Two links entering node 6 are disconnected (weights 

changed to take the value of 0).

5 The weights connecting neuron 5 with neuron 6 and 

neuron 4 with neuron 7 are swapped.

6 A constant perturbation is introduced to left motor 

output (neuron 6). 

7 No change

5. FAILURE DIAGNOSYS 

In this section we describe how the self-model constructed by 

the reflective controller can be used to diagnose the damage 

introduced to the innate controller. As previously described, the 

self-model was derived from data collected during normal 

operation of the robot.

First we introduce one of the failures described on Table 1 to 

the innate controller and we then let the robot to operate on its 

environment while executing its now damaged innate controller. 

During this period we collected time series of sensor Ze = {Zt

e : t 

 T} and motor Ue = {Ut

e : t  T} data.

It is natural to expect at this point a difference between the 

sensorimotor relationships observed under failure versus those 

already explained by the self-model. We also expect a reduction in

reward received from the environment. 

Since the self-model was proven to provide good behavioral 

explanations of the innate sensorimotor relationships it is 

expected that small deviations from that solution might have some 

explanatory power of the failure. 

Moreover we hypothesize that the topological distribution of 

these variations might have some degree of correlation with the 

actual source of the perturbation itself; we will further analyze 

these hypotheses in the remainder of this section. 

Figure 8. Flow diagram of the method to obtain a robot 

resilient behavior through controller self-diagnosis adaptation 

and recovery.

Self-model 

representation

Recovered?

Behavior 

monitoring

Failure 

diagnosis

Introduction of 

controller damage 

YES

NO

Recording I/O data 

time series while 

executing Innate-NN 

Table 1 

scenarios. 

Uses Self-model-NN 

as a seed to discover 

Diagnosis-SM

Recovery Synthesis of 

I/O modifiers. 

Self-modeling

Innate Controller

Construction of 

Self-model-NN 

User defined 

criteria. 

151
We used the following strategy for producing new diagnosis

self-models (Diagnosis-SM): Given the sensorimotor signals 

obtained during the perturbed functioning of the robot, we started 

a new genetic search over the same self-model search space that 

was described in section 3. A first population of diagnosis selfmodels was strongly seeded with the optimal self-model (c’*

). In 

this case the search was steered toward minimizing the difference 

between the Diagnosis-SM outputs versus the outputs observed 

during the operation of the damaged innate controller, this was

achieved by feeding each candidate Diagnosis-SM with the 

observed sensor time series Ze

, and by using the distance 

described in equation (4). If there were no damage at all, the 

solution would be the seed itself (validated by our experiments). 

In presence of damage, however, genetic search will steer the

Diagnosis-SM network to produce similar motor patterns as those 

provided by the damaged Innate-NN. We trained the DiagnosisSM using the same stopping criterion as described in section 3.

We note the resulting optimal Diagnosis-SM as c’’*

.

Before analyzing our diagnosis results we should note some 

hypothesis:

H1: The solutions for Diagnosis-SM, c’’* and for Self-Model,

c’* have a similar explanatory power over the innate system c.

Thus, any explanatory property assigned by an observer to the

Self-model must also hold for the Diagnosis-SM.

H2: The self-model synaptic difference vector c = |c’’*

- c’*|

represent topological changes of the self-model network, but does 

not necessarily have any counterpart in the real system.

This is since the topology of the self-model is different from the 

topology of the innate controller (with exemption of the examples 

presented here to test H3). 

H3: The topological changes represented byc might better 

approximate changes of a target system whose architecture is 

similar to the topology of the self-models.

A first estimation of the level of failure is given by quantitative 

measurements of parameter variation under each failure scenario. 

Figure 9 shows the standard deviation of the synaptic difference 

vector c that results when comparing the Self-Model with the 

Diagnosis-SM on each test scenario. The figure shows two cases. 

The first is when c is defined in a space of self-models of generic 

architecture (c = |c’’*- c’*|). The second is when c is defined in 

a space of self-models whose architecture intentionally match the 

innate architecture (c = |cT’’*

- cT’

*

|).

It is interesting to observe (see Figure 6 as reference) that when 

disconnecting a blue sensor at the left side (failure 1) the level of 

compensation is greater than disconnecting a red sensor (failure 

2). It appears that swapping the neuron connections also induces a 

small level of compensation (failure 5). The remaining failures {3,

4, 6} appear to induce a similar level of compensation as those 

required by failure 1. 

Another type of diagnosis deals with the topological localization

of the synaptic compensations that are hypothesized from the 

resulting difference vector c. We remark however, that this 

entails the careful consideration of the abovementioned 

hypotheses. 

Figure 9. Quantitative estimation of the level of damage 

introduced under each test scenario to the innate neural 

network. The estimation is a result of comparing the original

synaptic weights of the Self-model with the new synaptic 

weights resulting from evolving a Diagnosis-SM. Each bar

represents the standard deviation of the weight difference 

vector c. Results are presented for the generic self-model 

architecture as well as for a test architecture.

Figure 10 shows the localization of the synaptic weights that are 

experiencing a larger degree of change on each scenario. In the 

case of failure 1, it is interesting to observe that disconnecting a 

blue sensor induces a change on the bias of the same sensor as 

well as on the bias of the counter sensor of the opposite side. 

There is also a change on the synaptic weight of the counter motor

modulator.

1 2 3

4 5 6

Figure 10. Most relevant compensatory modifications resulting 

from comparing Diagnosis-SM with the Self-Model for each 

one of the damage test scenarios. Arrows or nodes in red 

represent parameters on the Diagnosis-SM deviating more 

than the typical standard deviation (2.0) from the Self-model.

In this case the Self-Model-NNT architecture was used.

0 1 2 3

6 7

4 5

0 1 3

6 7

4 5

0 1 2 3 2

6 7

4 5

3

6 7

4 5

0 1 2

0 1 2 3

6 7

4 5

1 3

6 7

4 5

0 2

152
Disconnecting a forward connection between neuron 4 and 

neuron 7 produces a larger degree of compensation over most part 

of the network (failure 3). Disconnecting two forward connections 

produces a strong compensation on the network as well, see

failure 4.

Figure 11 shows bar plots comparing the original Self-model 

synaptic weights (grey) versus the weights of resulting DiagnosisSM (black) on each test scenario.

6. ADAPTATION AND RECOVERY

In this section we study how to recover function by filtering the 

inputs and/or outputs of the innate controller which is being 

subject of different types of damage. The filters are implemented 

using RNN’s as described in [20]. Figure 1b shows a diagram of 

this procedure when applying the filters to the Innate-NN during 

robot functioning. The idea is to recover the innate function by 

synthesizing input/output modifier networks using the DiagnosisSM as a model of the damaged innate controller to be recovered, 

and the Self-Model-NN that was generated in 3, as a model of the 

target system to be recovered.

These self-models are exploited by re-injecting sensor signals 

that were recorded during the robot operation on a real 

environment. The procedure allows evaluating the quality of 

modifier filters without further accessing the damaged innate 

controller. More details of this process are presented in [19][20].

Figure 12 shows reward levels resulting from the operation of 

the robot on two different environments. The different damage 

scenarios are presented. In each case there is a bar representing 

the reward level obtained before (black) damage, after damage 

(grey) and after recovery (white). As it can be seen, the procedure 

allows recovering performance to a great extend in most of the 

failure scenarios under analysis. 

7. CONCLUSIONS

We have illustrated how the resiliency of a simulated robot 

increases using a self-reflection process that involves controller 

self-diagnosis, adaptation and recovery. 

The proposed approach might also be useful for reusing existing 

hardware for new tasks, by applying the presented monitoring and 

controlling stages. The algorithm could be implemented, for 

example, inside a pre-existing robot, and modify its behavior by 

modulating its original controller’s input and output signals.

In a broader sense, we have presented a case where system 

identification techniques can be used to infer the parameters of a 

controller, instead of the parameters of the dynamical system 

under control. While adaptive control aims to dynamically 

compensate for a plant whose parameters are uncertain, we 

address here the problem (and envision possible advantages) of 

adding uncertainty to the controller itself.

The proposed technique for reverse engineering a controller 

(section 3) can be of interest beyond robotics. For example, a 

common problem faced by growing production plants is related to 

the task of inferring the actual inner workings of their legacy 

control components.

Figure 11. Comparing synaptic weights of Self-model (grey) 

versus resulting Diagnosis-SM (black) on each test scenario.

Scenario 1

Scenario 2

Scenario 6

Scenario 5

Scenario 4

Scenario 3

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(a)

(b)

Figure 12. Reward levels obtained by the robot before failure, 

after failure and after recovery. Recovery results were 

obtained for the case of using self-models of ideal test 

architecture (solution cT’

*

) and the generic architecture 

(solution c’*). Resulting reward levels are shown for two 

different environments (a,b) and considering the different 

failure scenarios. The simulation was set to be deterministic, 

having the same initial conditions under each scenario. 

Although such knowledge is usually available for third party 

contractors it may be lost or too expensive. In theory, a meta 

cognitive system can be superimposed on an existing system, 

adding the possibility to monitor, control and further expand the 

capabilities of an existing system.

8. AKNOWLEDGEMENTS

This work has been supported in part by the U.S. National 

Science Foundation (NSF) Creative-IT program, grant #0757478

and Chilean research FONDECYT project number #3080048.

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154
Neurodynamics of Cognition and Consciousness 

Robert Kozma 

Department of Mathematical 

Sciences University of Memphis 

Memphis, TN 38152, USA 

1-901-678-2497 

Robert.kozma.ctr@hanscom.af.mil 

 Walter J. Freeman 

Division of Neuroscience 

University of California at Berkeley 

Berkeley CA 94720, USA 

1-510-624-4220 

dfreeman@berkeley.edu 

ABSTRACT

Human cognition performs a granulation of the seemingly 

homogeneous temporal sequences of perceptual experiences into 

meaningful and comprehendible chunks of fuzzy concepts and 

complex behavioral schemas, which are accessed during future 

action selection and decisions. In this work a dynamical Theoryof-Mind (ToM) is presented to interpret experimental findings. In 

our approach meaningful knowledge is continuously created, 

processed, and dissipated in the form of sequences of oscillatory 

patterns of neural activity described through spatio-temporal 

phase transitions. The proposed approach has been implemented 

in computational and robotic environments. 

General Terms

Algorithms, Experimentation, Theory. 

Keywords

Neurodynamics, EEG, Phase Transition, Autonomous Agent, 

Intentionality. 

1. INTRODUCTION 

During the past years, strong evidence has emerged in the 

literature about the existence of sudden jumps in measured 

cortical activities. Lehman [1] identifies “micro-sates” in brain 

activity and jumps between them. Rapid switches in EEG activity 

have been described [2-4]. Synchronization of neural electrical 

activity while completing cognitive tasks is studied in various 

animals, e.g., in cats, rabbits, gerbils, and macaque monkeys [5-

9]. Behavioral correlates of transitions between metastable 

cortical states have been identified [10-13]. A comprehensive 

overview of stability, metastability, and transitions in brain 

activity is given in [14-16]. One of the most influential theories of 

consciousness is the global workspace theory [17]. There is 

striking similarity between the cognitive content of phase 

transitions and the act of conscious broadcast in global workspace 

theory. It can be hypothesized that cortical phase transitions are in 

fact manifestations of such conscious broadcast events. 

Freeman interpreted these findings using dynamic systems theory 

[8, 18]. Accordingly, the brain’s basal state is a highdimensional/chaotic attractor. Under the influence of external 

stimuli, the dynamics is constrained to a lower-dimensional 

attractor wing. The system stays in this wing intermittently and 

produces an amplitude modulation (AM) activity pattern. 

Ultimately, the system jumps to another wing as it explores the 

complex attractor landscape. Evidence from AM pattern analysis 

during tasks requiring sensory discrimination demonstrates the 

potential existence of multiple modes in neocortex that are 

mutually exclusive and cannot interact when accessed one at a 

time. The perceptual content is found in the phase plateaus of 

human scalp EEG. The EEG shows that neocortex processes 

information in frames like a cinema. The phase jumps show the 

shutter. The coordinated activity reveals self-similarity of the 

global dynamics that may form Gestalts (multi-sensory percepts) 

[19]. In the present work we use the theory of Freeman’s K sets to 

model and implement biologically-motivated approach to 

intelligence.

2. NEURODYNAMIC MODEL USING K 

SETS 

A hierarchical approach to spatio-temporal neurodynamics, based 

on K sets, was proposed by Freeman in the 70’s [20]. K sets 

consist of a hierarchy of components with increasing complexity, 

including K0, KI, KII, KIII, KIV, and KV systems. They model 

the hierarchy of the brain starting from the mm scale to the 

complete hemisphere. Today, K sets are used in a wide range of 

applications, including classification [21-22], image recognition 

[23], and robot navigation [24-25]. Recent developments include 

KIV sets [26-28] for sensory fusion and modeling intentional 

behavior. They are applicable to autonomous control. 

Intentionality means in the context of the present approach the 

cyclic operation of prediction, testing by action, sensing, 

perceiving, and assimilation. The significance of the dynamical 

approach to intelligence is emphasized by our hypothesis that 

nonlinear dynamics is a necessary condition of intentional 

behavior and intelligence in biological systems [25]. Therefore, 

understanding dynamics of cognition and its relevance to 

intentionality is a crucial step toward building more intelligent 

machines [29]. Specifically, nonconvergent dynamics continually 

creates new information as a source of novel solutions to complex 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies are 

not made or distributed for profit or commercial advantage and that 

copies bear this notice and the full citation on the first page. To copy 

otherwise, or republish, to post on servers or to redistribute to lists, 

requires prior specific permission and/or a fee. 

Performance Metrics for Intelligent Systems Workshop (PERMIS), 

September 21-23, 2009, 2004, National Institute of Standards and 

Technology. NIST, Gaithersburg, Maryland, USA. 

155
problems. The proposed dynamical hypothesis on intentionality 

and intelligence goes beyond the basic notion of goal-oriented 

behavior, or sophisticated manipulations with symbolic 

representations to achieve given goals. Intentionality is 

endogenously rooted in the agent and it cannot be implanted into 

it from outside by any external agency. 

The KIV model of the brain consists of three major components: 

cortex, hippocampal formation, and midline forebrain. Further, 

the amygdala striatum and brain stem provide link to the external 

motor part of the limbic system [30]. In the model, three types of 

sensory signals are distinguished. Each of these sensory signals 

provides stimulus to a given part of the brain, namely the sensory 

cortices, midline forebrain, and the hippocampal formation, 

respectively. The corresponding types of sensory signals are (i) 

exteroceptors; (ii) interoceptors (including proprioception); (iii) 

orientation signals. The convergence location and output are 

provided by the amygdala. 

Experiments have been designed and implemented in computer 

simulation to demonstrate the potential of KIV operating on 

intentional dynamic principles. In the experiments we used an 

autonomous agent moving in a 2-dimensional environment. 

During its movement, the agent continuously receives two types 

of sensory data: (1) distance to obstacles; (2) and orientation 

toward some preset goal location. KIV makes decisions about its 

actions toward the goal. The spatio-temporal dynamics of this 

system shows sudden changes in the simulated cortical activity, 

which is in agreement with properties of metastable AM patterns 

observed in EEG data. These results indicate that the KIV model 

is indeed a suitable level of abstraction to grasp essential 

properties of cortical phase transitions as evidenced in intracranial 

and scalp EEG and MEG data. 

3. CONCLUSIONS 

Large-scale synchronization in the cortex, interrupted 

intermittently by short periods of desynchronization through 

phase transitions, is an emergent property of the cortex as a 

unified organ and it is an attribute to higher cognitive functions of 

the mind. Intensive studies are conducted towards the 

interpretation of the content of the metastable AM patterns 

separated by brief transitory periods of phase transitions. The 

intermittent synchronization-desynchronization cycle is a neurophyisiological correlate of intentionality and consciousness. The 

KIV model is capable of demonstrating this intentional dynamics 

and it is a candidate of implementing intentionality is artificial 

systems. Modeling is done in the context of the given level of 

knowledge available on the behavior of the autonomous system 

and its interaction with the environment. 

4. REFERENCES 

[1] Lehman D, Strik WK, Henggeler B, et al Int. J Psychophl., 

29:1–11, 1998. 

[2] Stam CJ, Breakspear M, Cappellen,et al Hum Brain Mapp

19:63-78, 2003. 

[3] Fingelkurts A .A., Fingelkurts A. A. Mind and Brain, 2:262–

296, 2001. 

[4] Fingelkurts A .A., Fingelkurts A. A. Int J.Neurosci,

114:843–862, 2004. 

[5] Barrie JM. Freeman WJ, Lenhart M. (1996) J. Neurophysiol. 

76: 520-539. 

[6] Ohl, FW, Scheich, H and Freeman, WJ Nature 412: 733-736, 

2001. 

[7] Ohl FW, Deliano M, Scheich H et al Biol. Cybernetics, 88, 

374-379, 2003. 

[8] Freeman WJ, Burke B, Holmes M. Hum. Brain Mapp. 19: 

248-272, 2003. 

[9] Bressler SL. Neuropsychopharmacology, 28:S35-S39, 2003. 

[10] Kelso, J.A.S.“Dynamic patterns: The self-organization of 

brain and behavior.” MIT Press, 1995. 

[11] Bressler SL, Kelso JAS. Trends in Cognitive Sciences, 5:26-

36, 2001. 

[12] Bressler SL. Current Directions in Psychol. Sci., 2002, 

11:58-61, 2002. 

[13] Kelso, J.A.S.,Engstrom,D.A.The complementary nature MIT 

Press, 2006. 

[14] Le Van Quyen M, Foucher J, et al J. Neurosci. Meth. 111: 

83-98, 2001. 

[15] Werner G. BioSystems, 87: 82-95, 2007. 

[16] Tsuda I. Beh. and Brain Sci., 24(5):793–810, 2001. 

[17] Baars, B. J. A cognitive theory of consciousness. MIT Press, 

MA, 1988. 

[18] Freeman, W.J. Clin. Neurophysiology, 116, 1118-1129, 

2005. 

[19] Freeman, W.J. Proposed cortical ‘shutter’ mechanism in 

cinematographic perception, in:“Neurodynamics of 

Cognition and Consciousness,” Perlovsky, L., Kozma, R. 

(eds), Springer, 2007. 

[20] Freeman, W.J. Mass Action in the Nervous System. Acad. 

Press, N.Y, 1975 

[21] Chang, H.J., Freeman, W.J., Burke, B.C. Neur. Netw., 11, 

449-466, 1998.

[22] Freeman, W.J., Kozma, R., Werbos, P.J. BioSystems, 59(2), 

109-123, 2001 

[23] Li, G., Z. Lou, L. Wang, X. Li, W.J. Freeman Springer 

LNCS, Vol. 3610, pp. 378-381, 2006. 

[24] Harter, D., Kozma, R. IEEE Trans. Neur. Netw., 16(4), 565-

579, 2005. 

[25] Harter, D., Kozma, R. Int. J. of Intelligent Systems, 21, 955-

971, 2006 

[26] Kozma, R., and Freeman, W.J. J. Integrative Neurosci., 2, 

125-140, 2003. 

[27] Kozma, R., Freeman, W.J., Erdi, P. Neurocomp., 52-54, 819-

825, 2003. 

[28] Huntsberger, T., Tunstel, E., Kozma, R. “Onboard learning 

strategies for planetary surface rovers,” in: Intelligence for 

Space Robotics, A. Howard, E. Tunstel (eds). pp. 403-422, 

TCI Press, S.A., TX, 2006. 

[29] Kozma, R., Fukuda, T. Int. J. Intell. Syst., 21(9), 875-879, 

2006. 

[30] Kozma, R., W.J. Freeman, Neural Networks, 22(3): 277-285, 

2009. 

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1

Theory of Mind, Computational Tractability, and Mind Shaping

2009 Performance Metrics for Intelligent Systems Workshop

Tad Zawidzki

Philosophy, Mind-Brain-Evolution Cluster, Mind-Brain Institute

George Washington University

1. Introduction

Philosophers and psychologists have traditionally understood theory of mind as a human 

capacity for understanding and predicting human behavior based on the attribution of 

unobservable mental states, like beliefs and desires. The classical model views the human “mind 

reader” as a kind of scientist, formulating hypotheses about the unobservable causes of the 

behavior of her fellows, and then testing them through observation (Gopnik & Wellman 1995). 

Many argue that the attribution of unobservable, theoretical mental states increases the power of 

human social cognition over mere sensitivity to patterns of observable behavior, of the kind that 

characterizes the social cognition of most, if not all non-human animals (Tomasello & Call 

1997). In this paper, I review some familiar problems with this view and suggest a novel 

strategy for dealing with them. In section 2, I explain why the timely and accurate attribution of 

mental states appears to be a computationally intractable task. In sections 3 and 4, I consider two 

standard models of human cognitive architecture aimed at mitigating problems of computational 

tractability: modularity and fast and frugal heuristics, respectively. I argue that these are unlikely 

to help in the case of theory of mind. In the final section, I show how “mind shaping” (Mameli 

2001; Zawidzki 2008) – roughly, the practice of socializing individuals in ways that make human 

populations more homogeneous – can mitigate some of the problems raised in the earlier 

sections.

2. The Apparent Computational Intractability of “Mind Reading”

In the philosophical literature on theory of mind, and in much of the psychological literature, 

beliefs and desires are taken to be the central mental states required to make sense of behavior. 

The central “law” of so-called “folk psychology” is, roughly, the following: if an agent desires 

that P, and believes that not P unless she does Q, then the agent will desire to do Q. However, 

this and related laws must inevitably be qualified by potentially indefinite numbers of 

exceptions. In principle, any behavior is compatible with any finite set of mental states, given 

enough adjustments in other mental states. For example, just because someone says she supports

Barack Obama, does not mean that she believes he is the best candidate, or that she will act to get 

him elected, etc. She might believe that John McCain is the best candidate, yet, at the same time, 

desire to conceal this fact from her interlocutors. 

Philosophers call this the problem of “holism” (Morton 1996, 2003). Behavior is not correlated 

with finite sets of mental states. Rather, behavior is correlated with whole systems of indefinitely 

many mental states (thus the term “holism”). The holism problem jeopardizes the timely

accuracy of any theory of mind based on the attribution of mental states like beliefs and desires.

Human social cognition is extraordinarily powerful, yet, at the same time, extraordinarily 

efficient. We can often accomplish dramatic feats of interpersonal coordination in constantly 

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2

shifting, dynamic social circumstances, where there does not appear to be enough time to 

consider and rule out all possible hypotheses about our interactants’ mental states. It seems 

unlikely that some kind of brute search through all possible sets of mental states compatible with 

behavioral cues emitted by our interactants can support such fluid socio-cognitive competence.

3. Why a Theory of Mind Module Cannot Help

“Modularity” is the classic response to issues of computational tractability. If the human mindbrain deploys encapsulated, computational modules, with pre-specified, domain-specific

databases, tractably searchable by dedicated processes, then, it is often claimed, problems of 

computational tractability can be avoided (Carruthers 2006). The idea is that, when confronting 

some domain-specific problem, e.g., predicting the behavior of another person, the human mindbrain need not search all information to which it has access. Such problems trigger activity in 

dedicated, domain-specific modules – in the case of the social domain, the theory of mind 

module – which are informationally isolated from other parts of the mind-brain, making the 

search for solutions exponentially more tractable. However, it is not clear that modularity can 

help in the case of social cognition. The reason is that, as Currie and Sterelny (2000) point out, 

any information might be relevant to the tasks of interpreting and predicting human behavior. 

They give the example of detective work: figuring out who committed a crime and for what 

reasons, and predicting the criminal’s next move, is precisely the kind of problem that a 

dedicated, informationally encapsulated module cannot solve. The reason we like detective 

fiction is that there is no way of knowing, in advance, what sorts of information might be 

relevant to cracking a case. 

Fodor (1983) introduced the notion of computational/cognitive modules, contrasting them with 

“central systems”, which, he argued, are responsible for most belief fixation in human beings. 

Unlike modules, central systems are, according to Fodor, “isotropic”, i.e., any information is 

potentially relevant to belief fixation. He focuses on examples from science, e.g., information 

about fluid behavior turned out to be relevant to fixing beliefs about the behavior of light in 

Nineteenth Century physics. Arguably, much everyday reasoning is similarly isotropic. There 

does not seem to be a way of pre-specifying what kinds of information might be relevant to 

selecting among products in a supermarket, or deciding whom to date, etc., in the way there

would need to be were such problems tractable by encapsulated modules. 

If Fodor is right that most human belief fixation is isotropic, and therefore a product of nonmodular central systems, then a general case can be made against the modularity of theory of 

mind. For, the goal of theory of mind is to determine what beliefs are likely operative in another 

agent. But, if Fodor is right, the processes by which an agent fixes her beliefs are isotropic and 

therefore non-modular. So any interpreter of that agent cannot, herself, rely on some modular,

informationally encapsulated theory of mind to determine which beliefs the agent will acquire 

and act on. If the interpretive target’s decisions are determined by non-modular processes then 

so must be the interpreter’s hypotheses about those decisions, if they have any chance of 

succeeding. Furthermore, besides figuring out how her target solves the belief fixation problems 

she faces, the interpreter must also determine to what information the target likely has access and 

what problems the target is most motivated by – problems the target need not solve. This 

compounds the problem facing the interpreter – not only must she, in effect, solve the same 

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3

isotropic belief fixation problems as her target, she must also determine the parameters 

governing her target’s solutions. Thus, the problems of computational tractability that arise for 

theory of mind do not appear to admit of a classical modularist solution.

4. Why Fast and Frugal Theory of Mind Heuristics Cannot Help Either

Fodorian modularity is an extreme solution to the problem of computational tractability, which 

seems unhelpful in many domains, particularly social cognition. However, there may be other 

kinds of modularity that evade some of the problems that have been raised for Fodorian 

modularity. Recently, Carruthers (2006) has defended a kind of modularity that is not based on 

some pre-specification of the kind of information that might be relevant to solving tasks in some 

domain. Instead, Carruthers argues that the problem of computational tractability can be solved 

using content-neutral, “fast and frugal heuristics” (Gigerenzer et al. 1999). Because such 

heuristics are content-neutral, there are no limits on the kinds of information they can consult. 

This is promising in the case of mind reading since, as we have seen, almost any kind of 

information can be relevant to this task. However, computational tractability is maintained by 

fast and frugal heuristics because there are strict limits on the quantity of information they can 

consult. 

For example, “Take the Best” is a well-known fast and frugal heuristic. It requires that one recall 

criteria previously used to distinguish between alternatives in some domain, determine which 

criterion distinguished best, and use that criterion on one’s current decision. For example, when 

asked which of two German cities is larger, one might recall that, previously, having a 

professional soccer team distinguished best between larger and smaller cities, and so one asks 

which, if either, has a professional soccer team. If neither or both do, one then proceeds to the 

next best criterion. In order to avoid intractable search, “Take the Best” has a “stopping rule” 

that suspends search if it cannot arrive at an answer after some small, finite number of iterations

(Gigerenzer et al. 1999; Carruthers 2006).

Fast and frugal heuristics combine computational tractability with openness to a wide variety of 

potentially relevant information. For example, although “Take the Best” is restricted to 

considering only information that a particular agent has recently consulted when reasoning about 

a certain domain, this restriction is content neutral. It includes different information for agents 

with different histories of reasoning about a domain. There is no reason why, for a different 

agent reasoning about relative population sizes of German cities, 1 “Take the Best” could not 

consult relative crime rates instead of presence of professional soccer teams (Carruthers 2006). 

Such heuristics are computationally tractable because they restrict search based on an agent’s 

current epistemic context, including relevant recent searches, not because they restrict search 

based on the content of the relevant domain, e.g., social, or physical, etc. So there is no need to 

pre-specify the kinds of information likely to be relevant to each domain. This mitigates the 

problem that Currie and Sterelny (2000) raise for modular theory of mind. Any information, 

e.g., the number of asparagus spears left on a plate, may be relevant to a theory of mind task. 

But only information that has recently been useful on similar tasks is consulted on any particular 

occasion.

                                                        1 Or for the same agent at a different time.

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4

Unfortunately, this proposal seems unlikely to work for theory of mind tasks. The problem is 

that fast and frugal heuristics work only in domains characterized by extreme homogeneity. 

“Take the Best” solves new problems based on strategies that a particular agent has successfully 

applied to similar problems in the recent past. Unlike more sophisticated, statistical learning 

algorithms, such heuristics make no attempt to insure that the sample from which they generalize 

is unbiased. Their quickness and frugality consists precisely in the fact that they avoid such 

formal niceties. Strategies that happen to have worked for a particular agent in the recent past 

are taken to be appropriate for current and future problems. Such contingent regularities can 

safely be assumed in certain extremely homogeneous, well-behaved domains. However, there is 

every reason to deny that the social domain is like this. 

Ironically, if human beings rely on fast and frugal heuristics to fix their beliefs, then the 

dependence of such heuristics on the idiosyncratic background knowledge of particular 

individuals is likely to make discovering their beliefs computationally intractable for fast and 

frugal theory of mind heuristics. This is because such social cognition would require quickly and 

frugally uncovering the idiosyncratic background knowledge on which one’s interpretive targets 

rely. But fast and frugal theory of mind heuristics can depend only on what has worked for the 

interpreter in the recent past, and there is no reason to think that this is any guide to the 

idiosyncratic background knowledge of a new interpretive target. Furthermore, as Sterelny 

(2003) emphasizes, human beings appear to have strong, biological incentives to behave in ways 

that are unpredictable relative to heuristics that their potential competitors have previously used 

to predict them. Lastly, there are good reasons to think that individual variation among human 

beings is extreme, compared with other species: we have unmatched capacities for creative 

cognition and conation which involve random processes heavily dependent on idiosyncratic 

learning history (Carruthers 2006), and extreme phenotypic plasticity is likely a human 

adaptation to extremely variable physical and social environments (Sterelny 2003). So, we have 

every reason to suppose that fast and frugal heuristics, the reliability of which relies on extreme 

homogeneity in the domains to which they apply, cannot support reliable social cognition. 

Perhaps there are more specific mind reading heuristics that can evade some of these problems. 

The most influential models of fast and frugal social cognition appeal to some kind of simulation

(Goldman 2006). Interpreters save on the computational costs of interpretation by simply 

projecting, in some sense, their own decision procedures onto others. But the accuracy of such 

simulation heuristics obviously depends on extreme homogeneity in human populations: 

interpreters and their targets must prioritize problems in similar ways and make decisions based 

on similar information and heuristics. And, as we have seen, there are good reasons to doubt that 

such homogeneity exists in human populations.

5. Mind Shaping as Human Homogenizer

Let me end by proposing a sketch of how I think humans solve the problems reviewed above. I 

propose that certain low-level, automatic mind-shaping mechanisms, prevalent in human 

populations, work to homogenize them, thereby making fast and frugal theory of mind heuristics 

more effective.

160
5

Suppose human beings have an automatic, default disposition to compare their own behavior to 

that of others, monitoring for any discrepancies. If the other is higher status, discrepancies tend 

automatically to issue in attempts at self-modification: one tries to change one’s dispositions 

such that one’s behavior better matches that of the high status model. If the other is lower status, 

discrepancies tend automatically to issue in attempts to modify the other: one tries to change the 

other’s dispositions, as in teaching offspring or punishing norm transgressors, such that the 

other’s behavior better matches one’s own. Assuming that judgments of status are largely 

homogeneous in a population, such mind-shaping dispositions would tend to further homogenize 

populations, counteracting the “centrifugal” forces causing individual variation. And such 

homogeneity would render fast and frugal theory of mind heuristics more reliable. For example, 

the “Take the Best” heuristic would be more likely to work. In a population of similarly 

socialized individuals, decision strategies that happen to have worked well in recent interpretive 

contexts are more likely to work in new circumstances. Similarly, variations of the simulation 

heuristic would also be more reliable: procedures that interpreters use in their own decisionmaking would be more likely to be used by their interpretive targets as well. 

Solutions to problems of computational tractability that arise for theory of mind, I want to urge, 

lie not within human mind readers, but, rather, outside of them. Rather than deploy intractably 

sophisticated theories of each other’s minds, we make use of a variety of low-level mind-shaping

dispositions to insure that our fellows are sufficiently familiar so that fast and frugal heuristics 

can help us accomplish our socio-cognitive goals. We teach our children to behave in ways that 

make them easier to interpret (Bruner 1983, Mameli 2001, McGeer 2001). We sanction those 

who behave in ways that are harder to interpret (think of the damage to status which often results 

from weakness of the will or absentmindedness) (Zawidzki 2008). We display unconscious, 

automatic, and irresistible tendencies to conformity, such as the “chameleon effect” (Chartrand 

& Bargh 1999). Such mechanisms shape our socio-cultural environment in ways that make 

coordination exponentially more tractable than it would be were we to exhaustively search the 

mental state hypothesis spaces compatible with the finite sets of potential-interactant behaviors 

with which we are familiar.

References

Bruner, J. 1983. Child’s talk. New York: Norton.

Carruthers 2006. The Architecture of the Mind. New York: Oxford University Press.

Chartrand, T.L., and J.A. Bargh. 1999. The chameleon effect: The perception-behavior link and 

social interaction. Journal of Personality and Social Psychology 76: 893 – 910.

Currie, G., and K. Sterelny 2000. How to think about the modularity of mind reading. 

Philosophical Quarterly 50: 145-60.

Fodor, J. 1983. The Modularity of Mind. Cambridge, MA: MIT Press.

Gigerenzer, G., P. Todd, and the ABC Research Group 1999. Simple heuristics that make us 

smart. New York: Oxford University Press.

161
6

Goldman, A. 2006. Simulating minds. Oxford: Oxford University Press.

Gopnik, A., and H. Wellman. 1995. Why the child’s theory of mind really is a theory. In Folk 

psychology, ed. M. Davies and T. Stone, 232 – 58. Oxford: Blackwell.

Mameli, M. 2001. Mindreading, mindshaping, and evolution. Biology and Philosophy 16: 597 –

628.

McGeer, V. 2001. Psycho-practice, psycho-theory and the contrastive case of autism. Journal of 

Consciousness Studies 8, no. 5 – 7: 109 – 32.

Morton, A. 1996. Folk psychology is not a predictive device. Mind 105: 119 – 37.

Morton, A. 2003. The importance of being understood. London: Routledge.

Sterelny, K. 2003. Thought in a Hostile World. Oxford: Blackwell.

Tomasello, M., and J. Call. 1997. Primate cognition. New York: Oxford University Press.

Zawidzki, T. 2008. The function of folk psychology: mind reading or mind shaping? 

Philosophical Explorations 11(3): 193 – 210

162
Data Collection Test-Bed for the Evaluation of Range

Imaging Sensors for ANSI/ITSDF B56.5 Safety Standard for

Guided Industrial Vehicles

Will Shackleford

National Institute of Standards and Technology(NIST)

100 Bureau Drive, Stop 8230

Gaithersburg, MD 20899

(301) 975-4286

shackle@nist.gov

Roger Bostelman

National Institute of Standards and Technology(NIST)

100 Bureau Drive, Stop 8230

Gaithersburg, MD 20899

(301) 975-3426

roger.bostelman@nist.gov

ABSTRACT

In this paper, we describe the process by which we collected

sensor data for the evaluation of 3D LIDAR (Light Detection

and Ranging). Data were also collected simultaneously from

SONAR (Sound Navigation and Ranging) sensors, navigation

systems, 2D Laser Measurement Sensor (LMS) and a color

camera. We describe software developed to perform data

collection and allow for evaluation of the data both offline and in

real-time during the data collection and briefly cover the

experiments themselves where various obstacles were placed in

front of a moving vehicle and results were recorded as to whether

the obstacle was detected or not.

Categories and Subject Descriptors

I2.10 [Vision and Scene Understanding]: 3D/stereo scene

analysis

General Terms

Performance, Design, Experimentation, Standardization, 

Keywords

LIDAR, LADAR, Data-Collection, Autonomous Guided

Vehicles(AGV), B56.5, Sonar.

1. INTRODUCTION

The Industrial Truck Standards Development Foundation

(ITSDF) manages the “ANSI/ITSDF B56.5 Safety Standard for

Guided Industrial Vehicles and Automated Functions Of Manned

Industrial Vehicles” as approved by the American National

Standards Institute (ANSI)[2]. The National Institute of

Standards and Technology (NIST) has been performing

measurements to be used as background information towards

changes in the standard. Automated Guided Vehicles (AGVs) are

typically programmed to follow prescribed paths but still need

sensors to detect obstacles such as closed doors, equipment,

personnel or material left temporarily in the vehicle's path.

Currently they rely heavily on 2D line scanners with a physical

bumper as the final backup to stop the vehicle in these cases.

The 2D line scanners work well against most vertical obstacles

but it takes many of them to completely protect against

overhanging obstacles and even then they do not scan the full

volume of space the vehicle will travel through. Flash LIDAR is

a relatively new class of range imaging sensors with the

potential to scan 3D volumes faster than the line scanning

systems. To evaluate them, a consortium of AGV vendors was

formed that took preliminary data with several Flash range

imaging systems and selected one for further development and

investigation. This is the sensor used for this work. In 2008

work was done with the stationary vehicle and with a manually

moved cart.[1] In 2009, the data collection system was integrated

with the Mobility Open Architecture Simulation and Tools

(MOAST) framework.[3] This allowed the system to collect data

while being driven autonomously.

2. Test-Bed Hardware

All of the sensors are mounted on a robot. The sensors include:*

 Spinning Laser Positioning System (SLPS) - Provides

absolute position using a spinning laser that detects special

reflective targets mounted on walls and fixed structures.

 Safety Laser Measurement Sensor(LMS) – 2D line scanner

which detects obstacles but only at a single height.

 FLASH – 3D Flash LIDAR Camera provides range/intensity

for every pixel in the image.

 Color Camera – Provides better documentation of each test.

 Positioning Camera (CamPos). -- A camera system pointed at

the ceiling to provide absolute position using special targets

mounted on the ceiling.

This paper is authored by employees of the United States Government and

is in the public domain.

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA.

ACM 978-1-60558-747-9/09/09

* Certain commercial equipment, instruments, or materials are identified in

this paper to specify the experimental procedure adequately. Such

identification is not intended to imply recommendation or endorsement by

the National Institute of Standards and Technology, nor is it intended to

imply that the materials or equipment identified are necessarily the best

available for the purpose. 

163
 SONAR – Sound navigation and ranging sensors mounted to

the vehicle to detect obstacles in the vehicle's path.

 The positions of the sensors are shown in Figure 1.

3. Software Architecture

3.1 Main Software Architecture

Figure 3.1 provides the main software architecture diagram for

the system. The moastLogRecordSuper supervises and

coordinates MOAST controller moves with the starting and

stopping of the data collection. The MOAST framework aids in

the development of autonomous robots. It includes an

architecture, control modules, interface specs, and data sets and

is fully integrated with the USARSim (Unified System for

Automation and Robot Simulation) system.[3] The MOAST

controller is used to generate trajectories for Player and send a

stream of desired translation and rotational velocities to Player.

Player is a cross-platform robot device interface and server that

supports a number of robot platforms and sensors including the

commercial platform used as our base and LMS1.[5]

3.2 Neutral Message Language(NML)

The Neutral Message Language (NML) [4] provides both the

communication system and the facilities for reading and writing

the data files in a portable, transport, platform and

programming language independent manner. NML is part of the

Real-time Control System (RCS) Library[11]. It provides a

common API (Application Programming Interface) for both

potentially remote TCP (Transmission Control Protocol) or UDP

(User Datagram Protocol) communications as well as faster

shared memory based communications. It is used for all interprocess communications within the system including internal

MOAST communications with the exception of the

communication between MOAST and Player [5] which uses a

Player-defined socket interface.

3.3 Sensor Subsystem Software

Architecture

Each sensor is handled by a similar subsystem as shown in

Figure 3.2 Each sensor comes on a data bus such as Firewire,

USB, RS232 Serial, or Ethernet with a format and protocol

usually unique to that model sensor. A separate process is used

for each sensor that essentially acts as a device-driver and

converts data received from the bus to an NML message and

writes it to both a queued and non-queued NML buffer. The

queued buffer is read by the LogRecorder which then writes the

data to disk. A non-queued buffer is used to provide a real-time

display of the data. The real-time display is needed during data

collection to ensure that the sensor is working, and that objects

of interest are within the field of view and to adjust sensor

configuration parameters. Using separate processes for the

NmlProducer and LogRecorder means that intermittent delays in

writing to the disk will not cause the system to miss frames as

the NmlProducer could continue filling the QueuedBuffer while

the LogRecorder is delayed. It also isolates the code most likely

to need debugging and not to be portable, which is the sensor

specific driver code. Each viewer is built so that it can display

either live data from the NML channel or logged data from files. 

moastLogRecordSuper

MOAST

Player/Servo

Goto {x,y}

1

,{x,y}

2

, ...

set_cmd_vel 

v,dTheta ... pos

Done

SLPS1

pos

Mobility

right vel,

left vel

LMS1

range1,range2,...

LMS1

NmlProducer

range

1

,range

2

,...

log

pos Recorder1

Collect Data

Collect Data

Camera 

Subsystem

CamPos1 

Subsystem

FLASH1 

Subsystem

Figure 2: Main Software Architecture Diagram

Figure 1: Sensor Positions

SLPS

(Spinning Laser 

Positioning System)

CamPos1

(looking up)

FLASH1

Color Camera

SONAR

Safety LMS1

164
The LogRecorder and Sensor NmlProducer are written in C++

for performance and low-level access to hardware or operating

system resources. Each viewer is written in Java so that the

logged data may be evaluated on any platform.

3.4 MOAST

MOAST is a control system framework that works both in

simulation and on real hardware. The simulation uses a

commercial gaming engine and is developed under the

Performance Simulation Project at NIST.[3][10] Figure 4 shows

the USARSim module/plugin showing a small simulated robot

in a warehouse setting and the RCS Diagnostics tool connected

to the bottom 3 levels of the MOAST (AM,PRIM,SERVO). The

Autonomous Mobility (AM) level combines data from the

various sensors into a map and uses the map to compute a path

from the vehicle's current position to the final goal position that

avoids all known obstacles. The PRIM level takes a list of

intermediate positions or arc segments produced by the AM level

and considers the dynamics and kinematics of the vehicle to

produce a series of SERVO commands sent one at a time as the

vehicle moves along the path. The SERVO level takes

commanded right and left wheel velocities and interfaces with

the hardware to achieve those velocities. The

moastLogRecordSuper can connect either to the PRIM or AM

levels. When commands are sent directly to PRIM the robot will

simply follow a set of way-points and ignore the sensors. When

commands are sent to the AM level the robot will use the sensors

to build a map and plan around obstacles. All of the tests done so

far have sent commands directly to the PRIM level which gives

us greater control over exactly where the robot travels but the

option of using the AM level for future tests remains available.

3.5 NML Packed Message Files

All of the data after collection is stored in NML packed message

files. The format will hopefully provide the openness and

flexibility of text, CSV (comma-separated values)[8] or XML

(Extensible Markup Language)[9] files and the efficiency in both

disk space and processing time of binary formats. This file

format allows for easy reading and writing of even complex data

structures. Generic tools can be used to display the contents of

the files or users can write their own programs to read and write

the files with a simple API. Also a memory map file listing the

offset to every variable allows the files to be accessed outside the

API. Tools were written to convert these files to plots, movies,

and separate still image files as appropriate.[6] A website is

under development that should allow users to access the

collected data using any of the tools discussed below, download

the message files, export appropriate subsections to a spreadsheet

, and etc.

The file sizes for one LIDAR data frame for the FLASH1, which

has a resolution of 144 pixels x176 pixels (25344 pixels), are

compared in Table 1. The packed format includes configuration

information and XYZ coordinates from every pixel as well as a

range and intensity value. Obviously if a display is all that is

needed, saving JPEGs requires by far the least data but it does

not include configuration data or the XYZ point cloud and even

the exact range and intensity values cannot be recovered from the

JPEG. The CSV and spreadsheet files with the same data are

larger and do not contain the configuration information or XYZ

coordinates although those could be added. There are many ways

the information could be stored in XML, but one of the most

straight-forward methods produced a file six times larger than

the original packed data.

Sensor

Sensor

NmlProducer

Log

Recorder

Queued NML buffer

Viewer Non-Queued

NML Buffer

Firewire/USB/RS232/Ethernet

File Storage

Figure 3: Sensor Subsystem Architecture

Figure 4: Left: Unreal Tournament with USARSim module

simulation/animation, RIGHT: RCS Diagnostics tools

showing AM,PRIM,SERVO levels of MOAST.

165
File Format XYZ ? Config.? Size(Bytes)

XML Yes Yes 3047494

Spreadsheet Yes No 2829824

CSV Yes No 1374147

NML Packed Yes Yes 507007

JPEG No No 3382

Table 1: LIDAR Data Frame File Sizes

3.6 Flash LIDAR Display

Flash LIDAR cameras generally provide both a range image

(Figure 5) and and intensity image (Figure 6). Although all tests

done this year were with the FLASH1, the data structure used to

store the data was originally developed with two other Flash

Lidar Cameras and therefore all tools should work with all three

cameras. It is often easier for people to see objects in the

intensity image. However, it is the range image that is of most

use for the AGV's obstacle detection and avoidance algorithms.

One goal of the experiments was to determine whether the

obstacles were detected by the sensor at various ranges.

Unfortunately this could be somewhat subjective. Just because a

person (especially one already familiar with the scene) could

make out something is no guarantee that it is possible to use the

data to build a reliable automatic obstacle detection algorithm.

For this reason the viewer includes its own obstacle image

classification window. The results of the obstacle detection are

shown in Figure 7.

The obstacle detection includes filters to eliminate points with

too high/low intensity, too high/low range values, isolated points

or points not near neighboring points. It then simply rotates the

point cloud to adjust for the mounting of the sensor and applies a

height threshold. Points below the threshold are ground and

points above the threshold are obstacles. All the parameters are

adjustable both in real-time and when displaying logged data

from a Graphical User Interface to allow for a very conservative

to very lenient obstacle detector. A text display allows min/max

and average intensity or range values to be obtained for any

selected rectangle in the image.

The sensor has two problems. First, it cannot distinguish

obstacles at multiples of its modulation wavelength (about 6 m).

i.e., if the modulation wavelength was 6 m an object 7 m away

returns the same value as one 1 m away. Second, when near

highly reflective surfaces such as the ones commonly used by the

AGV's navigation system the entire scene is strongly distorted.

For this reason all of our tests have the sensor pointing down

towards the floor which eliminates any possibility of an object

being farther away than the modulation wavelength and also

keeps the sensor away from the eye-level navigation reflectors.

3.7 Camera Positioning Navigation

System/Spinning Laser Positioning System

Spinning Laser Positioning System (SLPS) is typically used for

industrial AGV's. CamPos is a more recent alternative to the

spinning laser based navigation systems that uses a camera and

2D bar code targets mounted on the ceiling.[7] One set of tests

that we completed was to record both the CamPos and SLPS

positions simultaneously while driving the ATRV manually. We

purposely mounted the 2D bar code targets in a fairly regular

ceiling pattern of 1.2 m spacing and disregarded partially

occluded ceiling obstructions. Where obstructions mostly or

completely covered targets, we moved those targets to a less

obstructed ceiling location. Although the manufacturer suggests

non-obstructed targets, we are looking for ways to measure

performance of these systems when they are in the ideal and nonideal configurations. Figure 8 (left) shows clear view and

partially occluded views of ceiling-mounted 2D bar code targets.

A similar situation can occur with the SLPS positioning of wallFigure 5: Flash LIDAR Range Image of 3

Cylinders in front of AGV (Black=near,

White=far)

Figure 6: Flash LIDAR Intensity Image of 3

Cylinders in front of the AGV

Figure 7: Obstacle Detection Based On Flash LIDAR

Range Image (Green=ground,Red=Obstacle,

Blue=Unknown)

166
mounted reflectors as shown in Figure 8 (right). Here, reflectors

are shown in clear view and partially occluded views that could

also result in less than robust vehicle positioning. In previous

tests, we also found issues with this system when highly

reflective surfaces appear to the sensor as system reflectors.

For this recent experiment, we tested only the CamPos system

targets being partially obstructed. The experiment showed that

while the CamPos tracked the SLPS position well over much of

the approximately 36 m long x 10 m wide course, there were

measurement issues in places where the CamPos could not

simultaneously see more than one target (see Figure 9 left and

right). These were caused by an overhead crane system (as

shown in Figure 8 (left)) and ceiling supports that obscured a

few bar code targets.

The user of both the CamPos and SLPS systems can ideally

mount sensor targets appropriately to get maximum accuracy as

specified by the manufacturer. As targets are relatively

inexpensive for these sensor systems, adding more and

calibrating the targets mounted in non-occluded areas easily

solves these issues.

Both the CamPos and SLPS data are recorded with independent

running implementations of the sensor subsystem as described in

section 3.3. The data was plotted from the recorded data offline

using the plotter included with the RCS(Real-Time Control

System) Diagnostics Tool.

3.8 Safety Laser Measurement Sensor

(LMS)

The LMS is a laser scanning system that currently detects

obstacles at longer ranges and higher reliability than the Flash

LIDAR but only in a single plane. In Figure 10 the LMS was

scanning through a fence that will be placed around a robot work

station. In the raw sensor data the LMS sees both the fence and

the object on the other side of the fence. 

3.9 Color Camera

The main use of the color camera currently is to overlay images

from the 3D Flash LIDAR to better identify the source of

artifacts as shown in Figure 11.

4. Static Test Results

A series of static tests were performed using the sensors listed in

section 2 Test-bed Hardware. The tests included covering three

different test pieces with a variety of surfaces and testing with all

sensors recording at a variety of positions, orientations and

ranges. The test pieces included the two pieces already part of

the B56.5 standard, a 200 mm diameter x 600 mm long

horizontal cylinder and a 70 mm diameter x 400 mm high

vertical cylinder. The third test piece was the new 500 mm x

500 mm flat surface target. The coverings were selected to

change the reflectivity and specularity of the test pieces for the

optical sensors as well as the sound absorption properties for the

SONAR. 

For both optical and SONAR sensors, changing the angle of

reflection with the flat target had a significant effect on the

measured intensities and ranges and in some cases whether the

Figure 10: LMS1 data scanning both a fence and

obstacles on the other side of the fence.

Figure 9: (left): CamPos (pink) versus SLPS position

(green/yellow) plots; (right): Zoom of CamPos versus

SLPS position where ceiling barcodes are partially

occluded.

Figure 8: (left) Crane electrical bars partially occluding the

ceiling-mounted 2D bar code targets of the CamPos system;

(right): clear view of the right-most reflector of the SLPS

system and partially occluded view of the left-most reflector

by a robot cage

Figure 11: Color camera image overlaid with

obstacle detection data from the flash LIDAR sensor

Barcode Ceiling Targets

Left-most reflector Right-most reflector

167
sensor received a return or not. The reflectance and specularity of

the coverings also had an effect on the optical sensors however

none of the sound absorbing materials tested had a significant

effect on the SONAR.

5. Changes to the ANSI/ITSDF B56.5

Safety Standard for Guided Industrial

Vehicles

As a result of tasks conducted using this test bed, changes were

recommended to the ANSI/ITSDF B56.5 committee to add an

additional flat target, to test at a variety of reflectance and

specularity values and at different reflection angles and ranges.

There was also a recommendation to perform dynamic tests at

various vehicle speeds. We may provide further

recommendations after completing the dynamic tests.

5.1 Conclusions

NIST has created a unique test bed and data-collection platform.

Although its initial use was to provide input to the ITSDF

standard development process, it should be possible to provide

industry and/or the research community with independent

evaluations of sensor technologies or provide data for obstacle

detection algorithm development or verification. The test bed

will continue to be updated with additional sensors.

6. REFERENCES

[1] Roger Bostelman, MS; William Shackleford, “Time of

Flight Sensors Experiments Towards Vehicle Safety

Standard Advancements”, submitted to Computer Vision

and Image Understanding Journal

[2] Industrial Truck Standards Development Foundation,

(2005). ITSDF B56.5 Safety Standard for Guided Industrial

Vehicles and Automated Functions of Manned Industrial

Vehicles, http://www.itsdf.org.

[3] Mobility Open Architecture Simulation and Tools

(MOAST) framework,

https://sourceforge.net/projects/moast

[4] Neutral Message Language (NML) ,

http://www.isd.mel.nist.gov/projects/rcslib/NMLcpp.html

[5] Player Project, http://playerstage.sourceforge.net/index.php?

src=player

[6] NML Message Files,

http://www.isd.mel.nist.gov/projects/rcslib/message_files.ht

ml

[7] SkyTrax System,

http://www.sky-trax.com/products/STS.php

[8] Wikipedia: Comma-separated values,

http://en.wikipedia.org/wiki/Comma-separated_values

[9] W3C Extensible Markup Language(XML),

http://www.w3.org/XML/

[10] Performance Simulation Project,

http://www.nist.gov/mel/isd/ks/persim.cfm

[11] Real-Time Control Systems Library,

http://www.isd.mel.nist.gov/projects/rcslib/

168
Ground Truth Data Using 3D Imaging for Urban Search and

Rescue Robots

Nicholas A. Scott, Alan M. Lytle

National Institute of Standards and Technology

100 Bureau Drive Stop 8611

Gaithersburg, MD, 20899-8611, USA

{nicholas.scott, alan.lytle}@nist.gov

ABSTRACT

The National Institute of Standards and Technology (NIST)

is leading an effort to develop performance standards for urban search and rescue robots (US&R). An important component of developing performance standards for these robots

is capturing ground truth data that represents the geometry

of the robot operating environment. This paper describes

two ground truth data collection efforts conducted in 2006

and 2008 at the Texas Engineering Extension Service Disaster City training facility in College Station, Texas. Several indoor and outdoor training scenarios were captured

with 3D imaging systems and the data is now publicly available through NIST to support research and development of

robotic technologies for the US&R domain.

Keywords

robotics, ground truth, 3D imaging, urban search and rescue

1. INTRODUCTION

The National Institute of Standards and Technology (NIST)

is leading an effort to develop performance standards for urban search and rescue (US&R) robots [5]. As part of this

effort, NIST organizes events that allow emergency responders, robot manufacturers and robotics researchers to work

shoulder-to-shoulder within world-class responder training

facilities [4]. Commercial-off-the-shelf products and laboratory prototypes are operated by responders in realistic

operating scenarios while being observed and supported by

the technical experts. These events allow responders to better understand state-of-the-art robot capabilities and limitations, and provide manufacturers and researchers unfiltered

access to subject matter experts. An additional activity at

these responder events is the exercise of various test methods and performance metrics under development to support

the overall goal of creating a suite of performance standards

for US&R robots. An important component of developing

performance standards for these robots is capturing ground

truth data of the training scenarios and the test methods.

This paper is authored by employees of the United States Government and

is in the public domain.

PerMIS’09 September 21-23, 2009, Gaithersburg, MD, USA

ACM 978-1-60558-747-9/09/09.

This paper describes two ground truth data collection efforts conducted in 2006 and 2008 at the Texas Engineering

Extension Service (TEEX) Disaster City training facility in

College Station, Texas. Several indoor and outdoor training

scenarios and two test methods were captured with 3D imaging systems1 and the data is now publicly available through

NIST to support research and development of robotic technologies for the US&R domain.

This paper begins by describing the motivation behind

the data collection efforts. Section 3 provides information

regarding the captured scenarios and Section 4 presents metrics for the data collected. Finally, Section 5 discusses future

NIST efforts using this data.

2. MOTIVATION

When a disaster occurs, previously benign terrain may become difficult or impossible to traverse. Buildings collapse,

roads and bridges are destroyed, and previously smooth, obstacle free terrain may contain large obstacles and discontinuities. In order to perform search and rescue operations,

responders must assess the terrain in order to employ assets that possess the correct mobility to get to desired locations. For responders to effectively use robotic technologies on US&R missions, they must understand how different

robotic platforms perform in diverse terrain. Developing

tests and performance metrics to enable this understanding

not only supports the responder’s use of robots in the field,

but also provides important information to support further

research and development.

An essential element in defining these performance metrics is the independent capture of an accurate ground truth

representation of the robot’s operating environment. This

ground truth data can support a wide range of research

including mobility performance metrics, terrain characterization, mapping algorithm evaluation, and virtual environment construction.

For US&R robotics, both qualitative and quantitative measures of the environments in which platforms are tested and

deployed to support mobility performance metrics and terrain characterization are of great interest. For examples

of qualitative measures of an environment, consider trail

rating systems for ski slopes or the Beaufort Wind Force

Scale for estimating wind speed from sea state. Quantitative US&R terrain characterization metrics would enable

predictable and consistent ways of representing difficult ter1A 3D imaging system is a non-contact measurement instrument used to produce a 3D representation (for example, a

point cloud) of an object or a site [1].

169
rain (e.g., rubble) and provide fair comparison of platforms.

An example of quantitative metrics in the US&R context

could be a specific measure of the traversibility of the terrain surface derived using techniques such as height, slope,

and roughness estimation from plane fitting, fractal dimensional analysis or wavelet energy statistics. Traversibility is

a well-studied discipline, particularly in the context of unmanned ground vehicle path planning. The challenge is to

standardize a universally accepted measure for US&R robot

evaluation.

In addition to understanding the terrain, ground truth

data supports mapping algorithm evaluation and virtual environment construction. More and more developers are including map creation in their operator toolkits, and map

generation is a highly desired capability amongst responders. The ground truth data collected can serve as a baseline

for evaluating the performance of 2D and 3D mapping systems deployed on mobile sensors. Ground truth data used

to evaluate mapping can also be used in simulation environments. NIST, along with partner organizations, is investigating how to represent the point clouds and/or derivative terrain models within simulation environments such as

NIST’s USARSim [2]. Importing point, polygonal, or surface models of realistic training scenarios into simulation

systems can make the training scenarios themselves accessible to a wider set of developers. Responders, researchers,

developers, and other interested personnel will be able to

navigate the scenarios, to some degree of fidelity, without

having to physically travel to the location. Intelligent behaviors for semi-autonomous robots can also be virtually

tested within the models.

3. DATA COLLECTION

To support the need for ground truth data, NIST researchers gathered high-resolution 3D image data for five

training scenarios and two test methods at Disaster City, the

TEEX National Emergency Response and Rescue Training

Center in College Station, Texas. Disaster City is a 52-

acre site that provides full-scale collapsible structures, rubble piles, and wrecked transportation structures for search

and rescue training and is considered by many to be the most

comprehensive emergency response training facility presently

available. The ground truth data was collected during two

separate NIST organized events at Disaster City.

3.1 Collection One

The first data collection effort was held on April 4-6, 2006

and focused on outdoor US&R environments. Data for three

different scenarios was collected:

• Concrete Rubble Pile

• Wood Rubble Pile

• Passenger Trains

The concrete rubble pile scenario is depicted in Fig. 1.

This scenario simulates a fully collapsed concrete structure

with interior voids. The rubble pile primarily consists of

concrete and reinforcing bars (rebar). Large concrete slabs,

barriers, and pipes, generally several meters in length, provide the support for many of the voids. Small concrete rocks,

typically 30 cm to 50 cm in diameter, fill in the the space

around the larger concrete pieces. Rebar and other metal

Figure 1: Concrete rubble pile training scenario

which simulates a fully collapsed concrete structure.

structures are scattered throughout the rubble pile. Robots

are deployed from the perimeter either directly into subterranean voids or over top of the rubble pile to search for

victims and map the area.

The wood rubble pile scenario is shown in Fig. 2. This

Figure 2: Wood rubble pile training scenario which

simulates a fully collapsed wood structure.

scenario simulates a fully collapsed wood structure with interior voids. The rubble pile consists of several meter length

wood planks and wood pallets. Interior voids are created by

using several meter sections of concrete piping. Robots are

deployed from the perimeter of this pile by climbing, throwing, or launching into the central area to look for victims

and to map the area.

The passenger trains scenario is depicted in Fig. 3. This

scenario mimics the collision and partial derailment of passenger rail cars and industrial hazardous material tanker

cars carrying an unknown substance. Robots are deployed

from the perimeter of the wreck and circumnavigate the

trains, tracks, and rubble to map the perimeter of the scene

and determine the location of each car. The underside of

the elevated car and the interior of the car on its side is explored for victims and to look for placards describing what

hazardous material is onboard.

3.2 Collection Two

The second data collection effort was held on November 17-21, 2008 and focused on indoor US&R environments.

170
Figure 3: Passenger trains training scenario which

simulates the collision and partial derailment of passenger rail cars.

Data for two training scenarios and two test methods was

collected:

• Single Family Dwelling

• House of Pancakes

• Theater Maze

• Tube Maze

The single family dwelling scenario is depicted in Fig. 4.

This scenario simulates a partially-collapsed single family

Figure 4: Single family dwelling training scenario

simulating a partially collapsed home. The top picture shows the area inside one of the rooms of the

building and the bottom picture shows the building

from the outside.

home due to an earthquake. Entrances to the building and

doorways between rooms are compromised, with many either fully or partially blocked. The floors are scattered with

debris from the concrete structure and furniture. The ceiling

is mostly collapsed in one room and there is a large breach

in the floor in another room. There is also a basement accessible from the outside through a long set of stairs to a

welled exit. Robots are deployed into the building to identify victims, hazards, and all entrances and exits to inform

responders of the situation.

The house of pancakes scenario is depicted in Fig. 5. This

Figure 5: House of pancakes training scenario simulating a partially collapsed concrete building. The

top picture shows the main area inside with the collapsed sloped roof and the bottom picture shows the

building from the outside.

scenario mimics a partially collapsed concrete building of

unknown use. The roof structure is collapsed on one side of

the building causing the roof to angle downward such that

it is almost in contact with the ground. The interior of the

building contains various wood and concrete structures as

well as office desks and tables. Robots are deployed into the

building to search for victims and to map the environment.

The theater maze test method is depicted in Fig. 6. This

Figure 6: Theater maze test method which tests the

ability of a robot to navigate a complex environment

without getting lost.

environment tests the ability of the robot and its operator to fully explore a complex unknown environment for

victims and identify standard hazardous material placards

without getting lost. The maze consists of rolling wooden

floor planks on a slope and 2.44 m tall wooden walls.

The tube maze test method is depicted in Fig. 7. This

171
Figure 7: Tube maze test method which tests the

mapping ability of US&R robots in an environment

littered with occlusions.

environment tests the mapping and localization ability of

robots in an environment full of occlusions. The flooring is

made of angled sheets of wood. Rising up from the flooring

are PVC pipes of varying height that provide occlusions for

this environment.

4. DATA SETS

The 3D image data was collected using commercial laser

scanners. A laser scanner is a 3D imaging device that uses

a laser to measure the distance to an object. The laser

beam is scanned both horizontally and vertically over time

to image the operator-designated field of view. The distance,

azimuth, and elevation information collected from each measurement in the scan is used to create high-resolution point

clouds containing hundreds of thousands of points for a single scan.

Two different laser scanners were used in the two data

collection efforts. The data for collection effort one was collected using a pulse-based time-of-flight laser scanner. The

manufacturer specifies a range uncertainty of 7 mm and

a point uncertainty of 12 mm at 100 m range for this instrument. The data for collection effort two was collected

using a phase-based time-of-flight laser scanner. For this

instrument, the manufacturer specifies a range uncertainty

≤ 6 mm for ranges up to 50 m. A point uncertainty was not

specified for this instrument.

4.1 Sample Data and Metrics

Figures 8 and 9 show screen captures of scenes generated

in point cloud software for the tube maze test method. Each

point is colored based on the intensity of the laser return.

Within the software, camera viewpoints can be changed to

examine the 3D data from multiple viewing angles and measurements such as point-to-point distance can be readily determined.

Figure 8 shows an elevated view of the point cloud data for

a single scan of the tube maze. Since laser scanners are lineof-sight instruments, a single scan is unable to capture the

entire environment when there are occlusions in the scene.

Occlusions cause ”shadows” of missing data where the laser

scanner cannot sense. By design, the tube maze contains

many occlusions and areas of missing data (shown in black)

are prevalent in the individual scans.

Figure 8: An elevated view of the point cloud data

for a single scan of the tube maze test method. Occlusions cause ”shadows” of missing data.

To fill in the missing data, scans are taken from multiple locations around the scene. Individual scans are then

merged through a process called registration to create complete point clouds of the scenes. While some of the scans required manual registration, most of the scans were registered

using stationary targets placed in the scene to provide common points of reference to register the scans. The data was

registered and segmented using commercial software tools.

Figure 9 shows the complete registered point cloud data set

for the tube maze test method from a similar viewpoint.

Figure 9: An elevated view of the fully registered

point cloud data for the tube maze test method.

The number of points collected for each scenario is given

in Table 1 and the number of scans is given in Table 2.

4.2 Data Availability

All of the 3D image data outlined in this paper is available

free of charge to the public. Please contact the authors to

obtain any data of interest. In the near future, the data and

documentation of the file formats will be made available

through the NIST website.

5. FUTURE WORK

Stepfield pallets are a fabricated and repeatable terrain

for evaluating robot mobility [3]. As a first step towards developing terrain traversibility metrics, NIST researchers will

use the ground truth data presented in this paper to investigate the design of a multi-unit stepfield approximation of

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Table 1: The number of points for each scenario and

test method data set from collection efforts one and

two.

Collection Scenario/Test Method # Points (Millions)

1

Concrete Rubble Pile 5.77

Wood Rubble Pile 7.75

Passenger Trains 4.87

2

Single Family Dwelling 848.35

House of Pancakes 2550.60

Theater Maze 12.62

Tube Maze 296.90

Table 2: The number of scans for each scenario and

test method data set from collection efforts one and

two.

Collection Scenario/Test Method # Scans

1

Concrete Rubble Pile 45

Wood Rubble Pile 23

Passenger Trains 41

2

Single Family Dwelling 26

House of Pancakes 29

Theater Maze 2

Tube Maze 10

a representative segment of one of the Disaster City rubble

piles. If this is achieved, the existing mobility metrics captured for the stepfields can be applied to predict how well a

given mobility platform will perform in the rubble pile scenario and more generally, any terrain that can be modeled

in this fashion.

The ground truth data collected in this work will also be

used to explore methods for evaluating the quality of maps

generated by the US&R robots which traversed the same

scenario environments. While there is currently some work

being investigated for evaluating 2D maps, there is little

work being done for 3D maps.

Finally, the data will be used to support the modeling of

the Disaster City scenarios for use in virtual training and

testing environments such as USARSim.

6. ACKNOWLEDGMENTS

The authors would like to thank Adam Jacoff and Christopher Scrapper whose support made this work possible. Also,

a hearty thanks to Gerry Cheok, Kamel Saidi, and Nicolas

Philippon for their tireless work in helping plan and execute

these data collection efforts.

7. REFERENCES

[1] ASTM. ASTM E2544 - 09b Standard Terminology for

Three-Dimensional (3D) Imaging Systems. ASTM,

2009.

[2] S. Balakirsky, F. M. Proctor, C. J. Scrapper, and T. R.

Kramer. An integrated control and simulation

environment for mobile robot software development. In

Proceedings of the International Design Engineering

Technical Conferences and Computers and Information

in Engineering Conferences, August 2008.

[3] A. Jacoff, A. Downs, A. Virts, and E. Messina.

Stepfield pallets: Repeatable terrain for evaluating

robot mobility. In Proceedings of the 2008 Performance

Metrics for Intelligent Systems (PerMIS) Workshop,

2008.

[4] A. S. Jacoff and E. R. Messina. DHS/NIST Response

Robot Evaluation Exercises. In Proceedings of the 2006

IEEE International Workshop on Safety, Security and

Rescue Robotics (SSRR), January 2007.

[5] A. S. Jacoff and E. R. Messina. Urban search and

rescue robot performance standards: Progress update.

In Proceedings of the 2007 SPIE Defense and Security

Conference, June 2007.

173
Performance Measurements for Evaluating Static and 

Dynamic Multiple Human Detection and Tracking Systems 

in Unstructured Environments 

 

Barry Bodt, 

Richard Camden 

Army Research Laboratory 

{babodt,rcamden}@arl.army.mil 

 Harry Scott, Adam Jacoff, Tsai 

Hong, Tommy Chang, Rick 

Norcross, Tony Downs, and 

Ann Virts 

National Institute of Standards and 

Technology 

{harry.scott, adam.jacoff, tsai.hong, 

tommy.chang, rick.norcross, 

tony.downs, ann.virts}@nist.gov 

ABSTRACT

The Army Research Laboratory (ARL) Robotics Collaborative 

Technology Alliance (CTA) conducted an assessment and 

evaluation of multiple algorithms for real-time detection of 

pedestrians in Laser Detection and Ranging (LADAR) and video 

sensor data taken from a moving platform. The algorithms were 

developed by Robotics CTA members and then assessed in field 

experiments jointly conducted by the National Institute of 

Standards and Technology (NIST) and ARL. A robust, accurate 

and independent pedestrian tracking system was developed to 

provide ground truth. The ground truth was used to evaluate the 

CTA member algorithms for uncertainty and error in their results. 

A real-time display system was used to provide early detection of 

errors in data collection. 

Categories and Subject Descriptors

B8.2 [Performance and Reliability]: Performance Analysis and 

Design Aids; C.4 [Performance of Systems]: Performance 

attributes.

General Terms

Tracking, Algorithms, Performance, Measurement, 

Experimentation 

Keywords

Unmanned ground vehicle, experimental design, ground truth, 

pedestrian tracking, metrics, perception, performance evaluation 

1. INTRODUCTION 

The ARL Robotics Collaborative Technology Alliance 

(CTA) conducted an assessment and evaluation of multiple 

algorithms for real-time detection of pedestrians in Laser 

Detection and Ranging (LADAR) and video sensor data taken 

from a moving platform in January 2009. In the assessment, the 

robot vehicle equipped with two pairs of stereo cameras, two sets 

of General Dynamics Robotic Systems (GDRS) LADAR and two 

sets of SICK1

 lasers was driven by an operator through a straight 

route of approximately 240 m containing various configurations 

of eight moving pedestrians, four mannequins, four barrels, four 

cones, two trucks, two crates, seven tripods and trees. In addition 

to the complexity of the environments, the variables included 

multiple robot vehicle speeds (30 km/h or 15 km/h) and 

pedestrian speeds (1.5 m/s or 3.0 m/s). The environment was 

intended to provide some Military Operations in Urban Terrain, or 

MOUT, characteristics. 

The objective of the experiment was to capture the data 

necessary to evaluate the performance of each CTA team’s 

algorithm, to provide data to support further development of 

algorithms, and to produce performance analyses based on the 

captured data to support obstacle avoidance planning. An Ultra 

 

1

 Certain commercial equipment, instruments, or materials are identified 

in this paper in order to adequately specify the experimental procedure. 

Such identification does not imply recommendation or endorsement by 

NIST nor does it imply that the materials or equipment identified are 

necessarily the best for the purpose.

This paper is authored by employees of the United States Government 

and is in the public domain. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. ACM 

978-1-60558-747-9/09/09 

174
WideBand (UWB) system [1] employed by the National Institute 

of Standards and Technology (NIST) provided position tracking 

(≈20 cm uncertainty) of the moving and stationary humans, the 

robot vehicle, and other objects. Improved performance of the 

CTA tracking and recognition algorithms has called for 

improvements in the ground truth solution. Processing techniques 

were developed and implemented to produce higher quality 

tracking solutions than those provided by the raw data captured by 

the ultra wideband system. To address this, we developed a 

robust filter algorithm. To improve analysis of the performance of 

the CTA tracking systems, we also developed a temporally 

consistent algorithm for finding the correspondence between the 

ground truth data and the CTA tracking data. In addition, a 

display system was implemented to provide early detection of 

errors in data collection and to assist in data analysis. 

The paper is organized as follows: Section 2 presents a 

detailed description of the solution based on the UWB system for 

capturing the ground truth data. Section 3 introduces the filter and 

interpolation algorithms for improving the quality of the UWB 

data. Section 4 describes the visualization system for providing 

early detection of errors. Section 5 presents the correspondence 

algorithm for data analysis. Sections 6 and 7 present the 

performance metrics and analysis for evaluating the CTA 

algorithms. Finally, Section 8 provides a summary and 

conclusion. 

2. GROUND-TRUTH REFERENCE 

SYSTEM 

2.1 Ground Truth Setup 

NIST researchers have been working with an asset tracking 

system employing ultra wideband technology to capture 2-D 

location and path data for robots, vehicles, and personnel 

operating within scenarios set up to evaluate robotic perception 

systems. The goal is to capture quantitative performance data 

referenced to ground truth positions and time to help compare and 

improve sensors and algorithms in both indoor and outdoor 

scenarios. 

The tracking system uses state of the art ultra wideband radio 

receivers posted around the perimeter of the scenario to track 

multiple static and dynamic targets with badge-size transmitters 

(see Figure 1). It works in open outdoor areas and indoor areas 

through certain types of walls, though overall accuracy can vary. 

NIST has performed system characterization tests in ideal 

conditions to determine the best possible 2D accuracy of the 

system, which is approximately 15 cm. We have used it to track 

vehicles and personnel throughout an area over 80 000 square 

meters with an average accuracy of approximately 20 cm and an 

update rate of approximately 50 Hz, which is sufficient for 

tracking vehicles at highway speeds. We have also used it to track 

robots through random mazes with plywood walls (non-line-ofsight) achieving similar accuracies. We have not been successful 

tracking through concrete walls, but have used additional 

receivers in hallways to compensate during indoor building 

deployments. The total number of dynamic and static transmitter 

tags used simultaneously thus far has been approximately 15 and 

30 respectively for marking obstacles and known fiducial points 

to check accuracy. Setup time for a new site takes about 5 days. 

Returning to a previously setup site takes approximately two days 

for calibration prior to testing. 

(a) (b) (c)

Figure 1. (a) shows a receiver deployed in the field atop a 

mast centered over a known fiducial marker. (b) shows the 

asset tracking system components, ultra-wideband radio 

frequency receiver (shown with integrated high-gain antenna), 

1 W transmitter tag, and 30 mW transmitter tag. c) shows 

Several badge tags attached to helmets to track personnel in 

the scenario. Typically two tags are placed on moving vehicles 

to identify orientation. 

 Figure 2 shows a plot of the tracking results for a ground truth 

system coverage and accuracy test on the January course 

configured at NIST. Green and orange plots show the vehicle 

path and the other plots show pedestrian tracks. 

Figure 2. A calibration run with two transmitter tags mounted 

on a vehicle and two tags on each of two pedestrians to check 

coverage. 

2.2 Filter and Interpolation Algorithms for 

the Ground-Truth Data. 

The goal of the filter process is to remove outlier and error 

measurements from the ground-truth data. We identify outliers 

based on the maximum plausible speed of the tag. A polynomial 

least-squares algorithm filters the remaining data points. We then 

fit a spline through the filtered points to identify the tag’s position 

as a function of time. We interpolate the trimmed, filtered, and 

splined data at timestamps obtained from the CTA performers' 

data. This interpolated ground-truth is later used to establish 

temporal constraints for correspondence. 

The UWB position data contain anomalies that, while 

generally minor, diminish the usefulness of the data in subsequent 

evaluations and displays. The filter combines previous and 

subsequent readings to remove anomalies and identify a more 

accurate and timely position. For example, Figure 3 below is the 

UWB position data for tag A01D of Run3. The data show two 

significant anomalies: a gap in the center area and outlier points 

away from and along the track. 

The filter has three components: trim, filter, and spline. The 

green dots represent the raw data that were trimmed as outliers. 

The red dots are the remaining raw data points. The white line is 

the result of the filter. And the blue points are the positions at the 

CTA timestamps based on a spline fit of the filtered points. 

 The filter’s trim component removes outlier data points. 

Physical constraints limit the distance that an UWB tag can move 

between readings. The trim component computes the velocity 

175
between the current point and the last good point. The filter trims 

the current point when the velocity between these points is 

excessive. The filter checks subsequent points for a point that 

represents a reasonable velocity. The filter then uses that point as 

the last good point for subsequent evaluations. The filter passes 

the trimmed position list to the window component (see red in 

Figure 3). 

The filter component is based on the Savitsky-Golay 

algorithm [2]. Savitsky applies a polynomial least-squares fit to a 

set of points before and after the current point. The filtered value 

is the sum of the products of the points with an array of 

coefficients determined by the order of the algorithm (generally 3) 

and the size of the point set. The Savitsky algorithm relies on 

evenly spaced data. The gaps in the trimmed data would cause the 

Savitsky algorithm to inappropriately shift the data points. To 

compensate, our algorithm fills gaps in the trimmed data with 

linearly interpolated data points before applying the SavitskyGolay algorithm. Our algorithm discards the fill points prior to 

passing the data onto the spline component (see the white in 

Figure 3). 

The filter’s spline component is based on a cubic Hermite 

spline. The spline component identifies positions at a time of 

interest rather than at the time of data collection and allows 

researchers to determine the position of the UWB tag at times 

provided by the CTA systems (see the blue in Figure 3). 

Figure 3. Green is the raw data. Red is the trimmed data. 

White is filtered data. Blue is the interpolation data 

3. CTA REAL-TIME HUMAN DETECTION 

AND TRACKING ALGORITHMS 

In this experiment, six algorithms were included from the 

CTA. Five use LADAR sensing and one uses a vision system to 

provide data for the algorithms. During each algorithm cycle, the 

algorithm reports information about the detected humans. The 

report includes the number of detections, their locations, strength 

of detections, the time of detection, as well as vehicle status such 

as its location, speed, orientation, etc. All detections from the 

same algorithm cycle have the same detection time. The reports 

are collected and saved into files, one per algorithm. 

Reporting rate varies from algorithm to algorithm and may 

not be fixed within the algorithm itself. For example, an 

algorithm's cycle time may increase when the number of 

detections increases. 

In general, there are two data sets: ground-truth data and 

detection data. Detection data are the locations and detection 

times of all humans reported by a CTA algorithm, whereas 

ground-truth data are the corresponding UWB locations for the 

same time. 

Both the detection data and the ground-truth data are 

independently grouped with unique identifications (ID). For the 

ground-truth data, the group IDs are also referred to as the “tag 

ID". Different tag IDs always refer to different humans or physical 

objects. 

For the detection data, the group IDs are referred to as 

“tracking ID". With perfect CTA system detection and tracking 

performance, the number of tracking IDs would be the same as the 

number of tag IDs. In reality, the detection data can have more 

than one tracking ID for the same human due to occlusion or 

imperfect tracking capability of the algorithms. 

4. DATA VISUALIZATION FOR EARLY 

DETECTION OF ERRORS IN DATA 

COLLECTION. 

Data visualization is important for verifying the integrity of 

both the ground-truth data and the outputs of the CTA algorithms 

prior to, and during, the data collection. Bad data could arise due 

to sensor malfunction or unforeseen circumstances prior to or 

during the data collection. Since data collection is expensive, time 

consuming, and labor intensive, it is advantageous to detect bad 

data as soon as possible and prevent waste of resources. 

A software-based interactive viewer was developed for this 

purpose. The viewer uses various open source libraries and runs 

natively on Linux, Windows and Mac OS X. Figure 4 shows a 

typical screen-shot of the viewer displaying both the detection 

data from a CTA algorithm and the corresponding ground-truth 

data. An individual entity can be toggled on or off by clicking on 

its tag ID or tracking ID. 

176
Figure 4. CTAviewer screenshot showing both detection data 

and ground-truth. The left panel lists the tag ID and the right 

panel lists the tracking ID. An individual entity can be toggled 

on/off by its ID. 

Figure 5 and Figure 6 are examples of bad data. The plots show 

the locations of two ground-truth tags mounted on a moving 

vehicle. The two tags were separated by about 1 meter and the 

vehicle drove at a constant speed. 

The viewer software allows us to quickly view and evaluate 

the data immediately after the each run and investigate the cause 

of any anomalies. Problems in ground-truth can often be 

eliminated by placing more UWB receivers in the problematic 

areas. 

Figure 5. Correctable gaps in the ground-truth data. 

Figure 6. Uncorrectable severe distortion and gaps in the 

ground-truth data. 

5. MAP BETWEEN GROUND-TRUTH AND 

DETECTION 

All CTA teams output their results with time stamps which 

are used to synchronize to the UWB ground-truth data. Since each 

team has different output rates, the linear interpolation described 

in Section 2.2 is used to handle the different rates. All timestamps 

in the data collection systems come indirectly from a common 

clock source via a Network Time Protocol (NTP) server. All 

systems synchronize to the NTP server at the beginning of each 

run. A run lasts about 1 minute. This allows our data collection 

computers to stay synchronized to each other within 20 

milliseconds. 

All CTA algorithms output their results in a standard format, 

and are stored in Comma Separated Values (CSV) for viewing 

using the viewer described in section 3. 

The requirements for the performance evaluation of the CTA 

systems include: 

1. Timestamp correspondence between ground-truth and 

detection. 

2. Object/human correspondences between ground-truth and 

detection. 

3. Definition and computation of metrics and measurements 

for performance evaluation. 

Establishing time correspondence between the ground truth and 

the detection system is important for resolving ambiguity that 

involves time. Finding a mapping between the ground truth 

objects and the objects detected by the CTA algorithms is crucial 

for evaluating the algorithms’ performance. In Figure 7 using a 

nearest neighbor criterion, inside the red circle, the blue star T3

will correspond to the yellow T2 circle since the distance is less 

than the distance to the blue T3 circle. When time correspondence 

is established, the star T3 ground-truth will correspond to the 

ground truth represented by the blue T3 circle. The time 

correspondence algorithm will be described in section 5.1. Before 

defining the metrics, it is necessary to have a good way of 

assigning detected objects to ground truth. The detail of the 

correspondence algorithm will be described in Section 5.2. The 

assessment of the performance of the CTA tracking systems 

required several measurements. These measurements will be 

described in Section 5.3. 

177
Figure 7. The CTA data are represented by star shapes and 

the ground-truth data are represented by shaded circles. Tn 

represents the time n. It is sufficient to use time to match the 

closest detection ground-truth pair. The outer circle around 

the ground-truth data indicates the threshold radius used for 

establishing a spatial constraint. 

5.1 Establish Time Correspondences between 

Ground-Truth and Detection Tracking. 

After the filtering and interpolation process, all ground-truth 

data are interpolated at timestamps compiled from all the 

detection data. Two matching files are generated for each team - 

one containing the ground truth and the other containing the 

detection data. These two matching files have the same number of 

entries. Each entry contains a timestamp and information about 

object locations detected at, or interpolated to, that timestamp. 

Since the timestamps are matched, there is a one-to-one 

correspondence among the timestamps in the entries between the 

two files. 

5.2 Establish Object/Human 

Correspondences between Ground Truth and 

Detection Tracking 

Previously [3], correspondence was determined solely based 

on spatial constraints. One such constraint is the distance between 

the ground-truth and the detection data. Although spatial 

constraints are essential, they alone can not resolve ambiguities 

that arise when close data are taken at different times. Such 

ambiguity and an unnecessary spatial search can be avoided when 

we take the temporal consistency into account 

Several map correspondence algorithms [4][5][6][7] were 

investigated. The correspondence algorithm, adopted by 

Classifications of Events, Activities and Relationships 

(CLEAR)[8] evaluation workshop group, was implemented. 

Using matched-pair data from Section 5.1, the algorithm 

computed the averaged error distances over the cluster with 

respect to each object. Clusters were associated with ground-truth 

objects based on a minimum average distance subject to meeting a 

3 m proximity threshold based on our experiments. In addition, 

we used velocity to differentiate stationary from moving objects. 

Clusters were then labeled as a human (moving), mannequin 

(stationary human), misclassification objects (moving or 

stationary), or false positive. The results of this correspondence 

procedure will be used for computing several measurements for 

the analysis of the human detection in the following section. 

5.3 Post Process the Data Acquisition files for 

Human Detection Analysis 

In order to analyze the CTA tracking algorithms correctly and 

accurately, post processing of the data is necessary. CTA 

algorithms differed in cycle time ranging from 7 Hz to 20 Hz. At 

the end of each algorithm cycle, each algorithm reported detection 

information such as positions and velocities of the humans. The 

underlying assumptions for the outputs of the algorithms included 

the following: 

- Only obstacles seen and classified as human were reported. 

- Unique identification numbers were assigned to individual 

algorithm detections within a run. 

- Algorithms demonstrated tracking of an individual by 

maintaining the same ID in successive frames. 

- Algorithms also reported velocity of the detected humans. 

Since we only instrumented the ground-truth data in the 300 

m x 150 m test area, all detections are excluded if they occurred 

outside the test area. The correspondence algorithm described in 

Section 5.2 found the correspondence between the detections and 

the ground-truth based on location and time stamp. Detections 

were compared with all the ground-truth objects on the course. 

Absolute error distances were computed, summed, and averaged 

over the cluster with respect to each course object. The absolute 

velocity error between detection and ground truth objects was also 

computed and averaged over the cluster. Clusters were associated 

with a ground-truth object based on minimum average distance 

subject to meeting a 3 m proximity threshold. Using velocity to 

establish stationary/moving objects, clusters were then classified 

as a human, mannequin, misclassification, or false positive. Other 

values were reported in the post processing. For example, reports 

included the distance from the moving vehicle at the time of first 

detection for individual detections within the common ID cluster, 

the shortest distances and velocities, and dispersion measures for 

distance and velocity. 

6. PERFORMANCE METRICS 

Post processing of the data above results in a spreadsheet for each 

algorithm with metrics for analysis. A record is formed for each 

algorithm-reported human. Each algorithm assigns an identifier to 

an entity on the course classified by the algorithm to be a human. 

All information related to that algorithm identification is 

condensed to a single record. This record may hold information 

from many cycles of the algorithm. Post processing determines 

whether that entity is, in truth, a human or mannequin (true 

positive), another known course entity not human or mannequin 

(misclassification), or an unknown course feature with no 

associated ground-truth (false positive). Distinctions are also 

made between moving and stationary entities and various classes 

of nonhuman entities (e.g., barrels, cones, crates). Field notes 

178
describe test conditions under which the data were collected, 

absolute and relative positioning of the robot platform and 

detected entities recorded at the time detections first occurred for 

an identification, time and cycle number indicators of the 

persistence of detection, or the accuracy of the algorithm 

classification decision. 

7. EXPERIMENTS AND PERFORMANCE 

EVALUATION 

The purpose of this section is to outline the experiment and 

to illustrate the importance of the ground truth system in 

assessment of the algorithms. A complete analysis is not given. 

The principal experiment consisted of thirty-two runs conducted 

at the south end of Center Drive on the NIST campus (Figure 9). 

An autonomous vehicle platform, with sensors and algorithms on 

board as discussed above, was driven south to north over an 

approximately 240 m run. Scripted scenes with human motion, 

mannequins, and course clutter were sensed and interpreted and 

reported by the algorithms in real time. Eight humans were 

present in each run, four to either side of the road. Four moved in 

a manner parallel to the road, three at 45 degrees toward the road, 

and one perpendicular toward the road. Three parallel runs were 

receding from the platform and one was approaching. Among the 

45 degree runs, all were approaching the road, but only the run to 

the right was approaching the vehicle. 

Movements of the humans were choreographed and timed to 

ensure that regardless of test conditions the scene sensed remained 

consistent across runs. See Figure 8. All humans were upright in 

the principal experiment. Excursion runs not reported here 

explored other postures and group movement patterns. 

Figure 8. Human paths relative to platform route. The units 

are in meters. 

Test conditions were formed based on three factors: platform 

speed, human speed and course clutter. The platform was driven 

at either 15 km/h or 30 km/h, humans moved at 1.5 m/s or 3.0 

m/s, and the course was cluttered to approximate MOUT 

complexity or was open except for the human movers. The test 

conditions were allotted equally in accordance with a 2^3 factorial 

design with 4 replications per condition. Under the MOUT 

conditions only, 8 mannequins, 4 barrels, 4 cones, 2 crates, and 2 

trucks were included on the course. Seven NIST tripods were on 

the course for all 32 runs. Figure 9 shows a view of the course 

during a MOUT run. 

The assessment of algorithms focuses on the questions of what an 

algorithm saw, when it saw it, and how long the sighting 

persisted. These questions will be pursued for each algorithm and 

in the context of the experimental conditions under which the data 

were collected. The ground-truth system allows definitive answers 

to these questions. We share some preliminary high-level results 

to illustrate performance. 

Figure 9. Right side of course during a MOUT run. 

Table 1 summarizes the performance of the six algorithms in 

terms of detections, misclassifications, and false positives over the 

complete set of 32 runs. Entries are percentages except for the 

false positive entries, which report the number per run. Note that 

true positives are in bold, and false positives are in italics. All 

other entries show the algorithm misclassification of other course 

entities as human. At a high level, this table addresses what was 

seen. 

Table 1. Algorithm performance expressed in terms of the 

percentage of course entities detected and the number of false 

positives per run. 

 CTA Algorithm 

Object 

Type 

Alg 1 Alg 2 Alg 3 Alg 4 Alg 5 Alg 6 

Human(%) 97.3 90.8 98.4 98.0 89.5 85.7 

Mann.(%) 10.2 - 97.7 98.4 91.4 62.5 

Cones(%) 0.0 - 4.7 0.0 65.6 0.0 

Barrels(%) 14.1 - 54.7 70.3 89.1 0.0 

Crates(%) 46.9 - 100.0 90.6 100.0 50.0 

Trucks(%) 25.0 - 100.0 25.0 100.0 75.0 

Tripods(%) 1.3 46.7 53.6 60.7 58.9 29.8 

False 

Positives

29.8 77.9 155 37.3 29.8 1.3 

Performance varies widely across algorithms. While some 

demonstrate a high probability of detection, misclassification of 

other course entities is clearly a problem. Moreover, the number 

of false positives recorded, if not addressed, ultimately would 

provide a greater challenge for dynamic planning in an 

autonomous mode. 

179
In Figure 10, a boxplot (with mean) is shown for the distance 

between the platform and the target entity at the time of first 

detection. This distance is as perceived by the algorithm, but 

generally this does not vary greatly with the actual ground truth. 

Detections (green), misclassifications (yellow), and false positives 

(red) are shown. There are differences according to the type of 

obstacle. Tripods and trucks (Trks), for example, tend to be 

recognized in the neighborhood of 30 m away; whereas humans 

are detected on average at more than 50 m away. Confidence in 

this graph is based on the ground-truth system. A similar graph 

exists with actual distances based on the ground-truth, but a 

distance for false positives requires the algorithm-produced 

values. In this fashion, the question of when entities were seen is 

addressed. 

FP Brl Cone Crts human mannequin Tripod Trks

90

80

70

60

50

40

30

20

10

0

Object_type

Sub_to_Tar_FD (m)

Boxplot of Sub_to_Tar_FD (Alg 3)

Figure 10. Boxplots of distance from platform to targets 

detected by the algorithms for different object types. 

Figure 11 shows a boxplot for the duration of time all entities of a 

certain type were tracked. Ideally, humans and mannequins would 

be tracked persistently; whereas, other entities would not. 

FP Brl Cone Crts human mannequin Tripod Trks

40

30

20

10

0

Object_type

Det_Dur (sec)

Boxplot of Det_Dur (Alg 3)

Figure 11. Boxplots of duration of tracking for different object 

types. 

From Table 1 we learn that high detection rates are accompanied 

by higher than desired misclassification rates and numbers of false 

positives. The intent of examining data in Figure 11 is to 

determine if persistent tracking requirements might greatly reduce 

false alarms and misclassifications while retaining a high level of 

detection. In this case, at least 75 % of the false positives fall 

below the 25th percentile for humans and mannequins detected, 

suggesting persistent tracking may reduce false positives. 

However, tracking of misclassified entities would not be greatly 

influenced. 

308250 308275 308300 308325 308350 308375

4332950

4332900

4332850

4332800

4332750

4332700

Easting

Northing

False Positives (Alg 4, Runs 1-32)

Figure 12. Scatterplot of false positive locations for Alg4. 

Ground-truth allows a definitive decision on false positives. 

Figure 12 shows the false positives recorded for ALG4 over the 

32 runs of the experiment. The path of the vehicle (black) and the 

false positive locations (red) are shown. As part of the analysis, 

the cause of false positives (e.g., bushes, trees, high grass) will be 

pursued. 

One of the most advantageous features in the measurement 

technology employed here is the time sequenced display of 

detection. For individual runs, it is necessary to drill down into 

the data to investigate anomalies more carefully. A static display 

in Figure 13 shows a run for ALG4. Labels over the points for the 

path of the vehicle indicate when a specific moving human was 

detected during the run. The moving human is also plotted. In this 

instance, there are replicates for some of the moving humans (e.g., 

MUH4). This tells us that multiple unique identifications were 

assigned to this human. For some reason, the algorithm judged the 

human to be a different entity at different time. One possibility is 

occlusion, as might be the case here when MUH4 was obscured 

from view by crates during a MOUT run. Although informative, 

the static display does not reveal the same detail as movies of the 

run as it unfolds. The CTA Viewer illustrated in Figure 4 is 

critical to detailed analysis. 

308280 308290 308300 308310 308320 308330 308340

4332900

4332850

4332800

4332750

4332700

Easting

Northing

MHU8

MHU8 MHU4

MHU4 MHU3

MHU7

MHU4

MHU6

MHU3

MHU2 MHU1

MHU25

MHU8 MHU8

MHU4

MHU4 MHU3 MHU7 MHU4

MHU6

MHU3

MHU2

MHU1

MHU2 MHU5

Human Detection (Alg 4, Run 17)

Figure 13. Human detection for run 17 using ALG4. 

180
8. CONCLUSION 

We presented details of several components of a system for 

determining performance of sensors and perception algorithms 

tasked with detection, tracking, and classification of moving and 

fixed objects, including pedestrians, around a moving robot 

vehicle. We presented a filter algorithm developed to improve 

ground-truth data for analysis. In addition, we developed a groupbased correspondence between ground-truth and detection for 

data analysis and performance evaluation. 

From an analysis perspective, the advances in measurement 

technology of good ground truth data improve the assessment 

process markedly. The ground truth precision provides an 

objective evaluation of the results reported by the algorithms. It 

makes possible the exact tracking of moving entities on the 

course, essential given the planned assessment of the “detection 

and tracking” purposes of the algorithms. This was previously not 

possible. The CTA viewer has proven to not only be a useful tool 

in visual analytics, but has also provided an instant check during 

the conduct of the experiment as to whether or not data are being 

collected and whether systems are in good calibration. 

9. ACKNOWLEDGMENTS 

The authors would like to thank the CTA partners for their 

research and willingness to conform to the reporting format, and 

to thank Jeff Jones and his team at General Dynamic Robotics 

Systems (GDRS) in Westminster for their help with data 

collection. 

10. REFERENCES 

[1] Robert Fontana, Recent System Applications of Short-Pulse 

Ultra-Wideband (UWB) Technology (Invited Paper), IEEE 

Microwave Theory & Tech., Vol. 52, No. 9, Sept. 2004. 

[2] Abraham Savitzky and Marcel Golay, "Smoothing and 

Differentiation of Data by Simplified Least Squares 

Procedures". Analytical Chemistry 36 (8): 1627–1639, 1964. 

[3] Barry Bodt and Rickard Camden, “Detecting and Tracking 

Moving Humans form a Moving Vehicle,”, SPIE, Orlando, 

Florida, 2008. 

[4] Keni Bernardin, A. Elbs, Rainer Stiefelhagen, “Multiple 

Object Tracking Performance Metrics and Evaluation in a 

Smart Room Environment”. The Sixth IEEE International 

Workshop on Visual Surveillance, VS 2006, Graz, Austria, 

2006. 

[5] Kevin Smith, Sileye Ba, Jean-Marc Odobez and Daniel 

Gatica-Perez, “Evaluating Multi-Object Tracking”, 

Workshop on Empirical Evaluation Methods in Computer 

Vision (EEMCVF) San Diego, CA, 2005. 

[6] Sarma Pingali and Jakub Segen, "Performance Evaluation of 

People Tracking Systems," wacv, pp.33, Third IEEE 

Workshop on Applications of Computer Vision (WACV 

'96), 1996 

[7] Faisal Bashir and Fatih Porikli., "Performance Evaluation of 

Object Detection and Tracking Systems", IEEE International 

Workshop on Performance Evaluation of Tracking and 

Surveillance (PETS), June 2006. 

[8] Keni Bernardin, “ CLEAR 2007 Evaluation Plan 3D Person 

Tracking Task,”, Classification of Event, Activities and 

Relationships Evaluation and Workshop (CLEAR) 2007. 

[9] Richard Camden and Barry Bodt, “Safe Operations 

Experiment Report,” ARL-TR-3773, U.S. Army Research 

Laboratory: Aberdeen Proving Ground, MD, April 2006. 

[10] Elias Rigas, Barry Bodt and Richard Camden, “Detection, 

tracking, and avoidance of moving objects from a moving 

autonomous vehicle,” Proc. SPIE 6561 (2007).

181
Mathematical Metrology for Evaluating a

6DOF Visual Servoing System

Mili Shah

Loyola Univ. Maryland

Baltimore, Maryland

mishah@loyola.edu

Tommy Chang,Tsai Hong

National Inst. of Standards

and Technology

Gaithersburg, Maryland

{tchang,hongt}@nist.gov

Roger Eastman

Loyola Univ. Maryland

Baltimore, Maryland

reastman@loyola.edu

ABSTRACT

In this paper we develop a homogeneous matrix transformation to fit two streams of dynamic six degree of freedom

(6DOF) data for evaluating perception systems using ground

truth. In particular, we compare object position and orientation results from a 6DOF laser tracker that we consider to

be ground truth with results from a real-time visual servoing

system from the Purdue Robot Vision Lab. A problem that

arises when comparing these two data streams is that they

are not necessarily in the same coordinate system. Therefore, a method to transform one coordinate system to the

other is needed. We solve this problem by developing an optimization problem that minimizes the space between each

coordinate system. In other words, we construct a rotation

and translation which best transforms one coordinate space

to the other.

Categories and Subject Descriptors

C.4 [Performance of Systems]: Performance attributes;

B.8.2 [Performance and Reliability]: Performance Analysis and Design Aids; G.1.6 [Optimization]: Global optimization; I.4.8 [Scene Analysis]: Motion, Tracking; I.5.4

[Applications]: Computer Vision

General Terms

Computer Vision, Laser Tracker, Dynamic 6DOF metrology,

Performance Evaluation

1. INTRODUCTION

In previous work [2] we reported on experiments in the

evaluation of the performance of a real-time visual servoing

system using a highly accurate, dynamic, six degree of freedom (6DOF) laser tracker. The purpose of the experiments

was to demonstrate a method for evaluating real-time 6DOF

dimensional measurements of an object or assembly component under moderately constrained motion. By taking geometrically calibrated, time-synchronized data streams si-

(c) 2009 Association for Computing Machinery. ACM acknowledges that

this contribution was authored or co-authored by a contractor or affiliate

of the U.S. Government. As such, the Government retains a nonexclusive,

royalty-free right to publish or reproduce this article, or to allow others to

do so, for Government purposes only.

PerMIS ’09, September 21-23, 2009, Gaithersburg, MD, USA

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multaneously from the 6DOF servoing sensor system and

the laser tracker, the 6DOF system data can be evaluated

against the laser data serving as conventional ground truth.

In this paper we report on improved techniques for postexperiment and geometric calibration and evaluation of the

experimental data.

Reliable, accurate real-time systems for 6DOF perception

would have applications in advanced manufacturing robotics

and automation, as they would enable greater interaction

with objects in motion and more flexible robotic workcells.

However, despite considerable advances in real-time vision

and in laboratory demonstrations [7,16,17], these systems

have not yet been widely commercialized and this would be

assisted by reference metrology systems for empirical performance evaluation. Reference systems would include a

standard sensor system for ground truth along with appropriate metrics for the comparison of test systems with the

reference system. Standards and test procedures for dimensional metrology are well-established and highly accurate for

static measurements, with coordinate measuring machines

and laser trackers giving position measurements to microns.

However, the theory, technology, and test procedures are not

well established for dynamic dimensional measurements in

uncontrolled environments.

To assist in establishing these test procedures, the questions addressed in this work focus on calibrating and comparing two 6DOF data streams. We assume the two vector

data streams include position as X, Y, Z, pose as roll, pitch

and yaw, and that the two data streams have been timesynchonized so we have correspondence between individual

vectors in each data stream. But, we do not assume accurate

geometric calibration of coordinate systems between the two

data streams. During our initial experiments, accurate calibration proved difficult so we looked for a post-experiment

calibration approach that would compute an accurate transformation between two coordinate systems, taking into account all information in the 6DOF data. Once the two data

streams have been calibrated, we wish to compare the two

for the magnitude and nature of the differences in order to

characterize the 6DOF system under test.

The real-time visual servoing implementation used in this

study was developed at the Purdue Robot Vision Lab1us1Certain commercial equipment, instruments, or materials

are identified in this paper in order to adequately specify the

experimental procedure. Such identification does not imply

recommendation or endorsement by NIST nor does it imply

that the materials or equipment identified are necessarily

the best for the purpose.

182
ing a subsumptive, hierarchical, and distributed vision-based

architecture for smart robotics [3,6,16,17]. This is a robust,

advanced dynamic visual servoing implementation with a

high-level of fault tolerance to non-cooperative conditions

such as severe occlusions and sudden illumination changes.

The Purdue system combines a ceiling mounted camera with

a trinocular system mounted on the robot end-effector, and

uses position based visual servoing (PBVS). The work in

this paper is aimed at the evaluation of sensors for PBVS,

in which the servoing system senses the position and orientation of the part in 3D coordinates, as opposed to image

based visual servoing (IBVS), in which the servoing system

senses the position and orientation of the part in 2D image

coordinates.

2. PREVIOUS WORK

While pose estimation and visual servoing receive attention in the literature, evaluation of visual servoing usually

appears as a secondary element to the presentation of new

servoing approaches or algorithms. Many papers that present

a new approach include an empirical evaluation, but since

the paper emphasizes the development of the new approach,

the evaluation section is often brief. Two papers that do

focus on the evaluation of visual servoing algorithms are [1,

5]. In [5], there is a sensitivity analysis and simulation to

compute the contribution of image measurement errors to

the calculated pose and control trajectory for PBVS and

hybrid visual servoing. In [1], there is a modular analysis

of the elements of a visual servoing systems with the intention of supporting a design and evaluation framework, with

an emphasis on the control subsystem. The paper considers

many aspects of performance analysis for static and dynamic

cases, as well as accuracy and timing issues.

Most evaluation papers consider static pose only [6, 8]

and not 6DOF sensor measurements under motion. References [8, 9] use Monte Carlo simulation for the evaluation

of pose algorithm accuracy under noise and object orientations. In those articles, results are given for pose estimation

for a complex industrial part and the error from unidentified ground truth is plotted as position or orientation error

vs. the rotation of the object. The key result is to note

that the error as a function of part rotation varies considerably, spiking at ambiguous orientations of the object. Two

papers that do consider dynamic pose are [7, 11]. In [11] disassembly used car parts video sequences are used for tests of

a model-based algorithm with four parameter variations to

analyze the relative contributions of subcomponents such as

the edge detection operator or search technique. The results

are given as deviations from the results of the one parameter set that successfully maintained track through the video

sequences, but the nature and quality of this retrospective

ground truth is not described in the article. In [11] three

tracking approaches for 6DOF pose estimation and grasping of hand-held objects are evaluated using ground truth

from an unidentified infrared marker tracking system good

to 1.5 m in position but with no rotation accuracy or measurements per second cited. The three approaches run at

between 8 Hz and 25 Hz. The article gives results in graphs

that compare ground truth position and orientation data

to robot end-effector position and tracked position, but no

quantitative or summary statistics are given for the graphed

data.

The metrics used to evaluate pose estimation and visual

servoing systems vary. They include the mean and standard

deviation of a measure of error in world coordinates, including individual differences for each coordinate, a norm for

position and orientation separately, and rarely a combined

norm for all 6 degrees of freedom. The orientation can be

compared in roll-pitch-yaw, quaternion, or angle-axis representations. In experiments without ground truth in world

coordinates, or for IBVS in which pose in world coordinates

is not computed, errors are computed in the image domain.

In some visual servoing evaluations, the metric is the number of cases successfully completed during the experiments.

In physical experiments in the evaluation of pose estimation

or visual servoing, a mechanism must be used to generate

motion, frequently a robot arm [3, 4, 11]. [11] uses an arm

to move a camera towards a car battery through a known

trajectory linear in both translation and angle, and repeats

the motion 80 times to judge repeatability of the tracking

algorithm.

3. VISUAL SERVOING EXPERIMENTS

3.1 Purdue Data

The Purdue system produces a 6DOF pose at the rate

of 30 Hz. The output consists of 3 translations and 3 rotational angles, all relative to the robot base frame. The

object whose, pose is measured by the Purdue system, is

a typical engine cover about 0.5 m in width and 0.25 m in

height. Figure 1 defines the object frame.

A

B

C

O

Figure 1: Engine cover and the object frame: A,B

and C are coplanar in the YZ plane. O is centered

between A and B. OB is the Y axis, while the X-axis

is in the direction of the cross product of OB and

OC. The Z-axis is given by the cross product of axes

X and Y.

3.2 Laser Tracker Data

The laser tracker (LT) measures the 3D locations of a

smart track sensor (STS), which measures its own orientation. Together, the two measurements give a complete

6DOF pose of the STS at a rate of up to 150 Hz.

In our experiments, the STS is rigidly attached beneath

the engine cover (object) as shown in Figure 2. The laser

183
Figure 2: Engine cover (object) and the STS

tracker measures the position and orientation of the STS,

while the Purdue system measures the position and orientation of the engine cover (object). However, since the STS

and the engine cover (object) are fixed rigidly to each other,

the laser tracker can be utilized to compute the transformation between the two. A point of concern is that the Purdue

data is in the coordinate system of the robot base whereas

the laser tracker has its own coordinate system. Calibrating these two coordinate systems can be a daunting task.

Therefore, the objective of this paper is to use the data to

construct the best transformation of the robot base coordinate system into the laser tracker coordinate system. The

methodology of this process is shown in Section 5.

3.3 Synchronization of the two systems

In order to achieve a matched-pairs design, we take simultaneous measurements and thus minimize the difference

in system outputs due to independent measurements taken

at different times and different rates. Another advantage of

taking simultaneous measurements is that we do not need

to know the object motion.

Synchronization can be easily achieved through a common

external signal to trigger data acquisition. We use a 30 Hz

square wave signal as the Purdue system requires a steady

30 Hz data stream (limited by the cameras’ frame rate).

Although a common external trigger signal is used to trigger the data acquisition of both systems, the Purdue system

does not latch data instantly. This is because the cameras

in the Purdue system do not use a fixed shutter/exposure

time. After a trigger signal is received, the cameras open

their shutters for some amount of time to collect light. In

general, the amount of time changes from frame to frame,

depending on the lighting condition at the time. During

this exposure/integration time, motion blur can happen. Although the integration time is small, it can be a source of uncertainty in determining the exact pose of the object. Large

motion blur will increase pose uncertainty.

In order to be able to uniquely identify and track each

trigger signal, the data collection software from each system

maintains its own sequence counter and tags each count with

a time-stamp having microsecond resolution. Both data

collection software modules get their timestamps indirectly

from a common clock source via an Network Time Protocol

(NTP) server. However, instead of running an NTP client,

which attempts to model the clock drift over a long period

of time, we simply have the data collection computers synchronize the NTP sever every 10 s. We find this setup allows

our data collection computers to stay synchronized to each

other within 3 ms. In general, the clock circuits in today’s

consumer computers are precise but temperature dependent.

3.4 Experimental Setups

We conducted two sets of experiments, one with the object

stationary and one with the object moving with a simple

linear velocity.

3.4.1 Stationary Tests

The stationary tests allowed us to evaluate the basic performance of both systems and assure that the laser tracker

was performing to specification after shipping. The object

was placed in four positions and data were collected for 15 s

to 30 s each.

3.4.2 Linear Motion Tests

In the linear motion tests, the object was moved about

1.5 m left to right. For each trial, the motion was repeated

30 times as the object moved.

4. CALIBRATION

In order to compare data streams collected from the Purdue system with data streams collected from the laser tracker

system, which we consider to be ground truth, both systems

must first be placed in the same coordinate system. In other

words, a homogeneous matrix that transforms the Purdue

data into the coordinate system of the laser tracker system

data is needed. We define XHY as the homogeneous transformation from the coordinate system of Y to X. In other

words, XHY defines the 6DOF pose of Y in X coordinates.

Therefore in this paper, we are searching for LTHRB where

LT is the output of the laser tracker system and RB is the

output of the Purdue system. In [2], a description of the

methodology for the output of both the Purdue system and

the laser tracker system is provided. A review is given in

Section 3. Here, we will give an overview of the necessary

components (Figure 3).

The Purdue system provides RBHO, where RB denotes

the robot base and O denotes the object of interest. Similarly, the laser tracker system provides LTHSTS. However,

LTHO is what is needed. This can be calculated by noting

that

LTHO =LT HSTS ×STS HO (1)

and

STSHO =STS HLT ×LT HO (2)

is a fixed value and thus only one coordinate frame is needed

to construct it. STSHLT = (LTHSTS)

−1

and LTHO is constructed by using the laser tracker along with a spherically

184
Laser Tracker (LT)

STS

Object (O)

Robot Base (RB)

Figure 3: The necessary components of the Purdue

data stream and the laser tracker data stream.

mounted reflector (SMR) to calculate the Cartesian coordinate position of three features on the object. These three

features are enough information to identify the object’s reference frame [2].

The output for the laser tracker system is LTHO whereas

the output for the Purdue system is RBHO. Therefore, to

be able to compare the two outputs LTHRB is needed. The

following section describes a mathematical method that constructs LTHRB by forming the best homogeneous matrix

that fits the data RBHO from the Purdue system to the

LTHO of the laser tracker system.

5. MATHEMATICAL ANALYSIS

5.1 Homogeneous Matrix

In the previous section, an overview of how the data streams

are constructed from both the Purdue system and the laser

tracker system is given. The output of the Purdue system

is given as a series of homogeneous matrices

(RBHO)

i =

»

Rb i

bti

0 1–

(3)

for time steps i = 0, 1, , n − 1. Similarly, the output of the

laser tracker system is a series of homogeneous matrices

(LTHO)

i =

»

Ri ti

0 1–

(4)

for time steps i = 0, 1, , n− 1. We are interested in finding a

rotation R and translation t that best transforms the coordinate system of the Purdue data into the coordinate system

of the laser tracker data. Specifically, we want to construct

the homogeneous matrix

H =

»

R t

0 1–

that solves

min

H

kHP − Lk

2

where

P =

ˆ

(RBHO)0(RBHO)1. . . (RBHO)n−1

 ̃

and

L =

ˆ

(LTHO)0(LTHO)1. . . (LTHO)n−1

 ̃

.

Shah develops an algorithm for constructing such an H in

[10]. Specifically, the best rotation has to first be constructed as

R = VDUT

where the singular value decomposition of

XXb T = USVT

with

X =

ˆ

R0 t0 . . . Rn−1 tn−1

 ̃

Xb =

ˆ

Rb 0

bt0 . . . Rb n−1btn−1

 ̃

and

ti = ti − t with t =

1

n

nX−1

i=0

ti (5)

bti = bti − bt with bt =

1

n

nX−1

i=0

bti. (6)

Also

D =

(

diag(1, 1, 1) if det(VUT) = 1,

diag(1, 1, −1) if det(VUT) = −1.

Once the rotation R is found, the translation t can be constructed by setting

t = bt − Rt

where t and bt are defined in (5) and (6), respectively.

5.2 Error Metrics

Given a general homogeneous matrix H – made up of a

rotation R and translation t – a series of metrics is now offered to compare how well H transforms a given data stream

into another [10].

To see how well a given rotation R transforms a single

rotation Ri from the laser tracker data stream to a rotation

Rb i from the Purdue data stream, evaluate

kRRi − Rb ik

2 = kRRik2 − 2tr “

RRiRb T

i

”

+ kRb ik

2

= 6 − 2(1 + 2 cos θ)

where {1, cos θ±i sin θ} are the eigenvalues of RRiRb T

i

. Therefore,

0 ≤ kRRi − Rb ik

2 ≤ 8,

since −1 ≤ cos θ ≤ 1. Moreover,

0 ≤ 1 −

1

8

kRRi − Rb ik

2 ≤ 1.

defines a metric between 0 and 1 where 1 denotes a perfect

fit.

A similar metric can be constructed to compare a given

translation ti from the laser tracker data stream to a translation bti from the Purdue data stream. In this case we want

to see how close Rti + t is to bti. Thus, we consider the

dot product between these two normalized vectors. In other

words, we evaluate

0 ≤

(Rti + t)

T bti

‚

‚Rti + t

‚

‚

‚

‚bti

‚

‚

≤ 1

185
Once again this defines a metric with values between 0 and

1 where 1 denotes a perfect fit. One should note that this

metric loses valuable information regarding the scaling of

the problem. For example, if the vectors Rti + t and bti

point in the same direction (not necessarily the same magnitude), then the metric would give an accuracy reading of 1,

though the vectors may not be equal. However, this metric

is problem independent allowing one to compare two different problem setups. Another metric that can be used (but

is problem dependent) is to look at

kRti + t − btik.

However, this metric does not have a defined upper bound.

Instead, one could compare the magnitude of this metric

with the magnitude of the data used in order to calculate

the accuracy of the algorithm.

In the next section, experiments will be performed to see

how well the homogeneous matrix constructed in 5.1 performs using the metrics just defined.

6. EXPERIMENTS

The algorithm in 5.1 that constructed the best homogeneous matrix H to fit two streams of 6DOF data was applied

to data streams that were collected from the Purdue system

and a laser tracker system at Purdue University in April of

2008 [2]. These data streams were obtained from two experiments (see Section 3).

6.1 Stationary

In the stationary experiment, the object was placed in

four positions for 15 s to 30 s each. The mean distance

for each position was in the 3500 mm to 4100 mm range

with a standard deviation of 0.006 to 0.008 for the STS/LT

system. For the Purdue Line tracker system each position

was in the 2600 mm to 2700 mm range with a standard

deviation of 0.560 to 0.630 standard deviation. More details

can be found in [2]. Overall, the laser tracker system is two

orders of magnitude more accurate than the Purdue system.

The homogeneous matrix calculated from these stationary

data streams is

HStat =

2

6

6

4

−0.79 −0.61 −0.11 715.94

0.60 −0.79 0.07 2228.30

−0.13 −0.01 0.99 −1133.76

0 0 0 1

3

7

7

5

.

We calculated the accuracy of this homogeneous matrix using the metrics provided in the previous section. Not surprisingly, we have near 100 % accuracy for this homogeneous

matrix for both the rotation and translation as can be seen

in Figure 4. In addition, the translational error is around

12 mm – a two order decrease in magnitude compared to

the data position.

6.2 Linear Motion

In the linear motion experiment, the object was moved

1.5 m to the left and right. This motion was repeated 30

times for each trial and quickly returned back to the starting

position. It should be noted that this backward sweep was

ignored in the data collection for both systems.

The homogeneous matrix calculated from these linear mo0 6509 % Accuracy

Stationary

Index

0.5 0 1

Rotational

Translational

0.98 1.01

1000 1500

0 6509 Index

12

0

Translation Error in mm

Figure 4: Error metrics from the stationary experiment where the object was placed in four positions

for 15 s to 30 s each.

tion data streams is

HMove =

2

6

6

4

−0.79 −0.61 −0.08 666.20

0.61 −0.79 0.04 2271.00

−0.09 −0.01 1.00 −1238.45

0 0 0 1

3

7

7

5

which is not very different from the stationary homogeneous

matrix HStat. This should be expected since this experiment should not have much noise introduced from the simple linear motion. Moreover, the minimal noise results in

HMove having near 100 % accuracy for both the rotation

and translation as can be seen in Figure 5. In addition, the

translational error is only around 10 mm.

7. CONCLUSIONS

In this paper, we presented improved techniques for the

calibration of two 6DOF data streams. Previously, calibration was done by hand which was prone to errors. Here, the

data was used to mathematically find the best fit between

two given 6DOF data streams. Specifically, we constructed

the homogeneous matrix that best transformed the coordinate system of one of the two data streams into the other.

Moreover, metrics were offered to evaluate the effectiveness

of this transformation.

We tested this method on two data sets collected at Purdue University. The first consisted of the object being placed

in four positions for 15 s to 30 s and the second consisted

of the object moving in a linear motion. We found that the

homogeneous matrix fit the data almost perfectly for these

two systems.

8. ACKNOWLEDGMENTS

We thank Mike Shneier and Elena Messina for their fruitful discussions. We thank Avi Kak, Johnny Park, and Ger186
0 15792

% Accuracy

Move

Index

0.5 0 1

Rotational

Translational

0.98 1.01

2000 4000

Translation Error in mm

0 15792 Index

0 10

Figure 5: Error metrics from the move experiment

where the object was moved 1.5 m to the left and

right.

man Holguin from Purdue University for working with us on

the real-time data collection using their vision system, robot

arm, and rail. In addition, we would like to thank Jane Shi

from General Motors, Frank Maslar from Ford, and Kam

Lau from Automated Precision Inc. for their collaboration.

9. REFERENCES

[1] M. Bachiller, J. A. Cerrada, and C. Cerrada. A

modular scheme for controller design and performance

evaluation in 3d visual servoing. Journal of Intelligent

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[2] T. Chang, T. Hong, M. Shneier, G. Holguin, J. Park,

and R. Eastman. Dynamic 6dof metrology for

evaluating a visual servoing system. In Performance

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[3] K. Deguchi. A direct interpretation of dynamic images

with camera and object motions for vision guided

robot control. International Journal of Computer

Vision, 37(1):7–20, 2000.

[4] B. Espiau. Effect of camera calibration errors on visual

servoing in robotics. In International Symposium on

Experimental Robotics, pages 182–192, 1993.

[5] V. Kyrki, D. Kragic, and H. I. Christensen.

Measurement errors in visual servoing. Robotics and

Autonomous Systems, 54(10):815–827, 2006.

[6] C. B. Madsen. Viewpoint variation in the noise

sensitivity of pose estimation. In Conference on

Computer Vision and Pattern Recognition, pages

41–46, 1996.

[7] P. Preisig and D. Kragic. Robust statistics for 3d

object tracking. In International Conference on

Robotics and Automation, pages 2403–2408. IEEE,

2006.

[8] G. J. Reid, J. Tang, and L. Zhi. A complete

symbolic-numeric linear method for camera pose

determination. In ISSAC, pages 215–223, 2003.

[9] A. H. Rivera-R ́ıos, F.-L. Shih, and M. M. Marefat.

Stereo camera pose determination with error

reduction and tolerance satisfaction for dimensional

measurements. In International Conference on

Robotics and Automation, pages 423–428, 2005.

[10] M. I. Shah. Six degree of freedom point

correspondences. Technical report, Department of

Mathematical Sciences, Loyola College in Maryland,

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[11] M. Tonko and H.-H. Nagel. Model-based

stereo-tracking of non-polyhedral objects for

automatic disassembly experiments. International

Journal of Computer Vision, 37(1):99–118, 2000.

187
Ontology Formalisms: What is Appropriate for Different 

Applications? 

Craig Schlenoff 

National Institute of Standards and Technology 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

301-975-3456 

craig@schlenoff.com 

ABSTRACT

Ontologies can take many forms. There are ontologies that are 

extremely formal (e.g., using first order logic), and there are 

ontologies that are less formally defined (e.g., ontologies in the 

relational databases or dictionaries). Nonetheless, all of these can 

be considered ontologies and are appropriate in different 

situations. 

In this paper, I will present a view of levels of ontology 

formalizations and then describe three efforts that have applied 

ontologies to solve real-world problems. I will show where each 

of these efforts fall on the formalization spectrum and show why 

that level of formalization is appropriate for that application. 

Categories and Subject Descriptors

I.2.4 [Artificial Intelligence]: Knowledge Representation 

Formalisms and Methods Language – predicate logic, relation 

systems, representation languages, representations 

General Terms

Design, Experimentation, Standardization, Languages, Theory 

Keywords

Ontologies, Formalization, Robotic, Knowledge Representation 

1. INTRODUCTION 

Ontologies can take many forms. There are ontologies that are 

extremely formal (e.g., using first order logic), and there are 

ontologies that are less formally defined (e.g., ontologies in the 

relational databases or dictionaries). Nonetheless, all of these can 

be considered ontologies and are appropriate in different 

situations. 

Similarly, ontologies can play different roles. They can be used 

for common access to information, for search, as exchange 

languages and for reasoning. 

In this paper, I will present one view of different levels of 

ontology formalizations and then describe some efforts that have 

applied ontologies to solve real-world problems. I will then show 

where each of these efforts fall on the formalization spectrum and 

show why that level of formalization is appropriate for that 

application. Section 2 describes the formalization scale that will 

be using for this paper and Section 3 gives an overview of how 

ontologies have been used in real-world applications. Section 4 

describe the details of three projects that have used ontologies and 

how they fit into the classifications described in Sections 2 and 3. 

Section 5 concludes the paper. 

2. LEVELS OF ONTOLOGY FORMALISM 

For the purpose of this paper, I will loosely define an ontology as 

a knowledge representation that can be captured at different levels 

of formality, ranging from terms in a glossary or dictionary up to 

formal logic-based descriptions. Admittedly, this definition of an 

ontology is much broader than the commonly-accepted view. 

Often, people think of an ontology as a more formal 

representation; often one that can be reasoned over in an 

automated fashion. 

To describe the level of formalisms of an ontology, I will use the 

scale in Figure 1. This scale shows examples of ontologies listed 

from least formal (left side of the figure), to more formal (right 

side of the figure). The black diagonal line in the middle of figure 

shows the point at which ontologies can be reasoned over. One 

can run a reasoning engine over everything to the right of the line 

but cannot over the formalisms to the left of the line. This figure 

was not created by the author; it is often used in the literature but 

the author was unable to find the origin of it. 

Figure 1: Levels of Ontology Formalism 

The items in green can be thought of as standard glossaries or 

dictionaries, similar to the ones that you may have in your house. 

The items in purple are thesauri, taxonomies, and hierarchies. In 

this realm, one is starting to organize and categorize information. 

This paper is authored by employees of the United States Government 

and is in the public domain. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

ACM 978-1-60558-747-9/09/09 

188
These structures start to exhibit some superclass/subclass types of 

relationships, and can be used for application such as navigating 

web pages. The red items start providing a lot more structure to 

data, and provide a much richer set of relationships, such as partof, contains, spatial relations, etc. These structures are often used 

as specification for software, exchange languages, and ontologybased search. The items in blue can be thought of as formal 

ontologies and are often represented in logic-based languages 

such as the Knowledge Interchange Format (KIF) or description 

logics. The advantage of these types of formalisms is that they 

allow for inferencing. This allows one to discover additional 

information that is not formally represented and also allows one 

to identify inconsistencies in the knowledge that is represented. 

For the remainder of this paper, I will be using this formalization 

scale to characterize existing efforts in ontology development for 

robotics and related applications. 

3. APPLYING ONTOLOGY: THE BIG 

PICTURE 

Over the past two decades, ontologies have found a role in many 

different applications. Four ways in which ontologies have been 

used include: 

• Common access to information 

• Ontology-based search 

• Exchange language 

• Reasoning 

Each of these is discussed in more detail below. 

3.1 Common Access to Information 

It is often the case that multiple applications need access to the 

same information. This type of information could be a material 

database, a specification of a part to be manufactured, or terrain 

characteristics of an environment that an autonomous vehicle 

must traverse. Instead of requiring an application to encode this 

information in its own internal format, an ontology can provide 

this information in a neutral format that these different 

applications can reference. By not duplicating this information is 

numerous different applications, the ontology can allow the 

information to be represented only once, providing only a single 

source when information needs to be updated and ensuring that 

there is consistency between the information in different 

applications. In addition, the ontology provides a common set of 

vocabulary that all of the applications that access the ontology can 

reference. This will ensure that when information exchange 

between the applications needs to occur, mappings between 

concepts can be easily done based on the vocabulary in the 

ontology. The Road Network Database and the Intelligent 

Systems Ontology, discussed later in the paper, are examples of 

ontologies developed for common access to information. 

3.2 Ontology-Based Search 

It is not uncommon that a given concept can have two terms that 

correspond to it. For example, when a person goes to a web site 

and wants to buy a helmet that will protect their head at a 

construction site, they may type in either “hard hat”, “protective 

helmet”, “hard helmet”, or possibly other terms. Depending what 

the person types is, often different results will be displayed. This 

is because search engines often work on the term that is entered as 

opposed to the concept that is intended. 

Ontologies can help to address this issue by representing concepts 

by what they mean (as opposed to the terms that are used to 

represent them) and then mapping search terms and underlying 

information in product databases to those ontological concepts. 

This is not only true with products... it can also be done with web 

pages or any other item that needs to be searched. 

There are no specific examples of this type of ontology 

application in the paper but please contact the author if you would 

like to learn more about how this was applied in private industry. 

3.3 Ontologies as an Exchange Language 

Information often needs to be shared among different 

applications. This information is usually generated in one 

application and then needs to be sent to another application. An 

example of this is in the manufacturing domain where a person 

may use a process planning system to create a part in one 

application and then needs to send that information to a 

scheduling or production planning system to allow the part to be 

made. The problems with point-to-point translators between each 

pair of application are well documented, and these result in a very 

large amount of translators that need to be developed. Also, as a 

new version of the application is released, all of the translators 

that are written either to or from that application need to be 

updated. 

Ontologies have shown to be valuable in serving as a neutral 

representation to allow for the exchange of information between 

different applications. The ontology provides a common superset 

of all of the information structures that need to be exchanged 

between the applications. By having this common interchange 

structure, a given application would only have to write a 

translator to and from the ontology and then would be able to 

exchange information with any other application that has done the 

same. 

STEP [1] (STandard for the Exchange of Product model data) is 

perhaps the most widely used ontology for this purpose. In this 

paper, I describe the Process Specification Language (PSL) which 

is a more formal ontology that is used to exchange process data 

among applications. 

3.4 Ontologies for Reasoning 

When represented formally, ontologies have the ability to reason 

over information and provide additional information that was not 

previously formally represented. For example, when an 

autonomous vehicle is driving down a road and is presented with 

multiple paths, each of which that has an obstacle in its way, the 

ontology can reason about the expected damage that could occur 

by hitting each of the obstacles based on their known 

characteristics and those of the vehicle. Then a proposed path can 

be presented to a planner to determine how the vehicle should 

proceed. An ontology for navigation planning is discussed later in 

this paper which shows how ontologies can be used for this 

purpose. 

189
4. ONTOLOGY EXAMPLES 

In this section, I will describe existing and past efforts that have 

used ontologies for real-world applications. For each effort, I will 

characterize it with respect to its level of formality as described in 

Section 2 and what role it is playing as described in Section 3. I 

will start with ontologies that are considered to be less formal and 

then proceed to more formal ontologies. 

4.1 The Road Network Database 

For an autonomous vehicle to navigate a road network, it must be 

aware of and must respond appropriately to any object it 

encounters. This includes other vehicles, pedestrians, debris, 

construction, accidents, emergency vehicles ... and the roadway 

itself. The road network must be described such that an 

autonomous vehicle knows, with great precision and accuracy, 

where the road lies, rules dictating the traversal of intersections, 

lane markings, road barriers, road surface characteristics, and 

other relevant information. 

The purpose of this section is to provide an overview of the Road 

Network Database [2], which is to provide the data structures 

necessary to capture all of the information necessary about road 

networks so that a planner or control system on an autonomous 

vehicle can plan routes along the roadway at any level of 

abstraction. At one extreme, the database should provide

structures to represent information so that a low-level planner can 

develop detailed trajectories to navigate a vehicle over the span of 

a few meters. At the other extreme, the database should provide 

structures to represent information so that a high-level planner can 

plan a course across a country. Each level of planning requires 

data at different levels of abstraction, and as such, the Road 

Network Database must accommodate these requirements. In this 

section, I explore the contents of the Road Network Database and 

describe why it was represented in a database format as opposed 

to a more formal ontology. 

The fundamental components of the Road Network Database are 

described below. This is not an exhaustive list, but instead is 

meant to give the reader an idea of the type of structures that are 

represented in the database. 

• Junctions – A junction is a generic term referring to 

two or more paths of transportation that come together 

or diverge, or a controlled point in a roadway. 

Examples of roadway paths that could cause a junction 

are lanes splits, forks in the road, merges, and 

intersections. 

• Intersections - Intersections are a type of junction in 

which two or more separate roads come together. 

• Lane Junctions - A lane junction is a location in a 

junction in which two or more lanes of traffic overlap. 

• Road – A road is a stretch of travel lanes in which the 

name of the travel lanes does not change. An example 

is “Main Street” or “Route 95.” 

• Road Segment - A road segment is a uni-directional 

stretch of roadway bounded by intersections. A road 

segment is roughly analogous to a “block”. 

• Road Element - A road element is a uni-directional 

stretch of roadway bounded by any type of junction. 

Unlike road segments, road elements can be bounded 

by merging lanes, forks in the road, 

• Lane Cluster - A lane cluster is a set of uni-directional 

lanes (with respect to flow of traffic) in which no 

physical attribute of those lanes change over the span 

of the lane segment. Unlike a road element, lane 

clusters are not required to be bounded by junctions. 

• Lane - A lane is a single pathway of travel that is 

bounded by explicit or implicit lane marking. 

• Lane Segment - A lane segment is the most elemental 

portion of a road network captured by the database 

structure. Lane segments can be either straight line or 

constant curvature arcs. One or more lane segments 

compose a lane 

• Junction Lane Segments - A junction lane segment is 

a constant curvature path through a portion of a lane 

junction. 

As stated earlier, the data structures are designed to accommodate 

a control system that may contain planners with various levels of 

abstraction. The planners, their descriptions, and the data 

structures which best correspond to their level of responsibility 

are shown in Table 1. 

Table 1: Planner to Data Structure Mapping 

Planner 

Name 

Planner Description Appropriate 

Data 

Structures 

Destination 

Planner 

Plans the sequence of route segments 

to get to commanded destination goal. 

Outputs MapQuest1

-like directions 

Plans on the order of 1 to 2 hrs into 

the future 

Roads 

Road Segments 

Intersections 

Drive 

Behavior 

Planner 

Develops low-level behaviors for 

negotiating intersections and deciding 

when to change lanes. 

Plans on the order of 100 secs into the 

future. 

Plans up to 500 m 

Lane Clusters 

Lanes 

Intersection 

Elemental 

Maneuver 

Planner 

Carries out real-time maneuvers to 

slow down, stop, speed up, and 

change lateral position. 

Plans on the order of 10 secs into the 

future 

Plans up to 50 m distances 

Lanes 

Lane Segments 

 

1

 The name of commercial products or vendors does not imply 

NIST endorsement or that this product is necessarily the best for 

the purpose. 

190
This information is represented in a relational database. An 

example of the detailed information that was represented for a 

road segment can be seen in Table 2. The corresponding picture 

of what a road segment may look like is shown in Figure 2. 

Figure 2: Sample Road Segment 

A road segment is a uni-directional stretch of roadway bounded 

by intersections. A road segment is composed of one or more road 

elements and zero or more junctions. There are one or more road 

segments in a road. Unlike road elements, road segments are only 

bounded by intersection, not any type of junction. A road segment 

within a road must always be rendered in the same direction as 

the road. Road segments are used in the planning and control 

system to provide MapQuest-like directions to the vehicle to 

allow for route planning. 

This Road Network Database is represented as a database schema 

(on the left side of the formalization figure shown in Section 2). 

The reason why a more informal representation was chosen was 

because the database was meant to serve for common access to 

information (as described in Section 3). It was not anticipated that 

any reasoning would need to be performed on the data structures 

so a more formal type of representation (e.g., logic) was not 

needed. Conversely, since the database was expected to provide 

common access to information, more informal types of 

representations (glossaries, data dictionaries, informal hierarchies) 

were not used since they did not provide the level of specificity 

needed and provide too high a level of ambiguity in the meaning 

of the terms that were represented. 

Table 2: Road Segment Database Representation 

Attribute Data 

Type 

Value 

Restriction 

Point To Description 

ID Integer Any whole 

number 

greater or 

equal to 

one 

 A unique identifier for 

this entry in this table 

World_ID Integer World.ID A pointer to an element 

in the World table that 

indicates with which 

world this entry is 

associated. A road 

segment may only be 

associated with a single 

world. See 4.5.1. for 

information about 

worlds. 

Description Text A textual description of 

this field for human 

understanding 

Road_ID Integer Road.ID A pointer to the element 

in the Road table in 

which the road segment 

is a part of. 

Start_Point

_Adjacent_ 

Intersection

_ID 

Integer Intersecti

on.ID 

A pointer to the element 

in the Intersection table 

which precedes the road 

segment. 

End_Point_

Adjacent_ 

Intersection

_ID 

Integer Intersecti

on.ID 

A pointer to the element 

in the Intersection table 

which follows the road 

segment. 

Segment_ 

Length 

Double Measured in meters. The 

length of the road 

segment measured from 

center point to center 

point. This should be 

derived from the length 

of the road elements 

which compose it. 

Road_ 

Segment_ 

Class 

Integer RoadSeg

mentClass

.ID 

A pointer to an element 

in the 

RoadSegmentClassLook

up table which contains 

the class of road 

segment which applies 

to this road segment. 

4.2 Ontologies for Autonomous Navigation 

The field of autonomous vehicles has reached a level of 

maturity such that it could greatly benefit from leveraging the 

latest technologies in the area of reasoning over knowledge 

representations and ontologies.2 The use of ontologies and 

automated inference is a natural fit for representing and 

reasoning about world models (the internal knowledge 

representation) for autonomous vehicles. The goal for the 

effort described in this section is to apply ontologies to 

improve the capabilities and performance of on-board route 

 

2

 The 2004 AAAI Spring Symposium series includes a workshop 

on this the topic: “Knowledge Representation and Ontology in 

Autonomous Systems”. See: 

http://www.aaai.org/Symposia/Spring/2004/sssparticipation04.pdf 

191
planning for autonomous vehicles. More specifically, to apply 

ontologies to determine the extent to which a given object is an 

obstacle to a given vehicle in a given situation [3]. 

There are many potential benefits of introducing an ontology 

(or set of ontologies) into an autonomous vehicle’s knowledge 

base. One is the potential for reuse and modularity. For 

example, a general theory of obstacles could apply to a broad 

range of autonomous vehicles. In addition, ontologies provide 

a mechanism to allow for a more centralized approach to 

represent and reason about environmental information. 

Different modules in an autonomous vehicle would query the 

ontology, rather than having the information scattered among 

the modules. This has a corresponding benefit in cheaper and 

more reliable maintenance. Finally, there is the potential for 

increased flexibility of response for the autonomous vehicle. 

Methods that rely on pre-classification of certain kinds of 

terrain in terms of their traversability [4;5] are important, but 

do not support reasoning about objects in a more dynamic 

context. 

I start with the simple scenario illustrated in Figure 3. Our 

vehicle (labeled OV) is in the left lane of a four-lane, two-way, 

undivided highway. An object is detected in our lane. The goal 

is to formulate an optimal route plan that takes into account 

the potential damage from a collision with the object. The 

main role of the ontology component is [initially] to provide 

assessments of collision damage. I will take into account not 

only damage to the vehicle, but also damage to the payload 

and to the object, itself. This information is used to plan a 

route that either goes around the object, or collides with it. 

A number of parameters may be varied in this scenario. These 

include: the type of vehicle being controlled, the speed at 

which the vehicle is traveling, the payload being carried, and 

type of object in the path that may be an obstacle. For 

example, if the object is a newspaper in the middle of the 

roadway, then the ontology component will conclude that no 

damage will occur and the planner will conclude that the best 

course of action is to maintain the current lane (because 

changing lanes always accumulates additional risk over 

maintaining your lane). 

Figure 3. Simple Driving Scenario 

However, if the object were a large cinder block, significant 

damage would be likely and the final route should be quite 

different. The ontology component is equipped with 

knowledge about many kinds of vehicles, objects, and the kind 

of damage that can arise from different collisions. This is used 

to determine the damage that would be caused by a collision. 

The ontology includes objects, vehicles and situations with 

associated inference rules. Specifically, the ontology contains 

different types of objects that one expect to encounter in 

various environments, along with their pertinent characteristics 

and relationships to other objects. Initially the effort is 

focusing on on-road driving, so categories of objects such as 

other vehicles, pedestrians, animals, debris, speed bumps, etc. 

are represented. Each one of the objects that fall under these 

categories has a set of characteristics that describe them and 

help us to understand the damage that may be caused by 

colliding with them. For example, a certain type of debris may 

have a set of dimensions, a weight, a density, a velocity, etc. 

The rules determine the ‘degree of obstacleness’, which is 

ultimately expressed in terms of a cost. 

The ontology and its associated reasoning engine provides as 

an output, a damage assessment in the event of a collision 

between our vehicle and a given object based upon: 

• The type of autonomous vehicle; 

• The type of object being collided with; 

• The closing speed of our vehicle with the object; 

• The integrity of our vehicle, i.e., what damage has 

already occurred to our vehicle, if any. 

Based on this information, the ontology provides a damage 

classification pertaining to: 

• The vehicle’s integrity (initially only assigning 

damage to the bumper, wheels, and overall vehicle, 

but will eventually include other components of the 

vehicle). 

• The obstacle’s integrity 

• The vehicle payload’s integrity 

In order to provide the damage classifications, the 

expressiveness of the ontology must be such that it represents 

concepts such as: 

• The type of vehicle that is being autonomously 

controlled and its pertinent characteristics; 

• The objects that are being encountered in the 

environment and their pertinent characteristics; 

• The payloads that the vehicle is carrying and their 

pertinent characteristics; 

• Severity classifications of damage; 

• Damage types; 

• Terrain information (initially fixed as paved roads); 

• Collisions (e.g., a certain type of vehicle with a 

certain type of object). 

For the initial work, the levels of collision damage shown in 

Table 3 are assumed. 

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Table 3: Levels of Collision Damage 

Vehicle Object Payload 

None No damage to 

vehicle 

No damage 

to object 

No damage 

to payload 

Minor Damage to 

vehicle will not 

affect vehicle 

performance 

Damage to 

object will 

not affect 

object overall 

integrity 

Damage to 

vehicle will 

not affect 

payload 

Moderate Moderate 

probability of 

vehicle damage, 

maintenance 

required 

Damage to 

object will 

affect object 

integrity, but 

will not 

result in 

object 

destruction 

Moderate 

probability 

of payload 

loss 

Severe Major loss of 

functionality/ 

integrity of 

vehicle likely 

Major 

destruction 

of object 

Major 

payload loss 

Catastrophic Vehicle loss Object 

destruction 

Payload loss 

There are many approaches that could be used to estimate the 

actual collision damage. These include: 

• Numerical simulation tools which model the physics 

of weight, materials, shapes, density, momentum etc. 

to compute impact damage; 

• Probabilistic models;

• Fuzzy logic;

• Symbolic logic.

Not one of these techniques is likely to be adequate in all 

circumstances. The current work focuses on the symbolic logic 

approach. The hypothesis is that even when logic-based 

inference is not sufficient, the core ontology of objects and 

characteristics will remain useful as a conceptualization and 

vocabulary for expressing rules and procedures for estimating 

damage. 

For the initial experiments, a small ontology was constructed 

using OilEd [6]. Using a description logic [7] tool has two 

advantages for us. First, the classifier detects logical errors in 

the ontology, which greatly increases confidence that the 

ontology is correct. Second, it is very fast at doing inference. 

This is important because the planner needs to query the 

ontology component up to a few hundred times a second to get 

damage estimates for the many nodes being explored in the 

search space. 

A class called Situation was defined which has various 

characteristics or attributes, each modeled by functional 

relations with Situation as the domain. The key characteristics 

of a Situation that will determine the damage classification are 

the vehicle, the payload and the object with which the vehicle 

may collide. These functional relations are called hasVehicle, 

hasPayload, and hasPotentialObstacle, respectively. Attributes 

were also used to define the damage categories in Table 3. For 

example, the class VehicleIntegrityMinor is defined to be the 

class of all Situations such that the value of the functional 

relation hasVehicleIntegrity attribute is Minor. 

A simple ontology of physical objects was constructed that 

including various types of vehicles and other objects such as 

bricks, newspapers etc. that may be in the vehicle’s 

environment. These objects have characteristics such as 

weight, speed, density, etc. that are important in determining 

the damage category. Initially, some qualitative categories for 

measuring these characteristics were created, such as low, 

medium and high for weight, or density. 

Finally some axioms were created which specify how to 

classify a given situation in terms of the categories in Table 3. 

Here is a simple example: 

A Situation such that 

• The value of the hasPotentialObstacle relation is 

restricted to be of type SmallDenseObject. 

& 

• The value of the hasVehicle relation is restricted to 

be of type Car

is a subclass of VehicleIntegrityModerate. 

Some fictitious situations were created to test these axioms. 

For example, the situation whose hasPotentialObstacle 

relation is a brick, and whose hasVehicle relation is a Toyota 

Corolla will be classified by this rule under 

VehicleIntegrityModerate. This is inferable because a brick is 

a SmallDenseObject (by virtue of its weight and size), and a 

Toyota Corolla is a subclass of Car. 

This Autonomous Navigation Ontology is represented in 

description logic (near the right side of the formalization figure 

shown in Section 2). The reason why a more formal 

representation was chosen was because the ontology was 

developed to allow reasoning, which requires that the underlying 

representation be more formal. The effort clearly falls into the 

“Ontology for Reasoning” section described in Section 3. Full 

first order logic could have been chosen in this effort, but it was 

felt that it was overkill for the fairly simple examples that were 

anticipated. 

4.3 The Process Specification Language 

The Process Specification Language (PSL) [8] is addressing the 

software interoperability issue by creating a neutral, standard 

language for process specification to serve as an interlingua to 

integrate multiple process-related applications throughout the 

manufacturing life cycle. This interchange language is unique due 

to the formal semantic definitions (the ontology) that underlie the 

language. Because of these explicit and unambiguous definitions, 

information exchange can be achieved without relying on hidden 

assumptions or subjective mappings. 

193
Existing approaches to process modeling lack an adequate 

specification of the semantics of the process terminology, which 

leads to inconsistent interpretations and uses of the information. 

Analysis is hindered because models tend to be unique to their 

applications and are rarely reused. Obstacles to interoperability 

arise from the fact that the legacy systems that support the 

functions in many enterprises were created independently, and do 

not share the same semantics for the terminology of their process 

models. 

For example, consider Figure 4 in which two existing process 

planning applications are attempting to exchange data. Intuitively 

the applications can share concepts; for example, both material in 

Application A and workpiece in Application B correspond to a 

common concept of work-in-progress. However, without explicit 

definitions for the terms, it is difficult to see how concepts in each 

application correspond to each other. Both Application A and B 

have the term resource, but in each application this term has a 

different meaning. Simply sharing terminology is insufficient to 

support interoperability -- the applications must share their 

semantics. 

Figure 4: The Need For Semantics 

A rigorous foundation for process design, analysis, and execution 

therefore requires a formal specification of the semantics of 

process models. One approach to generating this specification is 

through the use of ontologies. A major goal of PSL is to reduce 

the number of translators to O(n) for n different ontologies, since 

it would only require translators from a native ontology into the 

interchange ontology. 

Within this work, the term “ontology” refers to a set of sentences 

in first-order logic, comprising a set of foundational theories and 

sets of definitions written using the foundational theories. In 

providing such an ontology, one must specify three notions: 

• Language 

• Model theory 

• Proof theory (axioms and definitions) 

A language is a set of symbols (lexicon) and a specification of 

how these symbols can be combined to make well-formed 

formulae (grammar/syntax). The lexicon consists of logical 

symbols (such as connectives, variables, and quantifiers) and nonlogical symbols. For PSL, the non-logical part of the lexicon 

consists of expressions (constants, function symbols, and 

predicates) that refer to everything needed to describe processes. 

The underlying language used for PSL is KIF[9] (Knowledge 

Interchange Format). Briefly stated, KIF is a formal language 

developed for the exchange of knowledge among disparate 

computer programs. KIF provides the level of rigor necessary to 

define concepts in the ontology unambiguously, a necessary 

characteristic to exchange manufacturing process information 

using the PSL Ontology. 

The primary component of PSL is its terminology for classes of 

processes and relations for processes and resources, along with 

definitions of these classes and relations. Such a lexicon of 

terminology along with some specification of the meaning of 

terms in the lexicon constitutes what this effort is calling an 

ontology. 

The model theory of PSL provides a rigorous mathematical 

characterization of the semantics of the terminology of PSL. The 

objective is to identify each term with an element of some 

mathematical structure, such as a set or a set with additional 

structure (e.g. a complete partial order); the underlying theory of 

the mathematical structure then becomes available as a basis for 

reasoning about the terms of the language and their relationships. 

The proof theory of PSL provides axioms for the interpretation of 

terms in the ontology. It is useful to distinguish two types of 

sentences in this set of axioms: core theories and definitions. A 

core theory is a set of distinguished predicates, function symbols, 

and individual constants, together with some axiomatization. 

Distinguished predicates are those for which there are no 

definitions; the intended interpretations of these predicates are 

defined using the axioms in the core theories. For these terms, one 

needs to describe the set of models corresponding to the intuitions 

that one has for them. Axioms are then written that are sound and 

complete with respect to the set of models. That is, every 

interpretation that is consistent with the axioms is a model in the 

set, and any model in the set is an interpretation consistent with 

the axioms. These axioms constitute the foundational theories of 

the ontology. The set of models form the semantics (or model 

theory) of the ontology. 

All other terms in the ontology are given definitions using the set 

of primitive terms. These definitions are known as conservative 

definitions since they do not add to the expressive power of the 

core theories, that is, anything that can be deduced with the 

definitions, can be deduced using the core theories alone. All 

definitions in an ontology are specified using the core theories; 

any terminology that does not have a definition is axiomatized in 

some core theory. Since all other terms are defined using these 

primitives, the set of models for them can be defined using the 

models of the core theories for the primitives. One can therefore 

assign semantics to the definitions using the classes of models that 

have already been specified for the core theories. 

The challenge is that some framework is needed for making 

explicit the meaning of the terminology for many ontologies that 

reside only in people's heads. Any ideas that are implicit are a 

possible source of ambiguity and confusion. For PSL, the model 

theory provides a rigorous mathematical characterization of 

process information and the axioms give precise expression to the 

basic logical properties of that information in the PSL language. 

So when one speaks about semantics for PSL, it is in reference to 

194
the axiomatization of core theories and definitions for the PSL 

terminology. 

The focus of the ontology is not only on the terms, but also on 

their definitions. An infinite set of terms can be included in the 

ontology, but they can only be shared if everyone agrees on their 

definitions. It is the definitions that are being shared, not simply 

the terms. A simple definition with the PSL ontology is shown 

below: 

Definition An activity is-occurring-at a timepoint p if and only if 

p is betweenEq the activity's begin and end points. 

(defrelation is-occurring-at (?a ?p) := 

 (exists (?occ) 

 (and (occurrence ?occ ?a) 

(betweenEq (beginof ?occ) ?p (endof ?occ))))) 

A simple axiom within the PSL ontology is shown below: 

Axiom. An object can participate in an activity only at those 

timepoints at which both the object exists and the activity is 

occurring. 

(forall (?x ?a ?t) 

 (=> (participates-in ?x ?a ?t) 

(and (exists-at ?x ?t) 

(is-occurring-at ?a ?t)))) 

This Process Specification Language is represented in full first 

order logic (all the way to the right side of the formalization 

figure shown in Section 2). The reason for this is two-fold: 

1. Precise semantics are needed to ensure that complete 

and unambiguous information exchange occurs between 

two applications, 

2. Reasoning must be performed over the concepts in the 

ontology to ensure that mappings between the 

applications ontology and PSL are complete and 

correct. 

The effort clearly falls into the “Ontology as an Exchange 

Language” section described in Section 3. 

5. CONCLUSION 

In this paper, different levels of ontology formalization are 

discussed and an overview of the ways in which ontologies have 

been used in practice over the past couple decades is described. 

Three examples are also provided of very different ontologies that 

have been developed to solve real-world problems in the 

autonomous vehicle and manufacturing systems integration 

domains. It is also explained why the formalisms that were used 

were the most appropriate for their intended purpose. 

There are many other ontology efforts which could have been 

used as examples, including an ontology for searching products 

on private company’s web site, an ontology for classifying robot 

capabilities to allow a first responder to find the best robot for a 

disaster site, and an ontology that was developed to classify 

autonomous vehicle behaviors so that the right behavior can be 

chosen when confronted with specific environmental conditions. 

The three that were chosen were done so because they provide a 

good spectrum of the types of formalism that can be used when 

developing an ontology. If the reader is interested in hearing 

about these efforts, please don’t hesitate to contact the author. 

6. REFERENCES 

 [1] S. Brooks and R. Greenway, "Using STEP to integrate 

design features with manufacturing features," in 

Computers in Engineering Conference New York, NY: 

1995, pp. 579-586. 

 [2] C. Schlenoff, S. Balakirsky, T. Barbera, C. Scrapper, J. 

Ajot, E. Hui, and M. Paredes, "The NIST Road Network 

Database: Version 1.0," National Institute of Standards 

and Technology (NIST) Internal Report 7136,2004. 

 [3] C. Schlenoff, S. Balakirsky, M. Uschold, R. Provine, and 

S. Smith, "Using Ontologies to Aid in Navigation 

Planning in Autonomous Vehicles," Knowledge 

Engineering Review, vol. 18, no. 3, pp. 243-255, 2004. 

 [4] J. J. Donlon and K. D. Forbus, "Using a Geographic 

Information System for Qualitative Spatial REasoning 

about Trafficability," in Proceedings of the Qualitative 

Reasoning Workshop Loch Awe, Scotland: 2003. 

 [5] R. M. Malyyankar, "Creating a Navigation Ontology," in 

Proceedings of the Workshop on Ontology Management, 

AAA!-99 Orlando, FL: 1999. 

 [6] S. Bechhofer, I. Horrocks, C. Goble, and R. Stevens, 

"OilEd: a reason-able ontology for the semantic web," in 

Proc. of the Joint German Austrian Conference on AI, 

number 2174 in Lecture Notes In Artificial Intelligence

Springer-Verlag, 2001, pp. 396-408. 

 [7] F. Baader, D. McGuinness, D. Nardi, and F. PatelSchnedier, Description Logic Handbook: Theory, 

Implementation and Application Cambridge University 

Press, 2002. 

 [8] C. Schlenoff, M. Gruninger, F. Tissot, J. Valois, J. Lubell, 

and J. Lee, "The Process Specification Language (PSL) 

Overview and Version 1.0 Specification," in NISTIR 6459, 

National Institute of Standards and Technology 2000. 

 [9] M. Genesereth and R. Fikes, "Knowledge Interchange 

Format," in Stanford Logic Report Logic-92-1 Stanford 

University: 1992. 

195
Universal Core Semantic Layer: A Roadmap to Semantic 

Interoperability

Lowell Vizenor 

National Center for Ontological Research 

University at Buffalo 

701 Ellicott Street, Buffalo NY, 14214 

lowell.vizenor@ctg.com 

Barry Smith 

National Center for Ontological Research 

University at Buffalo 

701 Ellicott Street, Buffalo NY, 14214 

phismith@buffalo.edu 

ABSTRACT

The Universal Core (UCore) is a central element of the US 

National Information Sharing Strategy that is supported by four 

Federal Government Departments (Defense, Energy, Justice, 

Homeland), by the Intelligence Community, and by a number of 

other national and international institutions. The goal of the 

UCore initiative is to foster information sharing by means of an 

XML schema providing consensus representations for four groups 

of universally understood terms under the headings who, what, 

when, and where. We here describe a project to create an 

ontology-based supporting layer for UCore, entitled ‘Universal 

Core Semantic Layer’ (UCore SL), and show how UCore SL is 

being used to further UCore’s information sharing goals. 

Categories and Subject Descriptors

D.3.1 [Formal Definitions and Theory]: Semantics 

General Terms

Design, Reliability, Standardization, Languages, Verification. 

Keywords

Ontology, Data Integration, Semantic Technology, OWL DL, 

Universal Core. 

1. INTRODUCTION 

The use of intelligent agents to assist the warfighter in obtaining 

accurate and relevant information about the battlespace and 

thereby reduce the overall impact of the fog a war is a vision that 

has yet to be fully realized. If intelligent agents are to scan a sea 

of information (in real time) to prevent mistakes, execute rules, 

discover anomalies and improve decisions, then it will be 

necessary to build logically and ontologically sound information 

artifacts that can truly support these next-generation goals. In this 

paper, we discuss a project to create an ontology-based supporting 

layer to further information sharing capabilities and act as a 

foundation to future applications that place actionable intelligence 

in the hands of commanders and intelligence analysts. 

1.1 The Universal Core 

The Universal Core (UCore) [1] is a US Federal Government 

information sharing initiative that is supported by the US 

Departments of Defense, Energy, Justice, and Homeland Security, 

by the Intelligence Community, and by a large number of other 

national and international agencies. UCore supports the principles 

of the Department of Defense (DoD) and Intelligence Community 

(IC) Data Strategies by defining a small set of common data 

elements that are implemented in a lightweight information 

exchange schema that is shared across multiple agencies. 

The prime focus of the UCore initiative is messaging. UCore is 

designed to promote information sharing across multiple message 

domains by means of a simple XML message format built on a 

taxonomical structure comprising four groups of terms under the 

headings who, what, when, and where. Table 1, below, represents 

the taxonomy as released in UCore Version 2.0, which is the 

version upon which we focus in what follows. Table 2 represents 

the relations contained within the UCore 2.0 XML Schema. 

UCore works by requiring message-creators to construct for each 

message a digest, a summary built out of a restricted vocabulary 

of UCore terms, and to link elements from the message payload to 

this digest. Developers of information systems are encouraged to 

use these terms wherever practical in order to realize the goal of 

facilitating automated sharing of information within and across 

agencies. To reap maximal benefit from its messaging resources, 

participants in the UCore initiative offer validation processes and 

tools intended to promote machine understanding of message 

content, and thereby prospectively enabling multiple different 

types of information retrieval, reasoning and consistency 

checking. 

The UCore taxonomy consists of terms (such as ‘Person’ or 

‘Organization’) which are universally understood in the sense that 

they require no domain-specific expertise for their understanding. 

The taxonomy can thereby be shared by many different types of 

users, and thus provides the opportunity for interoperability over 

many different sorts of domain-specific exchanges. As M. 

Daconta expresses it: 

if I have a UCore-wrapped National Information 

Exchange Model [NIEM] message from Immigration and 

Customs Enforcement about illegal immigrants wounded 

during criminal activity and I have a UCore-wrapped 

Health and Human Service Department message on 

visitors to emergency rooms, I have enabled immediate 

cross-domain search. ... UCore is a process of extracting 

cross-domain commonality from your message flows, 

thereby massively broadening the possible adoption and 

use of your shared information. In information sharing, 

adoption by consumers is the key value metric [2].

196
Table 1. UCore 2.0 Taxonomy 

uc:Entity uc:Event 

uc:Cargo uc:LivingThing uc:AlertEvent uc:LawEnforcementEvent 

uc:CollectionOfThings uc:Animal uc:CommunicationEvent uc:MigrationEvent 

uc:CyberAgent uc:Person uc:CriminalEvent uc:MilitaryEvent 

uc:Document uc:MicroOrganism uc:CyberSpaceEvent uc:NaturalEvent 

uc:Environment uc:Plant uc:DisasterEvent uc:ObservationEvent 

uc:Equipment uc:Organization uc:EconomicEvent uc:PlannedEvent 

uc:Facility uc:PoliticalEntity uc:EmergencyEvent uc:PoliticalEvent 

uc:FinancialInstrument uc:Sensor uc:EnvironmentalEvent uc:PublicHealthEvent 

uc:GeographicFeature uc:Vehicle uc:EvacuationEvent uc:SecurityEvent 

uc:GroupOfOrganizations uc:Aircraft uc:ExerciseEvent uc:SocialEvent 

uc:GroupOfPersons uc:GroundVehicle uc:FinancialEvent uc:TerroristEvent 

uc:InformationSource uc:Spacecraft uc:HazardousEvent uc:TransportationEvent 

uc:Infrastructure uc:Watercraft 

uc:HumanitarianAssistanceEven

t uc:WeatherEvent 

uc:InfrastructureEvent 

1.2 UCore and the Army Net-Centric Data 

Strategy 

UCore is designed not only to support messaging and the retrieval 

and analysis of message content. It is built also in such a way as 

to support interoperability of information systems of a variety of 

different types. That is, it is built in such a way as to serve as a 

basis for the construction of more inclusive artifacts that will 

serve as interoperability corridors tailored to the needs of groups 

of specialist users. 

Against this background, the Army Net-Centric Data Strategy 

Center of Excellence is supporting experiments to use UCore as 

the basis for fostering the interoperability of information artifacts 

created by Communities of Interest (COIs) in the Command and 

Control (C2) and other domains. The idea is that such COIs will 

create new vocabularies tailored to meet their unique 

requirements and thus go beyond the narrow set of UCore terms. 

UCore thereby serves as a vehicle which will maintain a joint 

community perspective by providing an evolving resource of 

common terms with shared definitions and associated logical 

resources. The long term goal is that these common terms will 

create a common reference platform allowing data from diverse 

COIs to be understood by systems across the DoD and IC. This 

approach is designed to allow a level of information sharing 

between unanticipated users and systems and to reduce the time 

and cost to implement information sharing across the DoD and IC 

enterprise, while allowing COIs to focus on their community 

specific needs. 

To achieve these ends, UCore will need to accommodate new 

requirements from its partner agencies, while at the same time 

remaining faithful to its key principle of providing a small set of 

essential terms and relations. The latter will however need to be 

expanded to some degree in order to include those universally 

understandable terms not so far included. UCore has accordingly 

established a Configuration Control Board (CCB), whose role is 

to manage change and versioning in such a way that UCore 

artifacts remain useable throughout the change lifecycle. 

2. UNIVERSAL CORE SEMANTIC LAYER 

We describe in what follows an initiative on the part of the Army 

Net-Centric Data Strategy (ANCDS) [3] Center of Excellence to 

create an analogous logical infrastructure in support of the UCore 

endeavor focusing especially on the application of UCore in the 

creation of domain and COI-specific extensions. The role of 

logical core is played in this case by the UCore Semantic Layer 

(UCore SL), version 1.0 which was released on June 15, 2009. 

UCore SL is the product of work by researchers from the National 

Center for Ontological Research (NCOR) in Buffalo, New York, 

with considerable input from the intelligence community under 

the sponsorship of the Office of the Director of National 

Intelligence (ODNI) CIO. 

Where UCore 2.0 is an XML artifact in which definitions are 

logically unarticulated, UCore SL is an OWL DL artifact based 

on logically articulated definitions. UCore SL is designed to work 

behind the scenes in UCore 2.0 application environments as a 

logical supplement to the UCore messaging standard. It thus 

supports UCore’s goals by providing additional resources on the 

side of logical structure. UCore SL offers the entirety of the 

content UCore-2.0, both taxonomy and relations, in a form which 

satisfies the needs of users needing enhanced logical resources. It 

provides for logical decomposition of terms and definitions, 

genuine reasoning based on the logical content of these 

definitions, and thereby also enhanced support for the creation of 

consistent extension modules. UCore SL is being used as a tool 

197
for validation of UCore itself and for the generation of proposals 

for changes and additions both to UCore 2.0 and its extensions. It 

also serves accessibility of UCore message content to W3Cstandard OWL-DL technology. 

Where UCore 2.0 provides through its XML framework and 

controlled vocabulary for syntactic interoperability, UCore SL 

offers a logically organized vocabulary of terms, relations and 

definitions which can serve the semantic interoperability of 

UCore message content. 

UCore SL is already helping to provide semantic interoperability 

in the results of work sponsored by the ANCDS COE on 

Biometrics and C2 Ontologies carried out by NCOR researchers 

in Buffalo. We are currently evaluating the ability of UCore SL to 

provide more powerful reasoning and message-checking 

capabilities as compared with UCore 2.0 without the added 

logical support. We are also testing the capacities of UCore SL to 

provide facilities for enhanced data sharing by helping to ensure 

that extension modules created by different domains or COIs, for 

example within the C2 framework, are created in a logically 

consistent fashion on the basis of logically sound and easily 

understood definitions. 

3. UCORE SL ENHANCHMENTS TO 

UCORE 2.0 

3.1 Mapping UCore SL to UCore 2.0 

The UCore SL Taxonomy (version 1.0) consists of 144 terms 

organized into an is-a (subclass) hierarchy, of which the top two 

terms are sl:Entity and sl:Event (see table below), corresponding 

roughly to the continuant and occurrent terms standardly used in 

upper-level ontologies such as BFO. The UCore SL taxonomy 

comprehends the entirety of the UCore 2.0 taxonomy in the sense 

that each one of the 55 terms in the UCore 2.0 taxonomy is 

mapped to a corresponding UCore SL term. As a result, it is 

possible to translate UCore 2.0 into UCore SL in order to take 

advantage of the latter’s enhanced logical resources. 

UCore SL contains 16 relations, with definitions relying on those 

provided in BFO [4]. 12 UCore SL relations have counterparts in 

UCore 2.0. In keeping with the W3C recommended best practice 

for reuse of OWL resources, ucore:DistinctFrom and 

ucore:SameAs are not mapped to corresponding UCore SL 

relations but rather to owl:differentFrom and owl:sameAs 

respectively. Four other UCore SL relations taken over from BFO 

do not correspond to any UCore 2.0 relations but are included in 

order to ensure logical decomposability of definitions. These are: 

inheres_in, part_of, participates_in and agent_in. 

We use the OWL import mechanism to import the UCore 2.0 

Taxonomy into the UCore SL taxonomy but not conversely. The 

import mechanism is uni-directional, which means that the UCore 

SL ontology contains the content of the UCore 2.0 Taxonomy but 

not vice versa. In other words, the UCore 2.0 Taxonomy can be 

used without any reference to UCore SL. 

The formal mechanism used to map a UCore SL term to a UCore 

2.0 term is the OWL property, owl:equivalentClass [5]. For 

example, UCore SL asserts that sl:Group is equivalent to 

uc:CollectionOfThings.(Terms prefixed with ‘sl:’ are UCore SL 

terms and terms prefixed with ‘uc:’ are UCore 2.0 terms.) In other 

words, every instance of sl:Group is an instance of 

uc:CollectionOfThings and vice versa. These equivalence 

statements are the logical crosswalk between UCore SL and 

UCore 2.0 that make it possible to enhance UCore 2.0 with the 

logical resources of UCore SL. 

Table 2. UCore 2.0 and UCore SL Relations 

UCore 2.0 

Relations 

UCore SL 

Relations 

rdfs:subClassOf rdfs:subClassOf 

ucore:AffiliatedWith slr:affiliated_with 

ucore:CauseOf slr:cause_of 

ucore:Controls slr:controls 

ucore:DistinctFrom owl:differentFrom 

ucore:EmployedBy slr:employed_by 

ucore:HasDestinationOf slr:has_destination_of 

ucore:HasFamilialRelationT

o slr:has_familial_relation_to 

ucore:HasOriginOf slr:has_origin_of 

ucore:InvolvedIn slr:involved_in 

ucore:LocatedAt slr:located_at 

ucore:OccursAt slr:occurs_at 

ucore:SameAs owl:sameAs 

ucore:SubordinateTo slr:subordinate_to 

ucore:WorksAt slr:works_at 

 slr:agent_in 

 slr:inheres_in 

 slr:part_of 

slr:participates_in 

The UCore 2.0 Taxonomy is relatively flat and semantically 

weak. For example, it contains no disjointness axioms, something 

which is essential to indentifying inconsistencies in an ontology. 

Two classes are declared to be disjoint if they share no instances 

in common. For example, Person and MilitaryEvent are in theory 

disjoint classes. That said, the UCore 2.0 Taxonomy lacks the 

logical resources to detect a case where something is asserted to 

be both an instance of Person and MiltaryEvent. The only way to 

detect this error would be through manual review. The problem is 

that information artifacts are large and complex and no amount of 

manual review will catch all the errors. This is why building 

systems that support automated reasoning are so important. 

In UCore SL the following equivalence statements are made: 

sl:Group ≡ uc:CollectionOfThings 

sl:GroupOfOrganizations ≡ uc:GroupOfOrganizations 

sl:GroupOfPersons ≡ uc:GroupOfPersons. 

Also, sl:GroupOfOrganizations and sl:GroupOfPersons are 

subclasses of sl:Group and are disjoint with one another. A few 

consequences of this are: 1) every instance of sl:GroupOfPersons 

is an instance of sl:Group, 2) every instance of 

sl:GroupOfOrganizations is an instance of sl:Group, and 3) no 

instance of sl:GroupOfPersons is an instance of 

198
sl:GroupOfOrganizations. On the UCore 2.0 side of things, uc:CollectionOfThings, uc:GroupOfOrganizations and 

Table 3. UCore SL Taxonomy 

sl:Entity sl:Event 

sl:InformationContentEntity sl:Infrastructure sl:Act 

sl:Analysis sl:Materiel sl:ActOfCommunication 

sl:Objective sl:Consumable sl:ActOfHumanitarianAssistance 

sl:ObjectiveSpecification sl:Organization sl:ActOfObservation 

sl:Opinion sl:Government sl:CriminalAct 

sl:Plan sl:PhysicalObject sl:ImmigrationEvent 

sl:TaskSpecification sl:LivingThing sl:LawEnforcementEvent 

sl:PhysicalEntity sl:Animal sl:TerroristAct 

sl:Agent sl:Person sl:CyberSpaceEvent 

sl:Artifact sl:InfectiousOrganism sl:Danger 

sl:ArtificialAgent sl:MicroOrganism sl:Disaster 

sl:Equipment sl:Plant sl:EconomicEvent 

sl:Facility sl:Vehicle sl:FinancialEvent 

sl:Sensor sl:SpaceRegion sl:EnvironmentalEvent 

sl:Environment sl:Property sl:Epidemic 

sl:GeographicFeature sl:Capability sl:EvacuationEvent 

sl:GeospatialBoundary sl:PhysicalProperty sl:HazardousEvent 

sl:GeospatialRegion sl:AtmosphericProperty sl:Incident 

sl:AdministrativeDivisio

n sl:GeographicProperty sl:InfrastructureEvent 

sl:ControlFeature sl:OceanographicProperty sl:MigrationEvent 

sl:CoverageFeature sl:SpaceEnvironmentProperty sl:MilitaryEvent 

sl:GeopoliticalEntity sl:Role sl:MissileLaunchEvent 

sl:Route sl:AffiliationRole sl:NaturalEvent 

sl:Track sl:AgentRole sl:AtmosphericEvent 

sl:Group sl:CargoRole sl:GeographicEvent 

sl:GroupOfOrganizations sl:ControlFeatureRole sl:NaturalEvent (cont.) 

sl:GroupOfPersons sl:ControlledSubstanceRole sl:OceanographicEvent 

sl:InformationBearingEntity sl:InformationSourceRole sl:SpaceEnvironmentEvent 

sl:Database sl:MaterielRole sl:PlannedEvent 

sl:Datafile sl:WaypointRole sl:PoliticalEvent 

sl:Document sl:PublicHealthEvent 

sl:Program sl:SecurityEvent 

sl:Website sl:NationalSpecialSecurityEvent 

 sl:SocialEvent 

sl:StructuralCollapse 

 sl:Task 

sl:TransportationEvent 

199
uc:GroupOfPersons are all sibling classes (i.e. no one class is a 

subclass of any of the others classes) and no one class is declared 

to be disjoint with any of the others. So, the fact that something is 

an instance of uc:GroupOfPersons does not entail that it is also an 

instance of uc:Group and 2) the fact that something is an instance 

of both uc:GroupOfPersons and an instance of 

GroupOfOrganizations does not entail a contradiction. 

This situation is improved by the fact that every term in the 

UCore 2.0 Taxonomy is mapped to a corresponding term in 

UCore SL. Once the OWL DL reasoner is run, the hierarchical 

structure and disjointness axioms in UCore SL are transferred 

over to UCore 2.0. Figure 1 shows the derived subclass 

statements. The bi-directional arrows indicate that the two classes 

are equivalent to one another and the uni-directional arrows 

indicate that one class is a subclass of another class. Postinference, uc:GroupOfOrganizations and uc:GroupOfPersons are 

now subclasses of uc:CollectionOfThings. Although not depicted 

in Figure 1, the reasoner would also detect cases where something 

was declared to be both an instance of uc:GroupOfOrganizations 

and uc:GroupOfPersons. 

Figure 1. Inferred Subclass Statements 

3.2 Extensions of UCore 

The purpose of UCore SL is to create a logical infrastructure in 

support of the UCore endeavor focusing especially on the 

application of UCore in the creation of domain and COI-specific 

extensions such as Command and Control (C2), Global Force 

Management and Cyberspace Operations. To this end, UCore SL 

can act an overarching framework that can constrain and guide 

the development of these various domain and COI-specific 

extensions of UCore. 

The mechanism for validating these extensions of UCore is 

similar to the mechanism described above to map the UCore 2.0 

Taxonomy to UCore SL. For example, in order for a C2 ontology 

to extend UCore SL, the C2 ontology would need to 1) import 

UCore SL and 2) subsume (directly or indirectly) every C2 term 

under a UCore SL term. At this point, it is possible to run an 

inference engine to identify inconsistencies between the C2 

ontology and UCore SL. In some cases, it will be necessary to 

validate multiple domain or COI specific ontologies against one 

another. Again, UCore SL can facilitate such efforts by providing 

the logical resources necessary for identifying inconsistencies 

across ontologies. It should be noted, however, that UCore SL is 

still in the early phases of development and in future releases 

more machine-readable definitions (i.e. owl restrictions) will be 

added in addition to a number of constraints that will check for 

bad practices in ontology design (e.g. multiple inheritance, use of 

plurals for class names, classes with only one child, etc.) 

Besides providing support for automated reasoning, another way 

that UCore SL is intended to support extensions of UCore 2.0 is 

through the use of logical definitions. Every UCore SL term is 

defined in terms of necessary and sufficient conditions following 

the Aristotelian schema, which defines each child term ‘A’ in 

terms of its immediate parent ‘B’ together with the differentia ‘C’ 

which determines what it is about the Bs which makes them As 

(as in: a human =def. an animal that is rational). One reason for 

this is that is instills discipline the Taxonomy development 

process. Another reason is that it specifies the intended semantics 

of the term in a human readable format that may serve as a guide 

for application developers who wish to implement this 

information artifact in one form or another. The implementation 

of UCore SL in OWL DL, for instance, is one such case, but there 

are any number of other ontology and data modeling languages in 

which one may need to implement UCore SL. To this end, the 

logical definitions (expressed in controlled way) serve as an 

application neutral expression of the intended semantics. 

3.3 Using UCore SL to Support Reason with 

UCore Messages 

As summarized in [6], we are developing a system which will 

allow software agents to better understand and reason with 

UCore-2.0 messaging content in an approach based once again on 

the logical resources provided by UCore SL. The underlying idea 

is to treat the XML-labels used in UCore 2.0 messages as 

annotations for particulars (for instance individual agents) about 

which these messages contain information. Some particulars are 

referred to in these messages directly (for instance the military 

unit that has been given an order to move from place A to place 

B); others are particulars that must exist for the messages to be 

correctly interpretable by software agents and whose existence 

can thereby be indirectly inferred. To make such inferences 

XML-labels are mapped to ontologies based on UCore SL. 

Depending on the quality of the mappings and the quality of the 

associated ontologies, more and better inferences can be made 

about the portion of reality described in the messages. 

We are working on a method to quantify the quality of these 

mappings and the ontologies in such a way that we can 

demonstrate that one ontology is to be preferred over another or 

that one mapping to an ontology is to be preferred over another 

mapping. By using such quantified measures, we can engineer an 

evolutionary improvement of ontology resources which can be 

used across the entire domain of messaging in areas such as C2, 

where tight integration of messages deriving from disparate 

sources is required. 

3.4 Use of Logical Definitions 

The UCore 2.0 definitions are derived primarily from the Concise 

Oxford English Dictionary (OED), which, while helpful to human 

users, unfortunately only goes part of the way in specifying the 

intended meaning of the terms in a fashion useful to computers. A 

200
further problem with this approach is that there are numerous 

cases where the provided definition is not in agreement with 

UCore’s own is-a hierarchy. An example is uc:Animal: 

A non-human organism which feeds on organic 

matter, has specialized sense organs and nervous 

system, and is able to move about and to respond 

rapidly to stimuli. (Derived from OED) 

Given that uc:Person is a subclass of uc:Animal, this definition 

entails that a uc:Person is a non-human organism. In this case, the 

UCore Configuration Control Board (CCB) has agreed to remove 

“non-human” from the definition of Human. 

Other examples of UCore 2.0 definitions are: 

• uc:GroupOfPersons =def A number of people located, 

gathered, or classed together. (Derived from OED) 

• uc:Organization =def An organized body of people with a 

particular purpose, e.g. a business or government 

department. (Verbatim from OED) 

• uc:PoliticalEntity =def An organized governing body with 

politcal responsibility in a given geographic region. (Derived 

from OED) 

The definition of ‘Organization’ does not make it clear whether or 

not organizations are groups of persons. The definition of 

‘PoliticalEntity’ suggests that it should be a subclass of 

‘Organization’, but this is not reflected in the UCore 2.0 

Taxonomy. 

UCore SL, in contrast, utilizes the structure of the Taxonomy in 

the formulation of its definitions. Examples from UCore SL are: 

1. sl:Government =def. An Organization with political 

responsibility for governing in a specified GeospatialRegion. 

2. sl:Organization =def. An Agent that has (1) members which 

are Agents, (2) one or more Objectives, and (3) 

MemberRoles (and other AffiliateRoles) which are realized 

in the pursuit of the Objective or Objectives 

3. sl:GroupOfPersons =def. A Group that includes only 

Persons. 

The fact that sl:Government is a subclass of sl:Organization is 

reflected in both the definition and the Taxonomy (see table 3). 

The UCore SL team recommended seven improvements to the 

UCore taxonomy: 

1. AlertEvent should be a subclass of 

CommunicationEvent, 

2. WeatherEvent should be a subclass of NaturalEvent, 

3. ExerciseEvent should be a subclass of PlannedEvent, 

4. FinancialEvent should be a subclass of EconomicEvent, 

5. FinancialInstrument should be a subclass of Document, 

6. CyberAgent should be a subclass of Agent. But the 

taxonomy does not contain the term Agent. The 

taxonomy needs to include Agent, 

7. PoliticalEntity should be a subclass of Organization. 

These changes should provide clarity to developers seeking to 

determine which term to use from the taxonomy. The added 

hierarchical structure effectively groups related terms together, 

providing developers with a clearer understanding of the available 

choices. 

4. CONCLUSION 

UCore SL, an ontology-based supporting layer for UCore, is 

designed to work behind the scenes in UCore 2.0 application 

environments as a logical supplement to the UCore messaging 

standard. UCore SL builds upon previous work in the biomedical 

domain on creating consistent extensions on the basis of a 

common core ontology in order to serve interoperability. UCore 

SL provides the logical resources for the UCore initiative to do 

this work. 

UCore SL is currently in the beta phase of development with 

several current and potential users who are testing it in their 

application environments and providing valuable feedback in 

order to help improve future versions of UCore-SL. In order for 

UCore SL to succeed, it is necessary to develop a vital user 

community around UCore SL, one where multiple extension 

ontologies are subjected to rigorous logical analysis and testing, 

linked together in a computable way and used to annotate large 

quantities of data. 

5. ACKNOWLEDGMENTS 

Thanks are due to Deborah Nichols, Randy Dipert, Ron Rudnicki, 

and James Schoening. 

6. REFERENCES 

[1] http://ucore.gov/. 

[2] http://www.gcn.com/Articles/2009/06/15/Reality-Checkcommentary-UCore-info-sharing.aspx. 

[3] Army Net-Centric Data Strategy (ANCDS). 

http://data.army.mil/ 

[4] B. Smith, W. Ceusters, B. Klagges, et al., “Relations in 

biomedical ontologies,” Genome Biology, vol. 6, 2006, R46, 

2005. 

[5] http://www.w3.org/TR/owl-ref/. 

[6] S. Manzoor, W. Ceusters, B. Smith. “Referent Tracking for 

Command & Control Messaging Systems,” submitted to 

Ontology for the Intelligence Community, Fairfax, Virginia, 

October 21-22, 2009.

201
Performance Measures of Agility for Mobile Robots

Alan Bowling and Shih-Chien Teng

Department of Mechanical and Aerospace Engineering

University of Texas at Arlington, USA

bowling@uta.edu, shih.teng@mavs.uta.edu

Categories and Subject Descriptors

C.4 [PERFORMANCE OF SYSTEMS]: Modeling techniques

General Terms

DESIGN, PERFORMANCE

Keywords

Performance measures

1. INTRODUCTION

Agility refers to a system’s ability to change some aspect

of its current situation. For example, consider agile manufacturing which concerns the ability to change a particular

manufacturing setup from one product to another without

much downtime [3, 8, 25]. In aeronautics, several scalar metrics have been proposed for aircraft agility which together

measure an aircraft’s ability to change its speed and direction [21, 1, 2].

In robotics, it is difficult to find a definition of agility or

any scalar metrics for measuring it, although several works

refer to it. This is true for all robots and is especially true

for legged robots. The proposed definitions are often more

implicit than explicit, but all discuss the robot’s ability to

change some aspect of its situation or state. In examining the field of legged robots, it is claimed that the robot’s

agility stems from its mechanical design, control, or both,

as summarized in the following two paragraphs.

Design: Several works claim agility by design, including

[17] because the multi-legged, segmented robot can change

its overall body configuration allowing it to follow the terrain’s contours. An innovative leg design is discussed in [19]

which is attributed to the hexapod robot’s agility discussed

in terms of its speed and its ability to climb obstacles. Similar work discusses the effect of actuation efficiency on agility

in terms a multi-legged robot’s speed and ability to climb

over obstacles [18]. In [16] an actuation scheme that allows a

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biped to perform acrobatic movements, exemplifying agility,

is explored. In [10] an analysis of the robots ability to change

the configuration of its legs was considered an indicator of

agility. A quadruped climbing robot in [24] is claimed to be

agile because of its ability to climb and change direction. A

turning criteria, or direction change, at a fairly high speed

is used in [15] for examining the agility of a wall climbing

robot. The design of a climbing robot in [22] also mentions

agility where the design is evaluated mainly in terms of its

speed. Other design studies discuss several issues, speed being a central one, but it is unclear how agility was quantified

[12, 11]. The work in [14] analyzes acceleration capability as

a measure of agility, similar to what is used herein, except

that a polytope characterization is used.

Control: In [7] agility in a hexapod micro-robot is obtained through fine tuning the control to increase its speed

and ability to change direction. A review of monopod robots

[23] mentions agility only in the abstract, but it has been

claimed that monopods are agile because their hopping motion allows them to change directions at each ground contact and to perform acrobatic movements [20]. A controller

for executing a self-righting motion, which could be considered acrobatic or agile, was developed in [9]. In [13] a

genetic algorithm was used to evolve a controller to execute dynamic maneuvers consisting of high-speed turns and

a running jump.

Nearly all of the works discussed in the “Design” and

“Control” sections above include the word “agile” or “agility”

somewhere in the paper, but only [14] presents a characterization to describe it. Herein the definition of agility proposed in [4] is used which measures a legged robot’s ability

to change its velocity. Acceleration is defined as the rate of

change of velocity, thus the robot’s ability to accelerate itself

can be used to measure agility. Agile robots can change their

velocity abruptly by generating high levels of acceleration.

However, it is often difficult to intuitively understand agility

in terms of acceleration capability. Thus the effort here is to

build a bridge between acceleration capability analysis and

more common measures of motion ability for mobile systems

in order to show the correlation between them. The metric considered is the time to maximum velocity. The study

is performed using an analysis of acceleration capability, referred to as the dynamic capability equations (DCEs), along

with simulation techniques in order to determine values for

these metrics. A hexapod and the tripod gait are used to

illustrate these ideas. This system is chosen because it is

simple to generate joint trajectories resulting in the desired

locomotion. However, there are some interesting challenges

202
x

z

y y

z

z

x

y

x

z

b b

1

2

3

4

5

6

1

2

3

6

5

4

1 2 3

Figure 1: The Hexapod.

involved in actually developing these simulations. These difficulties give one an appreciation for using a performance

metric or characterization, such as the DCEs, to aid in the

design as opposed to evaluating the metrics using a simulation.

2. THE SCENARIO

The goal of this work is to examine the effects of acceleration capability on locomotion. This effect is investigated

in simulation using a model of the hexapod shown in Fig. 1

as a test subject. In these test cases, the hexapod uses the

tripod gait to locomote, shown in Fig. 2. This gait is accomplished by placing the odd numbered feet on the ground

while lifting the even numbered feet in the air. The odd tripod is then swept forward which moves the body forward.

The even tripod is placed on the ground and the odd tripod is lifted off of the ground. The even tripod then sweeps

backward and the cycle is repeated. The same cycle can be

used for walking straight or turning.

The hexapod’s acceleration capability is affected by several different factors including its inertial properties, configuration, ground contact forces, and actuator torque capacity.

In order to examine how these factors affect its locomotion,

it is necessary to simulate the applied forces. This means

that a controller which can generate the tripod gait has to

be developed, and the contact forces have to be applied to

the dynamic model in a manner that is physically meaningful. These developments created some interesting challenges

in assessing how acceleration capability affects the turning

radius and achievement of the robot’s top speed. The next

sections discuss the elements of the simulation in detail.

3. THE SIMULATION

A hybrid dynamic simulation approach was used to deal

with the intermittent contact between the hexapod’s feet

and the ground. The equations of motion for the hexapod

can be expressed as

A(q) q ̈ + b(q ̇ , q) + g(q) = Γ = G

T Υ (1)

START MID-STANCE END

123

4 5 6

Figure 2: The Tripod Gait.

where q ∈ R

24 contains the generalized coordinates, and q ̇

and q ̈ are its time derivatives of velocity and acceleration.

The vectors b(q ̇ , q) ∈ R

24

, g(q) ∈ R

24

, Γ ∈ R

24

, F ∈ R

c

,

and Υ ∈ R

18 contain velocity, gravity, actuator torque, external, and actuator forces. The matrices A(q) ∈ R

24×24

,

G ∈ R

24×18

, Jc ∈ R

24×c

are the mass, actuator transmission,

and contact Jacobian matrices; c is the number of contact

constraints.

3.1 Contact Forces

When a foot touches the ground, no-slip, no rebound,

point contact is assumed. Thus when a foot touches the

ground its velocity is set equal to zero. This can be accomplished in several ways, but the details of what was done here

are given in [5]. The important thing to note is that (1) does

not include contact constraints. Thus they will have to be

included in some manner if (1) is used for purposes other

than numerical integration.

3.2 Actuator Torques

The computed torque method was used to execute the

tripod gait. Since a turning motion is required, the goal is

to avoid calculation of the inverse kinematics associated with

a turn. Thus an operational space approach was followed.

However, the relations in (1) represent an under actuated

system. Since the contact constraints are applied during

the numerical integration process, a control command which

accounts for the effect of the contact constraints is needed.

The constraints are found using a Jacobian of the form

θ =

[

v

T

b ω

T

b v

T

1 ⋅ ⋅ ⋅ v

T

6

]T

= J q ̇ (2)

where vb and ωb are the translational and rotational velocities of the torso and vi are the translational velocities of

the contact point on each foot. Without a loss of generality,

assume that the odd tripod is in contact with the ground:

θf =

[

v

T

b ω

T

b v

T

2 v

T

4 v

T

6

]T

= Jf q ̇ (3)

0 = θc =

[

v

T

1 v

T

3 v

T

5

]T

= Jc q ̇ = Q R ETq ̇ (4)

= Q [ Rc Rf ] E


where Q ∈ R

24×24 and R ∈ R24×c

are obtained from the QR

decomposition of Jc, and Rc ∈ R

c×c

is upper triangular and

full rank. Using (5) yields

q ̇ = E

[

−R

−1

c Rf

I

]

q ̇ f = P q ̇ f (7)

q ̈ = P q ̈f + E

[

−R

−1

c Q

T

J ̇

c q ̇

0

]

= Pq ̈f + P ̇ J ̇

cq ̇ (8)

Since the feet stick to the ground and do not rebound

we know that θc = θ ̇

c = 0 so θf is used to define the

operational space:

θf = Jf q ̇ = JfP q ̇ f (9)

θ ̇

f = Jf q ̈ + J ̇fq ̇ = JfPq ̈f + JfP ̇ J ̇

cq ̇ + J ̇

fq ̇ . (10)

All of the velocity relationships above have a dual involving

static forces which can be expressed as

(

P

T P

)−1

P q ̇ = q ̇ f −→ Γ = P

T

(

P

T P

)−T

Γf (11)

and from (1)

(

GGT

)−1

G Γ = Υ . (12)

The relations in (7)-(12) are used to transform the computed torque method into operational space as

θ ̇

d + Kv (θd − θf ) + Kp (xd − x) = Γ

∗

f (13)

P

T

(

AP ((JfP)

−1

(

Γ

∗

f − JfP ̇ J ̇

cq ̇ − J ̇

fq ̇

)) + b + g)

= Γf

(14)

(

GGT

)−1

GP

(

P

T P

)−T

Γf = Υ (15)

where θ ̇

d, θd, and xd represent the desired trajectories for

the torso and the feet; see (3). The terms Kp and Kv are

diagonal gain matrices. Note that x contains a set of Euler

angles to represent the arbitrary orientation of the torso. It

is possible to find other forms of (15), but the one given is

less likely to involve singular matrix products.

The different postures in the tripod gait are considered as

repeating via points for the desired trajectory [6]. The via

points are connected using cubic splines with zero velocity

at the start and end of each segment. The turning motion is

obtained by specifying the desired orientation of the torso.

3.3 Performance Analysis

The DCEs are used to analyze the robot’s performance.

A recent paper gave an extensive discussion of the DCEs

for legged robots in contact with the environment [4]. The

resulting characterization describes how well a legged robot

can use ground contact to accelerate itself. The performance

analysis is too involved to fully present here so only the

highlights will be given.

The goal is to determine to compare the torque required

to achieve desired motions, with the amount of torque available. In these simulations the available torque is determined

by the motors and has the form of a set of inequalities

Υlo ≤ Υ ≤ Υhi . (16)

In this work, each element in Υlo and Υhi is a constant.

After the contact constraints are applied to the equations of

motion in (1), they are substituted into (16) to obtain the

governing equations for the performance analysis. Further

information on this analysis is provided in [4].

The analysis is carried out in terms of balanced quantities

defined as

v ̇

T

b v ̇ b = ∣v ̇b∣

2 ω ̇T

b ω ̇ b = ∣ω ̇ b∣

2

(17)

where ∣v ̇b∣ is the balanced translational acceleration of the

torso. The leftmost relation in (17) represents a sphere with

a radius of ∣v ̇b∣. Note the difference between the radius ∣v ̇b∣

and the magnitude of the vector v ̇ b which is ∥v ̇ b∥. The idea

is to find the maximum values of the balanced quantities

subject to the (1) and (16). For a given configuration and

contact state, an analytic solution to this problem exists,

and it is the result of the DCE analysis referred to as the

dynamic capability hypersurface.

0 10 20 30 40 50 60 70

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

I

II

III

∣ω ̇ b∣ rad/s2

∣  ̇vb∣ m/s

2

Figure 3: Performance Curve.

A curve from the hypersurface is shown in Fig. 3. It shows

the combinations of translational and rotational acceleration

of the torso that is guaranteed to be achievable in and about

any direction.

A key feature of this analysis is that it can provide the

acceleration capability in a particular direction, by defining

balanced quantities such as

v ̇

2

b1 = ∣v ̇b1

∣

2

. (18)

This information is used in the example of Sec. 4.

4. TIME TO VELOCITY

In the automobile industry it is common to quote the time

it takes to accelerate from 0mph to 60mph as a measure of

performance. This type of measure is also examined for a

hexapod with a slightly different interpretation. These test

case were run for a set of trajectories which implement the

tripod gait. The trajectories for the different phases of the

gait were encoded as cubic splines with a start and final time

to completion. Therefore the amount of time to complete a

cycle of the tripod gait was specified.

A fast and a
0 1 2 3 4 5 6 7

z − Distance (m)

t=1s

t=1s

Figure 4: Low Acceleration Capability Hexapods in a 2.5 Second Race. Motion capture is taken at 0.5s

intervals. Both hexapods have a maximum torque of 500Nm. The top and bottom hexapods use the fast and

slow trajectories. The top hexapod’s actuators saturate, limiting its top speed. The time label at t = 1s shows

the relative position of the hexapods. The length of the arrow extending from each hexapod indicates its

acceleration capability in the forward direction; both hexapods have about the same acceleration capability.

0 0.5 1 1.5 2 2.5

−5

0

5

10

15

20

25

30

Time (s)

Velocity (m/s)

0 0.5 1 1.5 2 2.5

−5

0

5

10

15

20

25

30

Time (s)

Velocity (m/s)

(a) (b)

Figure 5: Velocity in the Forward Direction with

Peak Torque 500Nm. (a) Hexapod executing the

slow trajectories, and (b) the fast trajectories.

0 0.5 1 1.5 2 2.5

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75

1.8

1.85 x 104

Time (s)

Acceleration Capability(m/s

2

)

0 0.5 1 1.5 2 2.5

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75

1.8

1.85 x 104

Time (s)

Acceleration Capability(m/s

2

)

(a) (b)

Figure 6: Acceleration Capability in the Forward

Direction with Peak Torque 500Nm. (a) Hexapod

executing the slow trajectories, and (b) the fast trajectories.

were 500Nm and 1000Nm. If the controller commands more

torque than is available, the actuator is clipped to the maximum torque, as would occur in the real world. This clipping prevents the robot from asking for an infinite amount of

torque, but also limits its acceleration capability and therefore its speed.

This is illustrated in Fig. 4 where both hexapods have

actuators with a peak torque of 500Nm, but the top hexapod is commanded to perform the fast trajectories while the

lower one performs the slow ones; the hexapods are identical. The top hexapod moves faster than the lower one but it

reaches a maximum speed of about 25m/s as shown in Fig.

5b. The fast trajectories push the hexapod to move faster

than this, but its torque limitations prevent it.

Also notice in Fig. 5 that the speed varies greatly during

the tripod gait, so even if the hexapod has a large amount of

acceleration capability, as shown in Fig. 6, it cannot utilize

all of it all of the time due to the trajectories. Both hexapods

have roughly the same amount of acceleration capability in

the forward direction, as indicated by the arrows in Fig. 4

and in Fig. 6. The top hexapod has a little less because it

achieves higher velocities which create velocity forces that

require torque to overcome.

The lower hexapod gets of to a slow start, in Fig. 5a, but

quickly approaches its maximum velocity. If it had not had

this difficulty starting off, it would have kept pace with the

top hexapod. However, notice that the top hexapod also

had some difficulty getting started in Fig. 5b.

In Fig. 7 the peak torque of the top hexapod is increased

to 1000Nm. The bottom hexapod in Figs. 4 and 7 are

identical. The increase in peak torque gives the top hexapod

more acceleration capability thereby allowing it to achieve

the higher speeds required to execute the fast trajectories.

Comparing Figs. 4 and 7 notice that the top hexapod moves

farther in the 2.5 second run time. Fig. 8b shows that

the top hexapod did achieve a higher velocity 35m/s, as

compared to the top speed of 25m/s in Fig. 5b. Indeed

its acceleration capability in the forward direction was also

larger, as shown in Fig. 9b, than in the earlier race, Fig. 6b.

0 0.5 1 1.5 2 2.5

−5

0

5

10

15

20

25

30

35

40

Time (s)

Velocity (m/s)

0 0.5 1 1.5 2 2.5

−5

0

5

10

15

20

25

30

35

40

Time (s)

Velocity (m/s)

(a) (b)

Figure 8: Velocity in the Forward Direction. (a)

Hexapod executing the slow trajectories with peak

torque of 500Nm, and (b) the fast trajectories with

peak torque 1000Nm.

These two cases show how increasing the acceleration ca20
0 1 2 3

4 5 6 7

z − Distance (m)

t=1s

t=1s

Figure 7: High vs. Low Acceleration Capability Hexapod in a 2.5 Second Race. Motion capture is taken

at 0.5s intervals. The top hexapod has maximum torque of 1000Nm while the bottom one has 500Nm. The

higher acceleration capability allows the top robot to move farther in 2.5 seconds than in the previous race.

The time label at t = 1s shows the relative position of the hexapods. The length of the arrow extending from

each hexapod indicates its acceleration capability in the forward direction; the longer arrows show that the

fast hexapod has more acceleration capability.

0 0.5 1 1.5 2 2.5

1.5

2

2.5

3

3.5

x 104

Time (s)

Acceleration Capability (m/s

2)

Slow Hexapod

Fast Hexapod

Figure 9: Acceleration Capability in the Forward

Direction. (a) Hexapod executing the slow trajectories with peak torque of 500Nm, and (b) the fast

trajectories with peak torque 1000Nm.

pability can improve the performance of a hexapod. The

original hexapod can accelerate from 0-25m/s in 2.5s, but

after an increase in acceleration capability it could accelerate

from 0-35 in 2.5s. Here the increase in acceleration capability is due to increased actuator size. It is also possible that

the pattern of locomotion could be changed to increase its

acceleration capability to achieve the same effect.

What should be noticeable is the number of factors that

come into play in the simulation which affect the results.

Clearly a change in gait pattern would affect the performance. A performance metric which requires a simulation

to evaluate it would have to be run each time any of these

factors changes in order to evaluate the performance. This

is not a simple task when considering legged robots. It is

actually simpler and faster to use the analytic solution encoded in the DCE to design in the desired performance. A

simulation will most likely be needed in the end to check

the final conceptual design, but it need not slow the design

process down when a performance measure is available.

5. CONCLUSIONS

This paper showed that acceleration capability has a significant impact on a commonly used performance metric,

the time to a particular speed, which allows it to be used

in design in lieu of a simulation. This is advantageous because of the difficulty involved in simulating the locomotion

of legged robots. There are several factors which will affect

the simulation results, which are more easily addressed using

a performance characterization like the DCE. Future work

involves examining more agile motions like turning in order

to consider rotational performance.

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207
Measuring Robot Performance in Real-time for NASA 

Robotic Reconnaissance Operations 

Debra Schreckenghost 

TRACLabs, Inc 

1012 Hercules, Houston, TX 77058 

ghost@ieee.org 

Terrence Fong 

NASA Ames Research Center 

Moffett Field, CA 94035 

terry.fong@nasa.gov 

Hans Utz 

Research Institute for Advanced 

Computer Science 

Moffett Field, CA 94035 

hans.utz@nasa.gov 

Tod Milam 

TRACLabs, Inc 

1012 Hercules, Houston, TX 77058 

tmilam@traclabs.com 

ABSTRACT

Technical advances since Apollo make it possible to perform 

robotic reconnaissance to gain a better understanding of lunar sites 

prior to human exploration. NASA is conducting analog field tests 

to investigate these operations concepts with advanced robots and 

simulated flight operations. We have developed robot performance 

monitoring software for use during robotic reconnaissance 

operations. We measure robot performance by monitoring robot 

data in real-time and computing robot performance metrics from 

that data. Metrics are computed for two regimes of flight 

operations – remote supervision of autonomous robot operations 

and debrief support after a flight operations shift. In this paper we 

describe our performance monitoring software, define the metrics 

we compute, discuss how these metrics are used in flight 

operations, and summarize results from recent field tests. 

Categories and Subject Descriptors

C.4.3 [Performance of Systems]: Measurement techniques, 

Performance attributes, Reliability, availability, and 

serviceability; I1.2.9 [Artificial Intelligence]: Robotics - 

Commercial robots and applications, Operator interfaces

General Terms

Algorithms, Measurement, Performance, Experimentation 

Keywords

Operational performance metrics, robotic recon, space robotics. 

1. INTRODUCTION 

NASA’s plan to return humans to the moon raises questions about 

how to conduct lunar exploration missions to best utilize limited 

crew resources. Technical advances since Apollo enable the use of 

robotic systems to complement the human mission. Improved 

instrumentation makes it possible to perform robotic 

reconnaissance, or “recon”, to gain a better understanding of lunar 

sites prior to human exploration. NASA is conducting analog field 

tests to investigate these operations concepts with advanced robots 

and simulated flight operations [1]. 

We define robotic recon as operating a planetary rover under 

ground, or non-EVA astronaut, control to scout planned sorties 

prior to EVA activity. Scouting is an essential phase of field work, 

particularly for geology. Robot instruments provide measurements 

of the surface and subsurface at resolutions and from viewpoints 

not achievable from orbit. This surface-level data can then be used 

to select locations for field work and prioritize targets to improve 

crew productivity. Robotic recon can be done months in advance, 

or be part of a continuing planning process during human 

missions. [1]. 

We first began studying robotic recon during the June 2008 NASA 

Human-Robotic System (HRS) project analog field test conducted 

at the Moses Lake Sand Dunes, WA. During this test, an 

experimental ground control team located at the NASA Johnson 

Space Center (JSC) used a K10 planetary robot (Figure 1) to 

remotely scout a portion of the sand dunes. The data collected 

during the reconnaissance was then used to develop a plan for 

crew EVA in the same area. Lessons learned at Moses Lake were 

subsequently used to improve and validate robotic recon systems 

during Operational Readiness Tests (ORT) at the NASA Ames 

Research Center (ARC) in November 2008 and June 2009. Most 

recently, we conducted an experimental assessment of robotic 

recon as part of the 2009 Desert Research and Technology Studies 

(D-RATS) analog field campaign at Black Point Lava Flow, AZ. 

Figure 1. The K10 planetary rover is equipped with three 

instruments for robotic recon: a panoramic imager (PanCam), 

a 3D scanning lidar, and a terrain facing microscopic imager. 

208
Figure 2. An experimental ground control for robotic recon 

includes a science operations team and a flight control team. 

The roles and protocols used by this ground control are a 

hybrid of Apollo, Shuttle, Space Station, and MER concepts. 

For these ORTs and field tests, ground control (Figure 2) was 

conducted concurrent with robot surface operations. The Science 

Team builds robot plans to visit features of interest and take 

instrument readings to determine what features would benefit most 

from astronaut Extra-Vehicular Activity (EVA). During robotic 

recon operations, the ground control team reviews robot plans, 

then uplinks them to the robot. Similar to planetary rovers like 

JPL’s Mars Exploration Rover (MER), the K10 robot immediately 

begins to execute the uplinked plan. But, unlike MER, ground 

control supervises the robot as it executes the plan. They 

continuously monitor robot data in real-time and can intervene 

using tele-operations if opportunities or problems arise. 

To help improve the efficiency and effectiveness of robotic recon 

operations, we have developed real-time robot performance 

monitoring software. In the following, we describe our approach, 

define the performance metrics we compute, discuss how these 

metrics are used in robot operations, and summarize results from 

recent field tests. 

2. RELATED WORK 

The planetary surface environments in which recon robots must 

operate are highly variable. To maximize the value of the 

reconnaissance, the robot may be required to make observations in 

areas that are difficult to access or traverse. For example during 

the June 2009 ORT at NASA Ames Research Center, the K10 

robot operated near its safety limits to access desired geologic 

features on steep slopes. When communication latencies and 

bandwidth permit, tele-operating the robot in these conditions can 

be preferable to autonomous operations. Thus, robotic recon 

includes autonomous robotic activities interleaved with scheduled 

tele-operation. 

For this operations model, human-robot interaction metrics such as 

interaction efficiency [2] and neglect tolerance [3] are relevant. 

Interaction efficiency is improved by minimizing human 

interaction time. Neglect tolerance is a function of neglect time, 

the average time a robot can be ignored while keeping 

performance above some acceptable level. Larger neglect 

tolerance indicates greater independence in robot operations. We 

apply these metrics a bit differently for robotic recon, however. 

Central to these differences is the distinction between planned and 

unplanned human interaction. 

For lunar reconnaissance operations, minimizing all human 

interaction time may not translate to more efficient robot 

operations. In fact, it may often be more time and resource 

efficient to tele-operate the robot in difficult terrain than to operate 

autonomously. Thus, our objective is to minimize the time spent 

on unplanned interventions (such as anomaly handling). 

To do this, we make use of Mean Time to Intervene (MTTI) [4], 

which is the average time spent handling anomalies that interrupt 

planned robot tasks. Scheduled tele-operations are not included in 

MTTI. We also compute Mean Time Between Interventions

(MTBI) as the average time between unplanned interventions. 

Similar to neglect time, larger MTBI indicates improved humanrobot performance for reconnaissance. 

We measure the robot’s productivity as a function of the time the 

robot spends on reconnaissance tasks (called productive time). We 

compare productive time to time spent on other tasks (called 

overhead time), such as waiting for a reconnaissance plan or 

handling problems. One metric we use is Work Efficiency Index

(WEI) [5], which is the ratio of productive time to overhead time. 

We also compute the Percentage of Time on Task as the 

productive time normalized to the total elapsed time in the shift. 

Performance measures used for the MER robots include the total 

traverse distance over the lifetime of the robot [6]. Maximizing 

traverse distance for such exploration is desirable as an indicator 

of increased productive lifespan for the robot. But longer distances 

traveled or drive times do not necessarily correspond to better 

performance during robotic recon. Specifically, some plans may 

have short traverses interleaved with many data collection tasks. In 

such cases the robot may be performing optimally even with 

shorter drive times and less distance traveled. 

During the November 2008 ORT, for example, we observed the 

robot actually traveled greater distances when it was performing 

less well. The longest distance traveled over a shift corresponds to 

more human time spent unexpectedly tele-operating the robot out 

of difficult terrain. Comparing total distance traveled to the 

distance traveled performing planned tasks reveals this important 

distinction. Thus for robotic recon, a better indicator of good 

performance is the minimum distance traveled outside the plan 

instead of the maximum distance traveled. 

Performance measures are more effective for time and resource 

management if good estimates of expected task times and plan 

durations are available. These estimates define expected baseline 

performance useful in interpreting measures computed in realtime. Such information can be used to assess how efficiently the 

robot accomplished tasks under nominal circumstances and to 

determine what it is feasible to get done in the time remaining 

under contingency conditions. Algorithms for estimating task 

times can benefit from work on metrics for diagnostic and 

prognostic technologies [7, 8]. Such algorithms address detecting 

problems that can affect the quality of observations, which could 

be useful in pruning abnormal observations from computation of 

typical task times. 

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3. ROBOT PERFORMANCE MEASURES 

3.1 Software Approach 

We measure robot performance during reconnaissance operations 

by monitoring robot data in real-time and computing robot 

performance metrics from that data. Robot performance is 

computed remotely for use by flight operations personnel. The 

same robot data stream used for flight operations are used to 

compute performance of the robot in real-time. This includes 

detecting event signatures in data that affect robot performance 

(e.g., robot in motion). Metric values are displayed on web-based 

dashboards and plots for use by flight operations. Figure 3 

illustrates the key components of the performance monitoring 

software. 

Figure 3. Components of Performance Monitoring Software 

Metric algorithms are encoded as Java objects. At run-time 

algorithms are selected for execution and associated with robot 

data using configuration files. An instance of the algorithm’s class 

is created for each connection to a robot data item, permitting 

algorithms to be reused across multiple metrics. Complex 

algorithms are composed by connecting sequences of simpler 

algorithms (i.e., the output of one algorithm provides input to 

another algorithm). Computed metrics are distributed via a realtime data server and displayed as dashboards in a web page. 

Performance history is provided in two ways: plots of metric 

values computed over time and debrief reports of summary metrics 

computed over a shift. 

We evaluated our performance monitoring software during the 

June 2008 HRS field test at Moses Lake Sand Dunes, WA, to 

assess the feasibility of computing robot performance metrics in 

real time [9]. The objectives of this evaluation were to identify 

meaningful robot metrics, to assess whether these metrics can be 

computed using existing robot data, and to determine the impacts 

of remoteness on the computation of robot metrics on Earth. 

Subsequently we supported two ORTs at NASA ARC - one in 

November 2008 and another in early June 2009. During these 

ORTs we observed how performance metrics are used in 

operations. We also assessed how the human team design and 

protocols impact robot performance, such as robot utilization and 

robot wait time. 

Based on the results from these prior tests, we deployed and tested 

a revised system during the robotic recon portion of the 2009 DRATS field test at Black Point Lava Flow, AZ. Specifically, we 

computed performance metrics in two time regimes – for use by 

flight controllers during remote real-time flight operations and for 

use during debrief after an operations shift. To support flight 

operations, we also provided web-based dashboard displays of 

performance metrics computed from robot telemetry data. These 

displays were updated automatically with the latest computed 

value for performance measures. To support debrief meetings, we 

took snapshots of metric values at the end of each shift and 

performed additional computations on these values to produce a 

debrief report spanning each shift. The debrief report was 

generated in a web page using the eXtensible Markup Language 

(XML) stylesheets and Javascript. 

3.2 Metrics for Real-time Flight Operations 

To investigate the use of performance metrics during real-time 

operations, we supported two robot ground control positions [1]: 

the Flight Director, who is responsible for managing and 

coordinating the flight control and the Robot Operations 

Coordinator, who is responsible for the health and status of the 

K10 robot. In both cases, metrics are used in real-time for time and 

resource management. 

The Flight Director primarily uses performance metrics to manage 

the use of time. During operations, the Flight Director is 

concerned with whether the robot is operating normally. This 

includes both whether the assigned tasks complete successfully 

and whether these tasks can be accomplished in the allocated time. 

The questions asked by the Flight Director include: How much 

time before an ongoing task is completed? How much time it will 

take to complete the unfinished tasks in the current plan? and How 

much of the time allocated to a plan has been expended so far? 

This becomes particularly important when an anomaly occurs and 

the Flight Director must make choices about whether to abandon 

some tasks. 

To support the Flight Director in assessing and managing the 

timing of task performance, we provided a set of real-time metrics 

that measure how long tasks take and that compare these measures 

to expected performance. Specifically we computed and displayed 

the following five timers: 

1) Plan Timer: The Plan Timer counts down from the expected 

time it will take to complete a plan while the plan is being 

executed. The timer is updated each time an update about plan 

execution status is received until the plan is done. A plan is 

considered done when all tasks are either successful or aborted. 

This timer resets when a new plan is uplinked. The expected time 

is computed by summing the expected time for each planned task. 

Expected times for sampling tasks are based on typical 

performance during the ORT and field test. Expected times for 

210
traverse tasks are computed for a linear path between waypoints at 

default robot speed. The algorithm for the Plan Timer is shown 

below. 

 

2) Plan Wait Timer: The Plan Wait Timer increments when a 

plan is NOT active (i.e., no task is active, paused or pending). This 

includes time spent handling robot anomalies as well as idle time 

waiting for a new plan. This timer resets when a plan completes. 

Thus it measures all the “wait” time between plans. The algorithm 

for the Plan Timer is shown below. 

 

PlanWaitTimer(t) = PlanWaitTimer(t) + dtelapsed

where

x = elapsed time between adjacent timetags

when plan not active

 

3) Lidar Panorama Timer: The Lidar Panorama Timer counts 

down from the expected time it will take to complete a Lidar 

panorama while the lidar is active. The timer is updated each time 

an update about the Lidar subsystem status is received until the 

panorama is complete. This timer resets when a new panorama 

begins. The expected time for the Lidar panorama task is based on 

typical performance during the ORT and field test. The algorithm 

for the Lidar Panorama Timer is shown below. 

 

4) PanCam Timer: The Panoramic Camera (PanCam) Timer 

counts up while a PanCam panorama is being taken. For the 2009 

field test at Black Point Lava Flow, there are five different types 

of panorama: (1) Medium Width Image, Low Resolution, (2) 

Medium Width Image, Medium Resolution, (3) Wide Image, Low 

Resolution, (4) Wide Image, Medium Resolution, and (5) Narrow 

Image, High Resolution. Each of these types of panoramas take a 

different amount of time. Since the field test at Black Point Lava 

Flow was the first field test where we have used variable width 

and resolution PanCam imaging, we employ a “count up” timer to 

collect data on typical task times at each resolution. The algorithm 

for the PanCam Timer is shown below. 

 

5) MicroImager Timer: The MicroImager Timer counts up while 

a MicroImage is being taken. Microimaging takes between 10 and 

20 seconds. Because this task is so short, counting down from 

some expected time was not deemed useful. The algorithm for the 

MicroImage Timer is shown below. 

 

The Robot Operations Coordinator uses “low-level” performance 

metrics to manage robot resources such as remaining battery 

power. Such measures are needed to anticipate when robot 

maintenance is needed. Some batteries on the K10 robot provide 

feedback about remaining capacity and some do not. For batteries 

that are instrumented, we provide displays of the average battery 

capacity for each battery controller. Each controller manages eight 

batteries. For batteries that are not instrumented (such as the Lidar 

battery) we track battery usage (i.e., total runtime of the 

instrument using the battery) to give the Robot Operations 

Coordinator an idea of how much capacity remains. Utilization is 

tracked throughout the shift over multiple sampling intervals. 

When a Lidar battery is swapped out, we reset Lidar Runt Time. 

The algorithm used for Lidar Run Time is shown below. 

 

Finally we compute data communication quality metrics for use by 

all flight controllers. During the field test at Moses Lake, we 

observed two days with significantly degraded communication. 

The metric for communication quality during this field test was a 

count of the number of times the ground lost communication with 

the remote robot (called a data dropout, or Loss of Signal[LOS]) 

during a support period (for this test we compute a count over the 

shift). Larger dropout counts indicated more data were unavailable 

for computation, potentially impacting the accuracy of metric 

values. The duration of dropouts varied significantly at Moses 

Lake, however, prompting the definition of a second metric to 

compute the percentage of a support period (i.e., shift) that was 

spent without communication. This permits estimating the amount 

of data not available to flight operations and not included in 

metrics computed during real-time operations. The algorithms for 

both these metrics are shown below. 

 

 

211
3.3 Metrics for Shift Debrief 

Operationally, the ground control team holds a debrief meeting 

immediately after each shift (contiguous period of operations). We 

compute performance metrics over the course of each shift for 

building a debrief report. The debrief report has three sections: (1) 

a robot performance summary, (2) an anomaly summary, and (3) 

an event log. The performance summary provides metrics about 

robot productivity, task breakdown, and data collected during the 

shift. The anomaly summary provides metrics about unplanned 

interventions in robot operations, problems experienced by the 

robot, and loss of communication. The event log interleaves log 

notes by the flight team with events detected automatically in the 

robot telemetry stream. 

Three metrics are computed to assess robot productivity – time 

spent performing planned tasks (called productive time), time 

spent doing activities other than planned tasks (called overhead 

time), and the ratio of these two measures (called Work Efficiency 

Index; [5]). The algorithm used for each of these metrics is shown 

below. 

 

To assess task performance we compute the breakdown across the 

shift of robot drive time, time taking Lidar, time taking each of the 

five types of PanCam, and time microimaging. We also measure 

the number of samples taken by each type of instrument over the 

shift. Finally, we compute the total distance traveled by the robot 

during the shift. The algorithms used for these robot performance 

metrics are shown below. 

 

 

 

To assess anomalies in robot operations, we detect unplanned 

interventions in robot operations and use them to compute the 

Mean Time To Intervene (MTTI) and the Mean Time Between 

Interventions (MTBI). We show how many of each type of 

problem intervention occurred during the test, and the percent of 

the shift spent on each type of intervention. We also summarize 

communication quality by measuring the total number of LOS 

periods and the percentage of the shift spent in LOS. The 

algorithms for MTTI and MTBI metrics are shown below. The 

algorithms for communication quality were described previously. 

 

 

 

The event log combines information derived from the robot 

telemetry stream with log entries made by users. Events detected 

automatically include (1) Acquisition of Signal (AOS) and Loss of 

Signal (LOS), (2) instrument sample start and end, (3) problem 

start and end, and (4) plan uplink, start, and end. Problems 

reported include (1) emergency stop, (2) critical failure in robot 

subsystem (locomotor, navigation, or plan executor), (3) joint 

failure, and (4) navigation position error (usually due to a stuck 

wheel). These are problems where intervention by a person is 

likely required to fix the problem. User log entries are made from 

our real-time display. They are timestamped at the time of log 

entry. User log entries are distinguished from detected log entries 

using italics text in the display. 

4. RESULTS 

The performance monitoring software was used by K10 ground 

control during the robotic recon portion of the 2009 D-RATS field 

test at Black Point Lava Flow, AZ. In this section we summarize 

the results from using these metrics during field test operations. 

Instrument timers (Figure 4) were used in a number of ways 

during real-time operations. First, they were used to make better 

estimates of task duration for data acquisition tasks. Accurate 

estimates of the time to acquire the five types of PanCam 

panoramic images and the three types of Lidar scans were not 

available prior to the field test. We averaged sample collection 

times during operations early in the test to determine more 

accurate estimates. These estimates were used by the science team 

when building plans. They also were used by the Flight Director 

when monitoring robot progress. 

Figure 4. Example of Lidar Task Timer Display 

212
Second, the elapsed data acquisition time from an instrument timer 

was compared to the expected acquisition time for the sample type 

to determine the time remaining in an acquisition task. The 

medium and high resolution PanCam samples took between 10 

and 17 minutes to perform and the Flight Director frequently used 

these timers to determine time to task completion, 

Third, the instrument timers were used to detect data acquisition 

problems. These problems were evidenced by a larger than 

expected elapsed time with no indication that a sample had been 

taken (i.e., the sample count did not increase). The Lidar 

instrument had difficulty in completing a panorama on multiple 

occasions due to the extreme thermal conditions and the Lidar 

timer was useful in detecting these problems quickly. 

Finally, the Robot Operations Coordinator used the total Lidar 

runtime to estimate when to swap out the Lidar battery, since 

direct sensing of battery level was not available. During the field 

test we added the ability for the user to reset this timer after a 

battery swap. 

Plan timers were used by the Flight Director to identify the 

currently active task and when it was marked complete or aborted, 

and to determine what type of data was being acquired. The plan 

timers were helpful in determining when a plan had gone beyond 

the allocated time, but did not provide the Flight Director with 

sufficient timing information to detect when plan execution was 

getting behind early enough to take action before the allocated 

time was expended. When plan execution did get behind, the plan 

timer also did not aid the Flight Director in determining whether 

some portion of the remaining tasks might be completed in the 

time left. We developed new metrics during the field test to 

address these needs. Specifically we computed the time needed to 

complete the ongoing task and all pending tasks using estimates of 

task time previously measured. Using this information, the Flight 

Director could assess if adequate time remained and, if not, could 

inspect estimated task times to aid in selecting which remaining 

tasks to perform (a form of contingency re-planning). For the field 

test, this information was provided as a snapshot display from a 

checkpoint of metrics and thus did not update as plan execution 

continued. For future tests, we believe a version of this display 

that updates with progress on the plan would be preferable. Figure 

5 shows an example of the display used during the field test for 

these additional plan timing metrics. 

Figure 5. Display of Time Remaining in Plan 17A on June 20 

High environmental temperatures increased the risk of robot 

subsystems overheating during operations. The metrics for battery 

management were used by the Robot Operations Coordinator 

primarily to monitor battery temperature for potential overheating. 

To support this task, we computed the maximum temperature 

observed for each group of eight batteries and compared it to the 

current temperature. The Flight Director felt the thermal 

information currently provided should be supplemented with 

summary metrics of thermal performance, 

Communication quality varied significantly over the course of the 

field test. The average percentage of time spent in LOS for the 

entire field test was 13.4%, and varied from 1% to 38% of the day 

in LOS (Figure 6). Our metric for communication quality defined 

LOS as a dropout of all robot data. It was not uncommon, 

however, to experience dropped data messages without losing all 

robot data. As a result, the LOS metric is a conservative estimate 

of the quality of communication that can underestimate the impact 

of communication anomalies on data availability and quality of 

metrics. The Flight Director supplemented the LOS metrics with 

the message dropout warnings available for the individual robot 

subsystems to detect degraded communication due to dropped 

messages. 

213
Figure 6. Daily Percentage of Time in LOS for Field Test 

We produced a summary report at the end of each day using the 

metrics computed in real-time. These reports are intended to 

clarify how the robot spent its time, what data were collected, and 

what problems were encountered. These reports also should aid in 

identifying trends in expected robot performance over the field 

test. The summary measures in the debrief report identify how 

well the robot performed its tasks (see Figure 7 for an example of 

robot productivity on June 25) and what problems occur that affect 

that performance (see Figure 8 for an example of robot anomalies 

on June 25). An event log provides the details about what 

circumstances contributed to these summary measures. For 

example, one metric in the anomaly summary is the mean time 

spent by personnel intervening unexpectedly in robot operations. 

The log can be inspected to see exactly when samples were taken 

or when interventions were made. By inspecting adjacent events, 

the user may gain additionally insight into the operation. 

These reports were used as supplements to the console notes taken 

by the flight controllers. Both the Flight Director and Robot 

Operations Coordinator felt these reports would be more useful if 

flight operations personnel could edit them. The types of editing 

mentioned includes removing some data points from metric 

computations and adding comments after operations when 

reviewing and analyzing performance. At the end of the test these 

reports also represent a mission performance summary of robot 

productivity and robot reliability. 

Figure 7. K10 Robot Productivity on June 25 

Figure 8. K10 Robot Anomalies on June 25 

The average K10 robot productivity for the entire field test at 

Black Point was 37% productive time, 63% overhead time, and an 

average WEI of 0.73. Figure 9 shows these productivity metrics 

computed in real-time for each day of the field test. 

Figure 9. Daily K10 Robot Productivity Metrics for Field Test 

We further decomposed robot time into the percentage of time 

spent on each type of task. Figure 10 shows this breakout from 

June 17-26. We eliminated data on June 15-16 because of an error 

in the drive time computation used in real-time for those days. 

Figure 10. K10 Robot Task Breakout for the Field Test 

The average MTTI computed in real-time for the entire field test at 

Black Point was 5.6 minutes, ranging from a minimum of 1.6 

214
minutes to a maximum of 17.9 minutes. The average MTBI was 

24 minutes, ranging from a minimum of 5.5 minutes to a 

maximum of an hour. Figure 11 shows these reliability metrics 

computed in real-time for each day of the field test. 

Figure 11. Daily K10 Robot Reliability for the Field Test 

5. CONCLUSIONS 

We have described an approach for in-line computation of robot 

performance metrics to aid human-robot interaction during remote 

operation of robots in space. Results of evaluating our approach 

during recent field tests with the K10 robot indicate real-time 

computation of robot performance can aid both robot operations 

and debrief after operations. 

Plan and task timing metrics were used frequently during 

operations. These metrics were most meaningful when used in the 

context of expectations. Instrument timers were combined with 

knowledge of expected data acquisition times to monitor progress 

on the task and to detect when these task were not collecting data 

as expected. Similarly plan timers were combined with estimates 

of plan duration to monitor progress on the plan and to detect 

when the robot was getting behind. New metrics were identified 

and computed during the field test that compare the time left to 

complete the plan with estimates of the time needed to complete 

the remaining tasks. This information was used to determine 

whether adequate time remained to complete a plan and, if not, 

which of the remaining tasks should be performed. In all these 

cases, the expected task timing also was computed by the 

performance software. Comparing actual timing to expected 

timing is useful when inspecting performance after the mission as 

well. During the field test we computed the ratio of actual time to 

complete a plan with the estimated time to complete a plan. Using 

this metric we determined that 11 of 20 plans taken to completion 

were performed within the allocated time. 

Operational use of WEI and Percentage of Time on Task to 

measure robot productivity indicates that the Percentage of Time 

on Task is more meaningful in real-time. WEI is difficult to 

interpret for real-time use. When overhead time is very small, WEI 

can be very large (or can be undefined if overhead is zero). The 

meaning of such large numbers is not clear. By normalizing 

productive time to total time in operations (i.e., shift time), the 

Percentage of Time on Task is guaranteed to vary between 0 and 

100, ensuring greater consistency across operations and shifts. 

Communication quality varied significantly over the course of the 

field test, ranging from 1- 38% of daily operations spent out of 

communication. Additionally we observed frequent dropped 

messages that affected data availability and the resulting quality of 

metrics. As a result of this loss of data, we observed small 

inaccuracies in statistics on task durations and data sample counts 

due to communication anomalies. Additional metrics are needed 

that characterize how dropped messages affect data availability. 

The prevalence of dropped messages also indicates that metrics 

computed using this lossy data are subject to error due to missed 

data and could benefit from algorithms that consider the quality of 

the data messages used to compute them. 

6. FUTURE WORK 

Early detection of plan threats give ground control more flexibility 

in re-planning because more time and resource remain than if the 

threat is detected late. Plan threats include getting behind when 

performing a plan, using more resource (such as battery power) 

than planned, or losing robot capability that affects plan 

completion. We plan to investigate metrics for early detection of 

plan threats. For example, can we detect a robot getting behind as 

215
a divergence between the time remaining in the plan and the time 

needed to complete the plan? 

We also plan to investigate approaches for detecting when 

dropped messages impact quality of metrics. This includes 

performance algorithms that consider the quality of the data 

messages used to compute them. 

We believe the performance data we have computed over multiple 

NASA field tests can be useful for future field tests. It can be used 

to characterize typical robot performance for terrain types or 

specialized operations and thereby improve our interpretation of 

future robot performance (i.e., was this typical robot performance 

for this type of terrain?). It also can be used to establish realistic 

expectations when designing the activities for future field tests. 

7. ACKNOWLEDGMENTS 

This research was funded by the NASA Small Business Innovative 

Research program, Human-Robotic Systems project and ESMD 

Analogs program. We would like to thank Rob Landis, Timothy 

Kennedy, and Frank Jurgens for their feedback about flight control 

metrics. Thanks also to David Lees and Mark Allen for their 

assistance in integrating the performance monitoring software with 

the K10 ground data system, to Estrellina Pacis for her assistance 

in defining metrics for operations assessment, and to Matt Deans 

for information about the robotic recon planning process. 

8. REFERENCES 

[1] Fong, T., M. Bualat, M. Deans, M. Allan, X. Bouyssounouse, 

M. Broxton, L. Edwards, R, Elphic, L. Fluckiger, J. Frank, L. 

Keely, L. Kobayashi, P. Lee, S. Y. Lee, D. Lees, E. Pacis, E. 

Park, L. Pedersen, D, Schreckenghost, T. Smith, V. To, and 

H. Utz. Field Testing of Utility Robots for Lunar Surface 

Operations. AIAA Space 2008. San Diego, CA. 

[2] Jacob Crandall, Michael Goodrich, Dan Olsen, Jr., and Curtis 

W. Nielsen, “Validating Human–Robot Interaction Schemes 

in Multitasking Environments”, IEEE Transactions on 

Systems, Man, and Cybernetics—Part A: Systems and 

Humans, Vol. 35, No. 4, July 2005 

[3] Jacob Crandall and M. L. Cummings. Developing 

Performance Metrics for the Supervisory Control of Multiple 

Robots. Human-Robot Interaction 2007. March 2007. 

[4] J. Arnold, “Towards a Framework for Architecting 

Heterogeneous Teams of Humans and Robots for Space 

Exploration”, M.S. Thesis, Dept. of Aeronautics and 

Astronautics, Massachusetts Institute of Technology. 2006. 

[5] Mike Gernhardt, Work Efficiency Indices. Presentation at 

Johnson Space Center. November 15, 2005 

[6] Edward Tunstel, “Performance Metrics for Operational Mars 

Exploration Rover,” Journal of Field Robotics, Volume 24 

Issue 8-9, 651 – 670, September 2007. 

[7] Tolga Kurtoglu, Ole J. Mengshoel, and Scott Poll, A 

Framework for Systematic Benchmarking of Monitoring and 

Diagnostic Systems,” 2008 International Conference on 

Prognostics and Health Management Proceedings, October 

6-9, 2008. 

[8] Abhinav Saxena, Jose Celaya, Edward Balaban, Kai Goebel, 

Bhaskar Saha, Sankalita Saha, and Mark Schwabacher, 

“Metrics for Evaluating Performance of Prognostic 

Techniques,” 2008 International Conference on Prognostics 

and Health Management Proceedings, October 6-9, 2008. 

[9] Debra Schreckenghost, Terrence Fong, Tod Milam, Estrellina 

Pacis, and Hans Utz. Real-time Assessment of Robot 

Performance During Remote Exploration Operations. IEEE 

Aerospace Conference. Big Sky, MT. March 2009.

216
 A Biologically Inspired Sensory Driven Method for 

Tracking Wind-Borne Odors 

Brian K. Taylor 

Case Western Reserve University 

10900 Euclid Ave 

Glennan 418 

(216) 368 - 6044 

btaylor@case.edu 

Brandon L. Rutter 

Case Western Reserve University 

10900 Euclid Ave 

Glennan 418 

(216) 368 - 5216 

rutter@case.edu 

Roger D. Quinn 

Case Western Reserve University 

10900 Euclid Ave 

Glennan 418 

(216) 368 - 3222 

rdq@case.edu 

ABSTRACT 

The ability of a vehicle to track a wind-borne odor to its source 

has numerous applications such as search and rescue missions and 

security operations. The Sensory Coupled Action Switching 

Modules (SCASM) control concept, based initially on known 

neural pathways in stick insects, provides a way to control a 

system using only sensory feedback. To date, SCASM has only 

been implemented in legged locomotion systems. However, 

results from implementing SCASM on legs of differing 

morphology suggest that it can be applied to control paradigms 

beyond legged locomotion. Here, we apply SCASM to the 

problem of odor tracking. We first develop a degenerate-case 

SCASM controller for tracking a 2D continuous odor plume to its 

source. In simulation, we then test this controller in a variety of 

probabilistic plume conditions. Basic performance metrics are 

developed to determine how well the controller can “Succeed” at 

finding the source, and “Linger” near the source. Results indicate 

that high success rates ( ~80%) coupled with high lingering 

ratios ( ~15) lead to good source localization. Overall, the study 

begins to demonstrate the feasibility of applying SCASM to 

control paradigms beyond legged locomotion. 

Keywords

Biologically Inspired Robotics, Biologically Inspired Control, 

Odor Tracking, Chemical Plume Tracking, Pattern Generation, 

Sensory Coupled Action Switching Modules, Performance 

Metrics, Simulation 

1. INTRODUCTION 

1.1 The SCASM Control Concept 

Sensory Coupled Action Switching Modules (SCASM) is a 

control concept that coordinates a system to generate an emergent 

behavior (e.g., forward stepping) using sensory feedback. As an 

example, consider a legged engineering system. A SCASM 

controller would coordinate the joints of the leg using sensory 

feedback such as joint angles and leg load to achieve an emergent 

behavior (e.g. forward or side stepping). SCASM was initially 

based on known local neural circuits and sensory pathways in 

stick insects. Ekeberg et al. [1] implemented a dynamic simulation 

of the stick insect middle leg that only used the known local 

neural circuits and sensory feedback pathways to generate 

stepping motion. Their goal was to determine whether or not these 

influences (e.g., increasing leg load causing the thoraco-coxa joint 

to go from protraction to retraction) were sufficient to explain the 

stepping motion of the leg. Their simulation successfully 

reproduced the stepping motion of the stick insect middle leg and 

provided possible predictions about how the joints of the front and 

hind legs might be coordinated to generate stepping. Rutter et al. 

[2,3] followed up on this work by developing a robotic stick insect 

leg that uses the same neural circuits and sensory feedback 

information that is available to the animal. Lewinger et al. [4,5] 

implemented SCASM on a biologically inspired hexapod, where 

the SCASM controller coordinates the joints within a particular 

leg, and a behavioral controller coordinates the interleg motion. 

Also, Rutter successfully reorganized the stick insect robot 

controller developed in [2] and [3] to generate stepping motion in 

a robotic cockroach leg [6]. It should be noted that cockroach and 

stick insect legs are anatomically quite different from one another. 

An important feature of SCASM applied to these robotic models 

is that it has an elegant implementation and requires less 

computational power than traditional engineered control systems. 

At the same time, they are able to achieve substantial behavioral 

robustness to disturbances such as changes in substrate height or 

transient limb obstruction without the need for specific scenariohandling software often used in traditionally engineered legged 

systems. 

A high level way to represent a SCASM controller is an event 

space diagram (first used in [6] to describe the cockroach and 

stick insect leg controllers). A generic event space diagram is 

shown in Figure 1. 

Figure 1. Generic Event Space Diagram. 

Permission to make digital or hard copies of all or part of this 

work for personal or classroom use is granted without fee 

provided that copies are not made or distributed for profit or 

commercial advantage, and that copies bear this notice and the 

full citation on the first page. To copy otherwise, to republish, 

to post on servers or to redistribute to lists, requires prior 

specific permission and/or a fee. PerMIS'09, September 21-23, 

2009, Gaithersburg, MD, USA. Copyright © 2009 ACM 978-

1-60558-747-9/09/09...$10.00 

217
In Figure 1, we start with Input Sensory Events. These are events 

in the controlled system or its environment that can be detected 

via sensory feedback signals (e.g., leg load, joint angle, odor 

concentration). Some of these events may then be compounded 

together in some fashion (e.g., logic gates or weighted sums) to 

form higher level and/or more abstracted sensory feedback events. 

This is biologically plausible since neurons are capable of forming 

various kinds of logic gates and applying weights to input signals. 

Not all Input Sensory Events necessarily undergo compounding. 

After compounding, we have Action Switching Modules. Each 

module controls some piece of the system (e.g., in a legged 

system, a module would control a joint such as a knee). Within 

each module, there are actions (e.g., in a joint, possible actions 

might be flexion and extension). The individual and/or 

compounded sensory feedback signals elicit transitions between 

different actions within each module. For a given module, all 

actions are not necessarily reachable from one given action (e.g., 

in Module I, Action 3 is only reachable from Action 1, not Action 

2). To provide an example information flow, suppose that we have 

Sensory Events 1A and 2A. These signals would be “AND”ed 

together, and their combination would elicit transitions from 

Actions 2 or 3 to Action 1 in Module I. This transition could 

trigger Sensory Event 1C, which would cause a transition from 

Action 1 to Action 2 in Module II. This could trigger Sensory 

Event 3A, which could cause a transition from Action 1 back to 

Action 3 in Module I. In this way, we can see that we have Action 

Switching Modules that are Coupled through Sensory feedback. 

The color-coding between some of the modules and sensory 

inputs is to indicate that the actions defined within a particular 

module can elicit direct sensory feedback. For example, in a 

legged system, switching from flexion to extension in a joint 

would cause a change in the angle being sensed at that joint. In 

contrast, we can also use sensory feedback from the environment 

which is not inherent to the system itself. For example, in an odor 

tracking vehicle, odor presence will change the vehicle’s 

behavior. However, odor is a property of the environment, not the 

vehicle. Each action can be thought of as defining a dynamic 

system. For the legged robotic systems in [2,3,5], the dynamic 

system is affected by changing muscle model parameters. The 

muscle models at a joint determine motor commands, and the 

physical properties of the robot complete the system. 

We note here that a SCASM controller in some circumstances can 

be represented as a finite state machine. However, it is possible to 

construct a SCASM controller where action switching is not 

discrete, and a finite state machine representation cannot be 

created. Also, even if a SCASM controller is represented as a 

finite state machine, the connections in this kind of representation 

are not always useful from the standpoint of determining or 

modifying the underlying mechanisms that generate the system’s 

behavior. 

1.2 Applying SCASM to Odor Tracking 

With an understanding of SCASM control for the joints of a leg, 

our goal is to apply it as a generalized concept to a different 

problem: tracking an odor upwind to its source. The ability to 

track fluid-borne chemicals can allow vehicles to track and locate 

things such as: explosives, chemical/biological threats, people and 

animals in search and rescue scenarios, and illegal substances [7-

10]. [7] and [8] attempted to mimic the behavior of the tobacco 

hornworm moth, while [9] and [10] use a combination of 

biological and engineering approaches to track chemical plumes 

in air and water, respectively. The metrics used in these works 

focus on recording whether or not the agent successfully came 

within some acceptable distance of the odor source [8-10], and 

recording the raw time it took the agent to reach the source [8,9]. 

A SCASM based odor tracker would present a new method for 

chemical plume tracking, and may aid researchers in better 

understanding the odor tracking capabilities of biological systems. 

Using SCASM-controlled simulated and robotic models is already 

beginning to increase understanding of the legged locomotion 

capabilities of biological systems [1,6]. More importantly, beyond 

developing a new odor tracking strategy and aiding biological 

work, a SCASM based odor tracker would demonstrate that 

SCASM, although originally based in legged insect locomotion, is 

a more broadly applicable concept. Until now, it has only been 

implemented in legged systems. However, based on the success of 

implementing SCASM on legged systems of differing 

morphology and a multilegged system, it appears that the SCASM 

concept can be applied to a variety of other problems. We 

envision SCASM being applied to control paradigms where it is 

desired to have the system go though a coordinated and 

potentially repeating set of actions in concert with the 

environment. Furthermore, recall that SCASM appears to require 

less computational power than traditional engineering solutions 

for legged locomotion. This suggests that SCASM may have the 

potential to be a more computationally efficient method for 

controlling current and future systems. 

In this paper, we develop a SCASM controller to operate in a 

simplified 2D environment. In a MATLAB simulation, we then 

test this controller on a kinematic vehicle that uses standard 

vehicle coordinates under a variety of: plume conditions (e.g., 

environmental odor content), vehicle assumptions (e.g., is the 

vehicle’s velocity affected by the wind velocity), and controller 

parameters (e.g., constant vs. variable vehicle speed). Basic 

performance metrics that gauge whether the vehicle can find the 

odor source and how well the vehicle lingers near the odor source 

are developed to gain an understanding of how well the controller 

is able to localize the odor source under different environmental 

and controller conditions. Although they are simplified, the 

environments in this study give us a basic idea of what kinds of 

conditions are conducive to good performance. Overall, the study 

begins to demonstrate the feasibility of using SCASM control in 

non-legged systems. We note that the focus of this study is to gain 

insight into how varying the controller and environmental 

parameters affects the vehicle’s performance. We do not attempt 

to optimize the controller’s parameters. 

2. Methods 

2.1 Using a Simplified 2D Environment 

In general, real-world odor tracking is a 3D problem [7-10]. Also, 

a fluid-borne odor is usually discontinuous due to a turbulent fluid 

velocity field [7-13]. However, based upon the goals of this work, 

we use a simplified 2D odor environment, which allows for easier 

evaluation of the controller’s behavior and performance (more is 

said about our long term goal of 3D work in Section 2.2 and 

Section 4). The environment is infinite in the X and Y directions, 

contains a “triangular” plume, and employs uniform steady (i.e., 

constant in time) freestream wind velocity. While this is not a 

fluid mechanics based environment, it gives us a well understood 

model that eases our ability to test the controller’s basic behavior. 

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2.2 Developing an Event Space Diagram 

With an environment in place, we must now develop an event 

space diagram to represent an odor tracking SCASM controller. 

For odor tracking, the sensory feedback signals that the controller 

receives are egomotion (i.e., vehicle motion relative to the 

environment) and odor presence (i.e., does the vehicle sense odor 

at a particular point in space and time). Figure 2 illustrates the 

initial event space diagram for our system. 

Figure 2. Current event space diagram for the 2D controller. 

As shown in Figure 2, we currently have one action switching 

module which controls yaw motion. This module contains 4 

actions. Because we only have one action switching module, there 

is not any sensory feedback coupling between modules, so this is 

a degenerate case of a SCASM control network. Since our study is 

2D, egomotion is separated into the following cases: moving 

up/downwind, and moving left or right relative to the wind. The 

information flow in the controller is as follows. If the vehicle is 

moving upwind AND senses odor, then the controller transitions 

to the Move Straight action, which moves the vehicle in a straight 

line along its own x-axis. If the vehicle is moving downwind 

AND senses odor, then the vehicle is in the plume but facing in 

the wrong direction (we need to move upwind to find the odor 

source). In this case, the controller transitions to the Quickturn

action, which is a high rate clockwise turn that reorients the 

vehicle such that it faces upwind. If the vehicle is moving to the 

right relative to the wind AND does not sense odor, we might 

envision that the vehicle is on the right side of the plume and 

moving away from it. In this case, the controller transitions to the 

Turn Left action, which is a moderate rate counterclockwise turn. 

Likewise, if the vehicle is moving to the left relative to the wind 

AND does not sense odor, we might envision that it is on the left 

side of the plume and moving away from it. In this case, the 

controller transitions to the Turn Right action, which is a moderate 

rate clockwise turn. These actions seek to keep the vehicle in the 

plume and facing upwind. In all of the turning states, the vehicle 

still has a forward velocity. Speed and turn rates in this controller 

are constant (i.e., in any particular action, the vehicle moves in a 

straight line or a circle). We note that this controller is not 

optimized and that its performance is dependent on the vehicle’s 

starting location and pose within the world. However, despite its 

imperfections, it gives us a starting point for developing a 

SCASM based odor tracker. 

We also note that since we are in 2D, our event space diagram 

only has one action switching module for controlling yaw motion. 

A 3D controller would have pitch and/or roll modules in addition 

to yaw, and sensory events regarding the vehicle’s full orientation. 

This would create a large amount of sensory feedback coupling 

(e.g., roll feedback influencing the yaw actions). In addition, 

compared to previous SCASM controllers, the action switching 

module in Figure 2 has a large number of actions (action 

switching modules to date have had only 2 actions). This is due to 

the fact that actions in the yaw module are very simple (i.e., 

constant vehicle velocity and turn rates), so more actions are 

necessary to generate an emergent behavior. More complex and 

robust actions containing internal feedback would allow us to cut 

down on the total number of necessary actions. However, the 

simple actions in Figure 2 provide a starting point from an 

implementation standpoint. 

2.3 Plume Conditions 

The controller was tested in three kinds of “triangular” plumes: 

Deterministic, Uniform Probability and Distance Dependent 

Probability. In the Deterministic environment, if the vehicle is 

within the plume (i.e., within the triangular region), then it 

successfully detects the presence of odor. This is the plume 

condition that the event space diagram in Figure 2 is designed for. 

In the Uniform Probability environment, if the vehicle is within 

the plume, then it successfully detects the presence of odor with 

some preset fixed probability (e.g., if the vehicle is in the plume, 

then it successfully detects odor 75% of the time). This paradigm 

is used because a wind-borne odor is sensed in a discontinuous 

manner. With this condition, the vehicle will not detect odor 

100% of the time even if it stays in the plume. For the Distance 

Dependent Probability environment, if the vehicle is in the plume, 

then it successfully detects odor with a probability that decays as 

distance from the odor source increases. This is done because 

odor is more difficult to detect farther away from its source. For 

this study, the decay of probability away from the source is 

modeled by a sigmoid, which is shown in Eq. 1 

P = A

1+ ear Eq. 1 

Here, P is the probability of detecting an odor, a is what we refer 

to as the “sigmoid parameter”, A is a constant and r is the 

Euclidian distance between the vehicle and the odor source. A is 

set to 2 so that when the vehicle is at the odor source (i.e., r = 0), 

the probability of sensing odor is 1. This equation is plotted for 

various values of a in Figure. 3. 

Figure 3. Plot of Eq. 1 for various values of sigmoid parameter 

a. Odor content increases as a decreases. The vertical lines 

indicate the starting distances that were used in this study. 

Figure 3 shows that, for a fixed distance from the odor source, as 

a increases, the probability of detecting an odor drops. We 

therefore say that increasing a decreases the environment’s odor 

content. In this study, the following values of a were used 

(ordered from highest to lowest odor content): a {0.10, 0.25, 0.50, 

0.75, 0.90}. A sigmoid was used because it can be constructed 

219
such that it is unity at the odor source and decays monotonically 

to zero. 

2.4 Initial Simplifications and Trials 

For the initial part of the study, the simulation used the following 

simplifications and assumptions: 

1) The vehicle is a kinematic entity with no mass or inertia 

(i.e., a coordinate system whose motion and orientation 

is completely determined by speed and rotation rate 

commands). This level of abstraction can be used to 

understand ground or flight vehicle behavior. 

2) The vehicle can perfectly measure the true wind 

velocity and its ground velocity. 

3) The wind velocity cannot affect the vehicle’s velocity. 

4) The vehicle’s commanded speed is constant. 

5) The wind velocity is constant. 

In Section 3.1, we show one result from these initial assumptions 

to illustrate how the controller manifests its behavior. Specifically, 

we examine the controller’s performance in a Uniform Probability

environment and relate it to the event space diagram shown in 

Figure. 3. In Sections 2.5 and 2.6, we remove some of the above 

simplifications to gain insight into the vehicle’s performance 

under different operating conditions. 

2.5 Constant Speeds Influenced by Wind 

In this section, we remove simplification 3 described in Section 

2.4 by adding the wind velocity to the vehicle’s velocity at each 

time step so that the vehicle can be “pushed” by the wind. For this 

paradigm, we test the vehicle at 3 starting distances from the 

source. The baseline starting distance was determined by dividing 

the maximum vehicle velocity to be tested by the maximum turn 

rate to be tested (10 length units/second and 40o

/second, 

respectively), giving a distance of 14.3230 length units/second. 

Turn rate testing is still under investigation, and is not presented 

in this paper. Scaling this distance by nondimensional factors (R0-

Initial) of 0.5, 1.0, and 2.0, the starting distances used were 7.1620, 

14.3230 and 24.6478 length units. For each starting distance, the 

vehicle’s speed is varied through 5 different values: {0.1, 0.5, 1.0, 

5.0, 10.0} length units/second. For each vehicle speed, the 

controller is tested in each of the 5 Distance Dependent 

Probability environments. Figure 3 illustrates the starting 

distances that are used in relation to the initial probability of 

sensing odor. For each Distance Dependent Probability

environment, data was analyzed for 8 RCritical values. This gives us 

a 4-dimensional parameter space (R0-Initial: vehicle velocity: 

sigmoid parameter: RCritical). RCritical is a performance metric 

reference value that is defined in Section 2.7. 

2.6 Variable Speed Influenced by Wind 

In this section, we remove simplifications 3 and 4 described in 

Section 2.4 by varying the vehicle’s commanded speed 

throughout the course of a run and allowing the wind velocity to 

influence the vehicle’s velocity (the wind velocity is still 

constant). This was done based on the data collected from the 

protocol described in Section 2.5. The idea is that the vehicle 

speeds up when it is far from the odor source, and slows down 

when it is close to the odor source. This would make the vehicle 

“rush” to get close to the odor source, and then slow down to 

search a local area for the odor source. Because the vehicle does 

not explicitly have access to its position, distance from the odor 

source is approximated by counting the number of times an odor 

is sensed over a fixed time span. The farther away the vehicle is 

from the source, the less it should sense odor. With this in mind, 

the vehicle’s velocity is varied in the following way 

v = v( ) base

M

Number of times odor is sensed Eq. 2 

where vbase is a preset baseline vehicle velocity (set to 0.1 length 

units/second), and M is the number of time steps over which the 

vehicle counts whether or not it has sensed odor (set to 20 for this 

study). If the vehicle senses no odor over M timesteps, then the 

velocity is set to 1 length unit/second to prevent division by zero. 

2.7 Performance Metrics 

For the data collected in Sections 2.4 – 2.6, Success and Lingering 

metrics were employed to begin quantifying “good performance”. 

The success metric asks “Did the vehicle pass within a particular 

distance (RCritical) of the odor source?” This kind of metric has 

been used in both biological studies and engineered odor-tracking 

systems to determine whether the agent (animal or vehicle) has 

“reached” the odor [8-11]. We define a success percentage as 

Success% = Number of successful trials

N Eq. 3 

where N is the number of trials. The lingering metric seeks to 

determine how well the vehicle can localize the odor source. We 

use a lingering metric because it gives a description of the 

vehicle’s near source behavior. 

To compute the lingering metric, we first define a lingering 

distance as 

RLinger = qRCritical Eq. 4 

where RLinger is the distance from the source that defines lingering 

and q is a multiplying factor. For our study, q was arbitrarily set to 

3, indicating that the lingering vicinity was within three times the 

distance at which the vehicle was considered to have “reached” 

the point source. With this in mind, we can compute a lingering 

ratio in the following way: 

Linger Ratio = max(Actual Time in Linger Diameter)

Straight Line Time in Linger Diameter Eq. 5 

This ratio compares the maximum time spent near the odor source 

during a trial to the time it would take the vehicle to cross the 

lingering diameter in a straight line. Higher values imply that the 

vehicle spends a great amount of time near the source and lower 

values imply that the vehicle only passes by the source for short 

periods of time. For N trials, we define a lingering percentage as 

Linger% = Num _Trials above a specified linger ratio

N Eq. 6 

For source localization, we would expect relatively high success 

percentages coupled with relatively high lingering percentages 

(i.e., the vehicle finds the odor and stays near it). For this study, 

the specified lingering ratio was set to 15. This choice was based 

on preliminary observations of the controller’s performance. The 

success and lingering metrics are evaluated for multiple values of 

RCritical to understand how the quantitative performance of the 

vehicle changes when the reference value of the performance 

metrics is changed. RCritical is varied through the following values: 

RCritical = {0.25, 0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 2.00} length 

units. With these metrics, it is possible for the vehicle to 

“Succeed” but not remain near the source for a long period of 

220
time. It is also possible for the vehicle to “Linger” near the source 

without technically “Succeeding”. These metrics gauge how well 

the vehicle is able to find the source and remain near it. 

We note that in practice, the specific values of RCritical and q would 

depend on the desired performance of the vehicle for a particular 

scenario (e.g., high-resolution localization may demand values of 

RCritical and q that are smaller than what is presented in this study). 

The metric values used here were chosen to gain insight into the 

controller’s performance over a range of baseline values. 

2.8 General Trial Information 

For each parameter combination (R0-Initial: vehicle velocity: 

sigmoid parameter: RCritical), 20 trials were performed. At each 

time step, the vehicle sensed odor with a probability P as defined 

in Eq. 1. Also, for each trial, the initial starting location and 

orientation were randomized subject to the constraint of the 

specified starting distance. The vehicle was only allowed to start 

within the odor plume since it did not have a plume finding 

behavior such as casting (moving back and forth primarily across 

the wind with occasional downwind drifting) [7,11,12]. The same 

20 random number generator seeds were used for the trials at each 

parameter combination. The turn rates used were as follows: Turn 

Right = 20o

/second; Turn Left = -20o/second; Quickturn = 

40o

/second (turn rates are given relative to the vehicle’s 

coordinate system). 

3. Results 

3.1 Vehicle Behavior and its Relationship to 

the Event Space Diagram 

In Figure 4 we analyze an odor track from the Uniform 

Probability environment of P = 90% in reference to the event 

space diagram of Figure 2. 

Figure 4. A) Sample odor track from the P = 90% Uniform 

Probability environment. The vehicle starts at the open circle 

and ends at the cross. B) Zoomed in portion of the odor track. 

Black lines represent the Move Straight action, green lines 

represent the Turn Right action, magenta lines represent the 

Turn Left action and cyan lines represent the Quickturn

action. In this and all remaining figures, U is the wind 

velocity. 

In the first leg of the odor track (i.e., the long black line in Figure 

4B), the vehicle senses odor AND has a component of upwind 

velocity, so the controller transitions to the Move Straight action. 

The vehicle remains in this action until it leaves the odor plume, 

at which point the vehicle senses a lack of odor AND that it is 

moving to the left relative to the wind. This causes a transition to 

the Turn Right action. The vehicle stays in this action until it reenters the odor plume. However, now the vehicle senses that it is 

in the plume AND moving downwind, which triggers a transition 

to the Quickturn action. Once the vehicle has regained a 

component of upwind velocity in the plume, the controller 

transitions to the Move Straight action. This cycle of Move 

Straight, Turn Right and Quickturn repeats itself, which gradually 

moves the vehicle up the edge of the plume towards the source in 

a looping manner. Under these conditions, an unintended feature 

of the controller is that the vehicle is able to orbit the odor source. 

This capability was not explicitly built into the controller. It is a 

consequence of the sensory feedback connections in the event 

space diagram. We note that in Figure 4, the odor track does not 

form perfect lines or circles. This is due to the fact that the 

controller is in a probabilistic environment, and can therefore 

encounter sensory errors, resulting in intermittent transitions to 

other actions. For example, the main action of the first part of the 

odor track is Move Straight, but there are intermittent transitions 

to the Turn Right action (Figure 4B). 

3.2 Constant Speed Influenced by Wind 

Figure 5 shows the success and lingering percentages for the 

following conditions: R0-Initial = 0.5, Vehicle Speed = {0.5, 1.0, 

10} length units/second 

Figure 5. Success (Left) and lingering (Right) metrics for R0-

Initial = 0.5. For each plot, for a given sigmoid parameter, each 

bar corresponds to a different RCritical or RLinger value. 

For the success metric, as speed increases, the success percentage 

increases across sigmoid parameters. Also, with an increase in 

speed, the overall magnitude of success increases from 0.5 to 1 

length units/second, and then decreases for speeds greater than 1 

length unit/second (the data for V = 0.1 length units/second had 

maximum success rates of 85% and 40% in the a = {0.10, 0.25} 

environments respectively, and maximum linger rates of 15% and 

30% in the a = {0.10, 0.25} environments respectively). For the 

221
lingering percentage, the vehicle’s ability to linger is higher at a 

vehicle speed of 0.5 length units/second. Also, as the vehicle 

speed increases, the lingering percentage increases in the higher 

RLinger values and decreases in the lower RLinger values. Upon 

examining the odor tracks and comparing them to the plots in 

Figure 5, it appears that higher success rates coupled with higher 

linger rates for lower values of RLinger provide a useful description 

of source localization. The vehicle was still able to localize the 

source at vehicle speeds of 5 and 10 length units/second. 

However, the localization is not as good when the vehicle moves 

quickly. The lingering metric is not shown for V = {5, 10} length 

units/second because based on the lingering percentage definition 

given by Eq. 6, none of the trials for these vehicle speeds lingered. 

Overall, it appears that starting this close to the source, the vehicle 

has the greatest capability to “Succeed” and “Linger” in sigmoid 

parameter environments of 0.1 and 0.25. In cases where the 

vehicle failed to “Succeed” and/or “Linger”, the vehicle 

“spiraled” away from the source. This was due to the fact that 

when the odor was sparse enough within the plume, the vehicle 

would enter the “Turn Right” or Turn Left” actions, causing it to 

move in a circle. Since the vehicle had a component of wind 

velocity added to its own velocity at each time step, the vehicle 

moved in a circle while being “blown” away from the source 

(Figure 6-A1). Depending on the trial environment, this kind of 

behavior could occur even when the vehicle localized the source 

(Figure 6-B1). Figure 6 provides sample odor tracks that illustrate 

success vs. failure, good vs. poor lingering capability, and overall 

behavior. 

Figure 6. The top row (a = 0.1) illustrates trials where the 

vehicle “Succeeded” and “Lingered”. The bottom row 

illustrates trials with a success and/or lingering failure (a = 0.5 

for A1, a = 0.25 for B1 and C1). The vehicle velocities for the 

odor tracks that are shown are 0.5, 1, and 10 length 

units/second (left, middle and right columns, respectively). 

The vehicle starts at the open circles and ends at the crosses. 

Note that while C appears to linger, its lingering ratio is under 

15. 

Figure 7 shows the success and “Linger” metrics for a more 

distant starting position given by: R0-Initial = 2, Vehicle Speed = 

{0.5, 1.0, 10} length units/second 

Figure 7. Success (Left) and Lingering (Right) metrics for R0-

Initial = 2. For each plot, for a given sigmoid parameter, each 

bar corresponds to a different RCritical or RLinger value. 

Here, again, as vehicle speed increases, the success percentage 

across sigmoid parameters increases. Also, the success percentage 

increases from 0.5 to 1 length units/second, and then decreases for 

vehicle speeds greater than 1 length unit/second. From the 

lingering percentage, “tight” localization only occurs for a 

sigmoid parameter of a = 0.1. The vehicle still exhibits the 

behavior of tighter localization with smaller velocities (e.g., the 

lingering metric is higher for lower values of RLinger at a vehicle 

speed of 0.5 length units/second vs. a vehicle speed of 1 length 

unit/second). Success only appears to occur at the higher vehicle 

velocity values (plots for a vehicle velocity of 0.1 length 

units/second are not shown because the vehicle did not succeed or 

linger at this velocity for this initial distance). Lingering plots for 

vehicle speeds of 5 and 10 length units/second are not shown 

because the vehicle failed to linger at either of these speeds based 

on the lingering percentage definition given by Eq 6. Figure 8 

provides sample odor tracks. The failure mechanisms are the same 

as those described for the trials where R0-Initial = 0.5. 

222
Figure 8. The top row (a = 0.1) shows trials where the vehicle 

“Succeeded” and “Lingered”. The bottom row (a = 0.25) 

illustrates trials with a success and/or lingering failure. The 

vehicle velocities for the odor tracks that are shown are 0.5, 1, 

and 10 length units/second (left, middle and right columns, 

respectively). The vehicle starts at the open circles and ends at 

the crosses. Note that while C appears to linger, its lingering 

ratio is under 15. 

3.3 Variable Speed Influenced by Wind 

The results in Section 3.2 have two implications. First, when the 

vehicle is far from the source, moving faster increases the chance 

of success across sigmoid parameters. Second, it may be better to 

have the vehicle move slower when it is close to the source since 

this can increase lingering capability (i.e., high values of the 

lingering metric for lower values of RLinger). These hypotheses 

make physical sense. If the vehicle were far away from the source, 

it would make sense to move quickly to get into the neighborhood 

of the source as fast as possible, and then slow down to search a 

smaller area for the source. The hypothesis that varying velocity 

can lead to performance improvements was tested by varying the 

vehicle’s commanded velocity during a given run according to Eq. 

2. Results for starting distances of R0-Initial = 0.5 and R0-Initial = 2.0 

are shown in Figure 9. 

Figure 9. Illustration of the Success (Left) and Lingering 

(Right) metrics. For each plot, for a given sigmoid parameter, 

each bar corresponds to a different RCritical or RLinger value. 

The top 2 plots are for R0-Initial starting values of 0.5. The 

bottom 2 plots are for R0-Initial starting values of 2. 

From Figure 9, when the vehicle starts far away from the source, 

it only succeeds in a sigmoid parameter environment of 0.1. 

However, when it starts closer to the odor source, the vehicle is 

able to achieve success in all sigmoid parameters, with source 

localization occurring in sigmoid parameters of 0.1 and 0.25. This 

is consistent with the results from the wind-influenced constant 

speed trials in Section 3.2. 

4. Conclusions and Discussion 

We have developed a preliminary SCASM network for tracking 

odors in a simplified 2D environment. We tested the controller in 

an environment where the odor decays as the vehicle moves away 

from the source and where the vehicle’s velocity can be 

influenced by the wind. We used the results from these tests to 

begin understanding how the environment affects the controller’s 

performance by employing basic performance metrics. Overall, 

this study illustrates the development of a basic SCASM-based 

odor tracker and demonstrates the possibility of extending 

SCASM for use in control paradigms beyond legged locomotion. 

It appears that simultaneously having relatively high success rates 

(in the range of approximately 80% to 100%) and relatively high 

lingering rates (in the range of approximately 75% to 100%) 

results in tighter localization of the odor source (metric values 

taken from Figures 5, 7 and 9). As stated in Section 2.7, the 

lingering ratio of 15 was based on our initial observations of the 

controller’s performance. Based on the metrics developed here, 

the current controller’s preferred odor content environments 

appear to be a = 0.1 and a = 0.25 when the vehicle is started close 

to the source (both for constant and variable vehicle speed). When 

the vehicle starts farther away from the source, its preferred 

environment seems to be a = 0.1. This trend suggests that as the 

starting distance increases, the robustness of the controller across 

sigmoid parameters decreases, which makes sense based on 

Figure 3. At the farthest starting distance, the only environment 

that has a non-negligible probability of odor detection is a = 0.10, 

whereas at the closest starting distance, sigmoid parameters of 

0.10 and 0.25 have non-negligible probabilities of odor detection. 

At higher speeds, the vehicle is capable of succeeding in sigmoid 

parameter environments with odor contents less than those of a = 

0.1 or a = 0.25. However, due to the vehicle’s larger turning 

radius at these speeds, the controller does not seem to be able to 

linger as well, which can either decrease or destroy the vehicle’s 

localization ability. Changing the vehicle’s velocity from a 

constant to a variable value that depends on how often odor is 

sensed did affect the nature of the resulting odor tracks (i.e., for 

one action, an odor track can have different turn radii). Also, with 

a variable vehicle speed, for R0-Initial = 0.5, the vehicle seemed to 

exhibit better lingering performance in the lower values of RLinger, 

suggesting that for this initial condition, better localization may be 

possible with a variable vehicle speed. However, the success and 

linger metrics indicate that even with this change, the preferred 

odor content environments remained the same, so there was not an 

improvement in robustness across sigmoid parameters. 

Qualitatively, when the controller was able to “Succeed” and 

“Linger”, the odor tracks appeared to be indicative of the source 

223
location (e.g., Figures 6A and 8A show the vehicle orbiting the 

odor source with a relatively small turning radius as compared to 

Figure 6C). Even when the controller was not able to succeed, or 

linger that well, there were cases where it was still able to produce 

potentially useful behaviors, such as outlining one of the plume 

boundaries (data not shown). In fact, in some of the cases where 

the vehicle succeeded, the path that the vehicle took was along 

one of the plume’s edges. This occurred a great deal for a vehicle 

speed of 0.1 length units/second and R0-Initial = 0.5 (data not 

shown). The effect of the wind speed was to continuously push 

the vehicle away from the source. In successful runs, this could 

hinder the lingering capability of the vehicle (i.e., the vehicle 

would go through a cycle of finding the source and then being 

pushed away). In runs where the vehicle failed to locate the odor 

source (and even in some of the “successful” runs), the wind 

speed caused the vehicle to spiral away from the source because 

the vehicle entered a turning state due to a lack of odor and the 

wind velocity was continuously added to the vehicle’s velocity 

(Figures 6-B1 and 8-B1). As the vehicle speed increased, the wind 

speed’s influence became less and less pronounced, as one would 

expect. 

We note that the linger ratios had a large amount of variability, 

including a few distant outliers. A preliminary analysis revealed 

that the linger metrics may not be normally distributed. We plan 

to further investigate and quantify this variability in future studies. 

A number of steps can be taken to extend and improve upon this 

work. First and foremost, we would like to repeat this study, but 

looking at the effects of changing other controller and 

environmental parameters, such as the vehicle’s turn rate, wind 

speed, and the type of wind field used (i.e., unsteady in time 

and/or variable in space). Also, we plan to continue developing 

new performance metrics and refining the current ones. For 

example, the kind of statistical analysis that is applied to the odor 

tracks of Drosophila Melanogaster in [9] would provide a 

quantitative analysis of the odor tracks in addition to the success 

and lingering metrics. To quantify the vehicle’s ability to indicate 

the plume boundary and odor source, it may be possible to use a 

clustering algorithm on the action information that the vehicle 

records during the course of a run. With a given run’s actions and 

positions, a clustering algorithm may provide estimations of the 

odor plume structure and source, which could then be compared 

to the real boundary and odor source. In addition, we would like 

to increase the controller’s robustness by improving its ability to 

negotiate low odor content environments (i.e., a  0.25). This 

could be accomplished by incorporating a plume finding behavior 

such as insect casting into the event space diagram. We would 

also like to increase the realism of the simulation by incorporating 

dynamics and aerodynamics into the vehicle, and by eventually 

developing a 3D controller. Our long-term goal is the 

implementation of a SCASM odor tracking controller in 2D and 

3D hardware platforms (such as those described in [7] and [13]). 

5. ACKNOWLEDGMENTS 

We thank Nicole Doorly for her contributions in reviewing the 

data. This research was performed under an appointment to the 

U.S. Department of Homeland Security (DHS) Scholarship and 

Fellowship Program, administered by the Oak Ridge Institute for 

Science and Education (ORISE) through an interagency 

agreement between the U.S. Department of Energy (DOE) and 

DHS. ORISE is managed by Oak Ridge Associated Universities 

(ORAU) under DOE contract number DE-AC05-06OR23100. 

The opinions expressed here do not necessarily reflect the policies 

and views of DHS, DOE, or ORAU/ORISE. 

6. REFERENCES 

[1] Ekeberg, Ö., Blümel, M., Büschges, A. "Dynamic simulation 

of insect walking," Arthropod structure & development, vol. 

33, pp. 287 (14 pages), 2004 

[2] Rutter, B.L., Lewinger, W.A., Blümel, M., Büschges, A., 

Quinn, R.D. (2007) Simple Muscle Models Regularize 

Motion in a Robotic Leg with Neurally-Based Step 

Generation. Proceedings of ICRA 2007, Rome 

[3] Rutter, B.L., Lewinger, W., Taylor, B.K, Wilson, M., 

Blümel, M., Ekeberg, Ö., Büschges, A., Ritzmann, R.E., 

Quinn, R.D. (2006) “Neurally-Based Robot Control for 

Neuromechanical Modeling of Insect Stepping,” Soc. 

Neuroci. Abstr. CD ROM 32: 449.13.

[4] Lewinger, W. A., Rutter, B. L., Blümel, M., Büschges, A., 

and Quinn, R. D. "Sensory Coupled Action Switching 

Modules (SCASM) generate robust, adaptive stepping in 

legged robots," in CLAWAR 2006: 9th International 

Conference on Climbing and Walking Robots. 

[5] Lewinger, W.A., Rutter, B.L., Quinn, R.D., “Irregular 

Terrain Navigation and Leg Coordination Improve Walking 

Behavior for Small Legged Robots”, Adaptive Motion in 

Animals and Machines (AMAM) 2008, Cleveland USA 

[6] Rutter, B.L., Bender J.A., Taylor, B.K, Ritzmann, R.E., 

Quinn, R.D. (2008) “Experiments in Locomotion with 

Neuromechanically Based Robotic Insect Models” Soc. 

Neuroci. Abstr. 198.7. 

[7] Rutkowski, A.J., “A Biologically-Inspired Sensory Fusion 

Approach to Tracking A Wind-Borne Odor in Three 

Dimensions”, Ph.D Dissertation, Department of Mechanical 

and Aerospace Engineering, Case Western Reserve 

University, January 2008. 

[8] Edwards, S., et al. Moth-Inspired Plume Tracking Strategies 

In Three-Dimensions. in Robotics and Automation, 2005. 

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[9] Porter, M.J. and J.R. Vasquez. Bio-Inspired Navigation of 

Chemical Plumes. in Information Fusion, 2006 9th 

International Conference on. 2006. 

[10] Farrell, J.A., P. Shuo, and L. Wei, Chemical plume tracing 

via an autonomous underwater vehicle. Oceanic 

Engineering, IEEE Journal of, 2005. 30(2): p. 428-442. 

[11] Belanger, J.H., Arbas, E.A., “Behavioral Strategies 

Underlying Pheromone Modulated Flight in Moths: Lessons 

Learned from Simulation Studies”, Journal of Comparative 

Physiology A (2008), vol 183: pgs 345 – 360. 

[12] Budick, S.A., Dickinson, M.H., “Free-flight responses of 

Drosophila Melanogaster to Attractive Odors”, The Journal 

of Experimental Biology (2006), vol 209: pgs 3001 – 3017 

[13] Bailey, J.K., Willis, M.A., Quinn, R.D., “A Multi-Sensory 

Robot for Testing Biologically-Inspired Plume Tracking 

Srategies”, International Conference on Advanced Intelligent 

Mechatronics 2005, Monterey California, USA

224
A Confidence Measure for Segment Based Maps

Rolf Lakaemper

Temple University

Philadelphia, PA, USA

lakamper@temple.edu

ABSTRACT

Map confidence, or map quality based on regional consistency is an important measure to evaluate the quality of

robot maps. It is classically handled analyzing occupancy

grids, which is an unnatural choice if the map is not represented by data points, but by line segments. We define a

map-confidence measure that is tailored for segment based

maps, without leaving the compact data representation by

segments. The presented confidence measure is not based

on comparison to ground truth data, but evaluates the map

(ground truth free) based on map consistency.

1. INTRODUCTION AND APPROACH

The interest in robot mapping based on higher geometric

structures like linear elements is currently growing. Obvious advantages in runtime, memory efficiency and simpler

mid-level analysis capability make such mapping approaches

powerful competitors to the classic, point based techniques.

Computing the confidence in scans, and evaluating a global

map based on the confidence of aligned single scans in the

context of the global map is an important task in robot mapping. This paper gives an example, how map confidence can

be computed purely based on line segments. The presented

approach evaluates the confidence in each single segment; it

can be used to delete inconsistent segment data (’map cleaning’), as well as to score the quality of a given segment map

based on segment consistency.

The core algorithm was originally designed as a processing

module of a segment based robot mapping system (description of this system is part of a future publication). Its purpose there is to clean intermediate mapping results, consisting of a low number of aligned, segment based local maps,

from inconsistent or noisy segments. In a straightforward

manner, such a module can be extended to a global (or regional) confidence measure: the more consistent segments

(in a certain region), the better.

An important design paradigm of the presented research

is not to leave the very efficient and compact data reprePermission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies

bear this notice and the full citation on the first page. To copy otherwise, to

republish, to post on servers or to redistribute to lists, requires prior specific

permission and/or a fee.

Copyright 2009 ACM 978-1-60558-747-9/09/09 ...$10.00.

sentation by segments. Such an approach leads to multiple

advantages compared to point/grid based methods:

• The segment based approach captures structural information. This information goes significantly further

than the information of object presence, contained in

raw point data. Figure 3, (b,c,d) shows examples: the

red segments are detected as noise. In point density

based confidence approaches like occupancy grids [2],

the original data points would not only be labeled as

correct (since the point density in this area is high),

but would have enhanced the confidence in this region.

• The segment based approach is fast. In indoor environments or urban outdoor environments, a typical scan

consists of n < 20 segments of sufficient length, while

the number of data points is typically one to two orders

of magnitude (factor 10 − 100) higher. This becomes

especially important when point relations between different scans have to be evaluated, which usually implies algorithms with runtime between O(nlogn) and

O(n

2

).

• The segment based approach is memory efficient. Compared to occupancy grids, the memory consumption is

significantly lower.

• The segment based approach is precise. Segment endpoints don’t have to be adjusted to a resolution parameter, hence there are no quantization errors. This

is in contrast to grid based approaches.

The basic idea of the approach is to cluster segments,

based on an inter segment-distance measure. The quality

of clusters defines the confidence in the participating segments, which in turn defines the confidence in the entire

map. The main steps are i) the definition of a perceptually

consistent segment-distance measure, ii) the adaption of a

classic clustering technique (hierarchical clustering) to gain

a parameter free clustering system, and iii) a new measure

for intra cluster consistency, which directly leads to the final

goal, the confidence measure.

2. RELATED WORK

To the authors’s best knowledge, there are no publications available about generic map evaluation, the reason being that map evaluation is highly task specific. Task specific

map evaluation is usually performed in the broader environment of robot competitions, such as RoboCup [1] or the US

Department of Energy Grand Challenge [13]. Test arenas,

225
developed by the National Institute of Science and Technology (NIST) exist [4], as an effort to create robot maps

in standard environments. These arenas were used in various events, e.g. the RoboCup Rescue competition and the

Response Robot Evaluation Exercise [11].

An occupancy grid based evaluation tool, the Jacobs Map

Evaluation Toolkit [3], was utilized in the RobocupRescue

competition 2008. Aside from functionalities like ground

truth map creation, it consists in its core of a metric comparing the (grid/pixel based) maps. In short, correspondences between foreground points of the evaluated map and

a ground truth map are established. The correspondence

quality is computed using the spatial distance of the corresponding points.

In contrast, the presented evaluation method does not perform a comparison to a ground truth map, but aims to analyze the consistency of a single map. Working on a higher

data structure, line segments, it tries to capture regional

structural properties. These are evaluated based on their

ambiguity of representation: a single cluster represents a single feature, a high intra cluster distance can be interpreted

as ambiguity, or low confidence.

A more general introduction and overview of benchmarking and evaluation in robotics is given in [10].

3. INTER SEGMENT DISTANCE

This section introduces a distance measure between pairs

of line segments s1, s2. The basic idea of the distance measure is to merge two line segments to an ’average’ segment

s ̄. The distance is the merging cost, which consists of three

parts:

• the angular distance between si and  ̄s, i = 1, 2

• the spatial distance between si and  ̄s, i = 1, 2

• the spatial distance between s1 and s2.

The first two parts penalize the amount of ’non collinearity’ of the segments, the third part penalizes spatial distance.

Although used as a distance measure between two segments,

the design is based on comparison to a ’virtual’ average segment. This is motivated by certain experiments, suggesting

that human perception assigns or connects line segments to

larger structures under certain circumstances. For example, two collinear, overlapping line segments are perceived

as one line, i.e. both segments represent the same element

and should therefore have a distance of zero (which is the

case for our distance measure). Please note that such a distance measure is no metric. It already disobeys the most

’intuitive’ axiom of the metric axioms, the identity of indiscernibles (d(a, b) = 0 ↔ a = b), since two non identical

collinear segments s1, s2 with s1 ∪ s2 6= ∅ have a distance

d(s1, s2) = 0. This fact becomes important for the choice of

the clustering algorithm, see section 4

The definition of the measure is out of scope of this paper

and will be part of a future publication. Figure 1 gives

examples of segment configurations and resulting distances.

4. CLUSTERING

For the clustering, agglomerative hierarchical clustering

in ’single’ mode is utilized. This method seeks to build a

bottom up hierarchy of clusters, starting with each segment

0.0851622 0.487849

a b

0.521038 1.14809

c d

Figure 1: Segment configurations with increasing

distance. a) 0.09 b) 0.49 c) 0.52 d) 1.14. The thin

line is the merged segment. The increase in a)-c)

results from larger intra segment distance, while d)

results from angular distance.

being a single cluster, ending in a single cluster containing

all segments. Pairs of clusters are merged as one moves up

the hierarchy. The merge is determined in a greedy manner:

the two clusters with minimal distance are merged to a single

one. Hierarchical clustering allows for different strategies to

determine the distance of the newly emerged cluster to the

remaining elements. In our case, we use the ’single mode’

strategy: the distance between two clusters is the minimum

distance between their elements. There is a geometric motivation for the use of this mode: in the example of collinear,

slightly overlapping segments single mode clusters these segments to a single group — intuitively, single mode clustering

acts like a connected components algorithm, the necessary

topology being defined through the distance measure (small

distance = neighbors).

Hierarchical clustering has two main properties which suggest its use in the segment merging context: first, it is, in

its first stage, parameter free, i.e. no pre-defined number of

clusters has to be determined. Parameters might be introduced later in a follow up stage, which selects the level of

clustering (agglomerative hierarchical clustering always ends

in a single cluster). Second: it is simply based on mutual

distances between the data points (here: line segments), yet

without the need to embed them in a metric space. This

means, hierarchical clustering can deal with any distance

measure (especially non-metrics, as in the given case).

We want to illustrate the segment clustering by a simple

example, see Figure 2. The data set of this example consists of 15 segments, which can intuitively be combined to 3

clusters. Figure 2,b), shows the resulting dendrogram. Each

horizontal bar shows the linkage Li between two clusters, Li

is assigned the minimal distance between elements of the left

and right subtree of the linkage; in the dendrogram this cost

is displayed by the height of the bar. In this simple case, the

dendrogram clearly suggests the three clusters. The critical

step in hierarchical clustering is to define the step to end

the clustering process. We do so if a potential merge de226
 7 10 13 1 4 3 6 12 15 9 2 5 14 8 11

0

0.5

1

1.5

a b

Figure 2: Clustering example. Left: Segments.

Right: Dendrogram. See text for details.

creases the intra cluster consistency significantly. To determine clusters, we assign a consistency value c(Li) to each

linkage Li. c(Li) compares the linkage distance Li with all

linkage distances L

l

i

, Lr

i of the left and right subtree:

Li =

Li − mean(L

l

i ∪ L

r

i )

mean(L)

(1)

with L being the set of all linkages. Data elements s1, s2

(segments) belong to one cluster if all linkages connecting s1

and s2 do not exceed a certain threshold Tc. Normalizing by

mean(L) makes the approach scale independent. Our consistency measure has a clear geometric motivation, and performed well in different examples (see results in section 7).

Determining clusters from dendrograms can be performed

in different ways. More details about hierarchical clustering

can be found e.g. in [5].

5. CLUSTER QUALITY: THE CONFIDENCE

MEASURE

The main step in our evaluation is to determine the consistency of each cluster. Please observe that we already

computed an intra cluster consistency value c(Li) to determine the clusters. c(Li) has certain drawbacks handling

outliers, it is not necessarily consistent with the perceptual

consistency. We therefore introduce a new intra-clusterconsistency measure C which is stronger perceptually motivated and adjusts better to the specific problem. C is used

to re-evaluate each cluster, it is, however, too expensive to

be utilized in the clustering process itself. It is therefore

only applied after the clustering process is finalized.

In C, collinear structures are favored, while clusters containing wide-spread segment sets are penalized. Similar to

the segment distance measure, each segment in the cluster

is compared to an average cluster segment, the cluster representative. In analogy to classic intra cluster consistency

measures, the angular and spatial distance to this representative is taken into account to determine the cluster consistency. Intuitively, all angular distances of segments in

one cluster to the average cluster representative are computed, as well as the transitional distances. For angular

and translational distances, two separate confidence measures Ca, Ct ∈ [0..1] (angular/translational respectively) are

computed (see details below). The final confidence C is computed as

C = min(Ca, Ct). (2)

A high confidence ( 1) is therefore only assigned if both,

angular and translational confidence are high. Additionally,

clusters must contain a certain minimal number of segments

(in the current system: three segments), otherwise they are

assigned a confidence of C = 0. Figure 3 shows examples

for clusters and their consistency value C. Please note that

especially Figure 3 shows the superiority of a segment based

evaluation to point based occupancy grids. Figure 4 is a

1

2

3

4

5

6

7

8

9

10

(a)

2

3

4

5

6

7

8

12

13

Cluster Confidence

2: 0.0

3: 0.885

5: 0.669

6: 0.705

7: 0.636

8: 0.611

12: 0.279

13: 0.162

(b)

1

2

3

4

5

6

7

8

9

10

11

12 1

4

7

8

10

11

(c) (d)

Figure 3: Evaluating clusters using the confidence

measure C. The red/green-ness is determined by

confidence (the greener, the more confident). a) regions with non matching angles, widespread structures and areas of insufficient density are marked as

non confident. b) segment based confidence detects

structural inconsistency: the 45 degree corner scans

are detected as inconsistent. d) a magnified view

of the marked part of c): the correctly detected inconsistent segments have a huge overlap with consistent segments: detection of such areas is not possible with occupancy grids, but only with methods

detecting underlying structural information.

comparative example showing the performance of the two

confidence measures c(Li) and C(Ci): the tendency of both

measures is approximately equal (this is why we can use the

computationally cheaper c(Li) during the clustering), yet C

yields more perceptually consistent results.

5.1 Angular Confidence Ca

227
1

2

3

4

5

cluster 4

1 1.5 2 2.5 3 3.5 4 4.5 5

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure 4: Left: A simple example of a map with

high confidence in all 5 regions (clusters). Right:

(blue) cluster quality using the distance matrix

based method c(Li), evaluating cluster 4 (arrow in

left figure) to be of lower quality; (red)C, which is

in accord with the perceived high consistency of all

clusters.

For all segments si of a single cluster L, we compute their

angles ai ∈ [−π/2..π/2] with the x-axis. We define the cluster’s average angular direction θ using the weighted circular

mean wcm

θ = wcm(2ai, |si|)/2 (3)

where |si| denotes the length of segment si, used as the

corresponding angle’s weight (multiplication/division by 2

ensures correct handling of segment directions). The angular

distance da is computed as the weighted (by length) sum of

distances to θ:

da =

P

i min((|ai − θ|) mod π, π

P

− ((|ai − θ|) mod π)) li

i

li

.

(4)

Finally, the angular confidence is computed as

Ca = exp

d

2

a

2σ2

a

(5)

with a parameter σa = 0.05 which was experimentally determined and fixed.

5.2 Translational Confidence Ct

For the translational confidence, we compute the maximal

distance ti of each segment to a cluster-representative line S.

S is defined by θ and a point P =

P(pi)/#L, the average

center point (pi: center point of si, #L: number of segments

in cluster L). The translational distance is defined by

dt =

P ti

#L

(6)

Finally, the angular confidence is computed as

Cd = exp

d

2

t

2σ

2

t

(7)

with a parameter σa = 0.1 which was experimentally determined and fixed. Observe that σt is scale dependant. The

current value is determined for robot maps with scale unit

of one meter.

6. MAP EVALUATION

It is a small step from regional evaluation of single clusters to global map evaluation. Given all clusters Ci along

with their confidence measure C(Ci), we define the global

confidence M of a map by

M =

P

i #Ci C(Ci)

P

i #Ci

(8)

with #Ci denoting the cardinality of Ci. M computes the

average consistency of all segments, defining the confidence

of a segment by the consistency of the cluster it participates

in.

7. RESULTS

7.1 Random Distortion

1

2

3

4

5

6

7

1

2

3

4

5

6

7

a b

1

2

3

4

1 2 3 4 5 6 7 8 9 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

c d

Figure 5: Global map evaluation using M. A map

with high confidence was randomly increasingly distorted in 10 steps (Figure shows step 1(a),5(b) and

10(c)). (d) shows the global confidence diagram for

the resulting maps, x-axis: step 1-10, y-axis: confidence M.

In this experiment, a map with high confidence was randomly increasingly distorted in 10 steps. Figure 5 shows

steps 1, 5 and 10 and the confidence measures M for each

of the 10 distortion levels. Expectedly, the results show decreasing confidence.

7.2 Map Comparison

In this experiment, we compare results of two mapping

algorithms. The first algorithm [6] is a point based alignment (not segment based) algorithm. However, it results in

corrected poses of single scans. We used an algorithm explained in [8] to extract segments from these single scans,

and superimposed them, using the corrected poses (Figure

6,a). The second map (Figure 6,b) was computed by a new,

segment based algorithm, which will be topic in a future

publication. Both output maps consist of the same single

scans’ segments, yet aligned using different poses. It can

clearly be seen that the first map is less consistent. Our

evaluation algorithm does not only capture the overall difference in quality (quality of first map: M = 0.2769, quality

228
a

b

Figure 6: Evaluation of two maps of the data set

’Freiburg082’. a) Quality of map M = 0.2769. b)

M = 0.4355. Colors: level of green (vs red) shows

confidence: The greener, the more confident, the

more red, the worse. The higher regional confidence

in (b) leads to the better total confidence value.

of second map: M = 0.4355), but also identifies the confidence of single clusters (regions). It is interesting to show

segments above a certain confidence level only (Figure 7). In

this data set, this leads to structural de-noising of the map:

usually, large and static (in contrast to smaller and/or moving) objects in the environment yield high confidence representation. Therefore, the main structure of the environment

is highlighted (of course, using higher quality clusters only,

the map quality based on our evaluation measure increases).

Additional merging of the clusters to single segments yields

a clear map in a very compact representation (here: 80 segments). We performed a second comparative experiment

on a different data set (data set NIST), using the mapping

algorithms FFS [9] and FFS with Virtual Scans [7]. The

latter one is an extension of the first, and leads to (visually inspected) improved results. Numerical evaluation of

the results using the presented measure is consistent with

the visual impression, see Figure 8. The maps only differ

slightly in certain regions. However, the overall visual impression of (b) is slightly better than the one of (a), which

is also expressed in the evaluation. The experiments leading

to the respective maps are documented in [7].

a

b

Figure 7: Using segments of high confidence only

yields structural de-noising. a) segments of Figure 6,(b), belonging to clusters Li with a confidence c(Li) > 0.3 (80 clusters, overall confidence

M = 0.6554). b) clusters of (a) represented by single

representative merged segments (80 segments).

8. RUNTIME

The presented algorithm has an order of magnitude of

O(n

2

), n = total number of segments, which results from

computation of the pairwise segment distance matrix. The

MATLAB implementation of the algorithm needed 1 second for the experiment using data set NIST (332 segments),

and 5 seconds for the experiment using data set Freiburg082

(1975 segments), both on a 1.8GHz laptop PC.

9. CONCLUSION AND OUTLOOK

The presented confidence measure evaluates maps in consistency with visual perception. In its core, it uses a classical clustering algorithm, hierarchical clustering, which is

adapted to the current problem utilizing a segment distance

measure and a segment based cluster confidence measure.

Since segment based representation captures structural features better than its lower representation counterpart, point

based maps, erroneously mapped/aligned features can be

detected even if they overlap with correct features. This

leads to detection of structural consistency, which is the

main property evaluated by the presented approach. With a

re-definition of segment distance and cluster confidence, the

229
a

b

Figure 8: Mapping of data set NIST using algorithms FFS (a) and FFS with Virtual Scans (b).

The evaluation leads to values of M = 0.3386 (a) and

M = 0.3876 (b), reflecting the slight visual improvement of (b) over (a).

approach is extendable to 3D, which makes it interesting for

3D mapping algorithms based on planar elements, e.g. [12].

10. ACKNOWLEDGEMENTS

Thanks to Alexander Kleiner, University of Freiburg, for

the data set ’Freiburg082’.

11. REFERENCES [1] S. Balakirsky, S. Carpin, A. Kleiner, M. Lewis,

A. Visser, J. Wang, and V. A. Ziparo. Towards

heterogeneous robot teams for disaster mitigation:

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[2] A. Elfes. Using occupancy grids for mobile robot

perception and navigation. Computer, 22(6):46–57,

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[3] I. Varsadan, A. Birk, and M. Pfingsthorn. Determining

Map Quality through an Image Similarity Metric. In

Proceedings of the RoboCup Symposium, July 2008.

[4] A. Jacoff, E. Messina, B. Weiss, S. Tadokoro, and

Y. Nakagawa. Test arenas and performance metrics for

urban search and rescue robots. In Intelligent Robots

and Systems, 2003. (IROS 2003). Proceedings. 2003

IEEE/RSJ International Conference on, volume 4,

pages 3396–3403 vol.3, Oct. 2003.

[5] S. Johnson. Hierarchical clustering schemes.

Psychometrika, 32(3):241–254, September 1967.

[6] A. Kleiner and C. Dornhege. Real-time localization

and elevation mapping within urban search and rescue

scenarios: Field reports. J. Field Robot.,

24(8-9):723–745, 2007.

[7] R. Lakaemper. Improving sparse laser scan alignment

with virtual scans. In International Conference on

Intelligent Robots and Systems (IROS08), Nice,

France, September 2008. IEEE.

[8] R. Lakaemper. Simultaneous multi-line-segment

merging for robot mapping using mean shift

clustering. In International Conference on Intelligent

Robots and Systems (IROS09), St Louis, MO, USA,

September 2009. IEEE.

[9] R. Lakaemper, N. Adluru, L. Jan Latecki, and

R. Madhavan. Multi robot mapping using force field

simulation: Research articles. J. Field Robot.,

24(8-9):747–762, 2007.

[10] R. Madhavan, R. Lakaemper, and T. Kalmar-Nagy.

Benchmarking and standardization of intelligent

robotic systems. In 14th International Conference on

Advanced Robotics (ICAR 2009), Munich, Germany,

June 2009.

[11] NIST. NIST Response Robot Evaluation Exercise.

Search and Rescue: Texas Engineering Extension

Service (TEEX), November 2008.

[12] K. Pathak, N. Vaskevicius, J. Poppinga,

M. Pfingsthorn, S. Schwertfeger, and A. Birk. Fast 3d

mapping by matching planes extracted from range

sensor point-clouds. In International Conference on

Intelligent Robots and Systems (IROS09), St Louis,

MO, USA, September 2009. IEEE.

[13] S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens,

A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny,

G. Hoffmann, K. Lau, C. Oakley, M. Palatucci,

V. Pratt, P. Stang, S. Strohband, C. Dupont, L.-E.

Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van

Niekerk, E. Jensen, P. Alessandrini, G. Bradski,

B. Davies, S. Ettinger, A. Kaehler, A. Nefian, and

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grand challenge: Research articles. J. Robot. Syst.,

23(9):661–692, 2006.

230
Evaluation of RoboCup Maps

Benjamin Balaguer, Stefano Carpin

School of Engineering

5200 North Lake Road

University of California, Merced, USA

+1(209)228-4152

{bbalaguer,scarpin}@ucmerced.edu

Stephen Balakirsky

NIST

100 Bureau Drive

Gaithersburg, MD, USA

+1(301) 975-4791

stephen@nist.gov

Arnoud Visser

Universiteit van Amsterdam

Science Park 107

1098 XG Amsterdam, NL

+31 (20) 525-7532

A.Visser@uva.nl

ABSTRACT

This paper describes the steps taken to create the scoring

criteria aimed at measuring the quality of maps produced by

teams participating in the RoboCup Rescue Virtual Robots

competition. Since metrics have already been developed by

a few research groups, we start by highlighting the most popular solutions to this problem, emphasizing their strengths

and weaknesses. Having put the difficulty of creating map

benchmarks into perspective, we present our map benchmark suite, appropriate for Urban Search and Rescue missions, along with examples taken from former competitions.

Categories and Subject Descriptors

F.2.3 [Theory of computation]: Analysis of Algorithms

and Problem Features—Tradeoffs among complexity measures

General Terms

Performance, Measurement

Keywords

Map evaluation, RoboCup, Performance Metrics

1. INTRODUCTION

One of the competitions in RoboCup is the Virtual Robot

Rescue league, where participants are called upon to deploy

teams of robots capable of locating victims and hazards in

unstructured areas. As opposed to other RoboCup competitions [17, 20, 1, 11], the Virtual Robot Rescue league asks

robot teams to map unknown environments, with little a

priori information. The theme behind the league is Urban

Search and Rescue (USAR), where robots are deployed in

disaster scenarios (e.g. earthquakes, landslides) and have to

work cooperatively as unified teams while taking into account humans, whether they be victims or first responders

(e.g. firefighters, rescue teams). As such, the maps that

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies

bear this notice and the full citation on the first page. To copy otherwise, to

republish, to post on servers or to redistribute to lists, requires prior specific

permission and/or a fee.

Copyright 2009 ACM X-XXXXX-XX-X/XX/XX ...$10.00.

are generated by the robots need to incorporate useful information that first responders can exploit, adding a new

dimension to robot mapping. Indeed, robots now have to

generate multiple maps, some of which are for their own

needs (e.g. navigation) while others are explicitly for firstresponders (e.g. victim locations with safest paths to reach

them).

The Virtual Robot Rescue League uses the Unified System for Automation and Robot Simulation (USARSim) [7]

to simulate disaster scenarios. The simulation is realistic

due to a community of users and developers who strive to

validate each robot, sensor, or other physical properties [6,

8, 13]. This community involvement created a remarkably

accurate simulation capable of modeling multifaceted disaster environments ranging from traffic accidents to earthquakes and explosions, each possibly exploiting the effects

of smoke, fires, debris, water, to name a few. In addition to

the near-zero participation cost and the ability to create realistic city-sized disasters, the simulation offers ground truth

data that would otherwise be difficult to gather. The large

amount of robotic platforms and sensors in USARSim translates into a challenging situation for map scoring. Indeed,

each team solves the mapping problem differently using a diverse set of robots and sensor configurations, resulting in a

massive mismatch between maps, from scaling to rotational

differences. This puts us in the unique position of having to

come up with a map benchmark robust enough to take into

account all of these differences along with the opportunity

of having a tremendous amount of data available.

Evidently, and despite the fact that it is still frequently

employed, a qualitative approach is fundamentally insufficient for a competition where results have to be both repeatable and reliable. Not wanting to develop a map benchmark from scratch, and optimistically hoping that a solution had already been published, we performed an extensive

case study, a part of which is shown in Section 2. Realizing

that no current solution was robust enough for the problem at hand (i.e. teams would be able to take advantage of

the metrics’ weaknesses), we developed a mapping benchmark suite comprised of standards and categorized metrics,

which are described in Section 3 and 4, respectively. It is

worthwhile to note that the standards were so well received

that they have subsequently been implemented as part of

the Real Robot Rescue League. We close the paper with

concluding remarks and possible future work in Section 5.

While this paper focuses on mapping, a companion paper

highlights the overall RoboCup 2009 competition [3].

231
2. CASE STUDY

Map benchmarking is a relatively novel effort, and so is

robot benchmarking in general. Therefore, the amount of

formerly published scholar work is rather limited (the reader

is referred to a forthcoming special issue of the Autonomous

Robots journal on Characterizing mobile robot localization

and mapping). In this section, we quantitatively compare

some of the most popular benchmark metrics that have been

previously published. We run the metrics with two binary

occupancy grid maps, one generated by a robot and the

other being ground truth. Each grid cell can only have a

value of 1 for occupied space or a value of 0 for free space.

Please note that the discussion in this section is entirely

based on our binary map representation and that results

might be different with a probabilistic occupancy grid map.

Even though we have performed a full case study on different

environments, we only present a representative example in

Fig. 1 and Table 1 due to space constraints.

The first set of four metrics, namely the Map Score [14],

Overall Error [5], Normalized Map Score [16], and Occupied Map Score [16], represents an approach requiring pixelto-pixel comparisons between the ground truth and robotgenerated maps. The Map Score metric counts the number

of ground truth and robot map pixels that are the same. As

such, a perfect score would be obtained when the Map Score

metric equals the number of pixels in the map. The Overall

Error metric counts the number of ground truth and robot

map pixels that are different, where a perfect score would

be zero. The Map Score metric measures accuracy whereas

the Overall Error metric measures error and that adding

both metrics together will equal the total number of pixels.

The two aforementioned metrics are utilized over all the pixels, regardless of what they represent (i.e. occupied or free

space). Consequently, the two metrics are biased towards

maps with large regions of correct free space, as shown in

Fig. 1 and Table 1. From the table, Team A and Team

E have the best scores and, looking at the figure, the bias

is clear: the two maps with the smallest amount of discovered walls receive a higher score. Research groups have attempted to remove this bias by introducing the Normalized

Map Score and Occupied Map Score metrics. They work

the same way as the Overall Error metric (i.e. looking for

pixel mismatches) but are only run on the occupied space

of the maps. The Normalized Map Score runs on the occupied space of the ground truth map whereas the Occupied

Map Score runs on the occupied space of the robot-generated

map. Unfortunately, these metrics only move the bias, which

is now dependent on the occupied space. Using the Normalized Score metric, the robot maps that have thick walls do

better, as shown by Team C and Team E, since they do a

better job in replicating the wall thickness of the ground

truth map. In contrast, Team A and Team B do better with

the Occupied Map Score metric, due to their thin walls that

allow for a greater margin of error when compared to the

thicker ground truth walls.

Another interesting pixel-to-pixel approach is presented

through the Picture-Distance function [4]. In this metric,

the score represents the Manhattan-distance between an occupied pixel in the ground truth map and the closest occupied pixel in the robot-generated map. The process is

repeated over all the occupied pixels of 1) the ground truth

map and 2) the robot-generated maps. Finally, the result

is normalized by dividing it by the total number of pixels

considered. The Picture-Distance function is a measure of

map error and, as such, the best possible score is zero. A

look at Fig. 1 and Table 1 quickly shows that the two teams

who have explored the most, Team C and Team D, do better with this metric. From both the method used and the

experiment performed, it is clear that the method is also

biased, towards exploration (i.e. wall discovery).

Moving away from the bias of pixel-to-pixel comparisons

brings us to correlation coefficients, a comparison measures

valued between -1 and 1, with -1, 0, and 1 representing perfect inverse correlation, no correlation, and perfect correlation, respectively. The Baron’s Cross Correlation coefficient

[16] attempts to correlate two images by using the ground

truth and robot-generated pixels’ mean and standard deviation. Since averages are used, and the pixel’s values can

only be 0 or 1, the Baron’s coefficient rewards robot maps

that have a similar number of occupied and free pixels to

the ground truth. Consequently, the coefficient is influenced

both by wall thickness and exploration, as can be seen in Fig.

1 and Table 1 where Team C and Team E have the highest

scores. The Pearson’s Correlation coefficient [12] evaluates

the occupied space of the map as a spatial function, trying

to linearly describe one map from the other. The Pearson’s

coefficient requires an approximately similar point distribution between the two map. This drawback is evidenced by

the results for Team A and Team E, where, even though

both maps are very similar they have extremely different

Pearson’s coefficients. It is worthwhile to note that both

correlation coefficients can be unpredictable, as shown by

the scores of Team A and Team B.

Figure 1: Example set of maps used for the Case

Study, the results of which are in Table 1. The first

image is the ground truth with the remaining images

being, from left to right and up to down, Team A,

Team B, Team C, Team D, and Team E, respectively.

3. MAP REPRESENTATION STANDARDS

One of the principal obstacles impeding the development

of a consistent map benchmark comes from the lack of standards between the large amount of mapping algorithms that

have been developed, through the years, by various research

groups. Indeed, each algorithm works differently, from the

way they represent maps (e.g. occupancy grids, topological,

feature-based, etc...) to the different scales and rotations

232
Metric Team

A

Team

B

Team

C

Team

D

Team

E

Map Score

[14]

586779 586192 585049 585297 586815

Overall

Error [5]

56577 57164 58307 58059 56541

Normalized

Map Score

[16]

56065 55785 55227 55363 54367

Occupied

Map Score

[16]

512 1379 3080 2696 2174

Baron’s

Correlation

[16]

-0.005 0.017 0.036 0.032 0.098

Pearson’s

Correlation

[12]

0.298 -0.060 0.479 0.295 0.591

PictureDistance

[4]

210.09 254.37 129.89 189.44 221.61

Table 1: Metrics comparison for the maps shown in

Fig. 1. The seven rows represent each metric taken

from different publications. The bold font shows the

two best results for a specific metric.

that they may encompass. Having to rank maps generated

by many different robotics groups and, as a consequence,

facing the same map representation problems, we have imposed two mapping standards on participants, the GeoTIFF

[19] image format and the MIF [9] vector format. We have

found, over the years, that participants embrace them, primarily for their ease-of-use, while giving the administrators

powerful tools to generate a fair mapping benchmark.

3.1 GeoTIFF Image Format

The GeoTIFF image format embeds geographical information as an integral part of the map. The power of GeoTIFF lies in its ease of use, open standard, and layering

capabilitites. Indeed, it is very simple to geo-reference any

map, by providing an additional file comprised of six parameters, namely the X and Y positions of the upper-left pixel,

the scale of a pixel in the X and Y directions, the rotation, and the skew. These six parameters take into account

any potential differences in scale, translation, and rotation

between maps. GeoTIFF is an open standard, a fact that

translates into a plethora of open tools that work across

different platforms and programming languages. Last but

not least, it is very easy to embed multiple layers on top

of the original map, a powerful way to display varied information on the maps. Evidently, from a map benchmark

standpoint, the GeoTIFF image format allows every map,

including ground truth, to be overlaid on top of each other,

as shown in Fig. 2; making it straightforward to evaluate

the maps either quantitatively or qualitatively.

3.2 MIF Vector Format

The MIF vector format is similar to the GeoTIFF format in that it possesses the same qualities of allowing georeferencing, remaining easy to use, being an open standard,

and working well with layers. The difference between the

Figure 2: Examples of two robot-generated maps

(black) overlaid on top of the ground truth map

(gray) for an indoor environment.

two, however, lies in what can be represented. Whereas GeoTIFF represents images, MIF works with geometric primitives (e.g. points, lines, polygons) that can have an arbitrary number of attributes. The MIF vector format can be

best exploited to display topological or feature-based maps,

where labeled nodes or features can give high-level information or particular landmarks of interest to first responders.

Fig. 31shows some examples of what can be achieved with

a MIF vector file.

Figure 3: Four examples of MIF vector files, overlaid

on top of the robot-generated map. The upper-left

picture shows points representing victims’ location

labeled with various information about each victim.

The upper-right picture shows line segments highlighting the best path to reach each victim, labeled

with the victim’s information and path’s length.

The lower pictures display regions of interests, including a street (left) and a house (right).

4. MAP BENCHMARK

It is clear from the Case Study that no published algorithm is adequate on its own or as part of a map benchmarking suite. They each have some sort of bias and cannot

solve the problem of error propagation, the toughest challenge when evaluating maps, where similar mapping errors

can affect maps differently depending on when the error occurred. For example, an orientation error at the beginning

of a mission will result in a map that is wrong through the

rest of the mission, whereas the same orientation error at

the end of the mission will affect a much smaller portion of

1The text in the figures is provided to give the readers an

idea of the amount of information that can be included as

part of the MIF formats. It does not need to be read.

233
the map. It is our belief that the maps should be equally

deserving, provided that everything else is equal. Additionally, a map is application-specific and, in our case, both the

USAR scenario and the first responders have to be considered as part of the map benchmark. As such, we devised a

categorized benchmark comprised of Metric Quality, Skeleton Quality, Attribution, Grouping, Utility, and Creativity.

Each category possesses a weight, the combination of which

can be used to steer the competition towards one or more

research agendas.

4.1 Metric Quality

The Metric Quality tries to solve the same problem that

was studied in the Case Study: the comparison of the robotgenerated occupancy grid map to ground truth, from an

accuracy standpoint. In order to bypass the aforementioned

problem of error propagation, we further divide the Metric

Quality into Global and Local Quality. The Global Quality

is a measure of the number and severity of mapping errors

whereas the Local Quality is a measure of accuracy between

these mapping errors. Using Fig. 4 as an example, one can

see that both robot-generated maps are similar in terms of

Global Quality, each having a small error with the lower

hallway. The right map, however, is worst in terms of Local

Quality, since it is missing some walls in the center of the

map.

Figure 4: Example for the Metric Quality evaluation, where the upper map is ground truth and the

lower-left and lower-right maps are different robotgenerated maps.

4.2 Skeleton Quality

The Skeleton Quality evaluates a topological map rather

than an occupancy grid map, which can be more useful to

first responders. A first responder should be able to follow

a skeleton map to reach a chosen point. In this case, the

quality is determined from the number of false positives and

false negatives. A false positive occurs when a node cannot

be accessed whereas a false negative takes place when a clear

topological location is available but has not been included in

the skeleton map. Fig. 5 shows examples of skeleton maps

with similar qualities. The first map has a lot of false positives in the lower and right sections of the map, where topological locations have been identified in unexplored space.

The second map contains both false positives, where a topological node is inside a wall, and false negatives, along the

left side of the hallway.

Figure 5: Example for the Skeleton Quality evaluation, with two different robot-generated maps.

4.3 Attribution

The Attribution section of our mapping benchmark aims

to reward teams that can successfully deliver a feature-based

map with valuable information for first responders. The type

of information that can be embedded into the map is fairly

open, even though most teams deliver feature-based maps

indicating victim locations and information, best paths to

reach victims, robot paths, and important landmarks. The

Attribution is scored based on the amount and accuracy of

the data. As an example, Fig. 6 shows two maps, each providing victim locations and best paths to reach them. Both

maps provide accurate victim locations but the left one offers

a lot more information about the victim, ranging from the

sex, the condition, the priority given to get rescued, the ease

of accessibility, etc... Similarly, both maps provide paths to

reach the victims but the paths of the left map are inaccurate, going through a section of unexplored space. Based on

this example, the right map would get a better score.

Figure 6: Example for the Attribution metric for

two different-robot generated maps. The left and

right columns each represent a different robotgenerated map. The first row shows the victims’

attribution while the second row shows the victim

paths’ attribution.

234
4.4 Grouping

The Grouping metric is very similar to the Attribution in

that it is, essentially, a feature-based map aimed at helping

first-responders better navigate the environment. It differs

in that instead of being point-based, it groups and labels

regions of space. Grouping stems from the fact that a section

of occupied pixels represents particular landmarks that can

be labeled. Fig. 7 offers a contrasting example, where the

left map is comprised of a single group labeled ”Hazard”

and the right map contains many different groups labeled as

”House”, ”Street”, ”Vehicle”, among others. Once again, the

metric is scored based on the amount and accuracy of the

information provided and, in this example, the right map

would receive a better score than the first one.

Figure 7: Grouping example with two different

robot-generated maps.

4.5 Utility

The map Utility takes a look at the overall information

provided by the teams. In other words, the map Utility

aims at answering the question of how useful are all the

layers to a first responder. This metric regroups the other

metrics together but looks at a larger scope, where teams

have to balance the amount of information they provide with

the way it would look on the screen. As more and more

information is given, it is harder to display it neatly while

still making it easy to understand. The clever use of layers

greatly affects the utility of a given map.

4.6 Creativity

For the purpose of the competition, we have added an unorthodox metric that rewards teams for creative new ways of

representing valuable information to first responders. Teams

are given bonus points for innovative map layers that could

help first-responders better do their jobs. In the past, a

team came up with the geo-referencing of victims’ pictures,

a layer that was quickly adopted by the rest of teams in

later competitions. More recently, a team showed the best

communication coverage attained while navigating the environment so that first-responders could replicate it should

they need to establish a communication network. An example is shown in Fig. 8.

5. CONCLUSIONS

We have presented the necessary steps to come up with a

fair map benchmarking suite capable of scoring maps produced by USAR robots working in close cooperation with

first responders. We strongly believe in committing to easilyadoptable, yet powerful, open standards such as GeoTIFF

that take little additional work from programmers while providing great benefits. Similarly, we value open-source development by requiring teams to provide public access to

Figure 8: Example of a successful Creativity metric,

displaying a communication network. Each transmitter is shown as a point with the lines showing

the connections between each link. The point in the

left represents the base station.

their software and encouraging participants to share and

reuse code and ideas. In that sense, the competition can be

viewed more as an open workshop where teams are equally

looking to learn as they are to win. From a benchmarking

standpoint, the open-source phenomenon brings an interesting component, where algorithmic progress can easily be

measured from year to year due to the fact that the software is both available and archived. We hope that the community would follow in our footsteps and make algorithms

and data sets public, so that benchmarks can be accepted

and evaluated by an entire community rather than a relatively small research group. Two projects going in that

direction are OpenSLAM [15] and Radish [18]. OpenSLAM

provides open-source SLAM algorithms and Radish offers

laser range finder data sets. While we praise both initiatives, they are not as extensively used as they should and

are missing benchmark tools that would be used to evaluate

the quality of the SLAM algorithms (from a localization or

a mapping standpoint) for specific applications.

Throughout the years, we have devoted our map benchmarking endeavors to planar occupancy grid maps comprised of certainty values (i.e. either 0 for free space or

1 for occupied space). While this restriction has been reasonable over the last few years, mainly due to the popularity of occupancy grid maps, a surge of newly fashionable

robotic platforms ranging from underwater robots to unmanned air vehicles coupled with highly three-dimensional

terrain is slowly making two-dimensional occupancy grid

maps inadequate. Indeed, teams have already started to

explore three-dimensional mapping algorithms [10]. Evidently, the switch from two to three dimensional mapping

is not straightforward in terms of map benchmarking and

offers an interesting research avenue for future work. Furthermore, it is important to note that three-dimensional

mapping does not have a map representation that is well

recognized throughout the robotics community and that occupancy grids do not offer an easy transfer from two to three

dimensions due to the increase of space and time complexities. We contend that more work needs to be achieved to

come up with a community-accepted standard representation for three-dimensional maps.

All things considered, a general ”all-purpose” mapping

benchmark is still far from being developed due to the aforementioned problems of map representation, algorithmic differences, lack of open-source data or algorithms, and ap235
plication dependability. We are convinced that mapping

benchmarks need to be tied to the application at hand and,

as such, do not see a generalized map benchmark in the

near-future. It is rewarding to see, however, that there is an

increase in awareness as to the importance of the problem

and hope that this paper will help steer map benchmarking

towards the right direction.

Acknowledgments

An extended version of this paper appeared in [2].

6. REFERENCES

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S. Haidarian, Y. Mohammadi, M. Mojtahedi,

A. Taherinia, H. M, and H. Molla-Ahmadian.

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mashhad. Technical report, Ferdowsi University of

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[2] B. Balaguer, S. Balakirsky, S. Carpin, and A. Visser.

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[3] S. Balakirsky, S. Carpin, and A. Visser. Evaluating

the robocup 2009 virtual robot rescue competition. In

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evolutionary algorithm. In The Fifth Annual

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[5] J. Carlson, R. Murphy, S. Christopher, and J. Casper.

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C. Scrapper. Bridging the gap between simulation and

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[7] S. Carpin, M. Lewis, J. Wang, S. Balakirsky, and

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and education. In Proceedings of the IEEE

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[8] S. Carpin, T. Stoyanov, Y. Nevatia, M. Lewis, and

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accuracy. In Proceedings of PerMIS 2006, 2006.

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format specification. Technical report, MapInfo

Corporation, 1999.

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3D mapping: testing algorithms and discovering new

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workshop Robots, Games, and Research: Success

stories in USARSim, 2009.

[11] P. Dempsey. Engineering football mini robocup.

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[12] I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh.

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X. Liu. Experiments with attitude: Attitude displays

for teleoperation. In Proceedings of the 2003 IEEE

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Cybernetics, pages 1345–1349, 2003.

[14] M. C. Martin and H. P. Moravec. Robot Evidence

Grids. Technical Report CMU-RI-TR-96-06, Robotics

Institute - Carnegie Mellon University, March 1996.

[15] OpenSlam. http://www.openslam.org, 2009.

[16] S. O’Sullivan. An empirical evaluation of map building

methodologies in mobile robotics using the feature

prediction sonar noise filter and metric grip map

benchmarking suite. Master’s thesis, University of

Limerick, 2003.

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[18] Radish – the robotics data set repository.

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[19] N. Ritter and M. Ruth. Geotiff format specification.

Technical report, NASA Jet Propulsion Laboratory,

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The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

Evaluating Speech Translation Systems: Applying SCORE 

to TRANSTAC Technologies 

Craig Schlenoff 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

301-975-3456 

craig.schlenoff@nist.gov 

Brian Weiss 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

301-975-4373 

brian.weiss@nist.gov 

Michelle Potts Steves 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8940 

Gaithersburg, MD 20899 

301-975-3537 

michelle.steves@nist.gov 

Greg Sanders 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8940 

Gaithersburg, MD 20899 

301-975-4451 

greg.sanders@nist.gov

Fred Proctor 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

301-975-3425 

frederick.proctor@nist.gov 

Ann Virts 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

301-975-5068 

ann.virts@nist.gov 

ABSTRACT

The Spoken Language Communication and Translation System 

for Tactical Use (TRANSTAC) program is a Defense Advanced 

Research Projects Agency (DARPA) advanced technology 

research and development program. The goal of the TRANSTAC 

program is to demonstrate capabilities to rapidly develop and field 

free-form, two-way translation systems that enable speakers of 

different languages to communicate with one another in realworld tactical situations without an interpreter. 

The National Institute of Standards and Technology (NIST), 

along with support from MITRE and Appen Pty Ltd., have been 

funded to serve as the Independent Evaluation Team (IET) for the 

TRANSTAC Program. The IET is responsible for analyzing the 

performance of the TRANSTAC systems by designing and 

executing multiple TRANSTAC evaluations and analyzing the 

results of the evaluation. 

To accomplish this, NIST has applied the SCORE (System, 

Component, and Operationally Relevant Evaluations) Framework. 

SCORE is a unified set of criteria and software tools for defining 

a performance evaluation approach for complex intelligent 

systems. It provides a comprehensive evaluation blueprint that 

assesses the technical performance of a system and its 

components through isolating variables as well as capturing enduser utility of the system in realistic use-case environments. 

This document describes the TRANSTAC program and explains 

how the SCORE framework was applied to assess the technical 

and utility performance of the TRANSTAC systems. 

Categories and Subject Descriptors

I.2.7 [Computing Methodologies]: Natural Language Processing 

– machine translation, speech recognition and synthesis

General Terms

Algorithms, Measurement, Performance, Experimentation, 

Human Factors, Languages 

Keywords

Performance evaluation, speech-to-speech translation system, 

SCORE, TRANSTAC 

1. INTRODUCTION1

Performance evaluation of advanced technologies can often be 

very challenging. It is the authors’ belief that the design of an 

effective evaluation is as much a research issue as is the 

 

1

 Certain commercial products and software are identified in this 

paper in order to explain our research. Such identification does 

not imply recommendation or endorsement by NIST, nor does it 

imply that the products and software identified are necessarily the 

best available for the purpose. 

This paper is authored by employees of the United States Government 

and is in the public domain. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

ACM 978-1-60558-747-9/09/09 

237
The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

technology development itself. One must be able to accurately 

answer questions such as: 

• Does the overall system do what it claims to do? 

• What are the factors that would cause the overall system 

to fail? 

• Is the system useful to the end-user (whether it be 

military, law enforcement, first responders, industry, 

etc.)? 

• What are the key situations that the technology would 

be most useful for? 

• How well do the individual components of the system 

perform and what is their impact on the performance of 

the overall system? 

• How can we isolate specific capabilities of the system 

and test their performance? 

In order to address this, the SCORE Framework (System, 

Component, and Operationally Relevant Evaluations) Framework 

was developed. SCORE is a unified set of criteria and software 

tools for defining a performance evaluation approach for complex 

intelligent systems. It provides a comprehensive evaluation 

blueprint that assesses the technical performance of a system and 

its components through isolating and changing variables as well 

as capturing end-user utility of the system in realistic use-case 

environments. [1] 

The paper is organized as follows: Section 2 gives an overview of 

the TRANSTAC effort; Section 3 describes the SCORE 

framework; Section 4 describes how the SCORE Framework was 

applied to assess the TRANSTAC systems, Section 5 describes 

the metrics used in the TRANSTAC program, and Section 6 

concludes the paper. 

2. OVERVIEW OF THE DARPA 

TRANSTAC PROGRAM2

The Spoken Language Communication and Translation System 

for Tactical Use (TRANSTAC) program is a Defense Advanced 

Research Projects Agency (DARPA) advanced technology 

research and development program. The goal of the TRANSTAC 

program is to demonstrate capabilities to rapidly develop and field 

free-form, two-way translation systems that enable speakers of 

different languages to communicate with one another in realworld tactical situations without an interpreter. 

Several prototype systems have been developed under this 

program for numerous military applications including force 

protection and medical screening. The technology has been 

demonstrated on PDA (personal digital assistant) and laptop 

platforms. NIST was asked to assess the usability of the overall 

translation system and to individually assess each component of 

the system (the speech recognition, the machine translation, and 

the text-to-speech). 

 

2

 Due to DARPA restrictions, the results of the evaluation cannot 

be published. Instead, this paper will focus on the evaluation 

approach as opposed to the results. 

All of the TRANSTAC systems work fundamentally the same. 

Either English speech or an audio file is fed into the system. 

Automatic Speech Recognition (ASR) processes the speech to 

recognize what was said and generates a text file of the speech. 

That text file is then translated to another language using Machine 

Translation (MT) technology. The resulting text file is then 

spoken to the foreign language speaker using Text-To-Speech 

(TTS) technology. This same process then happens in reverse 

when the foreign language speaker speaks. This is shown in 

Figure 1. 

Figure 1: How Speech Translation Works 

3. OVERVIEW OF THE SCORE 

FRAMEWORK 

The SCORE Framework [2] [3] has been developed at the 

National Institute of Standards and Technology (NIST) over the 

past three years to provide formative evaluations of advanced 

technologies that are still under development. SCORE is built 

around the premise that, in order to get a true picture of how a 

system performs in the field, it must be evaluated at the 

component level, the system level, the capability level and within 

operationally-relevant environments. 

SCORE is unique in that: 

• It is applicable to a wide range of technologies, from 

manufacturing to defense systems 

• Elements of SCORE can be decoupled and customized 

based upon evaluation goals 

• It has the ability to evaluate a technology at various stages 

of development, from conceptual to fully mature 

• It combines the results of targeted evaluations to produce an 

extensive picture of a systems’ capabilities and utility 

To date, SCORE has been used to evaluate a wide range of 

advanced technologies, including Soldier-worn sensor systems, 

technologies allowing real-time multimedia information sharing 

among Soldiers in the field, two-way speech translation systems, 

and autonomous robotic platforms. It has been the foundation for 

ten technology evaluations involving Soldiers and Marines from 

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The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

around the country. SCORE has been used as the basis of two 

DARPA programs to evaluate advanced technologies. 

SCORE defines five evaluation goal types, as shown in Figure 2: 

• Component Level Testing – Technical Performance – 

involves decomposing a system into components to isolate 

those subsystems that are critical to system operation. 

• Capability Level Testing – Technical Performance –

involves decomposing a system into capabilities (where the 

complete system is made up of multiple capabilities). A 

capability can be thought of as an individual functionality, 

such as the ability for a sensor system to send and receive a 

picture or the ability for a translation system to identify and 

translate names (discussed below). 

• Capability Level Testing – Utility Assessments –assesses the 

utility of an individual capability. The benefit of this 

evaluation type is that specific capability utility and 

usability to the end-user can still be addressed even when 

the system and user-interface are still under development. 

• System Level Testing – Technical Performance –assesses 

the system as a whole, but in an ideal environment where 

test variables can be isolated and controlled. The benefit is 

that tests can be performed using a combination of test 

variables and parameters, where relationships can be 

determined between system behavior and these variables 

and parameters based upon the technical performance 

analysis. 

• System Level Testing – Utility Assessments –assesses a 

system’s utility, where utility is defined as the value the 

application provides to the system’s end-user. In addition, 

usability is assessed. which includes effectiveness, 

learnability, flexibility, and user attitude towards the 

system. 

Considering each of these evaluation elements, SCORE takes a 

tiered approach to measuring the performance of intelligent 

systems. At the lowest level, SCORE uses elemental tests to 

isolate specific components and then systematically modifies 

variables that could affect the performance of those components 

to determine those variables’ impact. Typically, this is performed 

for each relevant component with the system. At the next level, 

the overall system is tested in a highly structured environment to 

understand the performance of individual variables on the system 

as a whole. Then, individual capabilities of the system are isolated 

and tested for both their technical performance and their utility 

using task tests. Lastly, the technology is immersed in a longer 

scenario that evokes typical situations and surroundings in which 

the end-user is asked to perform an overall mission or procedure 

in a highly-relevant environment which stresses the overall 

system’s capabilities. Formal surveys and semi-structured 

interviews are used to assess the usefulness of the technology to 

the end-user. 

4. APPLYING SCORE TO TRANSTAC 

Technical performance of the individual components of the 

TRANSTAC system was performed using offline tests 

(represented by the red arrow in Figure 3). Both technical and 

utility performance of the entire system was performed using labbased evaluations of a laptop-based system (represented by the 

gray arrows in Figure 3) and more field-friendly utility systems 

(represented by the green arrows in Figure 3). Utility evaluations 

were also performed out in the field with the field-friendly 

systems (represented by the blue arrow in Figure 3). Lastly, the 

specific capabilities of the TRANSTAC systems (such as their 

ability to recognize proper names) were tested both for their 

technical capability and their utility (represented by the purple 

arrows in Figure 3). Each of these tests is discussed in detail 

below. 

Figure 2: SCORE Architecture 

4.1 Offline Evaluations 

The offline evaluation was performed to assess the technical 

performance of the TRANSTAC systems at the component level. 

There were three primary components that were being tested: the 

Automated Speech Recognition, the Machine Translation, and the 

Text-to-Speech. The offline evaluation was performed so that the 

component evaluation would be conducted on identical inputs for 

all systems. In advance of the evaluation, research teams were 

provided with the required log formats for storing the results of 

the offline processing. They were also provided with sample 

offline data that could be used to develop logging scripts and 

produce sample outputs. A verification script was provided to 

check the output for log format errors. 

During the offline evaluation, research teams provided the same 

versions of their systems that were used for the live evaluation. 

Research teams were provided with audio files for speech 

recognition and subsequent translation. Separately, they were 

provided with transcription files for text translation. 

Each system processed approximately 1000 audio files of 

utterances in each language and stored the results in system logs. 

In the context of this paper, an utterance is the words spoken by a 

239
The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

human from the time s/he starts speaking to the time that the 

TRANSTAC system begins to translate. An utterance can contain 

one or many concepts (individual pieces of information), but 

efforts have been made to have a comparable average number of 

concepts among all offline utterances from one evaluation to the 

next. 

For each audio file, the system stored the results of ASR, the 

translation based on ASR output, and time stamps marking the 

beginning and end of each process (recognition, translation, and 

TTS, if used). Outputs from the transcription inputs were the same 

except that results related to speech recognition were left blank. 

When processing was complete, a verification script was run on 

the logs to ensure that the output conformed to the required 

format. Logs were also checked for the correct number of outputs. 

In addition to the above, thirty well-formed foreign language text 

strings were fed into the TTS engine of the TRANSTAC systems. 

These engines read in the text strings and output audio files which 

contained the spoken version of the text. 

Figure 3: SCORE Applied to TRANSTAC 

Analysis on the offline evaluation focused on component level 

performance of the TRANSTAC systems using automated metrics 

and human judgments. The following metrics were used to 

analyze the offline data: 

 Human Judgment 

o Low-level concept transfer, performed by 

bilingual human judges 

o Likert judgment [4] at utterance level, 

performed by bilingual human judges 

o Likert judgment performed by bilingual 

human judges, to assess TTS 

 Automated Metrics 

o Word Error Rate (WER) to assess ASR and 

TTS 

o METEOR, BLEU – to assess ASR and MT 

together 

More details about these metrics can be found in Section 5. 

4.2 Lab-Based Evaluations 

The main difference between the offline evaluation described in 

Section 4.1and the live lab and field evaluations described in 

Sections 4.2 and 4.3 is that the live evaluations allow speakers to 

generate their own utterances of inquiries and responses while the 

offline evaluations uses scripted, recorded utterances by both 

speakers to provide an apples-to-apples comparison. 

Figure 4 shows an example of one of the teams’ TRANSTAC 

system. The main processing unit is a standard laptop, in which a 

head-mounted microphone (top right) and a speaker (bottom 

right) are plugged in. A hand-held control (bottom left) is as 

plugged into the laptop which allows the Soldier/Marine to let the 

system know when each speaker is about to talk. Each ‘START’ 

button corresponds to a different speaker and the button on the 

bottom allows the Soldier/Marine to replay the last audio that was 

output from the TRANSTAC system. 

Lab-based evaluations were used to assess the technical capability 

and utility of the TRANSTAC systems at the systems level. 

Approximately twenty scenarios are used to assess the 

performance of the TRANSTAC systems in a lab setting. These 

scenarios have either been structured scenarios or spontaneous 

scenarios. Structured scenarios provide a set of questions to the 

English speaker that they needed to find answers to. The foreign 

language speaker was given the answers to those questions in 

paragraph format. A dialogue occurred between the two speakers 

and the number of answers that the English speaker was able to 

obtain was noted. 

Figure 4: Example of Laptop-Based TRANSTAC System 

For spontaneous scenarios, a brief paragraph was provided to the 

English and foreign language speaker to give them the proper 

background to carry on a meaningful conversation. The 

background could state that they were performing a census survey 

and were going house to house gathering information about 

peoples’ living conditions. The direction that the Soldier/Marine 

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official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

takes the conversation was up to them, as long as it is within the 

bounds of the scenario description. There are advantages and 

disadvantages to both types of scenario, which is outside the 

scope of this paper. However, in both cases, the goal was to 

measure the number of meaningful interactions that the 

Soldier/Marine and the foreign language speaker has in a finite 

amount of time. 

In addition, after the interaction, questionnaires were provided to 

the English and foreign language speakers to gauge their 

perception of the TRANSTAC systems. 

All scenarios were performed in an indoor environment, usually 

in a conference room of a hotel. The Soldier/Marine and the 

foreign language speakers were stationary, with the TRANSTAC 

system on the table between them. All lab scenario runs were 

performed in this environment, with each scenario occurring 

within a ten minutes period. Noise masking technology was 

deployed to stop the speakers from hearing each other. They 

could only respond to what came out the TRANSTAC system. 

The goal of this type of evaluation is to place the systems in what 

many would consider an ideal environment (no background noise, 

minimal movement, etc.) to get an upper bound on how well they 

could perform. 

Because there were two physical systems (a laptop version and a 

more field-friendly) we used the same lab-based evaluation 

procedures for both systems. 

For the lab-based evaluations, the following metrics were used to 

analyze the data: 

• A count of high-level concepts found out by the 

Soldier/Marine in response to the questions he asked. 

• Analysis of the questionnaire performed by 

Soldiers/Marines and foreign language speakers after 

each scenario in which they participated. 

More details about these metrics can be found in Section 5. 

4.3 Field-Based Evaluations 

The field-based evaluations were used to assess the utility of the 

TRANSTAC systems at the system level. The field scenarios 

were performed outdoors with Soldiers/Marines wearing combat 

gear (body armor, helmet, gloves, etc.). They carried a “utility 

version” of the TRANSTAC systems while performing the 

scenarios. Following the scenarios, the Soldiers/Marines filled out 

questionnaires and participated in interview sessions with the 

evaluation team. 

The field environments were not intended to be completely 

representative of what the Soldiers/Marines would experience 

overseas. To replicate this type of environment would be a very 

difficult undertaking and it would not tell us much more than a 

more simplistic environment would. The reason for performing 

field evaluations was to subject the systems to the type of 

environmental variable that they would realistically be exposed 

to, such as wind, background noise, and the motion caused by the 

Soldier/Marine carrying the systems around with them. It also 

allowed the user to see how easy the system was to use while 

carrying around other gear such as bullet-proof vests and 

weapons. 

Figure 5: Example TRANSTAC Utility System 

An example of a utility version of the TRANSTAC system is 

shown in Figure 5. The “YOU” button on the microphone was 

meant to be push when the Soldier/Marnie was speaking (since 

they are the controller of the systems) and the “HIM” button was 

meant to be pushed when the foreign language speaker was 

speaking. A sample field environment that was used for testing 

is shown in Figure 6. 

For the field-based evaluations, the following metrics were used 

to analyze the data: 

• Analysis of the questionnaire performed by 

Soldiers/Marines and foreign language speakers after 

each scenario in which they participated. 

• Semi-structured interviews with the Marine/Soldiers 

and foreign language speakers. 

More details about these metrics can be found in Section 5. 

Figure 6: Sample Field Environment 

241
The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

4.4 Proper Names Evaluations 

The proper names evaluation was an example of a capability 

evaluation used to assess specific functionalities of the 

TRANSTAC system. The goal of the capability evaluation was to 

isolate specific functionalities of a system and test its performance 

with scenarios that are tailored to stress that functionality. The 

evaluation team focused on the ability for the TRANSTAC 

system to identify and convey proper names in a dialogue. In this 

context, proper names were people names, street names, and city 

names that were being conveyed from the foreign language 

speaker to the English speaker. Three unique, names-laden 

scenarios were created as scripted dialogues and recorded by 

unique speakers. Each scenario was very rich in proper names; 

they typically contained approximately 50 to 55 proper names 

within the 30 to 40 foreign language utterances. This recorded 

data was used to create the offline names evaluation. 

The offline names evaluation was run similar to that of the other 

offline evaluations. Specific recorded utterances were selected 

and fed directly into the TRANSTAC systems. However, the 

metrics from this test focus on how the systems specifically 

handle the translations of the proper names, as discussed below. 

The live names evaluation was run in a different manner than that 

of the live lab evaluation. The speakers were provided with the 

scripted names scenarios and instructed to read them verbatim 

into the TRANSTAC system. After hearing TRANSTAC 

translation of the English utterance, the foreign language speaker 

responded with their corresponding scripted utterance which 

again was spoken into the TRANSTAC system. That foreign 

language utterance was then translated into English. If the English 

speaker was able to understand the name that was 

translated/conveyed by the TRANSTAC system, they noted that 

and moved on to the next utterance. If the English speaker was 

unable to ascertain a name from the TRANSTAC output, then 

they were able to rephrase their original English utterance in any 

manner they saw fit. Likewise, the foreign language speaker, 

upon hearing the TRANSTAC output once the English speaker 

rephrased their utterance, could rephrase theirs accordingly to 

convey the desired name. The output of this evaluation produced 

both technical performance and utility assessment data. This took 

the form of measuring the number of names successfully 

transferred per unit time and collecting survey responses from the 

end-users regarding their specific names interactions. There were 

three names scenarios that were performed during the evaluation. 

To evaluate the live and offline names evaluation, each 

TRANSTAC output was analyzed to see how well the proper 

name was translated from the foreign language to English. This 

was performed by a panel of human judges. A score was provided 

to each output which classified each name translation as either: 

• Right name, right pronunciation 

• Right name, wrong pronunciation 

• Name translated as word (these were the cases where a 

proper name can also have a separate meaning... Black 

could be a person’s last name or a color) 

• Wrong name translation 

• Name not recognized 

5. METRICS APPLIED TO TRANSTAC 

In order to get a comprehensive picture of the performance of the 

TRANSTAC system, a large number of performance metrics were 

used when evaluating the systems. Many of these metrics are 

described below. The TRANSTAC community is in agreement 

that the two aspects that best characterize the performance of the 

systems are: (1) the semantic adequacy of the translations, leading 

to justified user confidence in the system’s translations, and (2) 

the ability of Marines/Soldiers and foreign language speakers to 

successfully carry out a task-oriented dialogue in a narrowly 

focused domain of known operational need under conditions that 

reasonably simulate use in the field. The metrics that were use to 

assess these capabilities are: 

1. High-Level Concept Transfer: Semantic adequacy of the 

translations was assessed by bilingual judges telling us 

whether the meaning of each utterance came across. The 

high-level concept metric is the number of utterances that are 

judged to have succeeded. Thus, failed utterances are not 

directly scored (other than taking up time). The high-level 

concept metric is an efficiency metric which shows the 

number of successful utterances per unit of time, as well as 

accuracy. This metric is roughly quantitative. 

2. Likert Judgment: A judgment of the semantic adequacy of 

the translations was performed by having a panel of bilingual 

judges rate the semantic adequacy of the translations, an 

utterance at a time. We asked our panel of five bilingual 

judges to assign a Likert-type score to each utterance, 

choosing from a seven-point scale. 

+3 Completely_adequate 

+2 

+1 Tending_adequate 

 0 

–1 Tending_inadequate 

–2 

–3 Inadequate. 

The judges were provided with a substantial set of exemplars 

showing utterances which were deemed to correspond to the 

four values (completely adequate, tending adequate, tending 

inadequate, inadequate) and were asked to choose the inbetween values only if on the fence between two of those 

values. 

3. Low-Level Concept Transfer: A directly quantitative 

measure of the transfer of the low-level elements of meaning 

in each utterance. In this context, a low-level concept is a 

specific content word (or words) in an utterance. For 

example, the phrase “The house is down the street from the 

mosque.” is one high-level concept, but is made up of three 

low-level concepts (house, down the street, mosque). 

We had an analyst who is a native speaker of each source 

language identify the low-level elements of meaning (lowlevel concepts) in representative sets of input utterances from 

242
The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

the offline datasets and then asked a panel of five bilingual 

judges to tell us which low-level concepts were successfully 

transferred into the target-language output (where failures 

are deletions, substitutions, or insertions of concepts). 

Progress from one evaluation to the next may be presented as 

an odds ratio. Odds of successful concept transfer is a more 

quantitative measure of translation adequacy than the Likerttype judgments of semantic adequacy — the Likert-type 

judgments give the bilingual judges the opportunity to take 

into account the relative importance of the various concepts 

while the low-level concept transfer does not. [4] 

4. Automated Metrics: A suite of automated metrics, intended 

to enable the research team to better understand what aspects 

of performance account for the end-to-end success of their 

systems. We hope to identify automated metrics that can be 

run quickly and easily yet will correlate strongly with 

judgments of semantic adequacy provided by bilingual 

judges. For speech recognition, we calculated Word-ErrorRate (WER) — using SCTK version 2.2.2 and automated 

procedures for normalizing the hypothesis and reference 

texts. For machine translation, we calculated BLEU [5] using 

four reference translations. We also measured MT 

performance by calculating a metric called METEOR 

defined by Alon Lavie of CMU. For both English and Dari, 

METEOR was run in the mode where it scores only exact 

matches (no stemming or synonymy). [6] 

5. TTS Evaluation: To assess the performance of a TTS 

component, human judges listened to the audio outputs of the 

TTS evaluation and compared them to the text string of what 

was fed into the TTS engine. They then gave a Likert score 

from 1-5 (five being the best) to indicate how understandable 

the audio file was in comparison to what was fed into it. In 

addition, these human judges transcribed what they heard in 

the audio file in the foreign language and then these 

transcriptions were compared to the input text files using 

Word Error Rate. 

6. Surveys/Semi-Structured Interviews: After each live 

scenario, the Soldiers/Marines and the foreign language 

speakers filled out a detailed survey asking them about their 

experiences with the TRANSTAC systems. The surveys 

explored how easy the system was to use, how well they 

perceived it worked, and errors that the users encountered 

when interacting with the system. In addition, after the field 

scenarios, semi-structured interviews were performed with 

all of the participants in which questions such as “What did 

you like?, What didn’t you like? and What would you 

change?” were explored. 

6. METRICS COMPARISON 

Although I cannot discuss detailed results in this paper due to 

DARPA restrictions, I can discuss, at a meta-level the level of 

consistency that was found by applying these metrics to the 

teams’ TRANSTAC output. For the purpose of this comparison, I 

will show the rank ordering of the teams’ performance by 

applying the follow metrics described in Section 5: high-level 

concept transfer, low-level concept transfer, Likert judgment, 

BLEU, and METEOR. This is shown in Table 1. 

Table 1: Metrics Comparison 

LANGUAGE

DIRECTION Metric

Team

1

Team

2

Team

3

Dari to English

High Level Concept

Transfer 1 2 3

Dari to English

Low‐level Concept

Transfer 1 2 2

Dari to English Likert Judgment 1 2 2

Dari to English BLEU 1 2 2

Dari to English METEOR 1 2 2

English to Dari

High Level Concept

Transfer 1 2 3

English to Dari

Low‐level Concept

Transfer 1 2 2

English to Dari Likert Judgment 1 1 1

English to Dari BLEU 1 2 2

English to Dari METEOR 1 1 1

 

As shown in Table 1, the numbers under Team 1, Team 2, and 

Team 3 show their relative score compared to each other teams 

when applying the metrics in the second column. For example, 

Team 1 had the highest relative score applying the high-level 

concept transfer metric looking at the translation from Dari to 

English. Team 2 had the second highest score and Team 3 had the 

third highest score. When two teams have the same number in the 

same row, it means that the scores were not statistically 

significant enough to be able to say that one score was better than 

the other. For example, Team 2 and Team 3 have very 

comparable scores when applying the low-level concept transfer 

metric in the Dari to English direction; hence they are both listed 

as the second ranked team. 

The table shows that there is significant comparability in the 

overall results when applying different metrics. In the Dari to 

English direction, Team 1 consistently was ranked #1 in all of the 

metrics applied and Team #2 was consistently ranked #2. The 

only difference was that there was a statistical difference between 

Teams 2 and Team 3 when applying the low-level concept 

transfer metric, where there was not a statistical difference when 

applying the other metrics. 

When looking at the English to Dari direction in Table 1, Team 1 

came out with the highest relative rank in all five metrics again. 

However, Teams 2 and Team 3’s scores varied depending which 

metrics was applied. Looking at Team #3, it was ranked third 

when applying the high-level concept transfer metric but was tied 

for first when applying the Likert judgment and METEOR 

metrics. In situations like this, one usually defaults to the metrics 

which involves humans, which is sometimes referred to as ground 

truth or the gold standard. The first three metrics (high-level 

concept transfer, low-level concept transfer, and Likert) all 

involved human judges. Unfortunately, this still doesn’t provide 

much insight as Team 3 is ranked #3, #2, and #1, respectively. As 

243
The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the 

official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense. 

such the only conclusion we can draw from this is that Team #1 

appears to be superior overall, while Team #2 and Team #3 are 

roughly tied for second. 

7. CONCLUSION 

In this paper, we have discussed the SCORE Framework and 

shown how it was applied to the DARPA TRANSTAC program. 

Using SCORE, we were able to evaluate the performance of 

speech translation systems by looking at the performance of: 

• the systems at the component level using offline 

evaluations, 

• the performance of the overall system in ideal 

environments using lab evaluations, 

• the performance of the system in operationally-relevant 

environments using field test, and 

• the specific capabilities of the systems to evaluate 

proper names. 

By putting together the results of all of these evaluations, we are 

able to gain a much more comprehensive evaluation of an overall 

system performance. 

SCORE has proven to be an invaluable evaluation design tool for 

the NIST Evaluation Team and was the backbone of eleven 

DARPA evaluations: six for the DARPA ASSIST program (not 

discussed in this paper) and five for TRANSTAC program. It is 

expected to play a critical role in the remaining ASSIST and 

TRANSTAC evaluations. 

The SCORE framework is applicable to domains beyond 

emerging military technologies and those solely dealing with 

intelligent systems. Personnel at NIST are applying the SCORE 

framework to the virtual manufacturing automation competition 

(VMAC) [7] and the virtual RoboRescue competition [8] (within 

the domain of urban search and rescue). Their intent is to develop 

elemental tests and vignette scenarios to test complex system 

capabilities and their component functions. The framework has 

proven to be highly adaptable and capable of meeting most any 

evaluation requirement. 

ACKNOWLEDGMENTS 

The authors would like to acknowledge the DARPA TRANSTAC 

program manager, Dr. Mari Maeda, and the members of the NIST 

IET for their continued support. 

REFERENCES 

 [1] B. Weiss, C. Schlenoff, G. Sanders, M. Steves, S. Condon, 

J. Phillips, and D. Parvaz, "Performance Evaluation of 

Speech Translation Systems," in Proceedings of the LREC 

2008 Conference Morocco: 2008. 

 [2] C. Schlenoff, M. Steves, B. Weiss, M. Shneier, and A. 

Virts, "Applying SCORE to Field-Based Performance 

Evaluations of Soldier Worn Sensor Technologies," 

Journal of Field Robotics, vol. 24, no. 8/9, pp. 671-698, 

Sept.2007. 

 [3] B. Weiss and C. Schlenoff, "Evolution of the SCORE 

Framework to Enhance Field-Based Performance 

Evaluations of Emerging Technologies," in Proceedings of 

the 2008 Performance Metrics for Intelligent Systems 

(PerMIS) Conference Gaithersburg, MD: 2008. 

 [4] G. Sanders, S. Bronsart, S. Condon, and C. Schlenoff, 

"Odds of Successful Transfer of Low-Level Concepts: A 

Key Metric for Bidirectional Speech-to-Speech Machine 

Translation in DARPA's TRANSTAC Program," in 

Proceedings of the LREC 2008 Conference Morocco: 

2008. 

 [5] K. Papineni, S. Roukos, T. Ward, and W. Zhu, "BLEU: a 

Method for Automatic Evaluation of Machine 

Translation," in Proceedings of the 40th 

Annual Meeting of the Association for 

Computational Linguistics (ACL)

Philadelphia, PA: 2002, pp. 311-318. 

 [6] S. Condon, J. Phillips, C. Doran, J. Aberdeen, D. Parvaz, 

B. Oshika, G. Sanders, and C. Schlenoff, "Applying 

Automated Metrics to Speech Translation Dialogs," in 

Proceedings of the LREC 2008 Conference Morocco: 

2008. 

 [7] S. Balakirsky and R. Madhavan, "Advancing 

Manufacturing Research Through Competitions," in 

Proceedings of the SPIE Defense Security and Sensing

Orlando, FL: 2009. 

 [8] S. Balakirsky, C. Scrapper, and S. Carpin, "The Evolution 

of Performance Metrics in the RoboCup Rescue Virtual 

Robot Competition," in Proceedings of the Performance 

Metrics for Intelligent Systems Workshop Gaithersburg, 

MD: 2007. 

244
Development and Internal Evaluation of Speech-to-Speech 

Translation Technology at BBN 

David Stallard 

BBN Technologies 

10 Moulton St. 

Cambridge, MA 

(617) 873-2825 

stallard@bbn.com 

Rohit Prasad 

BBN Technologies 

10 Moulton St. 

Cambridge, MA 

617-873-4785 

rprasad@bbn.com 

Prem Natarajan 

BBN Technologies 

10 Moulton St. 

Cambridge, MA 

617-873-5472 

prem@bbn.com 

ABSTRACT

Speech-to-speech translation (S2S) technology holds out the 

promise of allowing spoken communication across language 

barriers. Recently, there has been a great deal of progress in S2S 

technology, much of it under the sponsorship of DARPA’s 

TransTac program. In this paper, we present BBN’s S2S system, 

“TransTalk”, whose development has been funded under the 

TransTac program. We also present various performance metrics, 

and the result of detailed performance analyses, using the 

TransTalk system. 

Categories and Subject Descriptors 

I.2.7 [Artificial Intelligence]: Natural Language Processing – 

machine translation, speech recognition and synthesis. 

General Terms

Measurement, Performance, Experimentation, Human Factors. 

Keywords

Speech-to-speech translation, evaluation, performance analysis. 

1. INTRODUCTION 

Speech-to-speech translation (S2S) technology holds out the 

promise of allowing spoken communication across language 

barriers. Using a suitably configured S2S system, two people who 

do not share a common language can communicate with one 

another by speaking through the system. The system translates 

the speech of one party into synthesized speech in the language of 

the other party, and vice versa. S2S systems combine multiple 

technologies, including Automatic Speech Recognition (ASR), 

Machine Translation (MT), and Text-to-Speech synthesis (TTS). 

Recently, there has been significant progress in S2S technology. 

Much of this progress has taken place under the auspices of 

DARPA IPTO’s TransTac program (Dr. Mari Maeda, Program 

Manager). “TransTac” stands for “Translation systems for 

Tactical use”. The TransTac program sponsors S2S work between 

English and several foreign languages of interest to the US 

government, including Iraqi Arabic, Malay, Farsi, Dari, and 

Pashto. Systems have been developed for Iraqi Arabic, Malay, 

Farsi, and Dari. As of this writing, work is also underway for 

Pashto, one of the main languages of Afghanistan. These systems 

support conversations in the “force protection” domain, which is 

broadly construed to include not only checkpoints, searches, and 

military operations, but also rapport building, civil affairs, and 

basic medical. In addition to direct research funding, the Transtac 

program also funds data collection and periodic competitive 

evaluations, using live military users. Participating research sites 

are BBN, IBM, and SRI, with additional research contributions 

from USC and CMU. 

A key enabler of recent progress in S2S has been the recent and 

ongoing progress in Statistical Machine Translation (SMT). SMT 

uses statistical, corpus-driven approaches, as opposed to handwritten rules, and automated performance evaluation, as opposed 

to manual performance evaluation. The resulting labor savings 

has greatly accelerated the progress of MT. Moreover, the 

statistical paradigm generally provides better performance than 

hand-written rules, which are often brittle and in conflict with one 

another. In this way, SMT may be seen as following in the 

footsteps of ASR, which also underwent dramatic improvement 

following the adoption of statistical paradigms and automated 

evaluation. 

It should be noted, however, that S2S systems in their present 

form are not “universal translators”, ala Star Trek. On the 

contrary, they must be configured for particular language pairs by 

training ASR and translation models using speech and language 

data (recordings, transcriptions, and translations) in the relevant 

languages. While existing data can sometimes be found on the 

Internet or elsewhere, it is more frequently the case that the data 

has to be collected. Moreover, for optimal performance, the data 

collected should match the intended domains of conversation of 

the system. That is, if the intended domain is force protection, 

data should be collected for that domain. While data outside that 

domain can be helpful for general modeling of the given 

language, it often lacks the key concepts and constructions that 

are important in the specific application domain. In practice, the 

domain-relevant data is frequently conducted in simulated 

translingual dialogs between people playing the roles of soldier 

and civilian. 

In the remainder of this paper, we give an overview of BBN’s 

TransTac S2S system, “TransTalk” [1], and its component 

technologies. We follow this with a description of the automated 

metrics used in improving our system. Finally, we give results of 

some performance analyses of our system. 

2. SYSTEM OVERVIEW 

A block diagram of BBN’s TransTac system, “TransTalk” [1], is 

system is shown in Figure 1. The BBN TransTalk system uses 

245
BBN’s Byblos speech recognizer [2], BBN’s SMT engine, and 

Cepstral’s text-to-speech synthesizer. Various input modalities are 

supported, including both handheld and headset microphones. 

The primary physical interface is the “SuperMic”, a handheld unit 

developed by BBN, which encompasses a directional microphone, 

speakers, and two push-and-hold “listen” buttons, one for 

receiving the SME’s English speech, and the other for receiving 

the FLE’s foreign speech. Figure 2 shows the BBN TransTalk 

system running on an Ultra Mobile PC in the SuperMic 

configuration. 

English speech received through the physical interface device is 

sent to the English speech recognizer, which outputs the string of 

words in the recognizer’s vocabulary which it judges to be the 

most likely given the sounds received and given the recognizer’s 

Language Model (LM). This English text is then sent to both the 

SMT component and to a separate Question Canonicalizer 

component. The Question Canonicalizer tries to match the 

utterance to one of a set of utterances for which it has stored 

translations. If none is found, the output of the SMT component 

is used instead. Arabic speech corresponding to the translation is 

then played back. This may be a pre-recorded wave file, or more 

generally, the result of text-to-speech synthesis. 

The foreign language speaker’s reply (“Arabic” in Figure 1) is 

sent to the foreign language recognizer, which outputs text in the 

foreign language. This text is then sent to a second speech 

synthesizer, which speaks it out for the English speaker to here, 

and/or displaying it on a screen. 

A key requirement of TransTac systems is that they be 

“displayless”, aka “eyes-free”; i.e. that the user does not have to 

look at or interact with a screen display in order to use the system. 

Because of the requirement of displayless operation, our system 

uses audio confirmation. In particular, it uses implicit 

confirmation, in which the system speaks the confirmation 

utterance for the SME to hear. If the SME judges this correct, he 

takes no action, and the translation is played out for the FLE to 

hear. If the SME is not satisfied with what he hears, he simply 

presses the English “listen” button to barge in, interrupting system 

output and allowing him to and re-speak. 

In the following, we briefly discuss the component technologies 

involved. The BBN Byblos speech recognizer [2] models speech 

as the output of context-dependent phonetic Hidden Markov 

Models (HMMs). The outputs of the HMM states are mixtures of 

multi-dimensional diagonal Gaussians. Different forms of 

parameter tying are used in Byblos, including State Tied Mixture 

(STM) triphone and State Clustered Tied Mixture (SCTM) 

quinphone models. The mixture weights in both these cases are 

shared based on the decision tree clustering 

Speech recognition is performed using our patented two pass 

search strategy [3]. The forward pass is a fast-match beam search 

using an STM acoustic model and an approximate bigram 

language model. The output of the forward pass consists of the 

most likely word-ends per frame along with their partial forward 

likelihood scores. The backward pass operates on the set of 

choices from the forward pass to restrict the search space, and 

uses the more detailed SCTM quinphone model and a trigram 

language model to produce the best hypothesis. 

BBN’s Statistical Machine Translation (SMT) engine is a phrasebased translation system based [4]. Given, an input foreign 

language sentence ‘f’, we estimate the most likely translation into 

the target sentence ‘e’ as: 

Word alignments between source-target sentence pairs are 

generated using GIZA++ based on IBM’s Models 1 to 4 [5]. In 

order to improve the quality of the alignments, word alignments in 

the forward and backward direction are merged as in [6]. Phrase 

pairs are automatically extracted from the word alignments by 

merging neighboring alignment groups using a set of rules. The 

decoder uses a log-linear model of different features to choose 

between competing translation hypotheses. The parameters of the 

model are estimated using statistics of the phrase pairs extracted 

from the word alignments. The interpolation weights are 

optimized by minimizing the translation errors on a held out 

development set. 

3. EVALUATION METRICS 

In our work, we make frequent use of automated metrics to 

evaluate experimental configurations of our system, so as to 

determine whether or not to adopt a given new technique in 

modeling, data normalization, etc.. The advantage of using 

automated metrics is that it enables one to efficiently experiment 

with many different configurations, and to choose the best one 

according to the metrics. A fairly large “validation set” consisting 

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Conference’04, Month 1–2, 2004, City, State, Country. 

Copyright 2004 ACM 1-58113-000-0/00/0004...$5.00. 

Figure 2: BBN TransTalk System with the BBN 

SuperMic 

)|(maxarg

^

e feP

e

=

English

Recognition

Arabic Speech 

Playback

Question 

Canonicalizer

Answer 

Presentation

Machine 

Translation

Arabic

Recognition

Arabic 

Text

English 

Text

Speech 

File

Semantic Type

English 

Speech

Arabic 

Speech

Arabic 

Speech

English 

Speech

English 

Text

Machine 

Translation

English

Recognition

Arabic Speech 

Playback

Question 

Canonicalizer

Answer 

Presentation

Machine 

Translation

Arabic

Recognition

Arabic 

Text

English 

Text

Speech 

File

Semantic Type

English 

Speech

Arabic 

Speech

Arabic 

Speech

English 

Speech

English 

Text

Machine 

Translation

Figure 1: Block Diagram 

246
Metric R(T2T) R(S2T) 

TER 0.4450 0.5536

STER 0.4827 0.6077 

METEOR 0.5342 0.6295

Table 1: Comparison of Pearson correlation coefficient 

computed w.r.t Likert scores across different metrics.

of 11K utterances, is used to test the system performance. For 

more occasional subjective evaluations, we use the familiar 1 -5 

Likert scale. 

For ASR development, we of course use the standard Word Error 

Rate (WER) metric, which is based on edit distance. WER 

measures the number of insertions, deletions, and substitutions 

that would be required to transform the correct, or “reference”, 

transcription into the machine-generated one. For MT 

development, we use a combination of several automated metrics, 

including the widely-used BLEU [7] and METEOR [8]. BLEU 

measures n-gram precision, and also includes a brevity penalty. It 

tends to reward more fluent translations, but has no notion of 

word equivalence. METEOR by contrast measures primarily 

unigram recall, but uses WordNet [9], which recognizes 

synonymy and stemming equivalences between different words 

and different forms of the same word. METEOR tends to reward 

translation adequacy. 

We also make use of various metrics developed at BBN. One is 

the Translation Error Rate (TER) [10]. TER, like WER, is a 

measure based on edit distance. It differs from WER, however, 

in that it allows shifts of arbitrary sub-strings, so as to uncover 

matches that would otherwise be treated as substitution errors. 

Shifts are counted as an equally weighted component of error rate, 

along with insertions, deletions, and substitutions. Performing a 

shift can reduce the overall edit-distance error rate; however, it 

avoids the multiple substitution and insertion errors that would 

otherwise be incurred. 

A second metric developed at BBN is Semantic Translation Error 

Rate (STER) [11]. STER is based on TER, but adds the 

synonymy and stemming capabilities used by METEOR, in order 

to compute matches for alignment. STER also differs from TER 

in that it forbids the alignment, even as a substitution, of a 

concept and a non-concept word. (Non-concept words are 

function words like “the”, “is”, “of”, etc.) As a result, STER can 

produce alignments which are linguistically more intuitive than 

TER. An example is shown in Figure 3. 

4. STER and Human Judgment 

In this section, we evaluate STER in terms of its correlation with 

human judgments. The corpus used in all the experiments is the 

offline evaluation set used in the March 2006 TransTac 

evaluations. This set consists of 1440 spoken Iraqi Arabic 

utterances spanning four different domains: general survey, 

intelligence, medical, and municipal services. Each utterance was 

transcribed in Arabic, and given four reference translations into 

English. 

All SMT experiments provide two sets of scores. One set of 

scores evaluates translation performance on the reference 

transcriptions of the utterances (T2T). Another set of scores 

evaluates translation performance on speech recognition output as 

the source (S2T). 

In the first experiment, we compare the correlation of the various 

metrics with human judgment. A judge who was a native speaker 

of Arabic and fluent in English assigned 1-5 Likert scores to each 

translation output as a rating of their quality. In Table 1, we show 

the Pearson correlation coefficient, R, of Likert scores for every 

utterance against TER, METEOR, and STER scores respectively. 

From Table 1, we see that the STER metric is better correlated to 

human judgment than TER. Since TER and STER metrics have 

different edit costs for edits involving stop words, we performed 

another experiment to ensure the improved correlation results 

from the quality of the alignment and not due to edit costs. 

In Table 2, we provide correlation scores for STER and TER 

when the stop words have been removed from the hypothesis and 

reference. Since the edit costs for both the metrics are identical, 

the improvement in correlation reflects the improvement in the 

quality of the word alignments. 

The TER scores in Table 1 and Table 2 show that the removal of 

stop words results in a slight improvement in the correlation 

coefficient. However, removing stop words reduces correlation 

with human judgment for STER. Given STER aligns stop words 

independently of concept words, the reduction in correlation 

shows that human judgment is sensitive to non-concept words too. 

We believe this is due to the fact that stop words positively 

correlate with human judgment when it can be aligned with other 

stop words (as in STER) but negatively correlates with human 

judgment when it can align with both stop words and concept 

words (as in TER). 

Based on the results in Table 1, we can conclude that METEOR 

correlates best with human judgment. In Table 3, we compare 

METEOR to SMET, a metric derived from STER alignments as 

described above. The SMET instead of using the METOR 

alignments uses the STER alignment for computing the 

METEOR-equivalent score. As shown in Table 6, SMET has 

similar score as METEOR and is equally well correlated with 

human judgment. These results highlight the utility of SMET as a 

metric which is well correlated to human judgments and at the 

Best Ref: the house is smoking 

Orig Hyp: smoke is came from the home 

REF : the HOUSE is ****   SMOKING 

TER : [the] SMOKE is CAME FROM @ HOME 

REF : ** ** **** the house is smoking 

STER:@@ CAME FROM the home [is] [smoke ] 

Figure 3: STER Alignment Example 

Metric T2T S2T 

R Score R Score

METEOR 0.5342 0.6540 0.6295 0.5430

SMET 0.5331 0.6556 0.6270 0.5462 

Table 3: Comparison of METEOR and SMET metrics 

Metric R(T2T) R(S2T)

TER 0.4550 0.5661 

STER 0.4662 0.5875

Table 2. Comparison of TER and STER after 

removing stop words 

247
same time provides useful alignments for human driven analysis 

of the system output. 

An interesting possible consequence of the results in Table 3 is 

that scores equivalent to METEOR can be computed with a 

simpler algorithm than METEOR itself uses. We performed a 

detailed comparison of the individual unigram alignments for 

concept words in STER and METEOR, and found that only 0.5% 

of them were different. Notably, SMET does not require 

METEOR’s multiple stages of unigram matching for different 

word equivalency measures (WordNet, Porter stemming, etc). The 

STER alignment can also be viewed as enforcing the constraints 

that METEOR enforces on unigram alignments, namely making 

them one-to-one, and minimizing alignment crossings. 

5. PERFORMANCE ANALYSES 

In this section, we present two detailed performance analyses for 

our system. In the first, we seek to determine which types of 

machine translation error are the most harmful to performance. In 

the second, we evaluate the effectiveness of the so-called “backtranslation” strategy for user confirmation. 

5.1 Quantifying Damage Caused by Errors 

The MT component of an S2S system can make many different 

types of errors, both major (dropping concepts, using the wrong 

word sense, etc) and minor (plural vs. singular ending, etc). The 

question arises as to which of these types of errors are the most 

damaging to the overall quality of translation. Knowing this 

information can help direct research efforts towards those areas 

that are most likely to improve translation performance. 

To carry out this assessment, the MT component of our system 

was first evaluated subjectively on a test set consisting of 419 

Iraqi and 429 English utterances. A bilingual judge rated each 

MT output on a 1 – 5 Likert scale, where a score of 5 denoted 

perfect translation, 4 adequate translation, 3 semi-adequate, and 

so on. The results of this evaluation are shown in Table 4. The 

large difference in performance between E2I and I2E is due to the 

higher perplexity of the Iraqi set (586 vs. 54). 

Translation Type Likert 

E2I 

T2T 4.28 

S2T 4.05 

I2E 

T2T 3.85 

S2T 3.35 

Table 4: Likert Scores 

As part of the subjective evaluation, the bilingual judge 

categorized and labeled the specific translation errors made by the 

MT. The set of error categories was created based on an initial 

review of the MT output. There were approximately 15 

categories, which included major errors, such as dropping a 

concept or using the wrong sense of a word, and minor errors, 

such as using the singular form of a word instead of the plural. A 

principle goal of this effort was to quantify the relative importance 

of each error category in terms of the “damage” it did to the 

overall translation performance, so as to better direct our efforts 

towards improving the system. 

As a subjective measure of translation quality, we use the familiar 

1 – 5 Likert scale to rank both forward (i.e. English-to-foreign) 

translations, and back-translations. Note we do not assume that 

users will actually assign a Likert scores while using the system; 

but instead view the scores as numerical proxies for user 

reactions. We assign the following interpretations to the different 

elements of the Likert scale. 

5: Essentially a perfect translation, 

4: An adequate though somewhat disfluent translation which 

conveys the meaning of the utterance. 

3: A partial translation which is missing one or more 

concepts, or is severely disfluent. 

2: A translation which is missing most of the concepts. 

1: A translation with no apparent relation to the input. 

To quantify our notion of “damage”, we define the “Likert Error” 

(LER) for a translation as 5 minus its Likert score. We then 

define the “Total Likert Error” (TLE) of a set of translations as the 

sum of the LER’s of the translations. Table 5 gives TLE statistics 

for the utterances in I2E and E2I that contain errors. As can be 

seen, the average TLE per error and per utterance with error is 

higher for E2I, but I2E has many more utterances with an error. 

This is consistent with the lower average Likert score for I2E 

above. 

 #Utts #Errs Errs/ 

Utt 

Tot 

TLE 

TLE/ 

Err 

TLE/ 

Utt 

E2I 184 228 1.24 305 1.34 1.7 

I2E 273 383 1.40 484 1.26 1.3 

Table 5: Total Likert Error Stats 

To determine the damage done by each category of error, we make 

the simplifying assumption that the damage done by an individual 

error is at least approximately separable from and additive to the 

damage done by others. The relative importance of an error 

category C is then the fraction of the TLE that can be ascribed to 

its instances, or: 

TLE(C) = Count(C)*LER(C),

where LER(C) is the average damage done by instances of C, and 

quantifies the “seriousness” of the error. 

Estimating LER(C) is not wholly straightforward, because many 

sentences have both multiple errors. For example, the same 

sentence might have both a “Word Sense” and an “Incorrect 

Pronoun” error. So we cannot determine the LER simply by 

averaging over instances of C. The key question is how to 

apportion the blame between these errors. 

One might imagine various heuristic or hill-climbing approaches 

to this problem. Our approach instead views each annotated 

utterance as an equation, in which the annotator has asserted that 

the sum of the error labels equals the given Likert error value. The 

variables of this equation are the error labels, whose unknown 

values are the LER weights of the categories. The complete set of 

annotated utterances can then be viewed as a set of simultaneous 

equations over the LER’s. That is, we seek x such that Ax=k, 

where A is a matrix of coefficients for each equation, x is the 

vector of unknown LER weights, and k is the vector of annotatorassigned LER values. 

Due to the variability inherent in subjective analysis, one cannot 

in general expect this system of equations to be consistent. For 

example, a “Missing Concept” error might legitimately result in a 

248
higher Likert error in one sentence than in another, depending 

upon the missing concept. We must instead settle for an 

approximation Ax=k+e, where e is the difference between the 

predicted and actual LER values, and seek the x that minimizes 

|e|. Fortunately, this is just a least-squares problem, to which an 

exact solution can be found by solving the equation: 

A

TAx = AT

k

Once we have estimated the LER value for a category, we 

multiply it by the frequency of the category to estimate the 

category’s TLE. Table 6 gives the solved-for LER weights and the 

estimated TLE’s for each language direction. Note that the 

categories “Word Sense”, “Wrong Concept”, “Missing Concept”, 

and “Pronoun Error” account for the lion’s share of the TLE. 

(“Wrong Concept” is a word or phrase translation that is wrong in 

all contexts, regardless of word sense). All have high frequencies 

and, except for “Pronoun Error”, also high weights. “Pronoun 

Error” has a smaller weight (approximately 1.0), reflecting its 

lesser importance. The error “Wrong Polarity”, (e.g. “I am not

sick” instead of “I am sick”) is given a high weight as it should, 

but because its frequency is low, it contributes only a small share 

to the TLE. 

Iraqi-to-English 

%Count LER %TLE 

Word Sense 16.2 1.73 21.3 

Wrong Concept 13.3 1.96 19.9 

Missing Concept 13.1 1.73 17.2 

Pronoun Error 21.4 0.94 15.3 

Function Words 9.7 0.87 6.4 

Word Order 8.6 0.83 5.5 

Wrong Polarity 2.6 1.80 3.6 

Other 15.1 -- 10.8 

English-to-Iraqi 

%Count LER %TLE 

Word Sense 17.1 1.88 23.5 

Wrong Concept 14.5 2.00 21.4 

Missing Concept 10.1 1.94 14.3 

Pronoun Error 25.9 1.01 19.1 

Function Words 10.5 1.07 8.2 

Word Order 8.8 0.81 5.2 

Wrong Polarity 0.4 2.0 0.6 

Other 12.7 -- 7.7 

Table 6: Estimated Likert Error values 

Interestingly, the weights for some minor errors, such as “Word 

Order”, are driven below 1.0, even though 1 was the lowest Likert 

error the annotator could give a sentence that contained an error, 

since fractional scores were not allowed. Thus, the algorithm 

mitigates somewhat the rather severe quantization of the scoring 

system, which forces all imperfect but still adequate translations 

to have the same score. Of course, the advantage of the integer 

scale is that it is easier for annotators to use than real numbers. A 

useful future compromise would be to allow half-point scores. 

5.2 Evaluating Back-Translation 

A key issue in S2S systems is helping the speaker decide whether 

or not the system translated him correctly. If the user decides the 

system misunderstood or mistranslated what he said, he can take 

some form of remedial action in order to keep the dialog on track. 

Lacking such capability, translingual dialogs may swiftly founder 

due to mutual incomprehension. Many voice-only dialog and S2S 

systems use a “confirmation” utterance to convey the system’s 

understanding of what the user said. The user is then allowed to 

“barge in” and re-speak his utterance if he decides the system was 

incorrect. 

There are various approaches to generating the confirmation 

utterance. One is to simply read back the ASR output, on the 

theory that errors in concept words guarantee a mistranslation. 

However, this approach cannot detect errors that arise purely in 

the translation component, independent of ASR. An alternative 

approach is “back-translation”, in which the system re-translates 

the output of a source- to target-language translation back into the 

original source language. The idea is that if the back-translation 

is reasonably close to the original source language input, the 

speaker can have confidence that he was translated correctly. 

Possible objections to back-translation, however, are that 1) it 

might frequently produce garbage even for good translations, and 

2) even if garbage is not produced, the results of back-translation 

may yet be misleading, as merely using the translation model in 

the reverse direction may serve to mask errors in translation. 

The most obvious way to evaluate the efficacy of back-translation 

would be to run two complete sets of live evaluations, one with 

back-translation and one without, and compare the results on 

measures such as concept transfer, rate of concept transfer, user 

satisfaction, and the like. However, such evaluation is expensive 

to conduct and non repeatable. We must therefore look for an 

offline method for evaluating back-translation. 

Note that we are not interested in predicting the actual value of 

the Likert rating for the forward translation, but rather in simply 

predicting whether or not the forward translation’s Likert rating is 

above a certain threshold of acceptability. Therefore, we seek to 

use the back-translation for binary classification, rather than 

regression. As is pointed out for another context by [12], even a 

poor approximation of a function may be adequate for 

classification, if all we care about is the sign of the function’s 

value and not its magnitude. 

First, we choose a specific minimum acceptable Likert score F for 

the forward translation (say a score of 4). We then test various 

minimum thresholds B for the back-translation Likert score. In 

particular, for utterances whose back-translation score is at or 

above the threshold B, we test the prediction that the utterance’s 

forward translation Likert score will be at or above the threshold 

F, and thus acceptable. Below B, we predict that the forward 

translation Likert will be below F, and therefore unacceptable. We 

compute precision, recall, and F-measure for each such threshold. 

There are of course different kinds of costs for false acceptance (a 

failure of precision) vs. false rejection (a failure of recall). A false 

acceptance, by allowing an incorrect or garbled forward 

translation to be sent to the FLE, incurs the risk that the FLE will 

misunderstand or be confused. A false rejection, on the other 

249
hand, means that the user will have to repeat or rephrase his 

utterance unnecessarily, which not only wastes a dialog turn, but 

also opens up the possibility that the retry itself will be 

mistranslated. Different application domains may impose different 

weights on these types of costs, which can be straightforwardly 

taken into account by computing the F-measure with a weighted 

harmonic mean. 

To test the methodology outlined above, we used a set of 779 

English utterances that were spoken to our system by SME users 

during the TransTac live evaluation in June 2008, conducted by 

the US government’s National Institute of Standards and 

Technology (NIST). In this evaluation, active-duty military 

personnel played the part of the SMEs. Native speakers of Iraqi 

Arabic living in the US were recruited to play the part of FLEs. 

Participants worked through a set of specified scenarios in which 

each was briefed ahead of time with either the information he was 

to try to obtain (in the case of the SME), or the information he 

was to give (in the case of the FLE). The FLE wore noise-masking 

headphones so that he was not able to hear any of the English 

spoken by the SME, and thus had to rely on the system output 

only. The utterances of both parties, and the system’s ASR and 

MT outputs, were recorded for later analysis. 

The Iraqi translation output produced by the system for these 

utterances was Likert-scored by a native Arabic speaker 

experienced in the application domain. To produce the backtranslations, we ran (offline) our Arabic-to-English MT on the 

system’s Iraqi translation outputs. The back-translations thus 

produced were Likert-scored by a native English speaker 

knowledgeable in the application domain. For comparison, the 

same ranker Likert-scored the output of our system’s English ASR 

for these same 779 utterances. ). Half-scores (e.g. 4.5) were also 

allowed. 

Some examples of back-translations and their Likert rankings are: 

“Turn off your vehicle” (for “Turn your vehicle off”), ranked 5; 

“Construction prior experience do you have” (for “Do you have 

prior construction experience”), ranked 4; and “How many 

subcontracting work” (for “How many subcontractors work for 

you”) ranked 3. Table 7 shows the mean Likert scores for each of 

the conditions, namely, forward translation, back-translation, and 

ASR output of Likert scores for the back-translation. 

Forward BackTrans ASR

4.42 3.99 4.64

Table 7: Mean Likert Scores 

As can be expected, the highest mean Likert scores were produced 

on ASR output, which tends to overestimate the true (forward) 

Likert score, while the lowest were associated with backtranslation output, which tends to underestimate it. Both were 

approximately equally well-correlated with forward Likert score, 

however, with a correlation coefficient of approximately 0.60. 

Table 8 gives a more detailed breakdown of the score 

distributions. 

The large number of utterances scoring 5 for the ASR ranking is 

partly due to the low WER and low utterance rate for this set. The 

English ASR WER obtained on this corpus was 6.2%. And the 

English-to-Arabic BLEU score on this ASR output was 56.7%. 

Likert Forward BackTrans ASR 

5.0 0.47 0.27 0.72

4.5 0.22 0.08 0.03

4.0 0.14 0.26 0.10

3.5 0.06 0.17 0.08

3.0 0.07 0.16 0.05

2.5 0.03 0.02 0.00

2.0 0.01 0.03 0.01

1.0 0.01 0.01 0.00

Table 8: Likert Score Distributions 

To obtain results on back-translation efficacy, we set the desired 

forward translation Likert score threshold F to be 4.0. This may 

be considered a good minimum acceptable score for our purposes, 

as scores below 4.0 are associated with “semantic damage” to the 

translation. Table 3 gives acceptance rate, false rejection rate, 

false acceptance, F-measure, and precision-weighted F-measure 

for different back-translation Likert score cutoffs B. Each row of 

this table can be interpreted as a prediction rule, which predicts 

that an utterance whose back-translation Likert score is at or 

above the cutoff will have a forward translation whose Likert 

score will be 4.0 or higher. 

Cutof

f 

Acp

t 

FlsRe

j 

FlsAc

c 

FMsr WFMsr

5.0 0.27 0.68 0.02 0.48 0.58

4.5 0.35 0.59 0.03 0.57 0.67

4.0 0.61 0.29 0.04 0.81 0.86

3.5 0.78 0.14 0.08 0.89 0.90

3.0 0.94 0.02 0.14 0.92 0.90

2.5 0.96 0.02 0.15 0.91 0.89

2.0 0.99 0.00 0.16 0.91 0.88

1.0 1.00 0.00 0.17 0.91 0.88

Table 9: Precision and Recall for Back-Translation 

For many S2S applications, a false acceptance can be regarded as 

worse than a false rejection, because of the possibility of 

confusing the respondent, etc. For example, one might decide that 

a false acceptance is twice as bad as a false rejection. The 

rightmost column of Table 9 gives F-measure computed with 

these weights (0.67 vs. 0.33). 

The results in Tables 8 and 9 seem to show that the worst fears 

regarding back-translation are not realized. Back-translation does 

not yield garbage all the time, nor is it a totally faithless guide. 

Indeed, for cutoffs of 4.0 or higher, its false acceptance rate is 

actually quite low. This precision does come at the expense of 

recall, however, and in particular at a cutoff of 4.0 fully 39% of 

SME utterances would be rejected and have to be retried. A better 

strategy might be a slightly less strict cutoff of 3.5, which yields a 

low false acceptance rate of 8%, while falsely rejecting only 14%. 

This rule corresponds to a back-translation which subjectively 

seems rather poor, but which is not completely deficient. 

A key question to be addressed, however, is whether backtranslation is better than the strategy of simply reading back the 

system’s English ASR output. To address this question, Table 11 

repeats the above experiment, but with Likert rankings on the 

system’s English ASR output. It may be regarded as an 

250
experiment in which we pretend that the ASR output itself is the 

back-translation. 

Cutof

f 

Acp

t 

FlsRe

j 

FlsAc

c 

FMsr WFMsr

5.0 0.72 0.19 0.07 0.86 0.88

4.5 0.75 0.17 0.08 0.88 0.89

4.0 0.86 0.07 0.10 0.92 0.91

3.5 0.94 0.02 0.13 0.92 0.90

3.0 0.99 0.00 0.16 0.91 0.89

2.5 0.99 0.00 0.16 0.91 0.89

1.0 1.00 0.00 0.17 0.91 0.88

Table 11: Precision and Recall for ASR Output 

The false acceptance rate is higher than for back-translation, but 

the false rejection rate is much lower, yielding good F-measure 

scores at all values of the cutoff. For most cutoff values, the ASR 

read-back strategy even slightly out-performs back-translation on 

weighted F-measure. It might seem from this analysis that ASR 

read-back is therefore a superior strategy. It should be noted, 

however, that ASR read-back on this dataset has a floor of 7% 

false acceptance, below which it cannot possibly go. The backtranslation strategy, by contrast, can go as low as 2% false 

acceptance, albeit at the price of a very high false rejection rate. If 

rather than seeking to maximize F-measure, one were to instead 

stipulate a certain maximum allowable rate of false acceptance – 

say 8% – the back-translation strategy could be seen as slightly 

superior, resulting in a 14% false rejection rate as opposed to ASR 

read-back’s 17%. 

6. CONCLUSIONS 

We have presented our speech-to-speech translation system, 

TransTalk, and outlined several techniques used for automatically 

evaluating its performance. Among these techniques is an 

algorithm STER that highlights machine translation errors in a 

linguistically more meaningful way than other approaches. STER 

also correlates better with human judgment than does TER. We 

have also presented detailed analyses of TransTalk’s performance. 

In particular, we have presented a method by which we can 

apportion blame to different MT error phenomena, and shown that 

word sense errors are the largest contributor to error of all 

categories. We have also presented a method for evaluating the 

effectiveness of the back-translation approach to user 

confirmation, and shown that back-translation provides higher 

precision than the simple strategy of reading back the ASR, at the 

expense of recall. 

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NAACL/HLT, Edmonton, Canada, May 27--June 1 2003 

[5] Brown, P, Della Pieta, S, Della Pietra V, and Mercer, R. 

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[8] Banerjee, S. and Lavie A. 2005.. “METEOR: An Automatic 

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[9] Fellbaum, C., ed. 1998. “WordNet: An Electronic Lexical 

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[11] Subramanian, K., Stallard, D., Prasad, R., Saleem S., 

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1998. 

251








Probability of Successful Transfer of Low-level Concepts 

via Machine Translation: A Meta-evaluation 

Gregory A. Sanders 

National Institute of Standards and Technology 

100 Bureau Drive 

Gaithersburg, MD 20899-8940

(301) 975-4451

gregory.sanders@nist.gov 

Sherri Condon 

The MITRE Corporation 

7525 Colshire Drive 

McLean, VA 22102 

scondon@mitre.org 

ABSTRACT

In this paper, we present one of the important metrics used to 

measure the quality of machine translation in the DARPA 

TRANSTAC program. The metric is stated as either the 

probability or the odds of a machine translation system 

successfully transferring the meaning of content words (nouns, 

verbs, adjectives, adverbs, plus the most important quanitifiers 

and prepositions). We present the rationale for the metric, explain 

its implementation, and examine its performance. To characterize 

the performance of the metric, we compare it to utterance level (or 

sentence-level) human judgments of the semantic adequacy of the 

translations, obtained from a panel of bilingual judges who 

compare the source-language input to the target-language 

(translated) output. Language pairs examined in this paper include 

English-to-Arabic, Arabic-to-English, English-to-Dari, and Darito-English. 

Categories and Subject Descriptors

D 2.8 [Metrics] Performance Measures. 

I 2.7 [Natural Language Processing] Machine Translation.

:General Terms

Languages, Measurement. 

Keywords

TRANSTAC, low-level concept transfer 

Copyright

 © 2009 Association for Computing Machinery. ACM 

acknowledges that this contribution was authored or co-authored 

by a contractor or affiliate of the U.S. Government. As such, the 

Government retains a nonexclusive, royalty-free right to publish 

or reproduce this article, or to allow others to do so, for 

Government purposes only.

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

Copyright © 2009 ACM 978-1-60558-747-9/09/09...$10.00 

1. INTRODUCTION 

In this paper, we present one of the important metrics used to 

measure the quality of machine translation in the DARPA 

TRANSTAC program. TRANSTAC is intended to develop 

speech-to-speech machine translation that can be used by U.S. 

soldiers and marines who speak only English but who need to 

communicate with civilians in other countries who speak only 

other languages. The metric is stated as either the probability or 

the odds of a machine translation system successfully transferring 

the meaning of content words (nouns, verbs, adjectives, adverbs, 

plus the most important quanitifiers and prepositions) from the 

source-language spoken input, into the target-language output. 

TRANSTAC systems take spoken input via automatic speech 

recognition (ASR) pipelined with machine translation (MT) and 

then speak out the translation via a text-to-speech (TTS) process. 

We refer to the identified content words as low-level concepts.

The metric is explained in more detail in section 4 of this paper. 

Transfer of the low-level concepts is scored one concept at a time, 

as successfully transferred, deleted, or substituted (judges can also 

identify inserted concepts). Sanders, et al., (2008) provides some 

earlier analyses of the metric. 

In this paper, we present the rationale for the metric, explain its 

implementation, and examine its performance. To characterize the 

performance, we compare it to utterance level (or sentence-level) 

human judgments of the semantic adequacy of the translations, 

obtained from a panel of bilingual judges who compared the 

source-language input to the target-language (translated) output. 

 Language pairs examined in this paper include English-to-Arabic, 

Arabic-to-English, English-to-Dari, and Dari-to-English. 

Correspondingly, we will examine the low-level concepts in 

English, Dari, and Arabic. The Arabic dialect is Iraqi. The 

languages reflect countries in which many U.S. military people 

are deployed. 

2. MOTIVATION FOR THE METRIC 

One important motivation was to create a metric whose values 

would be intuitively meaningful. Another comes from the fact that 

the target languages in which translations are to be assessed in 

TRANSTAC are quite dissimilar. English, Dari, and Arabic come 

from different families of languages. Arabic is a ―central semitic‖ 

language very different from Indo-European languages. Dari, one 

of the two dominant languages spoken in Afghanistan, is a dialect 

of the Persian language. Dari is part of the Indo-Iranian group of 

Indo-European languages and is distinctly different from English, 

although not as different as Arabic. In the future, we expect to 

also deal with Pashto, the other dominant language of 

Afghanistan. The fact that the languages are so varied presents 

260
challenges for common automated metrics for machine 

translation, most of which were designed to assess translations 

into English and implicitly assume linguistic characteristics of 

English or similar languages. 

Most automated metrics for machine translation look at ―n-gram 

co-occurrence statistics.‖ For example, the commonly used BLEU 

metric (BiLingual Evaluation Understudy, Papineni, et al.; 2001, 

2002) compares a machine translation output to a set of (usually 

four) independent high-quality translations created by human 

translators. The BLEU metric looks at co-occcurrences of 

individual words (also called unigrams), pairs of words (called 2-

grams or bigrams), triples of words (3-grams or trigrams), and 4-

grams. We refer to these, collectively, as n-grams. The BLEU 

metric does a weighted average of the fractions of the unigrams, 

bigrams, trigrams, and 4-grams in the machine translation output 

that also occur in one or more of the human translations (reference 

translations). Looking at bigrams, trigrams, and 4-grams makes 

sense for English, because it is a measure of getting words in the 

right order, which is an important aspect of English syntax. 

Getting words in the same order as a human translator is also a 

useful indicator of fluency in English. But other languages 

communicate ―who did what to whom‖ by means of affixes on 

words (for example, ―whom‖ communicates that it is an object 

rather than subject by means of an ―m‖ affixed to ―who‖). In 

scoring translations into languages whose syntax relies more 

heavily on affixes rather than on word order, scoring the longer ngrams that BLEU examines can be counter-productive. 

A related problem with the longer n-grams is that a paragraphlength passage has about the same number of 4-grams as of 

unigrams. BLEU and similar metrics that consider longer n-grams 

(such as 3-grams and 4-grams) were designed with paragraphlength texts in mind. But for speech-to-speech machine 

translation, one is normally scoring a sentence-length utterance, 

which will have proportionately few 3-grams and 4-grams. For 

example, consider the greeting, ―Good morning, my name is Joe.‖ 

That greeting has six unigrams, but only three 4-grams. 

Correspondingly, the probability that the 4-grams will co-occur is 

lower, making the value returned by the metric be somewhat 

affected by the length of the utterance. 

Finally, the significance of a unigram error differs for languages 

that rely heavily on affixes. Consider the English phrase ―to the 

program‖, which consists of three words. Each of the three may 

independently co-occur (or not) between the machine translation 

output and a reference translation. Looking at only unigrams, the 

translation ―to a program‖ has two unigrams correct and one 

unigram (the vs. a) wrong. The Arabic equivalent of ―to the 

program‖ is للبرنامج) llbrnAmj) which has three elements: l-  ̳to‘, 

Al-  ̳the‘, and brnAmj  ̳program‘. The Arabic equivalent of ―to a 

program is لبرنامج) lbrnAmj). But the single error will cause 

automated metrics to in effect count all three elements as wrong in 

Arabic, where in English they would count only one as wrong.

Condon, et al., (2009) discussed relevant aspects of Arabic 

morphology.

All this led to the desire for a metric whose numeric values would 

be independent of the utterance length, comparable across very 

different language pairs (combinations of source language and 

target language), and intuitively meaningful. Extensive 

discussions within the TRANSTAC research community made it 

clear that some measure of low-level concept transfer could have 

those desired properties. Many possible schemes were proposed 

for what to take as those low-level concepts. Some of the 

proposals looked at structural relations among concepts and some 

proposals included the notion of more-important vs. lessimportant concepts. Arriving at a scheme that would have similar 

concept counts in very different languages was difficult. 

Agreement as to what to do did not materialize, however. 

At some point, Sherri Condon had suggested just using the 

content words as the low-level concepts. When circumstances 

finally forced a choice in order to be able to proceed, that was the 

approach that was chosen. A key goal of this paper is to examine 

how well using the content words has worked out. 

3. RATIONALE FOR CONTENT WORDS 

Content words occur in essentially all languages, and the number 

of content words in the source language input and the target 

language output tends to be quite similar. For that reason, a 

numeric measure of the probability that the meaning of the source 

language content words will be correctly transferred by the 

translation is somewhat language-independent. This was desired 

because we wanted to be able to say whether translations into one 

language were about as good as translations into another—for 

example, whether the translations from English to Arabic were 

about as good as the translations from English to Dari. We 

calculate the probability of success because probability is a linear 

scale that lends itself to calculating correlations to other metrics. 

We state the value as odds of success (the number transferred 

correctly, divided by the number of errors) because we can 

conveniently describe progress from one evalaution to the next as 

an odds ratio, which is a familiar statistic that is well understood. 

We chose to weight the content words equally because there is no 

agreed on way to measure the relative importance of different 

content words. Further, the usual metric for speech recognition 

accuracy (word error rate) weights all words equally, and that 

metric has worked out well in practice. This choice to weight the 

low-level concepts equally was somewhat controversial, so we 

want to look at how well that choice has worked out. 

But what are the advantages of using content words as our lowlevel concepts? There are three. First, it puts an upper-bound on 

the number of low-level concepts: one concept per content word. 

And that limit directly reflects choices made by the speaker. 

Second, it is a fact about human languages that any piece of 

meaning expressed by function words, syntax, context, and so 

forth can be expressed by a content word, should the speaker 

choose to give it greater prominence by doing so. Thus, the 

content words reflect the speaker‘s choice of what to make 

prominent. Third, content words tend to lexicalize entire 

complexes of meaning. Consider the noun ―steer.‖ Saying or 

writing that word conveys, in one word, that it is a male bovine 

that is castrated by humans, being kept by humans, and destined 

to be slaughtered for meat. A bilingual human judge can decide 

whether the meaning intended by the speaker‘s choice of the 

source language word ―steer‖ ended up being adequately 

conveyed by the target language translation. Thus, using the 

content words directly reflects the speaker‘s choices of how to say 

things. The probability or odds of a translation successfully 

transferring the meaning of the content words is a directly 

261
quantitative measure of the transfer of the low-level elements of 

meaning in each utterance. 

4. IMPLEMENTING THE METRIC 

We had an analyst who is a native speaker of each source 

language identify the low-level elements of meaning (low-level 

concepts) in the input utterances from several representative 

excerpts of conversations where both directions were translated by 

the TRANSTAC speech-to-speech machine translation systems. 

We then asked a panel of five bilingual judges to tell us which 

pre-identified low-level concepts were successfully transferred 

into the target-language output (where failures are deletions, 

substitutions, or insertions of concepts). 

4.1 Source-language Annotation 

To standardize the annotation of the source-language concepts, we 

created a fairly extensive guidelines document explaining what to 

mark as a concept. The guidelines document includes rules for 

what to group as a single concept. The following are some 

examples of groups of words that count as just one concept. 

words that make up a number: ―four hundred and twenty 

seven‖

words that make up a date: ―June 12, 1979‖

words that make up an address: ―1313 Mockingbird Lane‖

words that make up a verb tense in English: ―would have 

been known‖

noun-noun constructions: ―tea napkin‖ or ―Abrams tank‖

phrasal verbs: ―look up‖ (e.g., a word in a dictionary).

As a concession to make things easier for Arabic judges, we chose 

to group possessive pronouns with the thing possessed, for 

example ―his car.‖ 

Correspondingly, for source languages that rely heavily on affixes, 

the source-language annotator must sometimes mark affixes as 

independent low-level concepts. In some languages, including 

Arabic, ―two locks‖ would be a single word in dual number, 

which should be marked as a separate source-language concept. 

The source language annotators used a software tool called CTR, 

written by Sébastien Bronsart. 

Figure 1. CTR tool, in source language annotation mode. 

262
In figure 1, low-level Arabic concepts identified by the annotator 

appear as a vertical list in the bottom-center of the CTR tool. 

No matter what the language, the choices made by the sourcelanguage annotator were always reviewed in full detail with the 

first author (Sanders), in order to ensure that all issues became 

known and to promote uniformity across languages. 

4.2 Target-language Judging 

Later (after the evaluation had been run, generating the machine 

translation outputs from the systems) we had a panel of five 

bilingual judges compare the machine translation outputs (taken 

from the system logfiles) to these lists of pre-identified low-level 

concepts in each source language input utterance. The bilingual 

judges were all native speakers of the foreign language, 

reasonably current on the spoken language (not someone who had 

been in the U.S. for several decades), and had a high level of 

English proficiency. The judges were given detailed written 

guidelines for how to do the analysis and were trained by NIST. 

The bilingual judges did their analyses using the CTR analysis 

tool, in an ―output scoring‖ mode. The analyst worked through an 

utterance at a time. The reference transcription of the spoken input 

was presented across the top of the screen (much as the Arabic 

example in the screenshot above), with the machine translatiion 

output below. The list of pre-identified concepts appeared as a 

vertical list below that (just as in the screenshot above). The 

analyst worked down that vertical list of concepts, inputting their 

judgments using a relevant set of clickable buttons at the right. 

For each low-level concept, the analyst decided whether it was 

transferred successfully, was completely missing (a deletion 

error), or had morphed into something else (a substitution error). 

The analysts also identified concepts that had been inserted by the 

system (somewhere in the pipelined combination of ASR+MT). 

Immediately after scoring the low-level concepts for an utterance, 

the judge assigns a Likert-type judgment, as can be seen in the set 

of radio buttons in the lower-right corner of the following 

screenshot, where the judge is beginning to score an utterance. 

Figure 2. CTR tool in output scoring mode. 

The result was, therefore, a count of correctly transferred concepts 

and a count of the three types of errors. We computed our metric 

(Odds of Successful Transfer of a Low-level Concept) by dividing 

the number of successes by the number of errors. If, for example, 

there were 100 reference concepts, 75 transferred successfully, 

and there were no insertion errors, then the odds of success would 

be 3.0, which would also be the case if 79 transferred successfully 

and there were 5 insertion errors. In probability and statistics, the 

word ―odds‖ refers to the odds of the desired outcome — for 

example, if you roll one six-sided die, the odds of rolling 

something other than a six are 5 to 1). 

Comparisons across repeated evaluations can be stated as an Odds 

Ratio (the odds of success in the new evaluation, divided by the 

odds in the previous evaluation), which we believe will be easily 

263
understood. To continue our example from the immediately 

preceding paragraph, if the previous evaluation had 90 of the 100 

concepts successfully transferred and there were 10 errors (none 

insertion errors) then the odds of successful transfer are 90 / 10 or 

9.0. If the current evaluation has 95 of the 100 concepts 

successfully transferred, with 5 errors (none insertion errors), then 

the odds of success in the current eval were 95 / 5 or 19.0. Of 

course, odds can be re-stated as a probability, which we call 

―adjusted probability of correct‖ — adjProb(corr) amounts to 

numCorrectlyTransferred / (totalNum + numInsertionErrors).

5. LIKERT-TYPE JUDGMENTS 

Perhaps the most widely accepted benchmark for the quality of 

machine translation is to get judgments of semantic adequacy 

from a panel of bilingual judges. ―Semantic adequacy‖ means one 

asks the judges whether the translation conveys the meaning. 

As has been mentioned, immediately after scoring the low-level 

concepts for an utterance, the judge assigns a Likert-type 

judgment of semantic adequacy (as can be seen in the set of radio 

buttons in the lower-right corner of the screenshot in figure 2). 

Because the judge has just worked through the low-level concept 

analysis, the judge should have digested the machine translation 

output and thought about what the hearer of that translation 

would understand it to mean. 

In addition, the judges were given a substantial set of exemplars 

of each of the four Likert categories that have textual anchors, so 

as to calibrate their expectations to be as harsh/forgiving as the set 

of exemplars. These examplars came from judgments done by 

previous panels of judges. In practice, it has been necessary to pay 

considerable attention to getting all the judges to accept the sets of 

exemplars as correctly graded, and to not make their own 

judgments be more harsh or forgiving than the sets of exemplars. 

6. ASSESSING OUR METRIC 

Our metric is the probability of successful transfer of low-level 

concepts (content words). In general, the low-level concept 

transfer metric should be relatively independent of the identity of 

the source language. We have done what we could to ensure that 

source-language annotation of low-level concepts was done 

comparably in all source languages. And of necessity, the source 

language inputs are different for the different languages, being 

idiomatic spoken conversations with native speakers of the 

foreign language, intended to be representative of how U.S. 

soldiers and marines would use the systems in country. 

To assess the quality of our metric, we ask how closely its values 

correlate with the Likert-type judgments of semantic adequacy. At 

the utterance level, we have had three different machine 

translation systems in each evaluation, and we have assessed 

tranaslations of sixty to eighty utterances. The utterances were 

input to the systems as audio recordings, so that all three systems 

would have exactly the same inputs. The translations were judged 

as explained in the preceding sections of this paper. We then 

calculated Pearson correlations (Pearson‘s R) between the 

utterance-level Likert-type judgments of semantic adequacy and 

the values for probability of successful transfer of low-level 

concepts in each utterance. from each system. The resulting 

correlation values between our low-level concept transfer metric 

and the Likert-type judgments from our bilingual judges are 

substantially higher than the correlations of those judgments with 

the typical automated metrics (BLEU, METEOR, and so forth). 

For translations into English, the utterance-level correlations 

between our metric and the Likert-type judgments were usually 

above R = 0.7 

For translations into Dari, the utterance-level correlation between 

our metric and the Likert-type judgments was R = 0.72 

For translations into Arabic, the utterance-level correlation 

between our metric and the Likert-type judgments was 

consistently above R = 0.8 (with R

2

≈ 0.7) This raises the 

question as to why the correlations are higher than for the other 

two target languages. We really do not know why. One possibility 

is a wider range of difficulty in the source-language data; we don‘t

think that is the case. Another possibility is that it could be the 

case that for translations into Arabic any low-level concept error 

has a more direct effect on the actual semantic adequacy of the 

translation. We suspect that may be true, but we do not know. The 

Likert-type judgments for translation into Arabic are higher than 

for any other language pair that we have examined, so it also 

appears that the quality of these translations is better, and this 

could also be a factor. 

Why are the correlation values above (between our low-level 

concept transfer metric and the Likert-type judgments of semantic 

adequacy) not higher? That question is difficult to answer. We 

think much of the answer lies in the fact that our low-level 

concept metric gives the same weight to all content words. But 

some errors are far more serious than others. To provide an 

aritificial example, suppose a soldier asks a local collaborator 

about the state of the road to some nearby town. Suppose the 

collaborator says, ―There are new mines under that road.‖ If the 

automated speech recognition (speech-to-text) component of the 

system mis-hears this, the translation could easily be, ―There are 

no mines under that road.‖ Our low-level concept transfer metric, 

(as well as all the typical automated machine translation metrics) 

will find only one word incorrect and therefore give that 

translation an excellent score, where a bilingual human judge 

would recognize that the translation is ―inadequate.‖ 

7. REFERENCES 

[1] Condon, S., Sanders, G. A., Parvaz, D., Rubenstein, A., 

Doran, C., Aberdeen, J. and Oshika, B. 2009. Normalization 

for Automated Metrics: English and Arabic Speech 

Translation. Proceedings of Machine Translation Summit 

XII, (Ottawa, Canada). 

[2] Papineni, K., Roukos, S., Ward, T., and Zhu, W. 2001. 

BLEU: A Method for Automatic Evaluation of Machine 

Translation. (IBM research report available at 

http://domino.watson.ibm.com/library/cybergig.nsf/)

[3] Papineni, K., Roukos, S., Ward, T., and Zhu, W. 2002. 

BLEU: A Method for Automatic Evaluation of Machine 

Translation. In, Proceedings of ACL 2002, 311–318. 

[4] Sanders, G., Bronsart, S., Condon, S., and Schlenoff, C. 

2008. Odds of ssuccessful transfer of low-level concepts: A 

key metric for bidirectional speech-to-speech machine 

translation. In, Proceedings of LREC 2008

264
Automated Metrics for Speech Translation 

Sherri Condon, Mark Arehart, Christy Doran, Dan Parvaz, John Aberdeen, Karine 

Megerdoomian, and Beatrice Oshika 

The MITRE Corporation 

7525 Colshire Drive 

McLean, VA 22102 

00-1-703-983-5522

{scondon/marehart/cdoran/dparvaz/aberdeen/karine/bea}@mitre.org

ABSTRACT

In this paper, we describe automated measures used to evaluate 

machine translation quality in the Defense Advanced Research 

Projects Agency‟s Spoken Language Communication and 

Translation System for Tactical Use program, which is developing 

speech translation systems for dialogue between English and Iraqi 

Arabic speakers in military contexts. Limitations of the automated 

measures are illustrated along with variants of the measures that 

seek to overcome those limitations. Both the dialogue structure of 

the data and the Iraqi Arabic language challenge these measures, 

and the paper presents some solutions adopted by MITRE and 

NIST to improve confidence in the scores. 

Categories and Subject Descriptors 

D.2.8 [Software Engineering]: Metrics – performance measures

I.2.7 [Artificial Intelligence]: Natural Language Processing –

machine translation 

General Terms

Measurement, Performance, Experimentation 

Keywords

Speech translation evaluation, automated translation metrics, 

machine translation 

1. INTRODUCTION 

While human judgments are considered to be the gold standard 

for evaluating translation performance, it is the development of 

automated evaluation metrics that has facilitated significant 

advances in machine translation technology during the last 

decade. Unlike evaluation methods that involve human 

judgments, automated measures provide rapid, reliable feedback 

with relatively low cost. Both human judgments and automated 

metrics are limited in ways that are still not fully understood, and 

this report reveals some additional characteristics concerning the 

application of automated measures to speech translation between 

English and Iraqi Arabic. 

Approved for Public Release: 09-3822. Distribution Unlimited. The views, opinions, 

and/or findings contained in this article/presentation are those of the author/presenter 

and should not be interpreted as representing the official views or policies, either 

expressed or implied, of the Defense Advanced Research Projects Agency or the 

Department of Defense. Permission to make digital or hard copies of all or part of this 

work for personal or classroom use is granted without fee provided that copies are not 

made or distributed for profit or commercial advantage, and that copies bear this 

notice and the full citation on the first page. To copy otherwise, to republish, to post 

on servers or to redistribute to lists, requires prior specific permission and/or a fee. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

Copyright © 2009 ACM 978-1-60558-747-9/09/09...$10.00

The Spoken Language Communication and Translation System 

for Tactical Use (TRANSTAC) program has experimented with 

several evaluation strategies and metrics. Since the inception of 

the Defense Advanced Research Projects Agency (DARPA) 

tactical two-way speech translation programs, a MITRE team has 

coordinated with system developers to design collection methods 

for training data and evaluation methods to measure progress. 

More recently, The National Institute of Standards and 

Technology (NIST) has directed these efforts, and the MITRE 

team has focused on automated metrics. 

The evaluations have focused on the basic functionality of speech 

recognition and machine translation, and a major goal has been 

tests that incorporate users and domains which are representative 

of the military uses for which the systems are designed. 

Consequently, a significant challenge of developing useful 

evaluation methods for the TRANSTAC program has been the 

conflict between replicability and authenticity. Test conditions 

resembling real-world conditions require spontaneous interaction 

between representative users with meaningful goals in realistic 

situations and environments. However, these conditions are not 

repeatable due to the inevitable variation in human behavior. 

The strategy adopted for TRANSTAC evaluations has been to 

conduct two types of evaluations: live evaluations in which users 

interact with the translation systems according to several different 

protocols and offline evaluations in which the systems process 

audio recordings and transcripts of interactions. The inputs in the 

offline evaluation are the same for each system, and the automated 

measures used to evaluate system performance on those inputs 

produce scores in the same way each time they are computed. 

Therefore, the same tests can be repeated as the systems mature. 

Automated measures such as BiLingual Evaluation Understudy 

(BLEU) [10], Translation Edit Rate (TER) [13], and Metric for 

Evaluation of Translation with Explicit word Ordering 

(METEOR) [1] have been developed and widely used for 

translations of text and broadcast material, which have very 

different properties than dialogue. The TRANSTAC evaluations 

provide an opportunity to explore the applicability of automated 

metrics to translation of spoken dialogue and to compare these 

metrics to human judgments from a panel of bilingual judges. 

The evaluations also offer a chance to study the results of 

applying automated MT metrics to languages other than English. 

Studies of the measures have primarily involved translation to 

English and other European languages related to English. The 

TRANSTAC data present some significant differences between 

the automated measures of translation into English and Arabic, 

and our research has provided some insights into the reasons for 

these differences. 

265
2. AUTOMATIC TRANSLATION METRICS 

2.1 The BLEU Measure 

A fundamental problem of translation evaluation is that there are 

many possible translations from a source language input to a 

target language output. The IBM researchers who developed 

BLEU in 2001 provided a partial solution to this problem by 

creating test sets with more than one translation for each input. 

The machine translation output is then compared to these 

reference translations, and a score is computed based on the 

number of n-grams in the output that match the references. For 

example, Figure 1 provides a sample machine translation from 

Iraqi Arabic to English along with 4 reference translations. 

In Figure 1, 11 of the 12 words in the system output can be 

matched to words in the reference translations, producing a score 

of 11/12 for unigram matches. There are 11 bigrams (sequences 

of 2 words) in the system output, and 5 of them correspond to 

bigrams in the reference translations: he has, stomach pain, pain 

and, and always, and pain in. Therefore, the bigram score is 

5/11. The trigram score is 1/10: only pain and always can be 

matched to the references, and there are no matching 4-grams. 

The BLEU score is computed by micro-averaging [4] the n-gram 

scores of all the outputs in the test corpus, for n = 1, 2, 3, and 4. 

Then the geometric mean of the four n-gram averages is 

computed. Finally, the result is multiplied by a “brevity penalty.” 

The brevity penalty is assessed because without it, the score 

would not reflect portions of the reference translations that were 

completely missed. For example, suppose we added and I don’t 

know what to do to each of the reference translations. Without the 

brevity penalty, the BLEU score would not be affected. The 

brevity penalty lowers the BLEU score in proportion to the 

difference between the number of words in the system outputs and 

the number of words in the reference translations whose lengths 

are closest to the lengths of the outputs (combined across the 

entire test corpus). 

The example illustrates some of the limitations of the BLEU 

metric. Although the system output is not fluent English, the 

meaning expressed in the reference translations is easily inferred 

from the system output. The BLEU score cannot discriminate 

between a translation like the system output in Figure 1 and a 

translation like (1), which has the same number of matching ngrams. 

(1) he has some abdomen and always my and he says in his

The n-gram matching treats all words equally, regardless of their 

significance for the meaning. In the extreme case, a semantically 

loaded word like not is treated no differently than an optional 

conjunction like and. 

It has been observed that BLEU and measures derived from 

BLEU have become de facto standards in the machine translation 

community [7]. As automated measures are used more 

extensively, researchers learn more about their strengths and 

shortcomings, which allows the scores to be interpreted with 

greater understanding and confidence. Some of the limitations 

that have been identified for BLEU are very general, such as the 

fact observed earlier that the measure primarily reflects the 

accuracy of the words that the system produced with only a 

brevity penalty to assess what the system may have missed. This 

makes the measure more like a document similarity measure [9]. 

In fact, researchers often use information retrieval terms to 

describe this problem. BLEU scores measure precision: the 

proportion of words or documents that were correctly translated or 

retrieved compared to the total words or documents that were 

translated or retrieved. BLEU scores do not measure recall: the 

proportion of words or documents that were correctly translated or 

retrieved compared to the total words or documents that should 

have been translated or retrieved. 

2.2 The METEOR Measure 

Researchers have proposed dozens of alternative measures that 

seek to improve on BLEU, while retaining the basic insight of 

comparing system outputs to multiple reference translations. 

Many of these measures were compared in the NIST Metrics for 

Machine Translation 2008 Evaluation (MetricsMATR08) [8]. In 

addition to BLEU, the TRANSTAC program uses METEOR to 

score translations of the recorded dialogues. METEOR 

incorporates a unigram recall score that can yield higher 

correlations with human judgments than BLEU scores [1].

METEOR also addresses another problem that has been 

associated with BLEU. The ability of BLEU to take into account 

many possible translations for a given segment of language 

depends solely on the number of reference translations that are 

available for comparison. In contrast, METEOR accepts 

synonyms defined in a resource called WordNet [17], allowing 

additional options that are not present in reference translations. 

For example, METEOR would recognize the equivalence of pain

and ache in Figure 1. METEOR also uses stemming to remove 

inflectional affixes that may prevent translations from matching 

due to minor variation. For example, after stemming, METEOR 

would match cries and crying in Figure 1 because they are both 

forms of the verb cry. However, these enhancements are available 

only for English: there is no equivalent of WordNet for Iraqi 

Arabic, and Arabic affixes are often ambiguous out of context, 

making it difficult to stem words accurately. 

The METEOR score is computed by aligning the system output to 

the closest reference translation as in Figure 2. After stemming, 

cries and crying are considered a match, as are saying and says. In 

Figure 2, three words of the reference translation (in boldface) are 

not matched to the system output, and three words of the system 

output (not boldface) do not match the reference translation. 

Ref 1: he has some pain in his stomach and always cries and complains about stomach pain 

Ref 2: he has some pain in his stomach and he always cries and says I have a stomach pain 

Ref 3: he has some stomach pain and always cries saying my stomach hurts 

 Ref 4: he has a stomach ache and he always cries and says my stomach hurts 

System: he has stomach pain and always crying he says pain in stomach 

Figure 1: Sample Reference Translations and System Output 

266
Ref 3: he has some stomach pain and always cries saying my stomach hurts 

System: he has stomach pain and always crying he says pain in stomach 

Figure 2: METEOR Alignment of System Output and Reference Translation 

Therefore, recall is 9/12 and precision is 9/12. A weighted Fscore (harmonic mean of recall and precision) is computed with a 

penalty if any of the words have been aligned out of order. Recall 

is weighted more heavily than precision, though this can be 

adjusted by the user. 

Unlike the BLEU score, the METEOR score for the significantly 

poorer translation in (1) is lower than for the system output in 

Figures 1 and 2. For (1), recall is 8/12, precision is 7/12, and then 

the score is lowered by a penalty that applies to the match of my

because my occurs in a different position in the two sequences. 

2.3 The TER, STER and HTER Measures 

Another limitation of the BLEU metric is that it only indirectly 

captures sentence-level properties such as word order by counting 

n-grams for values of n that are greater than one. But syntactic 

variation can produce translation variants that may not be 

represented in reference translations, especially for languages that 

have relatively free word order [2,15]. For example, in the sample 

in Figures 1 and 2, the word always could appear in a variety of 

positions as illustrated in (2) for reference #4. 

(2) a. he has a stomach ache and he always cries and says my 

 stomach hurts (original reference) 

b. he has a stomach ache and he cries always and says my 

stomach hurts 

c. he has a stomach ache and always he cries and says my 

stomach hurts 

Although (2b) and (2c) may seem to be slightly less natural, they 

are certainly acceptable English forms. In other languages, word 

order is much freer than in English so that 3 or 4 reference 

translations will provide only a fraction of the options. METEOR 

allows users to adjust the word order penalty, but each word that 

must be moved or shifted in order to align with the reference 

translation is penalized separately. Therefore, when entire phrases 

in a language can freely occur in several positions, the translation 

is penalized for each word in the phrase. 

The TRANSTAC program has also experimented with the TER 

metric to measure translation quality. Unlike METEOR, TER 

allows any number of contiguous words to shift positions in a 

single move. Computation of the TER score is based on the 

Levenshtein edit distance measure for string matching [3], which 

counts the number of insertions, deletions, and substitutions 

required to transform one string into another. Figure 3 shows 

how the alignment in Figure 2 would be edited to transform the 

system output into the reference translation. The deletions and 

substitutions that transform he says pain in into saying my could 

have been aligned differently with no effect on the number of 

deletions and substitutions. 

The edit distance score is usually normalized by dividing the 

number of edits by the length of one of the strings, which would 

produce a score of 7/12 in Figure 3. When more than one 

reference translation is available, the denominator is the average 

length of the reference translations. 

Levenshtein edit distance does not allow for the possibility of 

aligning words that are out of order, as TER does. TER permits 

movement of words or contiguous sequences of words in order to 

align them, and the shifts are counted as edits along with 

insertions, deletions, and substitutions. With a slightly different 

reference translation, Figure 4 shows how allowing a shift 

produces a lower edit distance score. The TER score in figure 4 is 

7/13, whereas the Levenshtein edit distance score treats one he as 

a deletion and the other as an insertion, yielding a score of 8/13 

for the same pair. (Lower TER scores reflect better performance.) 

TER does not recognize synonyms, though the Semantic 

Translation Error Rate (STER) does use WordNet to align 

synonyms [17]. Instead, the inventors of TER introduced a 

variant that requires human intervention: Human Translation 

Error Rate (HTER). TER and HTER were developed for another 

DARPA machine translation program, Global Autonomous 

Language Exploitation (GALE), for which the machine translation 

evaluation is also conducted by NIST. In order to compute 

HTER, a human “post editor” edits the system output to produce a 

new reference translation that is maximally similar to the system 

output, while preserving the meaning of the reference translation. 

For example, a maximally similar reference for the system output 

in Figures 1-3 is (3). 

(3) he has stomach pain and always cries he says I have pain in 

 my stomach 

Computing TER using the reference translation in (3) results in a 

score of 4/15 (errors are in boldface), which is a significant 

improvement over the TER score computed using any of the 

reference translations in Figure 1. The lower error rate seems 

appropriate given our intuition that the meaning of the reference 

translations can easily be inferred from the system output. In 

contrast, consider the much poorer translation in (1), which is 

repeated as (4a). 

(4) a. he has some abdomen and always my and he says in his 

 b. he has some abdomen pain and always cries and he says

 my stomach hurts 

 Ref 3: he has some stomach pain and always cries saying my stomach hurts 

 System: he has stomach pain and always crying he says pain in stomach 

 Edits: insertion substitution deletion substitution deletion substitution deletion 

Figure 3: TER Alignment of System Output with Reference Translation and Edits 

267
 

 Ref 3: he has some stomach pain and he always cries saying my stomach hurts 

 System: he has stomach pain and always crying he says pain in stomach 

 Lev Edits: insertion insertion substitution deletion substitution deletion substitution deletion

 TER Edits: insertion shift1 substitution [ 1 ] substitution deletion substitution deletion 

Figure 4: TER vs. Levenshtein Edit Distance

(4a) receives a TER score of 8/12 when compared to the closest 

reference translation in Figure 1, which is a higher error rate than 

the system output‟s score of 7/12. The HTER score results in an 

even greater difference between the two translations: compared to 

the maximally similar reference translation in (4b), the HTER 

score for (4a) is 5/14 compared to 4/15 for the system output. 

The HTER measure does not need to use WordNet or stemming 

because the human post editor can incorporate synonyms and 

adjust inflections. Also, unlike human judgments of translation 

quality, HTER does not require bilingual judges. Monolingual 

post editors can produce the customized reference translations 

from a single reference translation. Although the HTER measure 

appears to be more sensitive than the TER measure, it requires 

human intervention. Therefore, the significant advantages that 

automated measures obtain by eliminating the time, expense, and 

variability of human evaluations are lost. Consequently, the 

TRANSTAC program has not used the HTER metric. 

2.4 More Issues for Automated Metrics 

One shortcoming of automated measures of translation quality is 

shared by human judgments, which are typically obtained by 

asking bilinguals to rate system outputs on a scale that ranges 

from poor to perfect. Neither automated measures nor human 

judgments provide feedback that is diagnostic or that specifies the 

problems in less-than-perfect translations. In fact, BLEU is 

designed to be computed on an entire test corpus, using microaveraging and calculating the brevity penalty based on all of the 

references and system outputs in the test set. NIST microaverages the HTER scores when reporting evaluation results for 

GALE [Le, personal communication]. The claim is often made 

that automated measures cannot be expected to correlate well with 

human judgments at a sentence or utterance level: the high 

correlations that are reported compare corpus-level scores among 

translation systems so that the statistic is typically based on only a 

dozen or fewer data points. The MetricsMATR08 evaluation 

computed both utterance and corpus level correlations, and the 

former were much lower [9].

Another issue that is relevant to TRANSTAC evaluations 

concerns the quantity of data required for reliable automated 

measures. TRANSTAC training data is difficult to collect (see 

Section 3) so that it is important to hold as little as possible back 

for evaluation. Fortunately, some recent work suggests that 

samples as small as 300 sentences can be sufficient to correctly 

detect significant differences between systems, though bootstrap 

sampling is recommended to assess the significance of differences 

in scores [15]. 

A related concern is the length of the inputs, which has particular 

importance for TRANSTAC data because spoken utterances tend 

to be shorter than written ones. For example Turian, Shen, & 

Melamed report that samples of reference translations from 

TIDES corpora averaged about 31 words per sentence [15], 

whereas 30 words is considered a maximum for inputs to the 

TRANSTAC speech translation systems. All of the automated 

measures of translation quality have been developed and tested 

using text data, whereas TRANSTAC data is speech data, which is 

structured very differently. In the next section the data collected 

for TRANSTAC systems is described, and additional features of 

those data that might affect automated metrics are discussed. 

3. TRANSTAC TEST DATA 

3.1 Data Collection 

Initially, TRANSTAC stakeholders agreed that domains and use 

cases should be narrowly defined in order to provide realistic 

goals for the speech translation systems. However, it quickly 

became clear that even the most routine interactions can easily 

veer out of domain when, for example, the driver at a checkpoint 

tries to explain why he has a sack of money in the trunk. 

Interviews with veterans of military operations in Iraq and 

Afghanistan initially resulted in about 50 scenarios that were used 

to elicit interactions in 6 domains, including checkpoints, 

searches, infrastructure surveys (sewer, water, electricity, trash, 

etc.), and training. Later, another 30 scenarios were developed 

with more diverse topics such as medical screening, inspection of 

facilities, and recruiting for emergency service professionals. 

Eventually scenarios were consolidated into six broad categories: 

checkpoints, civil affairs, facility inspections, medical, training, 

and joint operations. 

Scenarios provide each role-player with a description that sets the 

scene, identifies the role of the speaker, provides some 

background and motivation for the speaker, and may describe an 

outcome for the encounter. For example, the military speaker 

might be asked to imagine that he is at a checkpoint, that a car 

driven by a young man has approached, that a search of the car 

revealed a large bag of cash in the trunk, and that the man is 

detained for further questioning. Scenarios included an example 

interaction or suggested topics for discussion. Role-players were 

coached to prepare for their roles before recording. 

A variety of protocols were used in order to take advantage of 

role-players available at different data collection events and to 

maximize the number of interactions that were recorded. Large 

amounts of Iraqi Arabic data can be collected if Arabic speakers 

interact in Arabic. For authentic military English, dialogues were 

recorded in which an American soldier or Marine interacted with 

an Iraqi Arabic speaking civilian via a bilingual interpreter. This 

protocol made it possible to obtain a maximum amount of speech 

from the very limited time that we had access to military 

personnel. In earlier data collection events, an inoperable 

telephone handset or similar prop was passed to each role-player 

before he or she could begin to talk, which minimized overlap 

among the speakers. Later, lights were used to signal when 

268
participants could begin to speak. Additional data were collected 

by eliciting answers to prerecorded questions from native Iraqi 

Arabic speakers, and one of these collections was designed to 

elicit names of people, places, and organizations. 

All of the interactions were transcribed orthographically, and the 

transcriptions were translated into the other language (English to 

Arabic or Arabic to English) by professional transcribers and 

translators. Transcription and translation conventions were 

developed with input from developers, NIST, the Linguistic Data 

Consortium (LDC), and MITRE. Portions of the Arabic data 

were transcribed phonetically, and diacriticized lexica were 

created. Transcriptions included timestamps at the beginning and 

end of each segment. Some recordings, transcriptions, and 

translations were not distributed to the developers so that they 

could be used for evaluation. These data are referred to as the 

reserved data (see section 3.2). 

The data collection protocols resulted in speech that differs from 

the inputs that users produce when interacting with speech 

translation devices. Users communicating via a speech translation 

device quickly realize that they must speak clearly, avoid false 

starts and filler expressions such as „uh,‟ and keep inputs short 

and simple. In contrast, the training data resembles ordinary 

conversation with high frequencies of filler expressions, pauses, 

breaths, and unclear speech as well as lengthy utterances. Some 

examples are provided in (5).

(5) a. then %AH how is the water in the area what's the -- 

 what's the quality how does it taste %AH is there %AH 

 %breath sufficient supply? 

b. the -- the first thing when it comes to %AH comes to 

 fractures is you always look for %breath %AH fractures 

 of the skull or of the spinal column %breath because 

 these need to be* these need to be treated differently than 

 all other fractures. 

c. would you show me what part of the -- %AH %AH 

 roughly how far up and down the street this %breath 

 %UM this water covers when it backs up ? 

The examples in (5) illustrate the filler expressions such as „um‟ 

and „uh,‟ which are transcribed „%UM‟ and „%AH,‟ and false 

starts, which are represented by dashes, in the data. 

Another source of mismatches between training data and live 

evaluation inputs is in the transcription. Transcribers were 

instructed to divide sequences of speech from a single speaker 

into smaller units at reasonable logical break points. The 

guidelines indicate that there has been ongoing clarification of 

this directive, and it is clear that divisions were inconsistently 

applied. For example, the single segment in (5a) contains four 

separate questions, and (5b) was divided in the middle of a 

sentence where the asterisk appears in the text. There can be good 

reasons not to separate every distinct sentence-like unit in a steady 

stream of speech. If speakers do not pause between these units, 

then the speech cannot be divided cleanly due to co-articulation. 

3.2 Selection of Evaluation Data 

The TRANSTAC offline evaluations have primarily used two 

types of recorded dialogues. Reserved test data are subsets of the 

training data that are held back for evaluation instead of delivered 

to researchers for system development. Although reserved sets 

can be maximally representative of the training data, they are not 

ideal test sets because systems have been exposed to the voices 

and speech patterns of the speakers during training. Therefore, a 

special data collection using speakers who do not appear in any 

training data was conducted in order to create a test set that is 

sequestered for re-use. 

Training data were collected, processed, and released as separate 

corpora based on the data collection events at which they were 

produced. In order to identify a representative reserved set from 

each collection, the vocabulary in each dialogue was analyzed to 

provide the following information: 

1. Total word tokens and word types in the dialogue 

2. Number of tokens and types that are unique to the dialogue 

3. Percentage of tokens and types in the dialogue that occur in 

other dialogues 

4. Number of times a word in the dialogue appears in the 

corpus: average for all words 

From the dialogues that were in the mid-range for the percentage 

of word types that occurred in other dialogues, reserved dialogues 

were chosen so that each scenario topic was covered, a variety of 

speakers were represented, and the score in (4) above was 

maximized. Approximately 10% of the recordings were reserved. 

Before each evaluation event, the sets of reserved dialogues were 

analyzed, and a summary of information relevant to selecting the 

test dialogues was produced. This information included the 

scenario topics, gender of the speakers (most were male), the 

number of English and Arabic utterances, and information about 

the lengths of utterances in the scenarios. Selection of specific 

audio inputs for the offline evaluation requires several passes 

through the pool of dialogues available for the offline corpus. In 

the first pass, complete dialogues for the offline evaluation are 

selected based on the authenticity of the content, the range of 

scenarios, and the variety of speakers. 

From the selected dialogues, individual utterances were identified 

as candidates for the offline audio inputs. Utterances were 

selected to satisfy the following goals: 

1. Proportions of male and female speakers are similar to 

proportions in the training set 

2. Utterance lengths do not exceed 30 words with preference for 

5 - 15 words in length 

3. Minimize the frequency of false starts, pauses and filled 

pauses 

4. Avoid utterances that do not preserve structural and semantic 

coherence 

5. Avoid utterances that appear to overlap with other utterances 

according to the timestamps 

6. At least 400 utterances in each language 

After an initial pass through the dialogues to select utterances for 

an initial count, a second pass finalized the choices by eliminating 

additional utterances that were less desirable according to the 

criteria, while still preserving the goal of at least 400 inputs per 

language. In order to preserve the content and coherence of the 

dialogues, only the worst offenders of criteria 2-4 were excluded. 

As more data was collected, the number of utterances was raised 

to 600. The sequestered test set was selected in a similar manner. 

It includes 810 English utterances and 664 Arabic utterances. 

Timestamps were used to segment the audio recordings into a 

separate clip for each input. In addition, text inputs were 

269
produced from the transcriptions of the selected segments in order 

to provide measures of translation quality that were independent 

of speech recognition. Consequently, offline evaluations 

produced a set of results that included speech recognition word 

error rate (WER) for each language and BLEU, TER, and 

METEOR translation scores for spoken inputs as well as BLEU, 

TER, and METEOR scores for textual inputs. 

4. CORRELATIONS AMONG MEASURES 

Speech recognition performance is important because recognition 

errors usually result in translation errors. The speech recognition 

word error rate was measured using the NIST SCLite scoring 

software, which computes a score derived from Levenshtein edit 

distance by comparing system recognition outputs to 

transcriptions of the speech [12]. To address the variation that 

occurs in speech, NIST modifies the reference transcriptions, 

replacing each occurrence of an English contraction with the most 

likely expansion for that occurrence in its context. Further, words 

such as gonna, wanna, ’em and ’cause that represent phonological 

reduction are replaced by the unreduced equivalent. Compound 

words that are usually written as a single word are replaced by 

that form. Hyphenated words are rewritten as multiple words 

(replacing hyphen by space). Similar re-writes are done to the 

system output, except that contractions are replaced by an 

alternation, so that either version can match the reference. The net 

result of normalizing the system output and reference transcription 

files is to increase the number of matches (lowering the WER), 

make fairer comparisons among systems, and increase 

repeatability. 

For each evaluation, a sample of approximately 100 English-toArabic and 100 Arabic-to-English translations from the offline 

test data was also scored using two methods that involved human 

judgments. In one method, which will be referred to as Likert 

judgments, bilinguals classified the translations as completely

adequate, tending adequate, tending inadequate and inadequate. 

More recently these judgments have been modified to the 7-point 

scale in Figure 5. The same translations were scored using 

another method, developed by NIST, in which each open class 

content word (c-word) in the source utterance was identified, and 

bilingual judges determined whether the word had been 

+3 Completely adequate 

 +2 

 +1 Tending adequate 

 0 

–1 Tending inadequate 

–2 

–3 Inadequate 

Figure 5: Seven-value scale for semantic adequacy 

successfully translated, deleted, or substituted in the target 

utterance. The measure, which NIST refers to as low-level 

concept transfer, is computed as an odds score by dividing the 

number of c-words successfully translated by 1 minus the number 

of insertions, substitutions or deletions in the target [11].

Tables 1 and 2 show how the system scores from automated 

measures correlate with each other, with the human Likert 

judgments, and with the low level concept scores. Because TER 

and WER scores are error rates, they are subtracted from 1 to 

allow a positive correlation. “Concept Odds” refers to the low 

level concept measure described above, while “%Adequate” is the 

percent of utterances that were judged completely adequate in the 

Likert judgments. The correlations are typical of the correlations 

that developers of automated metrics of translation quality report. 

They are very high, but are based on only 5 systems and only on 

the samples of approximately 100 translations for each direction. 

Figures 6 and 7 present the scores obtained for each automated 

measure and each human-judged measure, including the live 

dialogues. In the latter, military English speakers and Iraqi Arabic 

speakers were asked to role play scenarios using the translation 

systems. To maintain consistency in the content of the unscripted 

interactions as they were repeated for each system, the same 

speakers were required to obtain and provide the same specific 

information using each system. Scores were based on a binary 

human judgment of translation adequacy for inputs produced in 

20 ten-minute dialogs [16]. The figures show similar patterns for 

all of the automated measures and for the human judged 

measures based on the offline data. The pattern for the live data is 

somewhat different, but for the most part, systems A-C score 

higher than D and E. 

BLEU METEOR 1 - TER Concept Odds %Adequate 1 - WER

BLEU 1

METEOR 0.994 1

1 - TER 0.994 0.993 1

Concept Odds 0.955 0.919 0.928 1

%Adequate 0.969 0.937 0.951 0.994 1

1 - WER 0.958 0.968 0.974 0.872 0.888 1

Table 1: English to Arabic Pearson Correlations among Measures for January 2007 Systems 

BLEU METEOR 1 - TER Concept Odds %Adequate 1 - WER

BLEU 1

METEOR 0.974 1

1 – TER 0.982 0.945 1

Concept Odds 0.978 0.990 0.972 1

%Adequate 0.979 0.988 0.930 0.971 1

1 - WER 0.813 0.906 0.756 0.847 0.880 1

Table 2: Arabic to English Pearson Correlations Among Measures for January 2007 Systems

270
 English to Iraqi Arabic Iraqi Arabic to English 

Figure 6: Automated Measures for Translations and Speech Recognition for January 2007 Systems A - E 

 English to Iraqi Arabic Iraqi Arabic to English 

Figure 7: Translation Quality Measures Involving Human Judgments for January 2007 Systems A – E 

5. CHALLENGES FROM ARABIC 

One fact about the patterns of scores has persisted in subsequent 

evaluations. Although the human judgments consistently suggest 

that translation from English to Arabic is more successful than 

translation from Arabic to English, the automated measures 

consistently suggest the opposite. Moreover, the WER for 

English is much lower than for Arabic, which should also make 

translation more accurate, as suggested by the human judgments, 

but not the automated measures. It cannot be expected that scores 

from automated metrics will be comparable across languages, but 

the concern is that the measures may be less indicative of 

translation performance for languages like Arabic. 

Several properties of Arabic challenge assumptions of automated 

measures. For example, it is assumed that words can be separated 

by spaces or punctuation, but six high-frequency words in Arabic, 

including the equivalents of „the‟ and „and‟ are attached to the 

word that follows them in Arabic orthography. The orthography 

of Arabic is extremely variable, with diacritic elements frequently 

omitted, so that string matching may fail due to a minor difference 

that would not obstruct understanding. Also, word order is freer 

in Arabic than in English. Furthermore, Arabic is more highly 

inflected than languages like English, though these differences 

have little effect on meaning. The examples in (6) illustrate that 

even in the absence of context, errors in inflectional morphology 

do not prevent communication of the sender‟s message.

(6) a. two book (two books) 

b. Him are my brother. (He is my brother) 

BLEU scores computed with reference to the correct versions in 

parentheses would be very low because the inflected forms do not 

match. METEOR provides a stemming operation that addresses 

this problem for English, but for many Arabic strings, complete 

stemming is not possible because the forms are ambiguous. 

Instead we experimented with light stemming, which has proven 

to be helpful in information retrieval tasks [6].

While NIST‟s normalization of references and outputs for 

computing WER has been uncontroversial, similar processes had 

not been proposed for automated measures of translation quality. 

However, normalization appears to be a simple way of handling 

superficial variation that would adversely affect accurate scoring 

of translations, just as it does for scoring WER. The TRANSTAC 

program has introduced normalization procedures for both 

English and Arabic to reduce variability before scoring with 

automated metrics. Norm1 performs rule-based normalization 

such as replacing contractions with full forms in English and 

removing all diacritics in Arabic. Norm2 performs word-based 

normalization such as the spellings of Arabic names in English. 

We experimented with two consequences of light stemming in 

Arabic: Norm2a separates the affixes, but does not delete them, 

while Norm2b deletes the affixes. 

We also experimented with an option in the BLEU metric that 

uses only the unigram scores to allow for the freer word order in 

Arabic. We used the human judged subset of the June 2008 

evaluation consisting of 109 English utterances (1431 words) and 

96 Iraqi Arabic utterances (1085 words) in excerpts from 13 

dialogs, each including about 7 exchanges. Table 1 provides 

Pearson‟s correlations among all the measures we have discussed 

for the English to Iraqi Arabic translations. Each correlation is 

computed over 39 data points (scores from 3 systems on excerpts 

from 13 dialogs). Correlations to the word-error-rate (WER) from 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Live Likert Concept

A

B

C

D

E

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Live Likert Concept

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1-WER BLEU 1-TER METEOR

A

B

C

D

E

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1-WER BLEU 1-TER METEOR

271
Table 1: Pearson’s R Correlations among the Metrics and Normalizations: June 2008 English to Iraqi Arabic

automated recognition of the English speech input are included in 

the first column. Next are correlations of Norm2, Norm2a, and 

Norm2b computed with BLEU_1 (BLEU with unigrams only) and 

with BLEU_4 (the more usual version with unigrams through 4-

grams). Correlations with the two human-judgment metrics are 

highlighted with grey background: “AdjProbCorrect” is based on 

the low-level concept transfer score described in section 4. 

The highest correlation in Table 1 is between the two types of 

human judgments. Also, it appears that WER is a good predictor 

of translation quality for the TRANSTAC systems. There is a 

steady increase in correlation from Norm2 to Norm2a to Norm2b. 

Norm2b scores correlate with the human judgments considerably 

more strongly than is the case for the Norm 2 and Norm2a scores. 

We believe this shows that human judges are more sensitive to 

errors on content words than to errors on the functional elements 

that are removed from Norm2b, but are only separated in Norm2a. 

6. CONCLUSIONS 

This report describes automated measures of translation quality, 

their limitations, and the issues encountered when applying the 

measures to speech translation data and to Arabic data. The report 

contributes to the research community‟s understanding of these 

measures, which have significantly advanced the development of 

machine translation systems. Results are based on a small sample 

of data, and additional data may change the patterns observed. 

7. REFERENCES 

[1] Banerjee, S. and A. Lavie. (2005). METEOR: An Automatic 

Metric for MT Evaluation with Improved Correlation with 

Human Judgments. In Proceedings of the ACL 2005 

Workshop on Intrinsic and Extrinsic Evaluation Measures 

for MT and/or Summarization, pp. 65-73.

[2] Chatterjee, N., Johnson, A., and M. Krishna. (2007). Some 

Improvements over the BLEU Metric for Measuring 

Translation Quality for Hindi. In Proceedings of the 

International Conference on Computing: Theory and 

Applications 2007, pp. 485-90. 

[3] Cohen, W., Ravikumar, P, and Fienberg, S. 2003. A 

Comparison of String Distance Metrics for Name-Matching 

Tasks. IIWeb 2003: 73-78. 

[4] Internet Information. Course Wiki. Accessed August, 2009. 

http://ilps.science.uva.nl/Teaching/II0607/twiki/bin/view/Ma

in/MeetingFeb22#1_1_1_Averaging. 

[5] Koehn, P. (2004). Statistical Significance Tests for Machine 

Translation Evaluation. In Proceedings of EMNLP 2004. 

[6] Larkey, L. and Connell, M. 2007. Light stemming for Arabic 

information retrieval. In Arabic Computational Morphology: 

Knowledge-based and empirical method, A. Soudi, A. van 

den Bosch, and G. Neumann, Eds,, Springer Verlag. 

[7] Lita, L.V., Rogati, M., and A. Lavie. (2005). BLANC: 

Learning Evaluation Metrics for MT. In Proceedings of 

HLT/EMNLP, pp. 740–747.

[8] Metrics for Machine Translation Evaluation (Metrics 

MaTr08). Accessed August, 2009. 

http://www.itl.nist.gov/iad/mig/tests/metricsmatr/2008. 

[9] Owczarzak, K., van Genabith, J., and A. Way. (2007). 

Dependency-Based Automatic Evaluation for Machine 

Translation. In Proceedings of HLT-NAACL 2007 AMTA 

Workshop on Syntax and Structure in Statistical Translation. 

[10] Papineni, K., Roukos, S., Ward, T., and W-J. Zhu. (2002). 

Bleu: A method for automatic evaluation of machine 

translation. In Proceedings of ACL 2002, pp. 311-318.

[11] Sanders, G., Bronsart, S., Condon, S., and C. Schlenoff, 

(2008). Odds of successful transfer of low-level concepts: 

A key metric for bidirectional speech-to-speech machine 

translation in DARPA‟s TRANSTAC program. In 

Proceedings of LREC 2008. 

[12] SCLite. NIST Multi-Modal Information Group. Accessed 

August, 2009. http://www.itl.nist.gov/iad/mig/tools/ 

[13] Snover, M., Dorr, B., Schwartz, R., Makhoul, J., and L. 

Micciula. (2006). A Study of Translation Error Rate with 

Targeted Human Annotation. In Proceedings of AMTA 

2006, pp. 223-231.

[14] Subramanian, K., Stallard, D., Prasad, R., Saleem, S., and 

Natarajan, P. Semantic translation error rate for evaluating 

translation systems. IEEE Workshop on Automatic Speech 

Recognition & Understanding, 2007, pp. 390-395. 

[15] Turian, J.P., Shen, L. and I. D. Melamed. (2003). Evaluation 

of Machine Translation and Its Evaluation. In Proceedings of 

MT Summit 2003, pp. 386-393.

[16] Weiss, B., Schlenoff, C., Sanders, G., Steves, M., Condon, 

S., Phillips, J., and Parvaz, D. (2008). Performance 

Evaluation of Speech Translation Systems. In Proceedings 

of LREC 2008. 

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http://wordnet.princeton.edu. 

Likert Content

English input BLEU_1 BLEU_4 BLEU_1 BLEU_4 BLEU_1 BLEU_4 Semantic Word

WER Norm2 Norm2 Norm2 Norm2a Norm2a Norm2b Norm2b Adequacy AdjProbCor

WER Norm2 1

BLEU_1 Norm2 -0.23 1

BLEU_4 Norm2 -0.03 0.81 1

BLEU_1 Norm2a -0.33 0.77 0.63 1

BLEU_4 Norm2a -0.18 0.81 0.89 0.79 1

BLEU_1 Norm2b -0.43 0.82 0.51 0.80 0.61 1

BLEU_4 Norm2b -0.38 0.76 0.63 0.64 0.66 0.84 1

Likert Sem Adeq -0.63 0.50 0.19 0.60 0.41 0.75 0.63 1

Adj Prob Correct -0.67 0.35 0.07 0.59 0.30 0.67 0.48 0.86 1

272
Utility Assessment in TRANSTAC: Using a set of 

complementary methods 

Michelle Potts Steves and Emile Morse 

National Institute of Standards and Technology 

100 Bureau Drive 

Gaithersburg, Maryland 20899-8940 USA 

1 301-975-{3537, 8239} 

msteves@nist.gov, emorse@nist.gov 

ABSTRACT

This paper describes the methods used during formative utility 

assessments for speech-to-speech, real-time translation systems 

intended for tactical use. The systems, while still prototypical, had 

hardware platforms that could be exercised indoors or outdoors by 

study participants with pertinent backgrounds and skills that are 

similar to the intended target users. English-speaking and foreign 

language subjects participated in exercises using the systems 

exchanging information without the aid of a human interpreter. 

Feedback on subjects’ experiences during the dialog interactions 

was collected via two primary methods: survey questionnaires and 

semi-structured interviews. In this paper, we describe our humancentered approach to utility assessment, how we combined the use 

of two feedback gathering methods, and discuss findings from use 

of this approach. 

Categories and Subject Descriptors

H.5.2 [Information Systems. Information Interfaces and 

presentation. User Interfaces] Evaluation/methodology

General Terms

Measurement, Performance, Human Factors 

Keywords

Speech-to-speech translation systems, evaluation, questionnaire, 

semi-structured interview, utility assessments, complementary 

assessment methods 

1. INTRODUCTION 

This paper describes the approach we have evolved to perform 

formative utility assessments for speech-to-speech translation 

systems. It describes briefly what utility is and the techniques 

used to assess utility for these prototype systems, namely, the 

combination of two methods: survey questionnaires and semistructured interviews. Further, we describe how we used these 

techniques during our most recent evaluation and provide some 

observations on the use of the approach. 

2. BACKGROUND 

2.1 TRANSTAC Program 

The systems being evaluated were developed by research teams 

participating in the Spoken Language Communication and 

Translation System for Tactical Use (TRANSTAC) program. 

TRANSTAC is a Defense Advanced Research Projects Agency 

(DARPA) advanced technology research and development 

program1

. The goal of the TRANSTAC program is to demonstrate 

capabilities to rapidly develop and field free-form, two-way 

speech-to-speech translation systems that enable speakers of 

different languages to communicate with one another in realworld tactical situations without an interpreter. To date, several 

prototype systems have been developed for force protection and 

medical screening domains in Iraqi Arabic (IA), Mandarin, Farsi, 

and Pashto. Systems have been demonstrated on both handheld 

and laptop-grade platforms with varying performance. Figure 1 

shows one such prototype in use during a recent evaluation. 

Evaluations have focused on assessments of both technical 

performance data and value to the intended end user. 

2.2 Utility Assessments for TRANSTAC 

Utility is an extension of usability [4] and describes the value that 

a system provides to the end user; usability, in contrast, addresses 

whether the system can be used. Utility and usability fall under the 

general heading of usefulness. For a detailed discussion of utility 

in the context of testing early prototype systems, see [3]. Utility 

assessments in TRANSTAC were intended to provide both 

quantitative and qualitative data about various aspects of these 

end-to-end systems. The interfaces between the systems and the 

users comprise both hardware and speech. Users are expected to 

 

1

 The views, opinions, and/or findings contained in this article are 

those of the authors and should not be interpreted as 

representing the official views or policies, either expressed or 

implied, of the Defense Advanced Research Projects Agency or 

the Department of Defense. 

Figure 1: TRANSTAC system prototype 

Copyright 

© 2009 Association for Computing Machinery. ACM 

acknowledges that this contribution was authored or co-authored 

by a contractor or affiliate of the U.S. Government. As such, the 

Government retains a nonexclusive, royalty-free right to publish 

or reproduce this article, or to allow others to do so, for 

Government purposes only. 

PerMIS’09, September 21-23, 2009, Gaithersburg, MD, USA, 

Copyright © 2009 ACM 978-1-600558-747-9/09/09...$10.00 

273
use a microphone and speaker/headphones as the hardware and 

use speech as the interaction mode. Both types of interactions are 

novel. More usual systems require keyboard input and text 

understanding. 

We believed that user-centered methods would allow us to collect 

both quantitative and qualitative measures of utility that could 

drive further development of the TRANSTAC systems. 

2.2.1 Techniques Used 

Over the course of the last three years, there have been five 

TRANSTAC evaluations. From the earliest of these evaluations, 

we have employed questionnaires to elicit opinions and attitudes 

of the subjects. Instruments have been developed for English 

speakers who are usually Marines and Soldiers, referred to as 

Subject Matter Experts (SMEs), and for Foreign Language 

Speakers (FLEs), who have always been bilingual in English and 

the target language. 

The second type of utility elicitation method that has been used is 

some form of interview. In early studies, observers often engaged 

in ad hoc debriefs with individual subjects. As the evaluation 

series evolved, we structured meetings with small groups (2-4 

subjects) of subject matter experts and a single facilitator at one 

time, and large group meetings (8-10 subjects) with 1-2 

facilitators for the foreign language speakers. Additionally, 

sessions in earlier TRANSTAC evaluations were essentially 

unstructured, in that the interviewer would rely on his/her recent 

observations of the subjects engaged in their tasks with the 

speech-to-speech translation system. More recently we have 

adopted a process that is referred to as semi-structured interviews 

[6]. This transition was made possible by our experience over 

time with the other interview methods and a focusing of areas of 

inquiry after learning the kinds of feedback subjects provided. 

Although the questionnaires that we used provided space for the 

subjects to make open-ended comments related to an issue, we 

found that since each subject completed many of these surveys 

over the course of a week of testing, the quantity of comments 

decreased and the quality of the content became repetitious. Semistructured interviews gave the subjects an opportunity to interact 

with one or more other subjects and with the facilitator; this 

situation seem to stimulate new ideas and the group tended to 

become newly motivated and to feed off each other. The structure 

provided by a core set of questions also allowed different 

researchers to act as facilitators. This latter aspect was important 

since several activities were running in parallel throughout the 

evaluation period and a single facilitator would never have been 

able to interact with all interview groups without severe changes 

to the master plan. 

2.2.2 Method 

This section gives an overview of the testing procedures used in 

the most recent study. Prior evaluations had similar procedures. 

SMEs and FLEs were trained just prior to the testing period. 

Training for both groups included an overview briefing on the 

program and its goals, the assessment goals and procedures for 

this testing period, administrative topics, and scenario topics. 

During scenario topic review, SMEs and FLEs were given some 

background information and suggested talking points for each of 

their assigned scenarios. Additionally, they were given the 

opportunity to step into their respective scenario roles to practice 

and try out various talking points with members of the evaluation 

team prior to interacting with a TRANSTAC system. 

Additionally, SMEs were provided TRANSTAC system-specific 

training just prior to a testing period. Four days of testing 

followed the training day. See Figure 2: Overall schedule. 

Day 1 Day 2 Day 3 Day 4 Day 5 

SME 

system 

training 

SME 

system 

training 

SME 

system 

training 

SME 

system 

training 

AM: 

testing 

AM: 

testing 

AM: 

testing 

AM: 

testing 

SME and FLE 

Training 

PM: 

testing 

PM: 

testing 

PM: 

testing 

PM: 

testing 

Figure 2: Overall schedule 

Figure 3: Daily testing schedule example shows a more detailed 

schedule of activities for subjects interacting with one research 

team’s TRANSTAC system, e.g., Team C, for the highlighted 

areas in Figure 2. Each of the other 2-3 research teams, their 

associated prototype systems, and assigned subject participants 

had similar schedules each of the 4 days of testing. 

Start End Station Activity SME FLE Obs.

8:00 8:45 N/A Morning Briefing ALL ALL

8:45 10:00 N/A Sys. Training 7, 8, 9 n/a

8:45 10:00 N/A Scenario Review as needed ALL

10:00 10:40 Station 3 Scenario 3 7 7, 8 C

10:40 11:20 Station 1 Scenario 1 8 1, 2, 3 C

11:20 12:00 Station 2 Scenario 2 9 4, 5, 6 C

12:00 12:45 Area 3 Semi-Struc t. Int 7, 8, 9 n/a C

12:00 12:45 Area 4 Semi-Struc t. Int n/a ALL D

12:45 13:45 N/A LUNCH

13:45 14:25 Station 3 Scenario 6 8 7, 8 A

14:25 15:05 Station 1 Scenario 4 9 1, 2, 3 A

15:05 15:45 Station 2 Scenario 5 7 4, 5, 6 A

15:45 16:30 Area 3 Semi-Struc t. Int 7, 8, 9 n/a A

15:45 16:30 Area 4 Semi-Struc t. Int n/a ALL D

Tuesday

TRANSTAC Research Team C

Figure 3: Daily testing schedule example 

3. TECHNIQUE IMPLEMENTATION 

The next section describes the detailed questionnaires that were 

developed for quantitative and qualitative subjective feedback. 

After that we describe the range of interview methods that were 

used in this study. Generally speaking, there are three main 

opportunities in a study for using questionnaires [1],[4] – pre-test, 

post-task, and post-test. We administered a pre-test survey to 

gather demographic data about our subjects; the content of the 

pre-test form is not shown here. The details in section 3.1 refer 

specifically to the post-task survey. SMEs completed a post-task 

survey roughly 8 times over the course of the 4-day evaluation; 

whereas the Foreign Language Speakers completed the post-task 

survey approximately 24 times during the 4-day evaluation 

period. The FLEs participated in more interactions, although 

typically two thirds of their interactions were of a secondary 

nature. We expected the observations of those who participated in 

a secondary manner to also be of value. There was no post-test 

survey in this study, but rather we used interview techniques to 

elicit the subjects’ feedback after all tasks had been completed. 

3.1 Survey Questionnaires 

Questionnaires were administered to English speakers and to 

foreign language speakers. During the first TRANSTAC 

evaluation exercise, our subjects filled in paper surveys. All 

subsequent studies were done on-line. The switch to on-line had 

274
several advantages: 1) elimination of data errors due to post-study 

transcription; 2) ability to enforce submission of data in required 

fields; and 3) subjects appeared to prefer interacting with a system 

rather than with paper. The first subsection describes survey items 

that produced quantitative data. The section after that addresses 

qualitative, open-ended comments. 

3.1.1 Quantitative assessment areas 

There were several areas that we wanted to probe and each 

category is detailed in the following subsections. Related sets of 

questions were administered in the order shown. Best practice in 

questionnaire design recommends that instruments be organized 

so that the most important information is elicited first and that 

more detailed information is requested later. Getting the most 

important things first enhances the likelihood of getting a 

subject’s freshest impressions. Asking detailed questions later 

tends to prevent a subject from being bored. Five-point Likert 

scales were used for each test item. Except for grouping related 

items, no headers, labels, or other indicators were used in the 

questionnaires themselves. 

3.1.1.1 Interaction factors 

The questions in this section are related to general usability of the 

speech-to-speech translation. We wanted to know at a high-level 

how the subject felt about the general context of the justcompleted task. While some of the items are highly correlated 

with one another, analysis of the data shows that subjects 

discriminate the nuanced meanings of the items. For example, on 

average, subjects might give high ratings to a system on the first 

three questions but significantly lower ratings for other questions. 

• I found the system easy to understand in this interaction. 

• What the system said made sense to me. 

• The system made it easy to have this interaction. 

• In this interaction, it was easy to get the information I 

needed. 

• I knew what I could say or do at each point of this 

interaction. 

• The system worked the way I expected it to in this 

interaction. 

All of the questions had response choices of 5=Strongly Agree, 

4=Agree, 3=Neutral, 2=Disagree, 1=Strongly Disagree, and N/A 

using a scale of 1 to 5. 

3.1.1.2 Speech characteristics 

Each characteristic was assessed by the SME with respect to 

English and the FLE with respect to the other language being 

assessed. The following characteristics were assessed: 

• The pronunciation of the <language> was clear and 

understandable. [Response choices were 5=Strongly Agree, 

4=Agree, 3=Neutral, 2=Disagree, 1=Strongly Disagree, and 

N/A, using a scale of 1 to 5.]

• The <language> words were put together in a way that was 

coherent and comprehensible. [Response choices were 

5=Strongly Agree, 4=Agree, 3=Neutral, 2=Disagree, 

1=Strongly Disagree, and N/A, using a scale of 1 to 5.]

• When the system didn't understand me, it was easy to correct. 

[Response choices were 5=Strongly Agree, 4=Agree, 

3=Neutral, 2=Disagree, 1=Strongly Disagree, and N/A, 

using a scale of 1 to 5.]

• How widely will the <language> spoken by the system be 

understood by other <language> speakers? [Response 

choices were 5=Nearly all, 4=Most, 3=Many, 2=Few, 

1=Almost None, and N/A, using a scale of 1 to 5.]

• How easily will other <language> speakers be able to speak 

in a way that is understood by the system? [Response choices 

were 5=Effortlessly, 4=With little effort, 3=Some effort, 

2=With difficulty, 1=Nearly impossible, and, N/A using a 

scale of 1 to 5.]

• How appropriate is the <language>spoken by the system to 

the situations simulated in today's tests? [Response choices 

were 5=Completely appropriate, 4=Rather appropriate, 

3=Minimally acceptable, 2=Somewhat inappropriate, 

1=Completely inappropriate, and N/A, using a scale of 1 to 

5.]

3.1.1.3 Satisfaction ratings 

Satisfaction, along with efficiency and effectiveness, are the 

primary measures of usability [2]. In some sense all questions on a 

survey are trying to assess a user’s subjective satisfaction. This set 

of questionnaire items particularly targets the ‘desirability’ aspect 

of the user’s interaction with the system. The first question 

regarding the speaker’s confidence was asked only of the native 

English speakers, while the other three questions were asked of all 

subjects. Since the English speaker was viewed in earlier 

TRANSTAC evaluations as the primary user of the system, it 

made sense to concentrate on his/her opinion about confidence. 

We have come to see that the opinions of the foreign language 

speaker diverge from those of English speakers and provide 

alternative viewpoints. Therefore, questionnaires in future 

evaluations will ask both speaker types about their confidence. 

• How confident were you in the system's ability to help you 

communicate effectively? [Response choices were 

5=Strongly Confident, 4=Confident, 3=Neutral, 2=Doubtful, 

1=No confidence, and N/A, using a scale of 1 to 5.]

• I would use this system in the field in its current state of 

functionality. [Response choices were 5=Strongly Agree, 

4=Agree, 3=Neutral, 2=Disagree, 1=Strongly Disagree, and 

N/A, using a scale of 1 to 5.]

• Based on my experience in this interaction, I would 

recommend this system for future similar interactions. 

[Response choices were 5=Strongly Agree, 4=Agree, 

3=Neutral, 2=Disagree, 1=Strongly Disagree, and N/A, 

using a scale of 1 to 5.]

• If the system had worked as intended in this interaction, I 

would recommend this system for future similar interactions. 

[Response choices were 5=Strongly Agree, 4=Agree, 

3=Neutral, 2=Disagree, 1=Strongly Disagree, and N/A, 

using a scale of 1 to 5.]

3.1.1.4 Problem detection and categorization 

Subjects were asked to characterize the causes of any problems 

they experienced during each interaction. This was assessed with 

the following question: If you experienced problems in the last 

dialog-interaction as reported above, please indicate the cause(s) 

of these problems. [Response choices were “Translation system 

problems”, “Human partner problems”, and “Other, please 

describe”.]

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3.1.1.5 Changing speech to match the system 

When we observed subjects using the speech-to-speech systems, it 

was clear that they tried a variety of ways of adjusting their 

speech input, especially when their utterances were not producing 

good results from their perspective. We asked our participants to 

tell us how they tried to deal with the system by changing their 

speech patterns. The list of possible changes was based on our 

observations and things subjects mentioned during interviews. 

• Towards the end of the conversation, I was interacting with 

the system in a different manner than at the beginning. 

[Response choices were 5=Strongly Agree, 4=Agree, 

3=Neutral, 2=Disagree, 1=Strongly Disagree, and N/A, 

using a scale of 1 to 5.]

• If you changed the way you interacted with the system, it 

involved the following (check all that apply): 

 Speaking more quickly 

 Speaking more ‘naturally’ for me 

 Speaking more slowly 

 Speaking more clearly 

 Speaking more loudly 

 Speaking more softly 

 Waiting more time to speak once my ‘speech button’ is 

pressed 

 Waiting less time to speak once my ‘speech button’ is 

pressed 

 Using simpler and or shorter sentences phrases 

 Using longer and or more complex sentences phrases 

 Other (Please describe) 

3.1.1.6 Miscellaneous 

Except as noted above, both English speakers and bilingual 

speakers were asked the same questions. One aspect of the test 

plan that differed for these two types of subjects was the training 

that people received. Since the English speakers were imagined to 

be the ones who would carry the physical system in actual use 

situations, they needed to be trained how to operate the physical 

device and given guidance on how to place it on their person. 

Training was provided by the developers of a system immediately 

before first use on each day that the system was tested. 

Foreign-language speakers played the role of citizens of their 

native country who might need to engage in conversation with the 

device-carrying English speaker. In such a scenario, there is no 

reason to provide training to these people. It would be important, 

however, for the foreign-language speaker to understand how he 

should act in the conversation. This information was provided by 

a set of instructions in the foreign language that the English 

speaker could invoke to be played from the TRANSTAC system 

for his intended conversational partner. 

The survey for foreign-language speakers had a five-point Likert 

item that addressed the adequacy of the instructional material. 

• The system instructions provided a clear understanding of 

how to use the system. [Response choices were 5=Strongly 

Agree, 4=Agree, 3=Neutral, 2=Disagree, 1=Strongly 

Disagree, and N/A, using a scale of 1 to 5.] 

3.1.2 Qualitative assessment areas 

The answers to all the questions in section 3.1.1 were entered by 

the subject using a radio button set or a drop-down list. The 

questions in this section are those that presented the user with a 

text box that accepted open-ended input. 

• What did you like most about this system, or what was most 

helpful and useful? 

• What did you like least about this system? 

• What would you change about this system? 

• What situations can you imagine that might best match this 

system's capabilities? 

• If the translation system seemed to have problems with 

certain words or phrases, please list any you can recall. 

• If you changed the way you interacted with the system, it 

involved the following “other” [strategies]: 

• What would you suggest to improve the training you 

received before starting the interactions? 

3.2 Interview topics 

Interviews were done each time that an English speaker 

completed a series of interaction scenarios using a single system. 

The schedule of activities was constructed so that two identical 

systems were in use by different English speakers. This situation 

allowed us to interview pairs of speakers who had had experience 

with the same system under very similar conditions. The next 

section shows topics that each interviewer covered during the 

interview session. Topics for FLEs follow in the next section. 

3.2.1 SME Topics 

• Things SMEs liked 

• Things SMEs didn't like 

• Things SMEs would change 

• Overall reliability and robustness (software/hardware) 

impressions 

• Form Factor Aspects 

• Microphone Use 

• Sound Output 

• Appropriateness 

• Comfort

• Control(s) 

• Speech Quality Aspects 

• Communication Successes/Failures 

• Speed Perception 

• Accuracy Perception 

• Error Issues 

• Use of confirmation 

• Frustration: SME and FLE 

• System feedback 

• Training/Instruction 

• Any other feedback 

3.2.2 FLE topics 

Foreign language speakers are not government employees but are 

considered members of the general public from a legal 

perspective. This fact complicates the use of ad hoc questions 

during evaluations. Due to restrictions imposed by the 

requirement for NIST to adhere to the Paperwork Reduction Act 

(PRA), all questions that NIST researchers would like to ask 

276
subjects from the general public must receive approval through a 

process managed by the Department of Commerce. The questions 

on surveys described in Section 3.1 have become standardized 

over time, it was possible to obtain permission to use those survey 

items. However, since interview topics are of a more ad hoc 

nature, it was not possible to get timely approval. Therefore, the 

only question we could pose to the foreign-language speakers 

during this evaluation was one requesting any feedback. 

4. DISCUSSION 

Each method has its own strengths. Questionnaires provide 

opportunities to gather both quantitative and qualitative feedback. 

Additionally all topics of inquiry were presented to each subject 

for feedback in the same way after each interaction and subjects 

could express their views without peer scrutiny. Interviews 

provide a way to modify the topics of inquiry as new points of 

interest arise. The interview facilitators had been scenario 

interaction observers, and were able to make just-in-time 

modifications to feedback topics relevant to interaction issues the 

participants had just experienced. Also, facilitator prompting and 

the group dynamics promoted deeper topic investigation and kept 

interviews fresher and less repetitive for participants. 

The use of complementary techniques gave us the opportunity to 

gain insights regarding the participant experience that each data 

gathering technique, used in isolation, would not have provided. 

In past evaluations, we attempted to use the qualitative responses 

from the surveys to provide context for the quantitative results. To 

some extent this was possible, but was completely dependent on 

the quality of the feedback provided by subjects prompted by the 

survey questions. We found that the semi-structured interview 

data greatly enriched the qualitative feedback we received from 

participants. This, in turn, provided a more informed context to 

interpret the quantitative findings. 

A striking example of how the semi-structured interview data 

provided a rich context for understanding quantitative results 

surfaced during the last evaluation. This example has to do with 

differences between SME and foreign language users regarding 

interaction factors for each of three systems assessed. During the 

course of the series of evaluations, we have seen that FLEs were 

often much more critical of the technologies than were the SMEs. 

This observation has held over time and has been an on-going 

question of interest for us. Figure 4 shows a graph of SME and 

FLE quantitative responses from a recent evaluation to our group 

of interaction factors questions listed below (also in 3.1.1.1). 

• I found the system easy to understand in this interaction. 

• What the system said made sense to me. 

• The system made it easy to have this interaction. 

• In this interaction, it was easy to get the information I 

needed. 

• I knew what I could say or do at each point in the interaction. 

• The system worked the way I expected it to in this 

interaction. 

All responses are on a five-point Likert scale, with 5 being the 

best response. SME responses are in blue and FLE responses in 

red as graphed. The general trends represented here illustrate the 

differences of opinions between the two major groupings of 

participants. 

Qualitative data from the questionnaires and FLE interviews 

indicated FLEs had significant dissatisfaction with the sentence 

construction, verb plurality, dropped words and concepts, among 

other issues, for all of the systems involved. The SMEs who 

possessed some knowledge of the target language were aware of 

these issues for the FLEs and noted them both in qualitative 

responses to survey questions and the interviews, however, the 

systems worked fairly well for the English speakers. On the 

surface, this seemed to explain the discrepancy between SME and 

FLE ratings for the systems. However, further probing by 

interview facilitators uncovered that SMEs are so keenly aware of 

the need for technology-assisted translation in the field that they 

were willing to take a chance on the current prototype systems. 

FLEs, on the other hand, being conversationally fluent in both 

languages, had no such real-world need and therefore gave a less 

biased judgment of system performance. In this case, the 

interview data provided a much richer context for understanding 

the quantitative results. 

Additionally, the interview feedback was much richer than the 

qualitative data provided in survey responses. We believe some of 

this is impact from method (discussed above) and some of it may 

be particular to our subject groups. For example, especially rich 

feedback was provided by the foreign language speakers from the 

one open-ended question posed: Any other feedback. The FLEs 

had high self-reported comfort levels with writing in English and 

use of computers. Yet despite this, verbal feedback was 

significantly richer. After a bit of reflection, this is not 

surprising... asking someone to respond verbally in a foreign (2nd, 

3rd or 4th) language vs. asking someone to write in that foreign 

language on a computer, of course, verbal responses will typically 

be richer. For example, during the interviews, FLEs introduced 

and discussed topics relating to how the technologies might be 

received in their native cultures and other cultural impact on 

expected concepts of operation and system form factors. These 

topics were not raised by FLEs in qualitative survey feedback. 

Similarly, Soldiers and Marines were more willing to discuss 

implications of system issues verbally than in writing. Likewise, 

they performed more brainstorming regarding system 

improvements during interviews than in survey feedback. 

Finally, the ability to modify topics of inquiry during the 

evaluation is a large bonus that the interview method provides. In 

the most recent evaluation, all systems used several forms of 

Figure 4: SME and FLE responses to interaction factors 

questions 

277
(English) confirmation. Only a small group of survey questions 

had been selected prior to the evaluation regarding backtranslation for inclusion in the survey instrument. The semistructured interview method provided the ability to probe the 

SMEs regarding all forms of confirmation and was very useful in 

providing pertinent feedback to the research teams on this topic of 

interest. 

As with all assessment efforts, there is a cost. In this case, adding 

the semi-structured interviews required additional time and effort, 

both in facilitator time to observe each of the interactions, and 

also time by both facilitators and subjects to participate in the 

interviews. We felt the time was well-spent, given the current 

stage of development of these systems, where utility is of interest 

as well as technical performance. 

5. CONCLUSION & FUTURE 

DIRECTIONS 

Early evaluations in this series focused heavily on technical 

performance of the systems. This was appropriate given the 

maturity level of the prototype systems and the understanding that 

insufficient speed and accuracy of translations would dominate 

any utility feedback during early stages of development. In 

accordance with this, we conducted data gathering using the 

survey questionnaires and ad-hoc interviews to provide some 

feedback to the research teams while refining our topics of inquiry 

and question sets. 

As the systems improved in translation speed and accuracy 

(technical performance) and form factor, a shift was made towards 

more emphasis on utility for the intended end user. When this 

occurred, semi-structured interviews were added to the post-task 

feedback gathering methods. While increasing the time required 

to gather utility feedback somewhat, the additional utility data 

provided new insights into the quantitative data gathered via the 

survey questionnaires and richer qualitative data overall. The use 

of these complementary methods improved the quality of the 

assessments that were collected and drawn from the data, and 

therefore provided richer feedback to the research teams to help 

direct their efforts in improving their systems as well as 

programmatic feedback on intended-user propensities relating to 

utility and acceptance. 

6. ACKNOWLEDGMENTS 

We especially want to thank the participants of these evaluations 

who strive to help move these systems toward deployment with 

their willingness to work with prototype systems and thoughtful 

feedback. 

7. REFERENCES 

[1] Dumas, J.S. and Redish, J.C. 1999. A Practical Guide to 

Usability Testing. Intellect, Portland OR. 

[2] ISO/IEC. 9241-14 Ergonomic requirements for office work 

with visual display terminals (VDT)s - Part 14 Menu 

dialogues, ISO/IEC 9241-14: 1998 (E), 1998. 

[3] Schlenoff, C., Steves, M. P., Weiss, B. A., Shneier, M., and 

Virts, A. 2007. Applying SCORE to field-based performance 

evaluations of soldier worn sensor technologies: Field 

Reports. J. Field Robot. 24, 8-9 (Aug. 2007), 671-698. 

[4] Scholtz, J. 2006. Methods for evaluating human information 

interaction systems. Interacting with Computers, Vol. 18 (4), 

July, 507-527. 

[5] Tullis, T. and Albert, B. 2008. Measuring the User 

Experience. Morgan-Kauffmann, Burlington, MA. 

[6] Wood, L.E. 1997. Semi-structured interviewing for usercentered design. Interactions: 4(2): 48-61. 

278
Performance measurement and its role in advancement for

intelligent systems: discussion points

Danil Prokhorov* and Yasuo Uehara

Toyota Research Institute NA

1555 Woodridge Dr.

Ann Arbor, MI 48105, USA

*dvprokhorov@gmail.com

ABSTRACT

This brief paper discusses the main theme of the PerMIS

2009 Workshop. It overviews three major areas of autonomous

intelligent systems and evaluates different issues of their performance testing.

Keywords

Autonomous intelligent systems, UGV, UAV, home robot

1. INTRODUCTION

The main theme of the PerMIS’09 Workshop is “Does

performance measurement accelerate the pace of advancement for intelligent systems?” This simple question apparently may not have an equally straightforward answer.

The simple answer is no. Performance measurement is

just a process to evaluate a system, and it is supposed to be

unbiased with respect to any higher-level intention, including the intention to accelerate the pace of advancement for

intelligent systems (IS).

However, the answer may also be yes because the higherlevel intention is self-evident as far as IS advancement goes

(one needs constantly advancing IS for accomplishing ever

more complex missions without human intervention), and

we need to be able to measure the progress of such advancement.

If the reader agrees with the affirmative answer (or at

least, admits that “yes” is more useful than “no”), then the

reader is welcome to proceed with reading further.

We change the question above slightly, hopefully without

loosing its value and importance, and turn it into a more

general discussion about the role of performance measurement in advancement for intelligent systems. Our focus is

on autonomous IS as exemplified by Unmanned Aerial Vehicles (UAV), Unmanned Ground Vehicles (UGV) and house

robots. We do not consider unmanned systems which operate under human operator control as in telerobotics (e.g.,

when a UAV is flown by a team of human operators).

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies

bear this notice and the full citation on the first page. To copy otherwise, to

republish, to post on servers or to redistribute to lists, requires prior specific

permission and/or a fee.

PerMIS’09 September 21-23, 2009, Gaithersburg, MD, USA.

Copyright 2009 ACM 978-1-60558-747-9/09/09 ...$10.00.

The next section introduces examples of performance measurement for autonomous IS. Section 3 summarizes our opinion about performance testing, including the use of simulators, followed by our final thoughts in Conclusion.

2. SPECIFICS AND EXAMPLES OF PERFORMANCE MEASUREMENT FOR AUTONOMOUS IS

Not every performance measure helps to accelerate the

progress of IS. Performance measurement seems to be only

useful for this purpose if it allows us to evaluate decision

making capabilities of IS. For example, just measuring maximum speed of an intelligent vehicle implies little about its

decision making capabilities, except maybe that the vehicle

needs some computing time to make appropriate decisions.

It is therefore our opinion that, instead of performance measurement, we should employ performance test (a sequence

of appropriate measurements in a form of scenario or procedure).

Performance test may or may not include components

which allow us to understand how to advance IS to the next

level of intelligence. The Turing test is well known among

proposed tests for intelligence. The original test itself is

probably not useful for autonomous IS, however its automotive variation has been suggested in [1] which is relevant

to a class of autonomous IS. We briefly discuss below other

tests in the context of autonomous flying, driving and home

robots and their corresponding performance metrics.

To be useful for advancing IS, performance tests should

contain clear directional information, i.e., information on

which components to improve in order to achieve the goals

of the test. For example, recognizing objects of interest

with the same accuracy as that of the trained human observer is essential to passing the test in any target recognition challenge. Matching or exceeding human recognition

accuracy constitutes an obvious direction to advance IS because humans often expect higher performance from robots

than from themselves (see, e.g., [2]).

2.1 Autonomous Driving

Though certainly not yet at the human level of driving,

autonomous surface vehicles have already demonstrated impressive achievements by advancing in just three years from

the level of DNF (did not finish) to the undeniable success

as evident from the series of DARPA Grand Challenges [3]

(Figure 1). Such challenges are examples of performance

tests, which greatly facilitated technology development for

279
autonomous driving. The next logical steps are autonomous

driving in less restricted and more complex environments

and races against human drivers.

Figure 1: Boss, the robot of the Tartan Racing team

is in action during the DARPA Urban Challenge.

In terms of durability (not getting tired, distracted, etc.),

the autonomous robots will be able to challenge humans even

sooner than their designers usually want to admit. Indeed,

autonomous driving across the NA continent will simply not

be possible for a single human driver because of the need for

rest, while a robot can drive essentially continuously (except

for short stops for fueling/electric battery replacement). It

is also interesting to ponder whether a conservatively driving

robot (one which is slow and stops often, letting other nonrobotic vehicles pass it, etc.) can still beat a human driver

which needs to rest in a race across the continent – a modern

version of the tortoise and the hare fable. By the way, this

example is another case in which performance metrics is less

of a typical one which measures speed of motion and other

physical quantities, but emphasizes safety.

2.2 Autonomous Flying

Similar to autonomous ground vehicles, autonomous flying vehicles have been making impressive advances as exemplified by aerial robotic competitions [4].

It is well known that many existing planes fly on autopilot

majority of time, especially during long flights. Even take

off and landing can be accomplished by robots on their own

(Figure 2). In fact, this unsophisticated level of automation

is already greater than what is currently available in the automotive world. Such automation level might be approachable in highway driving for automotive vehicles equipped

with next-generation adaptive cruise control (ACC) and lane

keeping assist (LKA) systems. Many people would have an

issue calling such automatic systems intelligent, for even specialized intelligence is generally believed to imply something

much more sophisticated than ACC/LKA.

2.3 Home Robots

Similarly, mobile robots with manipulators such as the one

shown in Figure 3 may some day reach the level at which

they can measure up against humans in performing various

useful tasks. Of course, prior to challenging humans in everyday tasks, home robots should pass much simpler tests

of reliably navigating in home environments, if only for the

Figure 2: The Soviet Space Shuttle “Buran” touching down automatically upon returning from space

in its first and only test flight in 1988.

purpose of re-charging their own batteries, as well as finding

and fetching appropriate objects based on their verbal descriptions, doing dishes without breaking them, etc. Various

universities are pursuing their in-door robotic research programs (e.g., [5]). New robotic challenges are contemplated

[6].

Figure 3: The home robot under development by

Toyota and University of Tokyo.

3. COMMON ISSUES OF TESTING

AUTONOMOUS IS

UGV, UAV and home robots generally operate in quite

different environments. UGV in the form of civilian robotic

cars will essentially operate in 2D (i.e., on the ground“plane”)

which is a safer environment than 3D operational environment of civilian UAV, especially when contrasted with the

risk of falling from the sky due to a mistake or a malfunction.

However, robotic cars will have to deal with broader variety

of situations on the road than their counterparts flying in

3D. Indeed, it is usually easier to collide with something on

the road than when flying due to higher density of potential obstacles in 2D. Though formally also operating in 3D,

home robots will have their own operation specifics, with

even greater variety of situations to handle than those of

robotic cars.

Yet, there are common issues of performance testing which

will apply to all of them. Several common issues are listed

below:

1) For autonomous IS, the performance is probably best

to be tested against humans whose functions such IS are

280
supposed to replace. It seems most useful to do so in a

succession of tests of increasing complexity, which will naturally facilitate the pace of IS advancement. As mentioned

above, the specific performance measurements such as average speed en route may or may not be of essence for the test

when contrasted with the IS operation safety.

2) An appropriate quantifiable metrics should provide a

clear indication of how well the IS accomplished the test

(e.g., the deviation from a planned route, the total number

of missed targets, etc.) [7]. Each test will certainly have specific metrics to measure the degree of success, but it remains

to be seen what quantifiable metrics to use when comparing with human-like behavior. For example, expert human

operators can always tell the difference between them and

a less skilled operator but their assessment that something

just does not feel right is often hard to quantify. Moreover,

operation differences such as feeling not-human-like must

not influence the performance assessment of the IS if they

are not essential to the chosen performance metrics.

Conceptually, imitating a human operator is easy: observe

inputs to the system under control of a human operator and

match them with the system outputs using an appropriate

machine learning method as part of the IS. This may or

may not be so easy in actual implementation, depending on

the control problem specifics. For example, like a human

operator any machine learning method must generalize to

similar but not exactly the same system conditions as observed during its training to imitate human behavior. This

is usually achieved by providing sufficient variety of training

examples from a human to the IS, but what constitutes sufficiency and how to guarantee adequate generalization are

subjects of on-going machine learning research. Sometimes

it is more effective to approximate a human evaluation (“reward”) function than a human controller itself from a modest

number of human demonstrations via inverse reinforcement

learning (see, e.g., [8]).

3) Performance testing in high-fidelity simulators is worthwhile to supplement testing in real-world environments, especially in early stages of IS development (e.g., simple scenarios, IS subsystem development, etc.). For example, there

is not much point in risking an expensive UAV if its attitude control system has not been tested thoroughly in highfidelity simulations.

Simulators should also be very helpful to test the behavior

of autonomous IS in rare/unusual situations because 1) their

very low rates of occurrence greatly complicate verification

of the IS performance in the real world, and 2) rare situations test for the IS ability of “exception handling” – the

quality usually attributed to human intelligence. For example, air or road accidents are rare events, but they can be

modeled if we understand what has happened and could describe it in computable terms. Such events as accidents are

not only rare but also prohibitively expensive, which makes

their high-fidelity simulation even more valuable as a tool

for IS performance assessment. The reader is referred to [9]

and [10] for examples of IS development simulators.

4. CONCLUSION

We think that performance measurement does help to advance the pace of IS development, especially if performance

tests are structured to have gradually increased level of difficulty and to compare different IS against each other and

against humans in tasks which humans would otherwise do

anyway.

We briefly discussed three classes of autonomous IS: UGV,

UAV and home robots. While seemingly different, they all

share the same set of performance testing issues as discussed

in Section 3. The main systems and subsystems of these IS

may be first subjected to tests in high-fidelity simulators,

followed by initially simple tests in real-life environment.

Gradually, tests will become more complex, with the purpose of verifying more advanced functions of IS. It is in this

progression of tests where we see the opportunity to accelerate the pace of IS advancement.

5. REFERENCES

[1] S. Kalik and D. Prokhorov. Automotive Turing Test. In

Proc. PerMIS’07.

[2] R. Murphy and B. Argrow. UAS in National Airspace

System: Research Directions. Final Report of the

NSF/AUVSI/FAA/DHS Wokrshop. Unmanned Systems,

June 2009, pp. 23–28.

[3] http://www.darpa.mil/grandchallenge/index.asp, DARPA

Urban Challenge 2007.

[4] http://www.auvsi.org/competitions/, AUVSI Student

Competitions.

[5] http://stair.stanford.edu/, STAIR: STanford Artificial

Intelligence Robot.

[6] C. Jenkins. The Future of AAAI Robotics. Challenges

and directions for building upward.

http://robotics.cs.brown.edu/ijcai09/aaai_challenges_mar2009.pdf

[7] E. Messina, A. Jacoff, and H. Scott. Performance

Evaluation of Autonomous Mobile Robots. In R.

Madhavan, E. Messina, J. Albus (Eds.), Intelligent

Vehicle Systems: A 4D/RCS Approach, pp. 247–282,

Nova Science Publishers, NY, 2006.

[8] P. Abbeel and A. Ng. Apprenticeship learning via

inverse reinforcement learning. In Proc. International

Conference on Machine Learning, 2004.

[9] http://usarsim.sourceforge.net/, USAIRSim homepage.

[10] http://www.tno.nl/downloads/PreScanBrochureDec2008.pdf,

PreScan brochure.

281
Collective Intelligence: Toward 

Classifying Systems of Systems 

 Alan J. Ramsbotham, Jr. 

Orion Enterprises, Inc. 

2300 Fall Hill Avenue, Suite 212 

Fredericksburg, VA 22401 USA 

+1.540.373.6025

ramsboth@oei-tech.com 

ABSTRACT

Intelligent systems are moving from science to more widespread 

engineering development and deployment. The objectives of this 

paper are to suggest design-oriented attributes that may provide a 

useful basis for classifying systems of systems. The discussion 

extends existing concepts, such as ALFUS, to complex ad hoc 

systems of systems wherein the individual elements can be 

geographically-dispersed and highly and independently mobile, 

and where the functions normally considered to comprise 

"intelligence" are distributed across the system. While not fully 

developed, the suggested extensions frame a discussion of how 

knowledge is obtained and distributed in such a system. Finally, 

the paper addresses some of the key challenges in predicting the 

performance of such complex intelligent systems of systems. 

Categories and Subject Descriptors

I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence, 

Multiagent systems 

General Terms

Performance, Design, Theory 

Keywords

Intelligent systems, collective intelligence, metrics, taxonomy, 

system of systems, complexity, receiver operating curve 

1. INTRODUCTION 

The following discussion draws heavily on the past efforts of 

others in autonomous and intelligent systems, unmanned systems, 

and knowledge management. [5][6][7] The purpose is to explore 

extensions of these existing concepts to move toward an effective 

ontology [13] for design and characterization of future “systems 

of systems” that exhibit collective intelligence. 

The benefit of a well-defined ontology is that it facilitates 

understanding in development and provides a framework for 

standardization in implementation and operation. The challenge 

is that an effective ontology depends upon a common 

understanding of its semantics—that is the meanings and 

relationships of the terms selected as labels in context. [2] No one 

size fits all. Even within a given set of operational requirements, 

conceptual design solutions, and implementation options there 

will always be outliers and exceptions. The set of principles used 

to classify systems of systems must be flexible enough to 

accommodate design trade-offs and changes. These criteria may 

sound intuitively obvious and simplistic. In practice, meeting 

them can prove to be a daunting challenge. It is essential to begin 

with some vision of the nature of the systems we will ultimately 

build. Specifically, the discussion addresses systems of systems, 

and specifically, systems that in aggregate exhibit intelligent 

behavior. 

2. BASIC OBSERVATIONS AND 

ASSERTIONS 

There exists within society a range of diverse applications that, 

for various reasons, cannot be (or are best not) performed by 

human operators. In some cases the human simply cannot 

survive in the operating environment—for example, inspection of 

six-inch pipes or operation in a highly radioactive environment. 

In others, the human may be present, but unable to perform 

essential functions safely or effectively—for example, control of 

high performance fly-by-wire aircraft with “relaxed stability.” 

[15] 

Across the broad spectrum of unmanned and machine-assisted 

applications systems are required to perform more or less 

autonomously; that is, without human intervention. Some level of 

autonomy is arguably essential for machine intelligence. 

However, autonomy does not equate to intelligence. Intelligence 

implies an ability to perceive and adapt to external environments 

in real-time, to acquire and store knowledge regarding problem 

solutions, and to incorporate that knowledge into system memory 

for future use. The simple examples cited above arguably do not 

exhibit intelligence, per se. At the current state of the art, pipe 

inspection systems are generally tele-operated. In the high 

performance aircraft the control laws, although complex, timecritical, and adaptive, are pre-programmed. Both incorporate 

human intelligence in their basic design or operation. These 

Permission to make digital or hard copies of all or part of this work observations lead to a few highly interdependent assertions. 

for personal or classroom use is granted without fee provided that 

copies are not made or distributed for profit or commercial advantage 

and that copies bear this notice and the full citation on the first page. 

To copy otherwise, or republish, to post on servers or to redistribute 

to lists, requires prior specific permission and/or a fee. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

Copyright © 2009 ACM 978-1-60558-747-9/09/09...$10.00 

282
A system need not be intelligent to solve complex problems and 

perform useful functions. 

A system can be both autonomous and adaptive without 

exhibiting intelligence. What matters is that the mission/task be 

performed adequately. As the complexity of the task increases 

and the system becomes more adaptive and autonomous in how it 

responds, the line between system complexity and machine 

intelligence becomes less distinct and meaningful. 

For the foreseeable future, systems will depend on human 

intelligence to perform their tasks. 

The dependence may be direct, as in the case of tele-operation, or 

it may be preprogrammed in the form of knowledge and rules of 

behavior. As we design and deploy systems that are capable of 

autonomous learning and self-modification, the distinction 

between system design and operation will blur. In a very real 

sense, we will design systems to re-design themselves. 

Regardless, human reasoning (or the fruits of human reasoning) 

will be essential elements of the system. 

All higher intelligence is collective in nature. 

Except for the most primitive instinctive functions, behaviors are 

acquired through observation of or imparted by others. In the 

case of autonomous intelligent systems, knowledge may be 

acquired through sensory perception and experience, and 

behaviors modified based on results of actions. Alternatively, 

knowledge/intelligence may be communicated from others. The 

rules and procedures whereby behaviors are modified may 

themselves evolve. But, as with human intelligence and 

collective behavior, they will evolve within practical constraints. 

The idea of "collective intelligence" is gaining importance in the 

field of information systems, and is a useful concept for designing 

and characterizing intelligent systems of systems. Also useful is 

the concept, articulated by the evolutionary biologist Gregory 

Stock, of a "superorganism" of humans and machines that adapts 

and responds to its environment in an organic manner. [20] 

For the foreseeable future, humans will be components of the 

system. 

It may be useful from an engineering design perspective to 

approach hardware and software as something that humans use. 

However, except for the most tightly-constrained tasks, humans 

will remain essential components of systems, if for no other 

reasons than those of legal liability. 

The ALFUS (Autonomous Levels For Unmanned Systems) 

provides a clear framework for characterizing human interaction 

with the mechanistic elements of intelligent systems (see Figure 

1). Significantly, for this discussion, the highest level does not 

postulate full autonomy, but specifies "near-zero human-robot 

interaction (HRI).” From a system of systems engineering 

standpoint, however, human operators are functional components 

integral to the system. 

Figure 1. Basic conceptual framework 

of the ALFUS levels of autonomy.

3. CONCEPT OF A “SYSTEM OF 

SYSTEMS” 

The term “system of systems” is a relatively new term. Its 

growing use is in large part a result of the U.S. Department of 

Defense's affection for the term in recent years. Much of the 

discussion is found in books, conference presentations, and 

symposia, as opposed to refereed journals. [8] While it is a truism 

that one man's system is another man's subsystem, the term is 

suggestive of a concept that is useful. It is a useful concept for 

this paper simply because some of the most promising uses for 

intelligent systems—transportation, military operations, and 

information infrastructure operations—will be systems of 

systems. 

For purposes of this discussion we will make several key 

assertions about those systems: 

• A system of intelligent systems will, by definition, 

exhibit some level of intelligent behavior and be an 

"intelligent system." A system of systems may exhibit 

intelligent behavior, even if none of the identifiable 

subsystems are "intelligent." 

• The functional elements of the system of system may be 

geographically-separated, and if not mobile, physically 

or electronically transportable/transferable between 

different locations. 

• While the maximum and minimum configurations of the 

system may be defined, at any given moment the 

operational configuration can be indeterminate and in 

many cases, indeterminable. 

For purposes of discussion, we will also postulate that the extent 

to which our system of systems will exhibit intelligent behavior 

with respect to both its external and internal environment is also 

an important design feature. The ability to modify and evolve its 

own internal environment autonomously is the essence of 

learning, and has been addressed in detail and with considerable 

technical rigor in recent applications of the development of the 

4D/RCS model architecture to learning. [1] It is the interfaces 

between the external environment and the intelligent systems that 

present the greatest design challenges and uncertainties.[19] To 

that end it is useful to decompose the functions comprising 

intelligent behavior into elements that correspond to specific 

hardware and software characteristics. For purposes of this 

283
discussion those will be to Sense, Perceive, Attend, Apprehend, 

Comprehend, and Effect Action (SPA2

CE (sm))

1

, as follows: 

• Sense: to generate a measurable signal (usually 

electrical) from external stimuli. A sensor will often 

employ techniques (for examples, bandpass filtering or 

thresholding) such that only part of the theoretical 

response of the transducer is perceived. 

• Perceive: to capture the raw sensor data in a form 

(analog or digital) that allows further processing to 

extract information. In this narrow construct perception 

is characterized by a 1:1 correspondence between the 

sensor signal and the output data. 

• Attend: to select data from what is perceived by the 

sensor. To a crude approximation, analogous to feature 

extraction. 

• Apprehend: to characterize the information content of 

the extracted features. Analogous to pattern 

recognition. 

• Comprehend: to understand the significance of the 

information apprehended in the context of existing 

knowledge--in the case of automata, typically other 

information stored in electronic memory. 

• Effect action: to interact with the external environment 

or modify the internal state (e.g., the stored information 

comprising the "knowledge base" of the system) based 

on what is comprehended. 

These are, in effect, a modest expansion of OODA (Observe, 

Orient, Decide, and Act). To a crude first approximation, the first 

four of these (sensing through attention, and in some 

configurations apprehension) comprise what we generally refer to 

as a sensory perception system, and thus to observation. 

Apprehension through comprehension map to orientation and 

decision-making. Effecting action corresponds to action, but 

includes, as an element of action, changes to the internal 

knowledge base and functions of the system. The distinction is 

one of focus and perspective: OODA speaks to the critical 

mission functions—the functions the end user (or in this case 

more accurately, the system of systems acting as an agent of the 

end user) must perform. The SPA2

CE framework defines 

functional elements relating to hardware/software design 

considerations. 

From a systems engineering standpoint, the owner does not care 

about the system architecture or its intelligence—only that the 

mission be successfully completed. 

Much of what we do as humans involves a combination of 

conditioned reflex and cognitive thought, depending on the nature 

of the action required. Intelligent systems will need to exhibit the 

same characteristic. The choice of which functions require 

automated reflexive action and which require cognitive decisionmaking, and the allocation of those functions will be important 

considerations in system of systems design. 

The priorities that the system assigns to attending to specific 

sensors or data may be driven by preprogrammed design, sensor 

inputs, or human intervention. Similarly the knowledge that the 

 

1

 The acronym SPA2CE is a service mark of Orion Enterprises, 

Inc. 

system relies upon will, again, for the foreseeable future, reside in 

the combined memories of the human operators and the 

information systems. Regardless of the source of knowledge the 

ability to optimize system performance will depend upon (and 

will at times be critically limited by) availability of computational 

and communications services. 

For the system of systems designer the choice of architecture and 

the details of functional allocations have major ramifications. 

These choices will drive trade-offs among: the cost and 

sophistication of individual nodes; the performance (bandwidth, 

latency, and availability) of the mobile ad hoc networks required 

to disseminate data and command and control; and mission 

performance and survivability. 

Collective mission performance can be optimized in many diverse 

ways. Table 1 provides a framework of examples in rough order 

of functional and architectural complexity. Less complex group 

behaviors include leader-follower and collective swarming. More 

sophisticated swarms exhibit individuated behavior where each 

member of the swarm is able to interact with its local environment 

to self-optimize its behavior. This implies situational awareness 

and adaptability on the part of system, but not necessarily at the 

level of the individual node. For example, the architecture may 

incorporate an all-knowing “dictator node” that directs and 

optimizes the actions of other nodes in the system. 

Basic learning behavior in artificial intelligence and swarming 

systems have been studied extensively. [3][10][11][12][17] This 

work forms a sound framework for designing and evaluating 

individual intelligent systems, homogeneous systems, and 

systems in which the functions comprising systems intelligence 

reside in a relatively small number of nodes. 

In an ad hoc mobile system of systems the number of possible 

architectural variations is practically unlimited. In the case of 

more complex ad hoc systems of heterogeneous mobile systems 

distribution of awareness and decision-making capabilities may 

be required. The holy grail of the intelligent system of systems is 

the case where individual nodes will sacrifice (i.e., sub-optimize 

individuated behavior) to optimize collective mission 

performance or survivability of the group. 

An example is the concept of future unmanned combat systems. 

As envisioned these will comprise of networks of unmanned 

smart sensors and robotic mobility platforms equipped for a wide 

range of land, air, and sea mission functions. The system is 

envisioned as reacting organically to maximize the forces 

operational effectiveness under any scenario. In this example an 

unattended surveillance sensor may optimize performance for a 

specific set of targets in a geographically-limited space at the 

expense of overall performance. Platforms may be "sacrificed" 

(that is placed in tactical situations where probabilities of survival 

are minimal) to achieve overarching objectives. As noted 

previously, in the specific tasks required of the integrated system 

and its component parts and the operational configuration of the 

system may be indeterminable at any point in time. 

284
Table 1. Examples of "Systems of Systems" 

Type of System Defining Characteristics 

Leader-Follower Intelligent behavior exhibited 

by single node, and replicated 

(sometimes with minor 

adaptation) by other nodes. 

Swarming (simple) Loosely structured collection 

of interacting agents, capable 

of moving collectively. 

Swarming (complex) Loosely structured collection 

of interacting agents, capable 

of individuated behavior to 

effect common goals. 

Homogenous intelligent 

systems 

A relatively structured 

collection of identical (or at 

least similar) agents, wherein 

collective system performance 

is optimized by optimizing the 

performance of individual 

agents. 

Heterogeneous intelligent 

systems 

A relatively structured 

heterogeneous collection of 

specialized agents, wherein the 

functions of intelligence 

distributed among the diverse 

agents to optimize performance 

of a defined task or set of tasks. 

Ad hoc intelligent adaptive 

systems 

A relatively unstructured and 

undefined heterogeneous 

collection of agent, wherein the 

functions comprising 

intelligence are dynamically 

distributed across the system to 

adapt to changing tasks. 

3. MOVING TOWARD A SYSTEM OF 

SYSTEMS ONTOLOGY 

Table 1 provided a notional framework for characterizing 

intelligent behaviors in systems of systems. An important step 

will be to understand how knowledge is acquired and 

disseminated through the system. Kennedy and Eberhardt [11] 

observe that new behaviors are acquired by experience or in the 

form of communication from others. In the case of an ad hoc 

mobile intelligent system, further distinctions must be made 

between knowledge that must be communicated in real-time to 

meet mission requirement, that which can be programmed in by 

the user, and that which is designed in a priori. As will be 

discussed in the next part of this paper each of these sources of 

knowledge are subject to uncertainty. 

The system architecture should be driven by the requirements, 

and taxonomies for our potential engineering solutions need to 

relate to the system architectures. For example, if the system 

requires persistent situational awareness of its surroundings and 

an ability to navigate autonomously in them, the design taxonomy 

needs to provide the necessary elements for sensing, signal and 

data processing, and simultaneous localization and mapping. 

The existing ALFUS construct provides such a clear framework 

for autonomy. It has evolved meaningful metrics for 

implementation. It also inherently suggests logical extensions for 

characterizing collective intelligence within an intelligent system 

of systems context. Those extensions are: 

• Dynamic architectural complexity—the number, 

diversity of intelligent behaviors, and mobility and 

spatial distribution of the nodes comprising the system 

of systems; 

• Functional complexity—specifically the extent to 

which the hardware and software functions comprising 

intelligence (i.e., SPA2

CE), are centralized or 

distributed across the dynamic system architecture; and 

• Capability of the computing and communications 

infrastructure to provide the necessary services to 

support and exploit those functions in real-time. 

The resulting framework, if not completely developed, is 

reasonably comprehensive. ALFUS provides a framework for 

characterizing functional divisions between human and machine 

intelligence and for classifying autonomy in terms of the 

complexity of the task and the operating environment where 

position on the axis correlates strongly with system intelligence. 

The discussion provides some initial suggestions for possible 

ways to classify implementation—how collective intelligence is a 

system of systems is attained. 

4. CHALLENGES TO DEVELOPING A 

USEFUL ONTOLOGY 

The challenges to developing an ontology that is sufficiently 

robust to provide a framework for systems of systems design and 

analysis fall into several broad categories: Mission uncertainty, 

complex interdependence of the diverse metrics; lack of accepted 

metrics for certain essential aspects of the system; and uncertainty 

associated with real-world physical limitation where mature 

metrics arguably exist. 

Mission Uncertainty: The potential applications 

cited―transportation, military operations, and information 

infrastructure―share a common characteristic. They involve 

scenarios where the sets of tasks to be performed may be 

essentially indeterminable. To the extent that approaches to 

machine intelligence require iterative training or optimization, the 

best we can do is approach some level of confidence for the 

highest priority tasks (that is, those most likely to be encountered, 

where the cost impact associated with performance of the tasks 

are the highest). 

Interdependence: At the system of systems level, the diverse 

metrics are highly interdependent, and the functions that comprise 

intelligent behaviors may be dynamically distributed across 

multiple elements of the system. Those elements exist in, and are 

integral features of the complex environment in which the system 

of systems must operate. For example, the electromagnetic 

transmission of active sensors and communications and the 

observable emanations of the physical elements of the system are 

all part of the complex signal environment against which those 

same subsystems and communications links must work. For 

intelligent transportation and military combat the system of 

285
systems will, in effect, constitute a significant part of its own 

environment. 

Inadequate metrics for enabling technologies: Computing is 

clearly a key enabling technology. Yet to date, quantitative 

metrics for predicting computational performance (and 

particularly software performance) elude us. The best we can do 

is rely on benchmarks (which are, at best, valid only within a 

fairly narrow problem set) and on qualitative measures, not of the 

software itself, but of the software engineering process. This has 

the potential to limit our ability to predict the performance of 

intelligent systems in complex, computationally-intensive 

applications. 

Reality and the laws of nature: Finally, where quantitative 

metrics exist (for examples: detection range, image resolution, 

data transfer rate, latency), our ability to predict their values 

accurately is limited by the laws of physics. Such limits are 

inherent when subsystems interact with the real-world 

environment—sensing, inter-system communications, and 

effecting external action. There are dynamic non-linear effects 

that the system cannot affect directly, such as: electromagnetic 

propagation; dynamic signal characteristics and noise; and effects 

of weather. 

The net result of these effects can be illustrated by reference key 

functions of a distributed intelligent system: 

Sensing, Perception and Classification. The outcomes of any 

binary classification process can be defined in terms of four 

possibilities, illustrated in Figure 2. [4] For physical sensors these 

are often characterized in terms of a receiver operating 

characteristic (ROC) curve shown in Figure 3. There is evidence 

of interest in using ROC analysis more broadly for evaluating 

classifiers for other purposes, including machine learning and data 

mining. [16][22] 2

 

The ROC has been used for many decades as a metric for radar 

and radio receivers. The curve illustrates an essential idea—that 

the probability of a true classification can be made arbitrarily 

high, as long as the system (user) can tolerate a corresponding 

increase in false positive identifications. 

A similar characteristic applies to distribution of knowledge in a 

mobile distributed ad hoc system. Shannon's theory of 

communications describes a level of uncertainty (entropy) 

associated with information, and the maximum theoretical 

information transfer rates that can be attained for a given 

transmission bandwidth (or bit rate) and signal to noise. 

Particularly in the case of mobile operations variations due to 

variations in path length, weather, multipath fade and 

interference, and signal blocking, predictions of signal-to-noise 

pose significant challenges. 

The same statistical principles apply to retrieval of information 

from very large data sets. A relatively recent development is a 

growing interest in the use of the ROC for other applications, 

including for data mining and for the extraction of digital 

information very large data sets. 

 

2 A Search of the ACM digital library for documents referencing 

ROC Analysis since 2005 returned 774 citations (June 16, 

2009).

Finally, readers familiar with the literature on evolutionary 

computing and genetic algorithms will recognize the similarity in 

the general shape and behavior of the ROC curve with plots of 

performance of genetic algorithms as a function of the number of 

iterations/generations run. [3][9][14][21] 

The primary significance of this discussion is to reaffirm the 

validity of the observation that "there is no such thing as a free 

lunch,” and to underline the basic fallacy of an all-too-common 

belief in “information assurance” as something attainable in an 

absolute sense. 

Figure 2. Matrix illustrating possible outcomes 

of classification; sometimes referred to as a 

“confusion matrix” 

Figure 3. Receiver operating characteristic 

(ROC) curves. The ratio of true-to-false positives (shown as 

moving from A to B), but only at a cost of increased 

sampling or processing time. 

286
5. CONCLUSIONS 

This paper suggests the utility of extending existing 

analytic/taxonomical frameworks to more explicitly address 

systems of systems. The specific framework suggested is 

intended to be illustrative, not definitive. Competing frameworks 

should be investigated. Once a suitable framework is established, 

much work will be required to bring new elements of the 

framework to a level of specificity comparable to ALFUS. But 

assuming that occurs, one can envision a new framework in which 

a composite measure of autonomy based on ALFUS, a measure of 

internal complexity, and measures for distributed computational 

performance form the axes of a system of systems metric. 

Even given a comprehensive framework, as we begin to build 

more complex intelligent systems of systems, we will need to 

acquire knowledge and improve analytic tools and metrics. 

Among the more important will be: 

• Better understanding of the complex interdependencies 

among existing metrics and system design; 

• More accurate and reliable predictive for models 

characterizing the effects of complex dynamic nonlinear physical effects on those subsystems that interact 

with external environments, and 

• Better models and metrics for characterizing limits of 

information assurance based on these effects. This will 

be both a critical need and a major challenge. 

ACKNOWLEDGEMENTS 

The author would like to gratefully acknowledge the contributions 

of PerMIS 2009 conference reviewer, whose comments led to 

substantial improvements in this paper. 

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analysis, Information Sciences , vol. 177 , no. 11, Elsevier, June. 

287
A Decision-Theoretic Formalism for Belief-Optimal

Reasoning

Kris Hauser

School of Informatics and Computing, Indiana University

Lindley Hall, Room 215

150 S. Woodlawn Ave.

Bloomington, Indiana

hauserk@cs.indiana.edu

ABSTRACT

Intelligent systems must often reason with partial or

corrupted information, due to noisy sensors, limited representation capabilities, and inherent problem complexity.

Gathering new information and reasoning with existing

information comes at a computational or physical cost.

This paper presents a formalism to model systems that

solve logical reasoning problems in the presence of uncertainty and priced information. The system is modeled a

decision-making agent that moves in a probabilistic belief

space, where each information-gathering or computation

step changes the belief state. This forms a Markov decision

process (MDP), and the belief-optimal system operates

according to the belief-space policy that optimizes the

MDP. This formalism makes the strong assertion that

belief-optimal systems solve the reasoning problem at minimal expected cost, given the background knowledge, sensing

capabilities, and computational resources available to the

system. Furthermore, this paper argues that belief-optimal

systems are more likely to avoid overfitting to benchmarks

than benchmark-optimized systems. These concepts are

illustrated on a variety of toy problems as well as a path

optimization problem encountered in motion planning.

1. INTRODUCTION

To complement the standard tool of benchmarking, computer scientists have theoretical tools to express algorithm

performance, such as big-O notation for worst-case asymptotic complexity. The power of these tools is that they express rigorous, theoretical performance bounds over all inputs. But for complex intelligent systems that interact and

reason about the world, benchmarks are often the most reliable way to measure performance. Indeed it is not clear that

worst-case complexity is meaningful in the physical world.

For example, the problem of navigation among movable obstacles is PSPACE-complete [20], but humans routinely navigate among movable obstacles without much difficulty. The

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worst-case problems that are truly hard are puzzles specifically designed to be hard. But benchmarking is not without its flaws; systems can perform well on benchmarks and

poorly in the real world, and it can be difficult to design

enough benchmarks to fully capture real-world performance.

This paper presents a new formalism to define rigorous,

but realistic, theoretical bounds on the performance of

systems that reason about and interact with an uncertain

world. Specifically, given a scalar valued performance metric, this paper derives a bound on the best expected-case

performance over a (typically infinite) space of problem

instances Ω. The system is treated as a decision-making

agent that operates in an “environment” drawn from Ω.

The agent does not know which instance was picked, and

must learn about the environment by probing it. This

perspective has often been taken for systems that operate

with physical uncertainty; the novelty in this work is to also

treat “mental” uncertainty in the same unified framework.

For example, it may know that f(x) = 0 but has not yet

computed the value of x. Though x is mathematically

defined, it is considered a random variable unknown to the

system until the system computes it.

The formalism casts the operation of the system as a

Markov decision process (MDP) (see [2] for a survey). At

every point in time the system is identified with a belief

state, and it has a set of available actions, such as gathering

sensor readings or performing computation. Executing an

action moves the belief state in a probabilistic way. To encode the performance metric, a utility function sums costs

and rewards encountered by the system before it terminates.

The belief-optimal system behaves according to the policy

that optimizes expected utility. This paper shows that if the

belief state encodes hypotheses about Ω accurately (through

appropriate definitions of background knowledge), then on

average a belief-optimal system performs better than any

algorithm on Ω.

Furthermore, this work highlights the danger of overfitting

on benchmarks when they are used to optimize program

performance. A program optimized on benchmarks Ω0 ⊂

Ω will perform as least as well as any other program on

Ω

0

, but may perform poorly on Ω. I argue that systems

optimized with decision-theoretic principles are often more

robust than those optimized on benchmarks, because any set

of benchmarks that represents a complex, high-dimensional

problem space is impractically large. The decision-theoretic

formalism approximates the problem space with statistical

models, which are more likely to generalize to new problems.

288
2. RELATED WORK

Decision theory is a well-known tool for modeling and designing systems that interact with the real world, but it

also has powerful applications to logical problem solving.

Decision-theoretic approaches have been explored for reasoning problems in a diverse range of fields, such as numerical analysis, artificial intelligence, and robotics, where

solutions are deterministically defined, and no inherent uncertainty exists except in the “mind” of the system. They

have also been used to model bounded rationality in humans [17].

Examples of applications include analysis of Monte Carlo

and quasi-Monte Carlo numerical integration and function

approximation techniques [19], and in sampling strategies

for stochastic numerical optimization [5, 21]. In artificial intelligence, efficient strategies have been developed for testing hypotheses represented as boolean formulas of uncertain

statements [8]. Decision theoretic models have been applied

to heuristic selection in heuristic search [4]. They have also

shown promise in the field of robot motion planning, with

speed gains of up to orders of magnitude [3, 11, 18]. My own

research has applied decision-theoretic approaches to several

motion planning subproblems [9], including path optimization, collision testing, configuration sampling, and contact

selection strategies for robotic systems with contact.

It has also been argued that certain heuristics for randomized algorithms can be understood in a decision-theoretic

sense, even if the heuristics are not themselves derived from

the same principles. For example, stochastic optimization

heuristics like simulated annealing, genetic algorithms, and

ant colony optimization can be interpreted has having implicit probabilistic models of the function space [21]. The

probabilistic roadmap (PRM) technique for robot motion

planning, which connects a network of randomly sampled

points in the robot’s free space, can be interpreted as having

implicit hypotheses about the shape of the free space [10].

This suggests that the performance of these algorithms may

be improved by using better initial hypotheses, or better

exploiting the information gathered during computation.

3. FORMAL MODELING OF BELIEF

SPACE REASONING

This section describes how to model an intelligent reasoning system as a decision-making agent, and describes the

associated POMDP formalism.

3.1 Assumptions

Consider a computer program that is given some background knowledge as input, and produces an output after

executing a sequence of tests, which may involve either computational reasoning or gathering sensor readings. Assume

that the sequencing of tests and termination conditions are

structured by the problem logic such that the output is always correct. The outcome and/or cost of each test behaves

with uncertainty, due either to stochasticity (a result of randomization or physical noise) or unpredictability (must be

treated as a black box with internal workings that are too

complex to be explicitly represented).

3.2 Modeling as a Belief-Space POMDP

The agent operates on a number of hypothetical logical

statements. To a statement S, assign a belief p in [0, 1], with

p = 0 meaning S is certainly false and p = 1 meaning S is

certainly true. An assignment of beliefs to all statements in

the scope of a problem defines a belief state, and the set of

all possible belief states is the belief space.

The program executes a sequence of tests, which are

atomic operations that modify the belief on hypothetical

statements. Upon observing the results of executing a test,

the program moves to a new belief state. Without loss

of generality, we define a test such that it determines the

factuality of a statement S exactly.

The problem is considered solved when the belief state

contains certain factual statements. The program may

choose to terminate, which produces an output. The

performance of the system is measured by a utility function

that sums the negative costs incurred during execution,

and positive rewards that assess output quality. Costs may

include execution time and resource usage; for example,

real-time constraints could be implemented by penalizing

time limit violations. Rewards and costs should be weighted

by the system designer so that the utility function measures

overall system performance.

In full generality, a belief state on statements S forms

a joint probability distribution P r(S|Z). Here, Z represents background knowledge established prior to the current

state. The importance of background knowledge will be discussed in Section 4.1. After executing a test T, the belief

state should change to P r(S|T succeeds, Z) with probability

P r(T succeeds|Z), and to P r(S|T fails, Z) with probability

P r(T fails|Z). These changes are called the transition dynamics.

With these definitions, the problem solver has been cast

as a belief-space Markov decision process. Because the variables that define the belief state are not directly observable,

the MDP is known as a partially-observable Markov decision

process (POMDP).

3.3 Illustration on Matrix Inversion

To illustrate the approach on a small example, suppose

we are designing a system to compute the inverse or pseudoinverse of a matrix, where the properties of the matrix

are unknown beforehand. Suppose the system has access

two three methods of computing inverses: Cholesky decomposition, LU decomposition, and singular value decomposition (SVD). The challenge in this particular system is that

Cholesky decomposition is much faster than LU decomposition when the matrix is symmetric positive definite (s.p.d.),

but this property is unknown beforehand (in fact, attempting a Cholesky decomposition is frequently used to test if

an unknown matrix is s.p.d.). Furthermore, LU decomposition is much faster than SVD when a matrix is invertible,

but again, invertibility is unknown beforehand. SVD will

compute a pseudoinverse or inverse for a general matrix.

Here, the belief space consists of four unknown properties of the input matrix: Square, Symmetric, Invertible,

and SPD (symmetric positive-definite). The tests available

to the system consist of two “probes”, Is-Square and IsSymmetric, and the three methods of computing inverses,

labeled Cholesky, LU, SVD. The operations listed in the order of increasing cost are Is-Square, Is-Symmetric, Cholesky,

LU, and SVD.

The optimal policy depends on the distribution of the

kinds of matrices in Ω. For example, if very few matrices

are invertible, then the policy of always using SVD would be

289
End

SVD

( • , • , • , • )

(a)

Is-Square

1 0

1 0

LU

End

End

End

SVD

SVD

( • , • , • , • )

(1, • , • , • )

(1, • ,1, • ) (1, • ,0, • )

(0, • , • , • )

(b)

Is-Square

1 0

1 0

1 0 1 0

Is-Symmetric

Cholesky LU

End SVD End

End End

End

SVD

SVD

( • , • , • , • )

(1, • , • , • )

(1,1, • , • ) (1,0, • , • )

(1,1,1,1) (1,1, • ,0) (1,0,1, • ) (1,0,0, • )

(0, • , • , • )

(c)

Figure 1: Three policies for the matrix inversion example which may be optimal, depending on the distribution of input matrices. Tuples (·, ·, ·, ·) indicate

belief states, where elements respectively indicate

that a matrix is square, symmetric, invertible, and

symmetric positive-definite.

optimal (Figure 1a). If many matrices are invertible but few

are symmetric positive-definite, then the policy in Figure 1b

may be optimal. If all kinds of matrices are well-represented,

then the optimal policy may gather as much information as

possible, as illustrated in Figure 1c.

The joint distribution of Ω over the belief space (which

can be represented by 16 real values), and the distribution

of costs associated with each test, is sufficient to compute the

optimal policy using the POMDP framework. This is not

difficult for a problem of this size, but can be tremendously

challenging for problems that are not much larger.

3.4 Illustration on a Household Mobile Robot

Consider another example of a mobile robot that is asked

to retrieve an item in a house. It has a map of the house,

and knows of N locations L1, . . . , LN where the item may

be, some of which are inaccessible (out of reach or blocked

by obstacles). The true location of the item is given by the

random variable I. The robot does not know a priori which

locations are inaccessible, and instead calls a planning subL1

: desk

L4: chest

L2: upper cabinet

L3: lower cabinet

R: robot

I: item

Figure 2: A household mobile robot is asked to

search for a cup. The robot does not know, a priori,

that the upper cabinet location is inaccessible. The

robot determines accessibility by running a planning subroutine. Physical uncertainty is present in

the location of the cup, and “mental” uncertainty is

present in the accessibility of locations.

routine Plan(Li) that either returns a path from its current

location R to the location Li, or reports that it is inaccessible. If a path is available, Travel(Li) moves the robot to the

location and looks for the item. This example combines both

physical uncertainty (where the item is located), as well as

“mental” uncertainty (which locations are accessible).

If we denote the accessibility of location Li with the

variable Ai, the belief state is defined over the statements

(I, R, A1, . . . , AN ). The program terminates when the item

is found, or the robot knows the item is in an inaccessible

location. The background knowledge will define the probability distribution on I and A1, . . . , AN , as well as the

expected costs of Plan() and Travel().

4. BELIEF-OPTIMAL POLICIES

The decision-theoretic formalism almost immediately defines the concept of a belief-optimal policy, and the performance bound associated with it. More precisely, let U(π, Z)

denote the expected utility of executing a policy π given

background knowledge Z. Then the belief-optimal policy is

π

? = argmax

π

U(π, Z).

The next section shows that U(π

?

, Z) is an upper bound to

the average performance for problem instances drawn from

Ω, so long as Z is properly defined.

4.1 The Role of Background Knowledge

Typically, Ω is enormous and unknown, so beliefs about Ω

must be represented in the background knowledge Z. Suppose that background knowledge is defined such that, given

any observed test results, the distribution of future test results can be inferred accurately. Then, the policy that optimizes the POMDP is the policy that achieves the lowest

expected cost over Ω.

290
Accuracy is defined as follows. Let μ(X) denote the probability that a problem instance is drawn from a subset X ⊆

Ω. Let ΩT be the set of instances that are consistent with a

history of observed tests T . Then the background knowledge

Z is accurate if

P r(T|T , Z) = μ(ΩT ∪{T })

μ(ΩT )

(1)

holds for any history of tests T and any future test T.

Appendix A shows that if background knowledge is accurate, then U(π, Z) is equal to the average performance

of π on the problem instances in Ω, and by consequence,

U(π

?

, Z) is an upper bound on performance. So in theory,

the belief-optimal policy and performance bound are rigorously defined, and can be computed for small problems. But

in practice, one can only hope for approximate solutions.

4.2 Representing and Computing Belief

States

It is usually extremely difficult to define a problem distribution Ω and μ, much less compute an accurate representation in background knowledge. So for algorithm designers

the perspective is reversed: background knowledge is chosen

explicitly, and the problem distribution is defined implicitly to be consistent with the chosen background knowledge.

Background knowledge can be encoded using a variety of

machine learning and statistical models, e.g., logistic models, Bayesian networks, decision trees, neural networks, etc.

Such models can encode prior beliefs as well as incorporate training on real problem instances. This in turn enables belief-optimal policies to generalize better to unseen

instances, as argued in Section 6.

It is also impractical to compute or explicitly represent

the joint distribution of the belief state because its size is

exponential in the number of statements. The belief state

and dynamics can be approximated by assuming statement

independence, in which case the joint distribution is simply

the product of distributions of individual statements in S.

More refined strategies might use assumptions of conditional

independence, for example, representing variables in a sparse

Bayesian network or other graphical models.

4.3 Optimizing POMDP Policies

Solving POMDPs in the general case is extremely computationally hard [15, 16]. In general there are two primary

approaches to solving large POMDPs (which are not mutually exclusive): exploit problem structure, or approximate.

Some problems have a structure for which exact solutions

can be computed efficiently, such as several variants of the

n-armed bandit problem [1, 4], and hypothesis testing of

two-level AND/OR boolean-formulas [8]. POMDP approximation in large belief spaces is an area of active research, and

perhaps the most promising results have come from sparse

sampling and depth-limited search techniques [12, 13].

The advent of POMDP optimization techniques for large

problems, on the order of thousands or millions of logical

statements, will greatly accelerate the rate of development

of complex intelligent systems. Intelligent systems will inevitably need to tackle computationally hard problems more

frequently in the future, and belief-based optimization is an

attractive, systematic approach to achieving good practical

performance in the face of discouraging worst-case complexity theories.

5. A PATH SMOOTHING EXAMPLE

Here we illustrate a practical application of the decisiontheoretic formalism to a path smoothing problem encountered in motion planning. Probabilistic roadmap motion

planners tend to produce jerky, unnatural-looking paths

due to their random exploration. A simple shortcutting

method [6, 7] smoothes these paths by picking two random

points A and B on a path, and testing the line segment

AB for feasibility. If AB lies in free space, it replaces the

portion of the path between A and B. After a handful

of iterations, the largest unnecessary jerks are likely to

be eliminated. However, it can take a huge number of

iterations before the process converges to a smooth path.

How many iterations are enough?

In a decision-theoretic formulation, the agent chooses

shortcuts as tests and receives rewards that are accumulated

over time. Each potential shortcut incurs a negative cost,

and produces a reward only if it is successful. Although it

is difficult to define background knowledge accurately, we

approximate it by making some independence assumptions.

The belief-optimal policy, given these assumptions ,is a

greedy strategy. Experiments demonstrate that the greedy

strategy converges much faster than picking random points,

and has a natural termination criterion: halt when no

shortcut has positive expected utility.

5.1 Problem Statement

Suppose the path y(u) is parameterized with u in [0,1].

Denote the length of the path between parameters u and

u

0

as l(u, u0), and denote the distance between two points

q and q

0

in C-space as d(q, q0). Testing the line segment

between u and u

0

incurs cost c(u, u0). If successful, y(u) is

replaced with the new path, and the planner receives reward l(u, u0

) − d(y(u), y(u

0

)) (the amount that path length

is reduced). Let p(u, u0) denote the estimated probability of

success. We assume c(u, u0) = csd(y(u), y(u

0

)), where cs is

a constant reflecting the amount of computation time one is

willing to spend for a unit decrease in path length.

This is essentially a “one-pull” variant of the well-studied

n-armed bandit problem [1]. In the n-armed bandit problem, the decision maker chooses from n actions, A1, . . . , An.

A stochastic payout zk is awarded after choosing Ak, and

payouts accumulate over time. If the distributions of each zk

are known and independent, the optimal strategy is greedy

and picks the choice with the highest expected reward. This

is also the case for “one-pull” bandits.

In other words, the greedy choice of u and u

0 maximizes

p(u, u0)(l(u, u0) − d(q, q0)) − c(u, u0). Dependencies between

two candidate shortcuts do occur when the range of the

shortcuts overlap, so the greedy strategy is not necessarily

optimal, but it does perform quite well in practice.

5.2 Belief Estimation

The performance of the greedy strategy depends on the

quality of the estimate p(u, u0). From first principles we

know that the probability that a random line segment is

feasible decreases with distance between its endpoints. Also,

a line segment is likely to be invalid if nearby line segments

are invalid. We keep a history of infeasible configurations I

(which may be initialized with samples from PRM planning)

and updated as shortcuts fail. We assign a belief to each

shortcut as a function of segment length and the distance

between the segment and the closest configuration in I.

291
(a) (b)

Figure 3: (a) PRM planners produce jerky paths. (b) Shortcutting produces a shorter path.

In the following experiments, we use a crude method

to estimate p(u, u0) given I. We tested the feasibility of

100,000 randomly sampled line segments, and built a histogram p0(d) of success rate indexed by distance d. We then

weight the baseline probability p0(d(q, q0)) by a function

of the distance d

0

to the closest infeasible configuration.

The experiments below used f(d

0

) = 1 − e

−αd0

, where α

was chosen by a small amount of tuning. A better method

might estimate the joint success rate as a function of d(q, q0)

and d

0

.

5.3 Experimental results

We demonstrate this for a C-space with regularly spaced

circular obstacles in a unit square (Figure 3). The shortest

path between two points may contain curves on the C-space

obstacle boundaries (this is also true in C-spaces for robotic

mechanisms with revolute joints). A good piecewise linear

approximation to the shortest path requires a huge number

of line segments, so shortcutting converges slowly.

To compare performance across varying start and goal

configurations, we normalize reward by dividing by the

straight-line distance between the starting points. We

set the cost proportionality constant cs to 0.01. Figure 4

compares the randomized strategy with a greedy, adaptive

strategy. 200 random start and goal locations were sampled

and connected using an RRT planner [14]. Starting at the

output path, random shortcuts were performed for 1000

iterations. For the same starting path, adaptive shortcuts

were performed until the strategy chose to terminate. The

set of infeasible configurations I is initially empty.

Initially, the adaptive strategy reduces the path length

quickly. Throughout execution, it achieves a given reduction

in path length about two or three times faster than the randomized strategy. As more iterations are taken, shortening

the path becomes increasingly harder. The adaptive strategy terminates naturally when the cost of making a shortcut

exceeds the expected reward. All adaptive runs terminated

by iteration 200.

6. AN ALTERNATIVE TO BENCHMARKS

6.1 An Argument Against Benchmarks

Benchmarks are a useful tool for performance optimization, but it is difficult to pick a representative benchmarking suite, and caution must be taken to avoid overfitting.

In general, there will exist a sub-optimal algorithm whose

benchmark performance is at least as good as the optimal algorithm. Existing techniques for avoiding overfitting

include using large benchmarking sets, regularization, and

cross-validation. Outside of the machine learning community, researchers rarely employ such techniques (I myself am

guilty of this), and even if they are employed, the size of the

benchmarking suite is rarely adequate to draw statistically

significant conclusions.

One reason why it is so difficult to choose a benchmarking

suite that accurately reflects real-world performance is that

any set of problem instances that is statistically identical

to Ω, as far as the algorithm is concerned, must span the

joint distribution of Ω in belief space. Such a set is practical

only if the belief space is tiny, or Ω happens to span a low

dimensional subspace of belief space.

I argue that performance optimization using the decisiontheoretic formalism can be more robust than benchmarks.

The system’s background knowledge Z can be defined to

capture the belief space distribution of Ω more accurately

and more broadly than a set of benchmarks.

A skeptic may raise two objections. First, background

knowledge should be trained on a set of problem instances,

which is essentially a set of benchmarks. Second, in practice,

background knowledge can only represent the distribution of

Ω approximately, and these modeling errors may reduce performance. To the first objection, I argue that background

knowledge can better generalize small datasets by taking

advantage of statistical and machine learning tools that are

based on sound and well-developed theory (some of which

were mentioned in Section 4.2). These tools will generalize

far better than ad-hoc techniques that try to encode generalization into a complex algorithm. To the second objection, the empirical success of the decision-theoretic approach

suggests that their behavior is relatively robust to approximation errors. An example will be shown in the following

section.

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Random

Adaptive

Figure 4: Results of shortcutting experiments, reporting relative path length, accumulated cost, and utility.

Averaged over 200 plans with random start and goal configurations. Iteration numbers are plotted on a log

scale.

6.2 Illustration on an Urn Problem

Consider a system that searches for a black ball placed in

one of two urns. In each problem instance, the black ball is

placed into an urn and both urns are filled with additional

white balls. The system has two types of tests: Ak asks the

number of balls in urn k, and Uk picks urn k and searches

through it. A search continues until it finds the black ball

or empties the urn completely (that is, it is not allowed to

stop midway through). A problem instance can be specified

as a 3-tuple (n1, n2, K), where n1 and n2 are respectively

the number of balls in the 1st and 2nd urns, and K is the

index of the urn containing the black ball. The cost of Ak is

1, and the expected cost of Uk is nk/2 if K = k, and is nk

otherwise.

Suppose the benchmark problem instances are (30, 1, 1),

(20, 15, 1), (40, 30, 2), and (35, 60, 2). A benchmark-optimal

strategy is to test A2, and if n2 ≥ 30, then to pick U2 first.

Otherwise, pick U1 first. Call this strategy π1. On the

benchmarks, π1 has average cost 18.5.

Suppose now that our approximate background knowledge

consists of a minimum and maximum estimate of the number

of balls in each urn, and the fraction of black balls found in

urn 1. Also, let the system assume that the number of balls

in an urn has a uniform distribution between the minimum

and maximum values, and is independent of the number in

the other urn. Given these approximations, the POMDP

expected utility of π1 is 35. Now consider a strategy π2 that

tests both A1 and A2, and then picks U1 first if n1 < n2

and U2 otherwise. The expected value of this strategy is

approximately 28.3. The worst-case cost of π2 is also better

than π1: 72 versus 81.

7. CONCLUSIONS

This paper presented a decision-theoretic formalism for

optimizing and bounding the expected performance of an intelligent system that interacts and reasons about the physical world. The formalism is based on casting the system

as a decision-making agent that faces uncertainty due to

sensor noise as well as uncertainty due to bounded rationality. This forms a partially-overvable Markov decision process

(POMDP) that can be optimized. I showed that if the beliefs

of the system accurately capture the problem space characteristics, then the strategy that optimizes the POMDP also

optimizes the average case performance over the problem

space. This paper also argued that the decision-theoretic

approach is an attractive alternative to benchmarking for

performance optimization that avoids the problem of overfitting to benchmarks. These principles are illustrated on

a variety of example problems. Future work should investigate the tractability and accuracy of approximation techniques for solving the large POMDPs that result from this

formalism, and also to apply the formalism to systems that

solve new and challenging problems.

8. ACKNOWLEDGEMENT

Many thanks to Jean-Claude Latombe for numerous helpful discussions during the early stages of this work.

APPENDIX

This appendix derives the result that if background

knowledge Z is defined such that (1) holds, then

V (π, Ω) = U(π, Z). It is largely a technical matter,

but is included here for completeness.

To define U and V more precisely we first need some preliminary definitions. Here we will represent a state s as a

history of tests {T1, . . . , Tn}, because the belief state and dynamics are fully determined by the background knowledge

(which stays constant) and the test history. Let π(s) denote

the action taken by policy π at state s, and in a slight abuse

of notation, also let it denote the result of the test π(s). Let

R(s, a) be the reward minus the cost of executing action a in

state s. Assume without loss of generality that Ω is defined

such that given a problem instance ω, transitions are deterministic (i.e., ω is the “true” value of the world state). Also,

assume terminal states are absorbing, and the performance

metric is bounded.

Define the utility function Uπ(s) as the unique solution to

the following system of equations over s:

Uπ(s) = R(s, π(s)) + P r(π(s)= 0|s, Z)Uπ(s ∪ {π(s)= 0})

+ P r(π(s)= 1|s, Z)Uπ(s ∪ {π(s)= 1}).

(2)

This is a slight abuse of notation; our original definition of

the utility function is U(π, Z) ≡ Uπ({}).

Let the trace of running policy π on instance ω be defined as (s0, s1, . . .). Define the return v0 using the recursive

formula

vi(π, ω) = R(si, π(si)) + vi+1(π, ω).

Then the average performance of π is

V (π, Ω) = Z

ω∈Ω

v0(π, ω)dμ(ω).

293
Let’s define

Yπ(s) = Z

ω∈Ωs

v|s|(ω, π)dμ(ω)

so that Yπ({}) ≡ V (π, Ω). Then, denote s

0 = s ∪ {π(s)= 1}

and s

00 = s ∪ {π(s) = 0}, and split Ωs into subsets Ωs0 and

Ωs00 depending on the results of π(s). We get

Yπ(s) = Z

ω∈Ωs

R(s, π(s)) + v|s|+1(ω, π)dμ(ω)

= μ(Ωs)R(s, π(s)) + Z

ω∈Ωs0

v|s|+1(ω, π)dμ(ω)

+

Z

ω∈Ωs00

v|s|+1(ω, π)dμ(ω)

= μ(Ωs)R(s, π(s)) + Yπ(s

0

) + Yπ(s

00)

(3)

Now if we let Xπ(s) = Yπ(s)/μ(Ωs), we have

Xπ(s) = R(s, π(s)) + μ(Ωs0 )

μ(Ωs)

Xπ(s

0

) + μ(Ωs00 )

μ(Ωs)

Xπ(s

00) (4)

Since background knowledge is accurate, then by (1) we

have

Xπ(s) = R(s, π(s)) + P r(π(s)= 0|s, Z)Xπ(s ∪ {π(s)= 0})

+ P r(π(s)= 1|s, Z)Xπ(s ∪ {π(s)= 1}).

(5)

Since Xπ solves for (2), then it must be identical to Uπ.

Then V (π, Ω) ≡ Yπ({}) = Uπ({}) ≡ U(π, Z) as desired.

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294
Evaluation of Automatically Generated Reactive Planning 

Logic for Unmanned Surface Vehicles

M. Schwartz 

Energetics Technology Center 

La Plata, MD, USA 

mschwartz@etcmd.org 

P. Svec 

University of Maryland College Park 

Department of Mechanical 

Engineering 

petrsvec@umd.edu

S. K. Gupta 

University of Maryland College Park 

Department of Mechanical 

Engineering and Institute for System 

Research 

skgupta@umd.edu

A. Thakur 

University of Maryland College Park 

Department of Mechanical 

Engineering 

athakur@umd.edu

ABSTRACT

Unmanned Surface Vehicles (USVs) often need to utilize high 

speed reactive planning to carry out certain mission tasks. 

Development of a robust reactive planning logic is a challenging 

task. We have been exploring the use of virtual environments and 

machine learning to automatically synthesize a reactive planning 

logic to block the advancement of an intruder boat toward a 

valuable target. An important component of our work is to 

evaluate the performance of the automatically generated planning 

logic. We have used a virtual environment based game to 

compare the efficiency of an automatically discovered decision 

tree representing a planning logic for blocking to the behavior 

exhibited by the human operators. During our testing we used 

four volunteers to play against each other and against the 

computer. In human against human testing, the four players took 

turns playing the role of the USV and the intruder. In computer 

against human tests the four players played the role of the intruder 

while computer played the role of the USV defending a target. 

The efficiency of the logic was measured in terms of the time 

delay applied on the intruder by the USV as the USV carried out 

blocking maneuvers to protect a target. Our preliminary results 

show that a genetic programming based framework is capable of 

generating decision trees expressing useful reactive blocking 

logic. 

Categories and Subject Descriptors

I.2.1 [Applications and Expert Systems] Games 

I.2.2 [Automatic Programming] Program synthesis 

I.2.6 [Learning] Knowledge acquisition, Parameter learning 

I.2.8 [Problem Solving, Control Methods, and Search] Control 

theory 

I.2.9 [Robotics] Autonomous vehicles, Kinematics and dynamics 

I.3.5 [Computational Geometry and Object Modeling] Physically 

based modeling 

I.3.7 [Three-Dimensional Graphics and Realism] Virtual reality, 

Color, shading, shadowing, and texture 

General Terms

Dynamics Simulation, Dynamics Meta-model, Evaluation, 

Evolutionary Computing, Genetic Programming 

Keywords

Autonomy, co-evolution, reactive planning logic, unmanned 

surface vehicle 

1. INTRODUCTION 

A major issue in the development of increased autonomy for 

robotic vehicles such as unmanned surface vehicles (USVs) is the 

time and expense of developing the software necessary to handle 

a large variety of missions and all the variations in the 

encountered environments. This is a truly challenging task and 

requires writing hundreds of thousands of lines of code by human 

programmers. 

We have developed a new approach for developing planning 

software that operates autonomous USVs. This new approach 

takes advantage of the significant progress that has been made in 

virtual environments and machine learning. The basic idea behind 

our approach is as follows. The USV explores the virtual 

environment by randomly trying different moves. USV moves are 

simulated in the virtual environment and evaluated based on their 

ability to make progress toward the mission goal. If a successful 

action is identified as a part of the random exploration, then this 

action will be integrated into the logic driving the USV. We 

anticipate that there may be portions of the mission, where trial 

and error alone will not be adequate to discover the right decision 

rule. In such cases, two additional approaches are utilized to make 

progress in acquiring the right logic. The first approach involves 

seeding the system with the logic employed by humans to solve a 

challenging task. The second approach is to restrict the action 

space based on some type of feasibility criteria. This paper mainly 

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PerMIS ’09, September 21-23, 2009, Gaithersburg, Washington, D.C., 

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Copyright 2009 ACM 1-58113-000-0/00/0004...$5.00. 

295
focuses on a reactive planning logic used for blocking the 

advancement of an intruder boat towards a valuable target. 

An overview of our overall approach is shown in Figure 2. The 

first major component of our approach is development of a 

physics-based meta-model. High fidelity simulation of USV is 

time consuming and cannot be used for discovering decision rules 

or trees used in planning. We have developed a meta-model by 

conducting off-line simulations of the USV in the sea. This 

simulation accounts for wave and USV interactions. The metamodel provides information about turning radius, steady state 

velocity, and acceleration as a function of rudder angle and 

throttle position. Section 4 provides more details on this 

component. 

We have developed a mission planning system whose main part is 

an evolutionary module for evolving planning decision trees. We 

used this system to automatically generate decision trees 

expressing blocking logic for the USV. This means that instead of 

automatically generating a program composed of low-level 

controller actions (steer left/right, go straight), we generate a 

program represented as a decision tree that consists of high-level 

controllers as building blocks (encircle intruder, go in front of the 

intruder, etc.) together with conditionals and other program 

constructs. The biggest challenge we faced was the efficient 

generation of various test-cases (intruder's attacking logic) for the 

USV blocking logic. To generate more general USV blocking 

logic, we employed the co-evolution approach. To make this 

approach feasible (time spent on the computation), (a) we had to 

seed a portion of both the initial populations for the USV and 

intruder by human-crafted initial set of blocking logic and (b) to 

evaluate the USV's blocking logic against a subset of the best 

intruders from the previous generations and vice-versa. Section 5 

provides more details on this. 

We have developed a virtual environment based game that allows 

human players to play against each other or against the computer. 

In the game, the player controlling the intruder boat must collide 

with a protected target, while the player controlling the USV must 

block and delay the intruder as long as possible. In addition to 

offering basic capabilities, the game provides collision detection 

and basic physics to the objects in the scene. The game logic is 

responsible for the rules of the game, game logging and replay, 

boat behaviors, and scoring. The game can be played on two 

computers over a network. Section 6 provides more details on this 

game. 

Deployment of autonomous USVs in critical missions requires 

that the performance of the autonomous system matches with that 

of a remote controlled vehicle. Therefore, we have started an 

effort to assess the performance of automatically generated 

blocking logic compared to blocking maneuvers exhibited by 

human operators. We have used the virtual environment based 

game to compare the automatically discovered decision trees 

representing the blocking logic to the strategies used by human 

players. During our testing we gathered four volunteers to play 

against each other and against the computer. In human against 

human testing, the four players took turns playing the role of the 

USV and the intruder. In computer against human evaluation 

tests, the four players played the role of the intruder while 

computer played the role of the USV. The efficiency of the logic 

was measured in terms of the time delay applied on the intruder 

by the USV. Section 7 provides more details on the evaluation 

methodology. 

Figure 1. Virtual environment for simulation 

2. Figure 2. Overview of the overall 

approachRelated Work 

Technological advances in unmanned surface vehicles have 

enabled unmanned boats to be involved in certain missions which 

could be potentially dangerous for humans. The use of USVs is 

being proposed for a myriad of tasks like surveying, marine 

research, mine sweeping, etc. Currently, the best known USVs are 

semiautonomous. This means that the way-points programmed 

into these semiautonomous USVs are initially determined by 

human navigators. The built-in navigation planners of these USVs 

employ deliberative and reactive obstacle avoidance (OA) 

modules to ensure safe movement between the way points. Some 

also compute new way points in response to fault conditions. The 

Space and Naval Warfare Systems Center, San Diego has 

developed an autonomous navigation and OA architecture for 

USVs that supports both deliberative and reactive OA [1]. The 

deliberative OA contains a quick path planner for diverting the 

route of a USV away from threats of stationary and moving 

obstacles in the far-field. In their further work [2], they extended 

the deliberative part of the navigation planner to generate paths 

consistent with the rules of the road during all stages of the 

planning. For a detailed description of the current state of USV 

autonomy, see [3]. 

Genetic programming (GP) [4, 5] as one of the robust 

evolutionary techniques has been used for automatically 

generating high-level controllers or planning logic in different 

domains. To be able to discover a general planning logic in a 

competitive setting, the co-evolutionary process is needed. This 

process automatically generates challenging test cases for the 

current individual being evolved. Co-evolution is thus an 

296
alternative to standard evolutionary methods and is based on the 

"Arms race" assumption [6]. According to this assumption, newer 

individuals are expected to perform better than their ancestors. 

Competitive fitness and co-evolution were first explored in the 

context of the Iterated Prisoner's Dilemma in [7]. Koza further 

discussed GP based co-evolution for a simple discrete planning 

logic game in [5]. Reynolds discussed how to use competitive coevolution for evolving strategies for players in the pursuit and 

evasion game positioned in a continuous geometric environment 

[8]. More recently, a game planning logic was evolved using 

competitive co-evolution for an ant taking part in the Ant Wars 

contest [9]. The task for the ant was to collect food in a toroidal 

grid environment in the presence of a competing ant. The coevolved planning logic is human-competitive in a sense that it 

was able to beat other human-programmed planning logic. 

Similarly, RoboCode [10, 11] is an another example of using 

simulated co-evolution for generating human-competitive game 

planning logic for competing tanks in a grid based environment. 

Multi-population competitive co-evolution is used for developing 

more general high-level controllers in car racing domain [12]. The 

work described in [13] shows evolutionary architecture for 

evolving team tactics for a combative 2D gaming environment 

using GP. 

Measuring the quality of task outcomes from different robotic 

domains (mapping and localization, obstacle avoidance, search 

and rescue, etc.) is necessary in order to be able to compare 

different techniques or algorithms. This can be done first in a 

simulator to prevent any possible damage to robots, followed by 

rigorous testing in a real environment. It is important for the 

benchmark itself to be accurate. This encourages other researchers 

to replicate it on the same problem so that they are able to 

accurately compare their own control or navigation strategies to 

existing ones. 

The benchmarking work in robotics is often performed through 

competitions. These are mainly student competitions like National 

Robotics Challenge [14], FIRST Robotics Competition (Lego 

League, Tech Challenge) [15], or Eurobot [16]. There are also 

technically more challenging competitions geared towards 

academic communities. For example, the world's largest robot 

competition RoboGames [17] or RoboCup soccer [18]. There are 

also robotic competitions appealing to much broader communities 

such as aerial robotic competitions (IARC [19]), ground robotic 

competitions (DARPA Urban Challenge [20]), or underwater 

robotic competitions (Autonomous Underwater Vehicle 

Competition sponsored by AUVSI and the U.S. Office of Naval 

Research). Others can be readily found on robotics related 

websites. 

In the field of UAVs, reference [21] describes an approach for 

evaluating algorithmic and human performance for UAV-based 

surveillance missions. Two main parts include an evaluation testbed consisting of 243 scenarios uniformly covering most of all 

possible missions and a decision-theoretic framework for 

measuring the performance of a surveillance method in a given 

mission. 

3. Reactive Planning Logic Executor 

Architecture 

The complexity of interactions of a mobile robotic system 

suggests structured (non-monolithic) high-level controller 

architecture. The unmanned boats must behave based on the 

effect of several independent threads of reasoning. This is implied 

by the highly parallel nature of events and processes in the real 

world. The high-level controller architecture can meet this 

requirement if it is modular, and when the modules can act 

simultaneously in a coordinated cooperation. 

The navigation system resides inside the USV and consists of 

perceptual, reasoning / planning, localization, and behavioral 

components. The USV's planning logic executor itself (see Figure 

3) is purely reactive which means that it interprets and triggers 

reactive planning logic in a strict timely fashion. It computes only 

one action in every discrete moment based on the current state so 

that it is able to cope with highly dynamic and unpredictable 

environments. 

Figure 3. Reactive planning logic executor architecture 

The reactive planning logic is a stored human-readable structure 

producing different motor actions (translation and steering 

velocities) in different situations. Its representation is a decision 

tree consisting of different high-level action controllers, 

conditional rules, standard boolean values and operators, 

conditional variables, program blocks, and velocity commands. 

The structure of a high-level controller is based on the behaviorbased subsumption architecture [22]. This architecture 

decomposes a complicated high-level intelligent behavior (a highlevel controller in our case) into a set of more solvable and simple 

behaviors (steer left / right, go straight, arrive) organized into 

layers. These primitive behaviors are finite state machines acting 

in response to sensor inputs and producing motor action outputs. 

Multiple behaviors of the high-level controller can be activated 

simultaneously producing different conflicting motor commands. 

This means that a certain amount of coordination is needed. Due 

to its robustness, we have chosen a priority-based arbitration 

mechanism, picking the motor action output of the behavior with 

the highest priority (e. g. obstacle avoidance) as the overall motor 

action output of the high-level controller. In this high-level 

controller structure, the behavior in the bottom layer has the 

highest priority (for example obstacle avoidance) while the 

297
behavior in the top layer represents the most abstract 

functionality. 

The high-level controllers, which are the main building blocks of 

a planning logic, can be parameterized. The parameters of a highlevel controller define its underlying property. For instance, for 

the GO-INTRUDER-FRONT controller, the parameter defines 

the USV’s relative goal position (in polar coordinates) in front of 

an intruder. This effectively allows the USV to cover all feasible 

positions, as defined by its planning logic around the intruder. 

The inputs into the reactive planning logic executor are sensor 

data, description of mission parameters, and the USV meta model 

itself. The outputs are translation and steering velocities for a lowlevel USV controller that are directly translated into motor 

commands for device drivers of a particular actuator. At any 

particular moment, the logic executor can be in only one state. As 

figure 3 shows, the planning logic interpreter takes the currently 

used planning logic as an input and produces action outputs. 

Based on the current sensor data readings, only one high-level 

controller inside the planning logic is activated. This high-level 

controller consists of multiple primitive behaviors and decides 

which one is used to produce the ultimate action output. 

4. Development of Physics-based Meta Model 

The system consists of a 6 degree of freedom USV simulation 

model, which is used for computing the dynamics of the USV 

under any given sea-state. The full fledged USV simulation is 

computationally expensive, so we created simplified meta-models 

in the form of lookup tables by performing exhaustive 

computations using the USV simulation model for each given seastate and USV model. The dynamic meta-models enable faster 

computation of fitness values in the evolution of the planning 

logic. We employed 6 degree of freedom nonlinear USSV 

dynamics model reported by Krishnamoorthy et al. as given by 

equation 1 [22]. 

() () ( )

( ) ⎪⎭

⎪

⎬

⎫

=

+=+++

vJp

FFpgvvDvvCvM

P

H H H PE

&

& (1) 

where, 

H RB += MMM A : is the (6X6) inertia matrix and is the 

(6X6) added mass matrix, 

M RB M A

H RB += CCC A : is the (6X6) Coriolis and Centripetal matrix 

and is the (6X6) hydrodynamic Coriolis and Centripetal 

matrix, 

CRB

CA

DH : (6X6) diagonal damping matrix, 

( ) pg : (6X1) restoring force vector, 

FE : (6X1) environment force vector, 

FP : (6X1) actuator force vector, 

P J : (6X6) Jacobian matrix 

v : (6X1) velocity vector relative to inertial frame and expressed 

in body-fixed frame 

p : (6X1) vector representing position of USV relative to as well 

as expressed in the inertial frame of reference 

We computed the added mass matrix using strip theory as 

explained by Fossen [23]. Unlike the approach suggested by 

Krishnamurthy et al. to estimate the restoring force vector using 

an approximated simplified formula we computed the 

instantaneous restoring forces and moments 

M A

(pg ) by actually 

intersecting the USV geometry with the wave at the given 

location of the USV resulting into a more accurate estimation of 

restoring force and moments. To compute the environment force 

we only considered the wave forces and ignored the effects of 

wind ( ) E = FF W . To compute the wave force we used the equation 

2. 

⎥

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎢

⎣

⎡

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ ⎟ × ⎠

⎞ ⎜

⎝

⎛ ∇∇+

∂

∂ −

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ ∇∇+

∂

∂ −

=

×

×

∫∫

∫∫

13

13

.5.0 ˆ

.5.0 ˆ

B

B

S

S

W

dSnr

t

dSn

t

F

r φφ φ ρ

φφ φ ρ

 (2) 

where, 

φ : Velocity potential 

B S : Instantaneous wet surface area of USV 

ρ : Density of water 

nˆ : Normal surface vector 

r

r : Position vector of points on the surface in body –fixed frame 

of reference 

The simulation technique described above is computationally 

expensive because at every time step it performs the geometric 

computation of the wet area of USV and the surface integration. 

To use the simulator in reactive planning we employed a look up 

table based meta-model. The lookup table meta-model is prepopulated by running the simulator under various sea-states. The 

meta-model is then used in subsequent computations as described 

in the following sections. 

The structure for the meta-model that we employed is shown in 

Figure 4. 

class MetaModel { 

 SeaState seastate; 

 double AvgMaximumVelocity; 

 double StddevMaximumVelocity; 

 double AvgPositionalError; 

 double StddevPositionalError; 

 double AvgMaximumAcceleration; 

 double StddevMaximumAcceleration; 

 double AvgTurningRadius; 

 double StddevTurningRadius; 

}; 

Figure 4. Structure of the meta-model 

298
5. Automated Generation of Blocking 

Decision Tree 

Our solution to the problem of reactive planning was the use of a 

computer simulated evolution based on the Darwinian principles 

of survival and reproduction of the fittest. Using this phenotype 

evolutionary process, we were able to evolve the actual decision 

tree representing a planning logic. The specific evolutionary 

method we used is the strongly-typed genetic programming (GP) 

[4, 5]. This is a robust stochastic optimization method that 

searches a large space of candidate hypotheses (programs) while 

looking for the one with the best performance (fitness value). 

During the evolution itself, a population of USV decision trees is 

being stochastically transformed into a new population with a 

better average fitness value using standard evolutionary operators 

like crossover and mutation. 

We were particularly interested in automatically discovering a 

blocking logic for slowing down the movement of the intruder 

toward the protected object. This blocking logic is defined in the 

context of a simple mission. During this mission, the USV must 

protect an oil tanker by patrolling around it while avoiding 

collisions with friendly boats and scanning the environment for a 

possible intruder. The environment around the oil tanker is 

divided into danger and buffer zones (see Figure 5). Once the 

intruder enters the buffer zone, the USV approaches the intruder 

boat and circles it for surveillance purposes. If the intruder enters 

the danger zone, the USV does its best to block the intruder, 

slowing the intruder’s progress toward the protected object. 

Figure 5. Simple mission: USV protects area containing 

objects of interest 

Instead of generating a decision tree composed of low-level 

controller actions (e.g. steer left / right, go straight), we utilized 

our evolutionary framework to automatically generate a decision 

tree consisting of high-level parameterized controllers as building 

blocks (see Table 1). Other components of this decision tree are 

conditional rules (IF-THEN-ELSE), standard boolean values and 

operators (TRUE, FALSE, AND, OR, NOT), conditional 

variables (see Table 2), program blocks (SEQUENCE), and 

velocity commands (SET-VELOCITY, SLOW/NORMAL/HIGHVELOCITY, STOP). 

The final decision tree expressing the blocking logic had to be 

general enough to cope with a broad variety of enemies. The key 

was to let the intruder develop its own attacking logic thereby 

competing with the USV’s blocking logic. To generate a more 

general USV logic, we employed a simulated competitive coevolutionary process [6] using which the USV’s and intruder’s 

logic was being improved simultaneously. During this process, 

the improved intelligent intruder was taking advantage of 

inefficiencies in the blocking logic used by the USV. It was 

gradually developing its own set of rules exploiting the weak 

points of the blocking logic. 

The competitive co-evolution is a form of evolution in which the 

fitness function of an individual is completely dependent on other 

individuals. This means that fitness evaluation requires interaction 

between multiple individuals. We used two populations 

representing two different sets of individuals (USV and intruder 

decision trees) as they are made by different program primitives. 

During the evolutionary process, a newly created USV individual 

is evaluated against a particular intruder in three different test 

scenarios. In each scenario, the intruder had a different initial 

orientation and distance from the target, and the USV always 

started from an initial position close to the target. The fitness 

function is defined as the squared distance between the intruder 

and the target over all time steps. This squared distance is 

normalized due to the different initial distances of the intruder 

from the target in the test scenarios. The fitness function is 

defined as 

∑=

⎥

⎦

⎤ ⎢

⎣

⎡ =

T

i

i

d

d

T

F 1

2

0

1 (3) 

where is the total number of time steps, is the distance of 

the intruder from the target at time step i, and is the initial 

distance of the intruder from the target in a particular test case. 

When evolving the blocking logic for the USV we are trying to 

maximize its fitness value and vice-versa when evolving the logic 

for the intruder. 

T i d

0 d

Due to the stochastic nature of the simulated evolution and 

various co-evolutionary dynamics [6], not all co-evolutionary 

runs are successful. To make the co-evolutionary run feasible in 

terms of probability of successfully evolving a solution and time 

spent on the computation, we had to (a) seed a portion of both of 

the initial populations (for the USV and intruder) by an initial set 

of simple human-crafted planning logic and (b) evaluate the USV 

blocking logic against an archive of previously encountered 

intruder champions from previous generations and vice-versa. 

The evaluation process of an individual is based on an archive of 

previously encountered champions from the other population. 

Using this archive, the individual being evaluated is pitted against 

a randomly sampled set of best adversaries from previous 

generations. The final fitness value of the individual is then 

computed as an average of all fitness values resulting from each 

single competition. 

The initially seeded individuals provide a baseline from which the 

co-evolution can start without having to spend much valuable 

time on evolving the basic functionality from scratch. This 

baseline also serves as a basic evaluation standard in further 

generations for penalizing suboptimal novel solutions. This means 

that a newly created individual is evaluated not only against the 

best adversaries from the archive but also against the baseline 

individuals. The initial values of all high-level controller 

parameters inside the initial set of randomly generated decision 

trees are randomly generated. They are further mutated in the 

299
course of the evolution process, possibly improving the 

functionality of their parental high-level controllers. 

In order to pick the most successful blocking logic from a 

particular co-evolutionary run, we developed a tool analogous to 

the Master Tournament matrix as defined in [23]. We also needed 

to analyze whether a given co-evolutionary run produced logic 

which could cope with all previously encountered adversaries. 

This is due to the fact that the co-evolution is prone to various 

complex dynamics, such as overspecialization, which are often 

difficult to analyze. This tool allowed us to detect failures during 

the co-evolutionary search, identify similar phenotypes related to 

similar efficiency, identify early convergence to a particular type 

of logic, and identify possible breakdowns in the "Arms race" due 

to overspecialization. 

Table 1. High-level controllers of reactive planning logic for 

USV and intruder 

GO-INTRUDER/USV: FRONT, FRONT-LEFT/RIGHT, 

LEFT, RIGHT, BACK, BACK-LEFT/RIGHT 

GO-STRAIGHT 

TURN-LEFT/RIGHT 

ENCIRCLE-INTRUDER/USV 

INTRUDER-SEEK-PROTECTED-OBJECT 

Table 2. Conditional variables of reactive planning logic for 

USV and intruder 

INTRUDER/USV: ON-THE-LEFT/FRONT-LEFT/FRONTRIGHT/RIGHT, IN-FRONT, AT-THE-BACK/BACKLEFT/BACK-RIGHT 

USV/INTRUDER: PROTECTED-OBJECT-ON-THELEFT/RIGHT, 

INTRUDER/USV: CLOSE 

6. Virtual Environment-Based Game 

We have developed a virtual-environment-based game to evaluate 

the automatically generated blocking logic represented as a 

decision tree as described in Section 5. The game software 

consists of two parts: a virtual environment and game logic itself. 

The virtual environment offers a realistic 3D immersive world by 

implementing physics based scene, incorporating rigid body 

dynamics, waves, and dynamic obstacles. The virtual 

environment also handles user input. User input consists of 

keyboard strokes, mouse clicks and movements, or a Microsoft 

XBox controller interface. The XBox controller offers a very 

intuitive and familiar user interface, especially for the 

experienced gamers. It is important to make the user interface as 

intuitive and as simple as possible so that the performance of the 

human operators, when compared to the performance of the 

computer reactive planning logic, is not biased by a poor user 

interface. The XBox interface allows the human player to not 

only control a boat, but also control the translation and rotation of 

the view and switch between different vantage points. 

All the boats in the environment are represented with realistic 3D 

models created using a CAD tool. The ocean is rendered using a 

dynamically generated triangulated mesh that is linked to a height 

map. The same height map is used to calculate bobbing motion 

for all the boats in the environment. Up to a certain distance from 

the view point, the triangles which make up the ocean surface are 

constantly updated and redrawn based on the height map queries. 

The dynamic ocean mesh was broken up into a grid of tiles, where 

each tile represents an independent object. As the user’s view 

moves and rotates, the virtual environment uses an efficient 

method to check which tiles are in view and which tiles are no 

longer within the view angle. Tiles which are not within the view 

angle are made invisible to prevent the system from rendering 

unnecessary triangles. The dynamic ocean implementation also 

supports multiple levels of detail (LOD). Ocean tiles with high 

LOD level have more triangles than those with a low LOD level. 

The detail level increases for the tiles that are closer to the view 

and decreases for those that are far from it. When the system 

signals an ocean tile to change its LOD level, the triangulated 

mesh for a specific tile is regenerated. 

The game logic is responsible for the rules of the game, game 

logging and replay, boat behaviors, and scoring. Players must 

navigate the boats around various obstacles to perform their 

respective missions. Once models of all the boats are loaded into 

the scene, a physics-based dynamics model is used to govern the 

boat behavior. In human versus human mode the game is played 

on two computers over a network using User Datagram Protocol 

(UDP) and a client-server architecture. The game also supports 

the concept of a limited visual range. Each player is only able to 

see objects which are within a certain configurable radius of the 

boat they are driving. 

The game supports collision detection for all objects in the 

environment so that objects are not allowed to pass through each 

other. Some basic boat physics has been implemented to deal with 

boat collisions. When two boats collide, they deflect each other 

depending on the collision point for each boat, each boat’s 

velocity, mass, and each boat’s direction of travel. The deflection 

involves both translation and rotation. Upon collision, each boat 

receives a certain amount of damage. Too much damage results in 

the sinking of the boat. The computer driven boat is equipped 

with visibility sensors used for avoiding obstacles. 

The game also supports logging so that it can be played back from 

different perspectives for visual analysis and inspection. 

The logging system records the movements of all objects during 

the game. 

The boats serving as obstacles have randomly generated 

structured motion. During the initialization of the environment, 

a randomly generated sequence of actions, as well as their 

duration, is built for each boat. 

7. Evaluation 

The virtual environment based game that we developed allows 

human players to play against each other or against the computer. 

In the game, the player controlling the intruder boat must reach a 

protected object as quickly as possible, while the player 

controlling the USV must block and delay the intruder for as long 

time as possible. This game allows us to compare the efficiency 

of the automatically discovered decision tree expressing the 

USV’s blocking logic to the behavior exhibited by human players. 

300
7.1 Evaluation Protocol 

For our testing, we gathered four volunteers to play against each 

other and against the computer. In human to human testing, the 

four players took turns playing the role of the USV and the 

intruder. Each of the four players played nine games resulting in a 

total of 36 games played during the testing. In computer to human 

benchmarking tests the four players took turns playing the role of 

the intruder while computer played the role of the USV. In this 

second round of gaming, each volunteer played 3 times resulting 

in a total of 12 games played. 

The efficiency of the logic was measured in terms of the time 

delay applied on the intruder by the USV as the USV carried out 

blocking logic to protect a target. The maximum time for the 

game was set to five minutes. The speed of the USV was set to be 

25 per cent higher than the speed of the intruder. 

7.2 Preliminary Results and Discussion 

The results of our evaluation tests depicted in the graph in 

Figure 6 show that the human operators performed better in terms 

of delaying the intruder than the computer generated decision 

trees. The blocking logic currently being generated by the 

computer using many simulations is highly reactive in nature. In 

other words, the computer generates simple, short term plans in 

response to the intruder’s actions. No long term planning or 

reasoning is performed by the computer. Human operators, on the 

other hand, are capable of perceiving long term outcomes such as 

congested regions predicted based on the current direction of 

travel and the speed of obstacles. 

Figure 6. Evaluation results 

The graph in Figure 6 shows the results of human to human and 

human to computer competitions. Each pair of vertical bars in the 

graph represents the scores for a particular human player, while 

that player controlled the intruder boat. The score represents the 

number of seconds it took for the human operated intruder to 

reach and collide with a protected target. A high number means 

that the intruder performed poorly and the USV performed well. 

The first bar in each pair (XHH) represents the scores for that 

player after playing against other human operated USVs. The 

second bar in each pair (XHC) shows the scores for this player 

after playing against the computer operated USV. For example, 

the first 1HH case shows the average time (66.5 s) player 1 took 

to get to the protected object when playing against USVs driven 

by other players. This number is computed as an average of three 

games played by intruder 1 against USV 2, USV 3, and USV 4. 

Each game played by intruder 1 against each USV consisted of 

three rounds. The second 1HC case shows how much time on 

average (55 s) intruder number 1 took to get to the protected 

object when playing against a USV driven by a computer. The bar 

associated with each number shows maximum and minimum 

values. These results show that human players consistently 

outperformed the computer in blocking another human player. 

To get a fair assessment of computer performance versus human 

performance, the time values being compared must be normalized 

by 46 seconds baseline. This baseline represents the amount of 

time needed to reach the target if the intruder is completely 

unobstructed. Any additional time above this baseline thus 

represents the effective delay time of the intruder when being 

blocked by the USV. 

During the testing, we noticed that the computer and the human 

players had different strengths and weaknesses. The computer 

was able to precisely and swiftly direct the USV into 

advantageous blocking positions around the intruder. This is 

mainly due to rapid reaction of the USV to sudden changes in 

intruder’s velocity, position, and orientation. The computer was 

also more efficient at taking the shortest path to a waypoint, while 

the human players tended to weave side to side. This is due to the 

underlying precision of the computer’s control system compared 

to the relatively imprecise control of the human players. Humans 

on the other hand had the ability to perform better long term 

prediction. For example, humans were better able to observe a set 

of moving obstacles and predict natural barriers for the intruder as 

a result of high congestion and traffic. A human could then stay 

closer to less congested routes to the protected target. Since the 

current type of the planning logic is not able to perform long term 

planning, this capability was not available to the computer. 

Human players were also able to spot repetition in computer’s 

behavior and take advantage of it. For example, once some of the 

human players noticed that the computer tends to over shoot a 

little in the process of blocking, they would often suddenly slow 

down and turn toward the stern of the blocking USV, passing the 

USV from behind. 

66.5

55

64

55

73 72

63.5

54

0

10

20

30

40

50

60

70

80

90

1HH 1HC 2HH 2HC 3HH 3HC 4HH 4HC Players 

Intruder's time (s) 

We noticed a number of blocking logic emerging during the 

human to human testing. In the human to human testing, the best 

blocking logic for the USV was to block the intruder far from the 

target. By doing this, the USV found more possibilities for 

blocking the intruder and had more time to recover in case of a 

mistake. It was also beneficial for a player controlling the USV to 

stay close to the protected object and wait for the intruder. Once 

the intruder approached the base, the USV would start to actively 

block it. In order for this to work, however, the human player 

controlling the USV had to maintain a certain distance from the 

protected object and be skillful at controlling the boat. Otherwise, 

the intruder could take advantage of the USV’s close proximity to 

the base and lack of adequate space for maneuvering. The 

blocking logic, in this case, aimed to predict the future position of 

the intruder and use that prediction to make sure that USV was 

constantly between the intruder and the protected base. The 

intruder’s solution in this case was to drive at high speed around 

the base, waiting for the USV to make a mistake and leave an 

opening. Figure 7 shows an example from a game played by a 

human intruder against a computer-driven USV during which the 

USV utilizes a blocking maneuver in front of the intruder. 

301
2. Larson, J., et al., Advances in Autonomous Obstacle 

Avoidance for Unmanned Surface Vehicles. AUVSI 

Unmanned Systems North America, 2007. 

3. Cornfield, S. and J. Young, Unmanned Surface Vehicles - 

Game Changing Technology for Naval Operations, in 

Advances in Unmanned Marine Vehicles, G.N. Roberts and 

R. Sutton, Editors. 2009, The Institution of Electrical 

Engineers: Stevenage, United Kingdom. 

4. Koza, J.R., et al., Genetic Programming IV: Routine HumanCompetitive Machine Intelligence. 2003: Kluwer Academic 

Figure 7. Example of a game played by a human intruder Publishers. 

against a computer driven USV 5. Koza, J., Genetic Programming: On the Programming of 

Computers by Means of Natural Selection. 1992: MIT press. 

8. CONCLUSIONS AND FUTURE WORK 6. Miconi, T., The Road to Everywhere: Evolution, Complexity 

and Progress in Natural and Artificial Systems. 2007, School 

of Computer Science, The University of Birmingham. p. 197. 

We have developed a mission planning system for automatically 

generating a reactive planning logic for USVs. The planning logic 

is represented as a decision tree which consists of high-level 

controllers as building blocks, conditionals and other program 

constructs. We used our strongly-typed GP-based evolutionary 

framework for automatic generation of planning logic for 

blocking the advancement of an intruder boat toward a given 

target. We employed a simulated competitive co-evolutionary 

process to improve the general capability of this blocking logic by 

pitting the autonomous USV against a set of different, 

simultaneously evolved enemies. 

7. Axelrod, R., The Evolution of Strategies in the Iterated 

Prisoner’s Dilemma. Genetic algorithms and simulated 

annealing, 1987: p. 32-41. 

8. Reynolds, C. Competition, Coevolution and the Game of 

Tag. 1994. 

9. Jaskowski, W., K. Krawiec, and B. Wieloch, Winning Ant 

Wars: Evolving a Human-Competitive Game Strategy Using 

Fitnessless Selection. Lecture Notes in Computer Science, 

2008. 4971: p. 13. 

10. Shichel, Y., E. Ziserman, and M. Sipper. GP-Robocode: 

Using Genetic Programming to Evolve Robocode Players: 

Springer. 

An important part of our work was to fairly assess the 

performance of the blocking logic. For this reason, we developed 

a virtual environment based game to compare the efficiency of the 

computer generated planning decision tree to planning skills of 

human operators. 

11. Sipper, M., et al., Designing an Evolutionary Strategizing 

Machine for Game Playing and Beyond. IEEE Transactions 

on Systems, Man, and Cybernetics, Part C: Applications and 

The results of our tests show that human players driving the USVs Reviews, 2007. 37(4): p. 583-593. consistently outperformed the computer driven USV in blocking 

another human driven intruder. From purely reactive point of 

view, the performance of humans and machine were quite similar. 

However, the human players demonstrated better long term 

planning and were better at discovering weaknesses in the 

computer planning logic. We are planning to combine deliberative 

planning with predictive capabilities in the next round of testing. 

12. Togelius, J., P. Burrow, and S. Lucas. Multi-Population 

Competitive Co-evolution of Car Racing Controllers. 

13. Doherty, D. and C. O’Riordan. Evolving Agent-Based Team 

Tactics for Combative Computer Games. 2006. 

14. National Robotics Challenge. 2009 [cited 2009 May]; 

Available from: http://www.nationalroboticschallenge.org. 

15. US FIRST. 2009 [cited 2009 May]; Available from: 

We recently started testing physical radio controlled boats so that http://www.usfirst.org. 

we can evaluate the generated planning logic in a real 

environment. Testing in a real environment adds some additional 

challenges due to the differences with the virtual environment. 

We are planning to improve the virtual environment based boat 

simulations (high and low-level controllers, environment 

dynamics, sensors, etc.) inside the mission planning system in 

order to generate a planning logic reflecting the real world 

dynamics. 

16. Eurobot. 2009 [cited 2009 May]; Available from: 

http://www.eurobot.org. 

17. Robogames. 2009 [cited 2009 May]; Available from: 

http://www.robogames.net. 

18. RoboCup. 2009 [cited 2009 May]; Available from: 

http://www.ai.rug.nl/robocupathome. 

19. International Aerial Robotics Competition. 2009 [cited 

2009 May]; Available from: http://iarc.angel-strike.com. 

20. DARPA Urban Challenge. 2009 [cited 2009 May]; 

Available from: http://www.darpa.mil/grandchallenge. 9. ACKNOWLEDGMENTS 21. Freed, M., R. Harris, and M. Shafto. Measuring Autonomous 

UAV Surveillance Performance. in Proceedings of PerMIS 

'04. 2004. Washington, D.C. 

This research has been supported by Office of Naval Research 

and NSF Grants OCI-0636164 and CMMI-0727380. Opinions 

expressed in this paper are those of the authors and do not 

necessarily reflect opinions of the sponsors. 22. Brooks, R.A., Intelligence Without Representation. Artificial 

Intelligence, 1991. 47: p. 139-159. 

10. REFERENCES 23. Nolfi, S. and D. Floreano, Coevolving Predator and Prey 

Robots: Do "Arms Races” Arise in Artificial Evolution?

Artificial Life, 1998. 4(4): p. 311-335. 1. Larson, J., M. Bruch, and J. Ebken. Autonomous Navigation 

and Obstacle Avoidance for Unmanned Surface Vehicles. in 

Proceedings of SPIE Unmanned Ground Vehicle Technology 

VIII. 2006. 

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303
Fork Lift Awareness 

Mark E. Austin

Occupational Safety and Health Administration 

Baltimore Washington Area Office

1099 Winterson Road, Suite 140

Baltimore, MD 21090

mark.austin@dol.gov 

ABSTRACT 

In this paper, we discuss the Occupational Safety and Health 

Administrations’ statistics on fork lift accidents and injuries 

caused by these accidents. Fork lift benefits and the operating 

environments where these manned machines work are 

considered. Methods used to reduce fork lift accidents as well 

as reported cases of lost time at work due to fork lift accidents 

will be discussed, along with the percentage of lift truck 

accident causes. 

Categories and Subject Descriptors 

B.0 [HARDWARE/GENERAL]: Fork Trucks; 

K.m [MISCELLANEOUS] 

General Terms 

Human Factors, Performance

Keywords 

Fork lift, accidents, injuries, lost work time, OSHA, pedestrians 

1. PRESENTATION 

Fork Lift Accidents

l OSHA estimates that there are 110,000 accidents each 

year. 

l Approximately 31,600 employees suffer some type of 

injury. 

l Losses affect employees through physical and mental 

suffering. 

Benefits of Fork lifts 

l Assist in the movement of materials 

l Reduce employee injuries 

Fork Lift operating environments include:

l Pedestrians 

l Blind spots 

l Indoors/Outdoors 

l Narrow aisles 

l Building columns 

l Operate 24 hours per day 

l Turning radius 

Fork Lift and Pedestrians

l Pedestrians contribute to accidents 

l Pedestrians do no understand stopping distances 

l Pedestrians tried to “beat” a lift truck 

Methods used to reduce Fork Lift Accidents

l Training of drivers 

l Maintenance of equipment 

l Areas of operation 

Fork Lift Accidents

l Losses affect employers 

– damage to equipment 

– loss productivity 

A breakdown of the 1,158,870 Report Lost Time Cases in 2007:

l 11,040 Involved Fork Trucks 

l 5,730 Involved Transportation and material moving 

l 1,630 Involved Production Worker 

l 1,360 Involved Office Or Administrative Workers 

Source: Powered industrial truck accidents report through 

Bureau of Labor Statistics 2007 

Type Body Parts Affected:

Lower Extremities 6,040 

– Foot, Toe 3,290 

– Knee 400 

Trunk 1,780 

This paper is authored by employees of the United Back 1,090 

States Government and is in the public domain. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, 

USA. 

ACM 978-1-60558-747-9/09/09

Shoulder 280 

Event or Exposure

l Contact with Object, Equipment 4,540 

– Caught in object, equipment 1,630 

304
– Struck against object 1,600 

– Struck by object 1,000 

In percent what Causes Lift Truck Accidents? 

305
Where AGV’s and Forklifts Roam: 

Preserving Operational Safety in a Shared Workspace 

Richard H. Ungerbuehler 

Co-Founder and Chief Engineer 

Sky-Trax Incorporated 

98 Quigley Blvd. 

New Castle, DE 19720 

USA 

(302) 395-9540 

RHU@Sky-Trax

Operational safety can be assured in facilities where industrial 

utility vehicles are used to transport goods only when safe 

practices are clearly established and carefully monitored. For 

example, forklift trucks, pallet jacks, buggies and carts can 

safely share operational space if safety is designed into the 

operation and vehicle operators follow safety rules and remain 

alert. 

In facilities where autonomous vehicles are used, a different set 

of safety requirements is needed. Autonomous vehicle control 

systems must assure that inter-vehicular collisions are prevented, 

and the vehicles must be equipped with safety devices, such as 

laser bumpers, to prevent collisions with people or equipment. 

Shared Operational Space

When considering the design of a new storage facility, designers 

may choose between implementing a manual operation using 

driven vehicles such as forklifts, or a fully automated operation 

that employs autonomous vehicles or an automated storage and 

retrieval system. By making a choice of one or the other, 

managers commit to a non-reversible decision to invest capital 

in a facility with predestined operational efficiency. 

In the event that a company wishes to utilize driven and 

autonomous vehicles in a shared space, two basic safety 

strategies exist: (1) separate by subdividing and sharing space, 

(2) separate by sharing time. Facilities can be subdivided into 

multiple areas; perhaps one where humans work, another where 

driven vehicles are permitted, and another where automated 

vehicles operate. This strategy results in a high degree of safety, 

but often complicates operations and significantly limits 

efficiency. Another example would be to separate driven 

vehicles from autonomous vehicles by utilizing forklifts during 

one time period and autonomous vehicles during another period. 

As an example, imagine a warehouse that allows powered 

vehicles to operate during the day shift, while precluding 

pedestrians from entering the facility. Then, on the night shift, 

no vehicles are operated, while workers have full access to the 

facility. This plan implements a time-shared facility. 

An alternative would be the space- shared facility, where 

workers, forklifts, and autonomous vehicles operating during 

both shifts, but impervious barriers separate the three. Neither 

arrangement proves very practical in the industrial setting. A 

combination of time and space separation is needed, and that’s 

where modern technology steps in. 

Collision Avoidance: The Time/Space Problem

Traditionally, collision avoidance strategies that were designed 

to separate driven vehicles, autonomous vehicles, and people, 

have relied on facility design, safety procedures, and driver 

training and compliance. While these strategies will always be 

elements of workplace safety programs, collision statistics 

clearly indicate that training, signage, and floor markings for 

traffic control are not enough to assure a safe environment. 

Avoiding collisions between powered vehicles, or between 

pedestrians and vehicles, is based on a simple principal; all 

pedestrians and all vehicles must be kept separated in time and 

space. 

The key to understanding the safety versus efficiency dilemma 

is to understand that time separation and space separation, which 

have always been effective, are inefficient, while simultaneous, 

real-time monitoring and control can improve both safety and 

efficiency. 

Safety Makes Good Business Sense

Leading businesses know that safety is not incompatible with 

efficiency – instead, it can improve efficiency and enhance 

productivity. These companies use safety as a competitive 

advantage. Since the majority of serious accidents involve 

stability incidents and vehicular collisions with pedestrians, the 

new safety systems solution will integrate technology addressing 

both problems with intelligent speed control, vehicle tracking, 

and pedestrian tracking that can provide both improved safety 

and increased productivity. 

306
With the possibility of improving efficiency, a strong business 

case can be made for investing in safety systems and technology 

to eliminate hazards and prevent accidents. 

Collision Avoidance: A Technological Approach

Technological solutions are now becoming available to allow 

safe operations in facilities where mixed manual and automated 

equipment is used; for example, in a factory or warehouse where 

driven and autonomous vehicles co-exist. 

Sensor technology today can provide the ability to detect and 

track the location and proximity of vehicles and pedestrians in 

industrial facilities. Further, sensors can work in localized areas, 

over large areas, or throughout entire facilities. Whether tracking 

pedestrians or trucks, the best safety technologies will have the 

following capabilities: 

 Location determination accurate to within a meter or less; 

 Velocity determination; 

 Determination of orientation or direction of travel; 

 Ability to identify individual vehicles. 

Technology for Detecting Pedestrians

To systematically improve safety, safety engineers recommend 

the following strategy for hazard control: 1) Remove, 2) Guard 

& 3) Warn. Strategy 1 strives to remove the hazard by 

eliminating the danger by designing it out of the system or 

environment. For example, closing a blind door that opens into a 

warehouse can eliminate the possibility of a pedestrian walking 

into forklift traffic. 

If the hazard cannot be eliminated, Strategy 2 is to guard the 

hazard by installing safety apparatus to prevent exposure to the 

danger. Even if pedestrians and drivers are distracted, guards 

can protect both from harm. For example, automatic barrier 

guards can be installed to prevent fork trucks from falling off a 

vacant receiving dock. A good example is the Rite-Hite RHH 

dock leveler with the Safe-T-LipTM barrier1

, which prevents 

forklifts from running off an open dock and can stop a 10,000 lb. 

forklift traveling at up to 4 mph. 

Finally, if the hazard cannot be eliminated, or exposure to the 

danger prevented, Strategy 3 is to warn workers of the hazard. 

Ideally, specific alerts should be communicated only to those 

directly involved in the hazard situation and only where and 

when a danger actually exists. For example, warning lights can 

be installed at blind corners to warn of oncoming forklifts with a 

system like the Wickham Fork-AlertTM product2

. 

Safety system designers now have new technologies to consider 

for hazard control, particularly for detecting collision and 

speeding hazards. 

Two types of pedestrians can be found in industrial settings; 

employees who work in an area with vehicular traffic, such as a 

hand truck operator or an order picker, and visitors, who are less 

likely to understand, remember, and comply with safety 

requirements. Safety system designers must address the risk and 

establish effective measures for both classes of pedestrians. 

The Accident Research Centre at Monash University (Victoria, 

Australia), a leader in evaluating technology for preventing 

forklift-pedestrian accidents, has developed a system employing 

a simple RF-tag placed in safety vests worn by warehouse 

workers3

. An RF receiver was installed on each truck alerting 

drivers to the presence of any workers within the detection 

radius of the receiver. The researchers found this wearable RF 

tag prototype to be a low cost solution that they recommend to 

be used along with other safety measures. 

A product from ProxAlert4 takes the Monash University 

prototype concept to the next step. ProxAlert places an RF 

transceiver on each vehicle. A similar battery-powered portable 

transceiver is clipped onto any pedestrian entering the 

warehouse. As illustrated; the transceiver range creates a virtual 

protection zone around the vehicle or person. When the zones 

intersect, the transceivers energize a warning signal for both the 

pedestrian and the vehicle operator. This approach is a viable 

solution for workers and pedestrians. 

Figure 1. Proximity Detection and Alert 

Tracking Pedestrians with Machine Vision

Over the last 10 years, image processing research has made 

great progress in developing methods for detecting, identifying, 

and tracking people in video images. Driven largely by the need 

for smart surveillance and security systems, the technology has 

moved beyond military uses and is now used in commercial 

applications. Brickstream (Atlanta, GA) has marketed a 

pedestrian tracking system since 2002 that tracks and analyzes 

the movement of customers in commercial buildings5

. 

Processing images from overhead cameras, the system 

determines the number of customers entering a store and the 

exact paths taken by customers shopping in the store. In retail 

and banking applications, the technology is used to track queues 

of customers and to signal when more check out lanes need to be 

opened. 

While this technology has not yet been applied to collisionavoidance systems, it can be expected in the near future. 

Because industrial spaces are less diverse and more orderly than 

public areas like streets and stores, the application of machine 

307
vision for pedestrian tracking in warehouses should be very 

feasible. Location accuracy is likely to be less than a meter, and 

the pedestrian’s direction of movement and speed will also be 

provided. These capabilities are needed for detecting pedestrian 

hazards in areas where forklifts operate. 

Technology for Detecting Vehicles

A wide spectrum of technologies can be used to detect industrial 

utility vehicles. These can be functionally categorized as 

follows: 

 Presence Detection 

 Presence & Distance Detection 

 Presence & Identification 

 Location and Tracking 

Presence Detection

Presence detection sensors indicate that a vehicle is within the 

detection distance or zone of the sensor. In most cases, there is 

some ability to configure or engineer the detection distance. 

Inductive or capacitive proximity sensors and photoelectric 

sensors, all of which are familiar to automation engineers, fall 

into this category. 

Proximity sensors detect the presence of a large metal mass like 

a truck within their detection range – usually less than 1-2 

meters. This short detection range makes this type of sensor 

most applicable for detection at “chokepoints” such as dock 

doors. Photo detector sensors are also used for this purpose. 

Wickham Fork-Alert TM and Alert Safety Products offer safety 

products for the warehouse based on this technology. Fork-Alert 

employs an invisible infrared light beacon mounted on the top of 

the vehicle. An infrared receiver can detect the infrared light up 

to 25 meters away and trigger warning lights or audible alarms 

for pedestrians and other drivers. Alert Safety Products 

combines the light source and the detector in a single unit that is 

mounted on a wall or post. Strips of reflective tape are applied to 

both sides of forklifts so the vehicles can be detected when 

traveling by the sensor. 

Microwave sensors work similarly and can shape the detection 

zone to match an area of interest. For example, products from 

Door-Man6

 and Alert Safety Products7 offer warehouse 

intersection warning products using microwave sensors. Four 

sensors and a warning light are hung above an intersection with 

microwave sensors aimed in all four directions. A vehicle 

approaching the intersection is detected and triggers the 

appropriate warning light. 

Presence and Distance Detection

The next class of sensors not only detects a target but can 

accurately measure the distance from the sensor to the object. 

The principal technologies here are ultrasonic range sensors and 

laser time-of-flight sensors. Ultrasonic sensors emit highfrequency sound waves which are too high for the human ear to 

hear. When waves are reflected back from a solid object, the 

sensor can determine the distance from a few centimeters up to 

10 meters. 

Laser systems can measure distances with higher accuracy and 

longer ranges. Found typically in high-end safety systems on 

automated guided vehicles, these sensors measure distances very 

accurately with time-of-flight calculations on the reflected laser 

light. A commercial safety laser scanner from SICK GmbH8 can 

be programmed for different scanning areas and distances and 

configured to have both warning and emergency stop thresholds. 

Presence and Identification

RFID technology has received extensive press for inventory 

tracking applications in warehouses. Typical applications use 

passive RFID with inexpensive tags that can be read (detected 

and identified) by an RFID reader, but the read distance is small 

- usually less than a meter. Longer read distances of up to tens of 

meters are possible with active RFID systems. These systems 

detect and identify a tagged entity within the proximity of the 

RFID reader. This capability has been employed widely for 

security and access control applications. 

Location and Tracking Systems

Systems that can accurately track industrial vehicles will have 

great impact on creating the next generation of safety systems 

for warehouse operators. This new technology is known as real 

time location systems (RTLS). Radio frequency RTLS and 

optical RTLS systems are available today. 

Radio frequency RTLS tracks vehicles that carry an RFID tag. 

The identifying tag can be read simultaneously by multiple RF 

receivers in the detection region. Using one of several different 

sensing algorithms, a high speed computer applies triangulation 

techniques and computes a location estimate for any tag that is 

read by three or more sensors. Overall accuracy in industrial 

buildings currently is approximately 2 to 10 meters. Leading 

suppliers of RF RTLS technology include AeroScout9

, 

WhereNet10, and Ekahau11. 

The latest technology for tracking vehicles in warehouses is 

machine vision for optical RTLS. Machine vision (image 

processing) has been used widely in industrial automation for 

high speed package sortation, automated product inspection, and 

robotic guidance for the past 20 years. Sky-Trax Inc. has 

adapted this technology to provide accurate and reliable tracking 

of industrial vehicles inside buildings. With the Sky-Trax Indoor 

Position SensingTM (IPS) technology12, vehicles are tracked in 

real time to accuracy of 5-20 cm. Important to many safety 

applications, IPS systems determine the instantaneous speed and 

orientation (heading or direction of travel) of each tracked 

vehicle. 

308
Figure 2. Indoor Position Sensing system 

IPS (Figure 2) employs a small imaging sensor mounted on each 

vehicle that views upward toward the ceiling, where an array of 

encoded position markers is visible. The imaging sensor 

includes image processing intelligence to capture and analyze 

pictures of the ceiling several times per second. From ceiling 

scene analysis, IPS calculates X and Y position as well as 

angular direction of the vehicle. Velocity data is calculated from 

location changes noted from frame to frame. Position data are 

transmitted wirelessly to a computer which collects data on the 

location and status of all vehicles in real time. 

Speed Control for Safety

Speed is usually a contributory factor to both collisions and 

stability-induced incidents (tip-over) which together represent 

well over half of all serious accidents. Monash University 

researchers report that 75% of side tip-over’s occur when a 

forklift is empty, leading them to conclude that these incidents 

are due more to speeding than other causes. Systems to control 

speed will be a significant way that technology will improve 

safety. The best solutions will do this without impacting 

productivity. 

Like automobiles, forklifts cannot safely stop on a dime; and 

panic stops create additional hazards with loss of handling 

capability and unstable loads. Stopping distance is a function of 

speed, mass, driver reaction time, driving surface conditions, 

and braking system performance. Speed and reaction time are 

the key variables that can be controlled by the driver, and are the 

two areas where technology can help. Technology can provide 

advance warning of hazards (earlier reaction time) and can 

directly limit speed to assure adequate stopping distance based 

on location, load, vehicle type, and known hazards. 

Safety system analysis begins with understanding safe stopping 

distances. If an empty forklift truck moving at 10 mph requires 

40 feet for a safe stopping distance, the driver needs to allow at 

least 40 feet to react once a pedestrian hazard is recognized. If 

this is not practical, for instance at blind intersections, speeds 

need to be reduced in order to allow for proper stopping 

distance. 

Speed limits are established by rule and drivers are expected to 

recognize and obey the appropriate speed limits. As on the 

highways, these rules are often violated and difficult to enforce. 

Many forklifts do not have a speedometer, and speed limits are 

not always posted. Complicating the situation, the safe speed 

changes as load mass changes, and driving surface conditions 

vary at different locations in the facility. Unfortunately, drivers 

sometimes experience more pressure to be efficient (drive fast) 

than to be safe. 

Some believe that installing speed limiters on trucks is the 

solution to control speeding; however, this recreates the safety 

versus efficiency dilemma. While reduced speeds are necessary 

in some areas and conditions, speed reductions are an 

unnecessary restraint on productivity in other areas and 

circumstances. Speed control must balance productivity with 

safety to permit a vehicle to travel at the fastest safe speed for 

the specific location and conditions. Allowable speed must vary 

as the vehicle moves from location to location and as conditions 

change. This can be accomplished with technology that monitors 

the conditions, location, direction, and speed of the vehicle and 

of all the other vehicles and pedestrians in the area. 

The Intelligent Safety System 

The Intelligent Safety System (ISS) will include a direct means 

for alerting drivers and pedestrians when hazards exist and a 

direct means of automatically limiting speed. ISS will utilize 

data collected from on-board sensors and facility monitoring 

systems to: 

 Accurately track the location, direction, and speed of all 

vehicles; 

 Accurately track the location and movement of all 

pedestrians; 

 Know the status of each vehicle (driver ID, load, current 

task, impact events, etc.) 

Given an abundance of real-time data, ISS intelligence will 

predict collision hazards and initiate action to warn or eliminate 

hazards. ISS will have communication and control links with 

drivers for hazard alerting, with trucks for automatic speed 

limiting, and with facility safety systems for intersection control 

and other intelligent warning systems. 

Figure 3. A Basic Intelligent Safety System (ISS) 

309
Figure 3 illustrates a simplistic ISS. Safety zones are defined 

and configured in ISS software; for example, safe, caution, and 

danger zones are designated for intersections with the green, 

yellow, and red shading in the illustration. As truck A 

approaches the intersection from a safe zone ISS, which controls 

the intersection traffic lights, gives a green light to Truck A and 

to pedestrians, even though Truck B and Truck C are in the 

intersection’s caution zones. With knowledge of the exact 

location, direction of travel, and speed of vehicles and 

pedestrians, ISS determines that Truck C is moving away from 

the intersection and presents no danger to vehicles A or B, or to 

the pedestrians. Likewise, Truck B’s orientation and speed 

indicate that it is putting away a load and not entering the 

intersection. 

An unintelligent intersection safety signal system would 

illuminate caution or stop signals for Truck A based on the 

proximity of Truck B and Truck C. Importantly, ISS preserves 

productivity - Truck A will not be slowed - while establishing a 

safer workplace. 

Figure 4 illustrates an example of collision prediction based on a 

more sophisticated ISS. Four utility vehicles are tracked secondby-second by IPS while ISS makes instantaneous calculations of 

collision potential. Vehicle location, speed and heading are fed 

into the ISS “engine”, which uses the operating parameters for 

each vehicle and the facility layout to predict collision 

probabilities. When warranted, control or warning signals are 

transmitted to an AGV to slow or stop the vehicle, or to a 

forklift operator for driver action. 

Safety Wins

Perhaps highway safety will someday become as safe as the ISS 

environment. Technology will continue to offer the ability to 

reduce or eliminate vehicle stability accidents, multiple vehicle 

collisions, and vehicle/pedestrian collisions, using new sensors 

and intelligent, automated safety solutions. As this progress is 

increasingly recognized by industry leaders, regulatory agencies, 

and safety researchers, it is expected that industrial vehicle 

manufacturers will incorporate the new safety technology into 

their products, and that market-leading companies will take 

advantage of the new capabilities. 

Everyone is in favor of increased safety, especially when it 

enhances productivity and the bottom line. Technology cannot 

replace the basics of safety – strong management commitment, 

good operations design, training, and accountability. But when 

safety is introduced into a safety conscious culture, technology 

will provide the tools for transforming safety into a competitive 

advantage. 

Figure 4. A Sophisticated Intelligent Safety System (ISS) for Inter-Vehicular Collision Prediction

310
_______________________ 

1 Rite-Hite (Milwaukee, WI) RHH dock leveler with the Safe-T-LipTM barrier; www.ritehite.com 2 Wickham (Victoria, Australia) Fork-AlertTM; www.wickhamplastics.com 3

 Industrial Forklift Trucks – Dynamic Stability and the Design of Safe Logistics, 2003; Accident Research Centre at Monash 

University, Australia. 4 ProxAlert (Phoenix, AZ); www.proxalert.com 5 Brickstream (Atlanta, GA); www.brickstream.com 6 Door-Man (Auburn Hills, MI); www.door-man.com 7 Alert Safety Products (Cincinnati, OH); www.alertsafetyproducts.com 8 SICK GmbH (Waldkirch, Germany); www.sick.com 9 AeroScout (Redwood City, CA); www.aeroscout.com 10 WhereNet (Oakland, CA); http://zes.zebra.com 11 Ekahau (Reston, VA); www.ekahau.com 12 Sky-Trax Inc. (New Castle, DE); www.sky-trax.com

---- < End > ---- 

Contact 

For more information or discussion on this topic, contact Richard Ungerbuehler by email at RHU@sky-trax.com, or visit 

www.sky-trax.com. 

Credits 

Some content was originally published by Larry Mahan in an article for Industrial Utility Vehicle magazine. The author wishes to thank 

Mr. Mahan and the staff of Sky-Trax for their help in preparing this paper and the accompanying slide presentation. 

Abstract 

Everyone wants to work in a safe environment where no hazards exist, close calls never happen, and personal injuries and property 

damage simply do not occur. As companies strive to maintain safe operations by establishing safe practices, and using accepted safety 

controls and equipment as necessary for their industry, new technology is helping to bring a new level of safety to facilities where driven 

and automated vehicles operate. 

Author’s Bio 

Richard H. Ungerbuehler is a founding partner and Chief Engineer of Sky-Trax Inc. Rich enjoyed a twenty five year career with DuPont 

where he was involved with the development of specialized product inspection instrumentation and automation systems. He led the

internationally recognized DuPont DART team, which coordinated data collection applications throughout the company. Rich represented 

DuPont on international bar code standards organizations, and participated in the authoring of ANSI MH10.8 – the American National 

Standard for Bar Code Shipping Labels. He co-founded Sky-Trax with Larry Mahan and David Emanuel in 2004 and serves today as Chief 

Engineer. Rich received a Bachelor of Science degree in Electronic Engineering Technology from Spring Garden College and he holds 

various meritorious awards and certifications from the United States Army Signal Corps.

311
Performance Measurements Towards 

Improved Manufacturing Vehicle Safety 

Roger Bostelman 

NIST 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

(301) 975-3426 

roger.bostelman@nist.gov 

Will Shackleford 

NIST 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

(301) 975-4286 

shackle@nist.gov 

ABSTRACT 

In this paper, we describe the current 2D (two dimensional) 

sensor used for industrial vehicles and ideal sensor 

configurations for mounting 3D imagers on manufacturing 

vehicles in an attempt to make them safer. In a search for the 

ideal sensor configuration, three experiments were performed 

using an advanced 3D imager and a color camera. The 

experiments are intended to be useful to the standards 

community and manned and unmanned forklift and automated 

guided vehicle industries. The imager that was used was a 3D 

Flash LIDAR (Light Detection and Ranging) camera with 7.5 m 

range and rapid detection. It was selected because it shows 

promise for use on forklifts and other industrial vehicles. 

Experiments included: 1) detection of standard sized obstacles, 

2) detection of obstacles with highly reflective surfaces within 

detection range, and 3) detection of forklift tines above the floor. 

We briefly describe these experiments and reference their 

detailed reports. 

Categories and Subject Descriptors 

I2.10 [Vision and Scene Understanding]: 3D/stereo scene 

analysis 

General Terms 

Performance, Design, Experimentation, Standardization 

Keywords 

3D Flash LIDAR, Forklifts, Powered Industrial Trucks, 

Automated Guided Vehicles (AGV), ANSI/ITSDF B56.5 

1. INTRODUCTION 

The National Institute of Standards and Technology (NIST), 

Intelligent Systems Division (ISD) has been performing 

measurements to be used as background information for 

advancing standards and for the manned and unmanned vehicle 

and sensors industries in an attempt to make forklifts and other 

vehicles safer. The Occupational Safety and Health 

Administration states: [1] “Each year, tens of thousands of 

injuries related to powered industrial trucks (PIT), or forklifts, 

occur in US workplaces. Most incidents also involve property 

damage, including damage to overhead sprinklers, racking, 

pipes, walls, and machinery. Unfortunately, most employee 

injuries and property damage can be attributed to lack of safe 

operating procedures, lack of safety-rule enforcement, and 

insufficient or inadequate training.” The statement suggests the 

need for improving driver’s knowledge, although safer vehicles 

can also help. Obstacle detection sensing that completely 

surrounds the vehicle could augment the driver’s or autonomous 

vehicle’s environmental awareness. Driver alerts and/or 

autonomous slow or stop vehicle operations are then possible 

based on this sensor information and, therefore, could provide 

safer vehicles. 

NIST ISD has been working for several years with the Industrial 

Truck Standards Development Foundation (ITSDF) which 

manages “ITSDF B56.5 Safety Standard for Guided Industrial 

Vehicles and Automated Functions Of Manned Industrial 

Vehicles” [2] as approved by the American National Standards 

Institute (ANSI). NIST’s involvement with the B56.5 standard 

includes performance measurements of advanced non-contact 

sensors for automated guided vehicles (AGVs), and has led to 

proposed changes to the standard. AGVs are typically 

programmed to follow prescribed paths but still need sensors to 

detect obstacles such as closed doors, equipment, personnel or 

material left temporarily in the vehicles’ paths. Currently, they 

rely heavily on 2D line scanners, while some are equipped with 

a physical bumper as the final backup to stop the vehicle. The 

2D line scanners work well with ground-based vertical obstacles 

but it takes many sensors to completely protect against 

overhanging obstacles and even then they do not scan the full 

volume of space through which the vehicle travels. Figure 1 

shows how 2D line scanning sensors are typically oriented on 

vehicles to aid detection of overhanging obstacles. The lower 

triangular detection region near the vehicle would be undetected 

at vehicle startup. The longer upper non-detect area is never 

detected and the sensors themselves may be struck by 

overhanging obstacles, like a crane hook. Side and top sensors 

in the figure could themselves become obstacles if mounted as 

shown. The red dotted lines depict sensor scan-lines. From the 

side view, it is clear that 2D sensors of this type may not detect 

obstacles that are not within the sensor scan-line or have already 

passed through the scan-line. As shown, the scanner can miss 

an overhanging obstacle completely when directly in front of 

This paper is authored by employees of the United States 

Government and is in the public domain. 

PerMIS'09, September 21-23, 2009, Gaithersburg, MD, USA. 

ACM 978-1-60558-747-9/09/09

312
the sensor housing, or if the obstacle is detected, it may be too 

late to stop or slow the vehicle. 

 

Figure 1 – Front (top) and side (bottom) views of typical 2D line 

scanning LIDAR sensors mounting configuration on AGV’s and the 

areas they detect. The upper-right portion of the bottom view shows a 

crane hook that would never be detected. 

3D Flash LIDAR technology has led to a relatively new class of 

range imaging sensors with the potential to scan 3D volumes 

faster than the 2D scanning systems. Capabilities of this type of 

sensor could dramatically change the way sensors are used on 

manufacturing vehicles. This concept will be further explained 

in Section 2. 

To evaluate this class of sensors, a consortium of AGV vendors 

was formed that took preliminary data with several flash range 

imaging systems and selected one for further development and 

investigation. The one selected by the AGV consortium is the 

sensor used for this work. The data collection system was 

integrated with a NIST-developed vehicle control system, the 

Mobility Open Architecture Simulation and Tools (MOAST) 

framework. [3] This allowed the system to collect data while the 

vehicle was driving autonomously. 

Three experiments were completed providing background data 

towards illustrating the usefulness of advanced 3D Flash LIDAR 

cameras on forklifts and PITs. Experiments included: 1) 

detection of obstacles specified in the ITDSF/ANSI standard, 2) 

detection of obstacles while highly reflective surfaces are also 

within the camera’s field of view, and 3) detection of forklift 

tines above the floor. Each of these experiments is briefly 

explained in Section 3. 

2. IDEAL 3D VEHICLE SENSING 

CONCEPT 

Ideally, based on proposed B56.5 standard changes, the volume 

that completely surrounds the manned or unmanned vehicle 

should be sensed to ensure a safe manufacturing environment. 

Ideal 3D vehicle sensing volume concepts are depicted in the 

graphics shown in Figures 2 and 3. Figure 2 shows top and 

front views of the ideal 3D sensing volumes for an AGV in 

green completely surrounding the vehicle, extending beyond the 

vehicle to include a safe stopping distance. Figure 3 depicts the 

concept of using multiple 3D imaging sensors to measure the 

volume surrounding an AGV (Figure 3, top) and a forklift 

(Figure 3, bottom). Each orange triangle represents a sensor’s 

field of view (FOV). The bottom graphic shows a forklift 

carrying a double-height load and a movable arm (green) that 

carries a 3D imager to look over, and in front of, the load. Also, 

especially for high reach forklifts, a sensor is needed on the 

mast top to sense overhead obstacles such as ceilings. 

No detect 

region until 

scan beam 

is reached 

and at 

vehicle 

startup 

maximum 

vehicle 

stopping 

distance 

maximum 

vehicle 

stopping 

distance 

Figure 2 – Top view (top) and front view (bottom) of the ideal 3D 

sensing volume for AGVs. 

3. 3D IMAGING EXPERIMENTS 

As a preliminary to the implementation of the 3D sensing 

scenarios posed in the previous section, performance 

measurements are required of advanced 3D imagers. 3D Flash 

LIDAR, a time-of-flight range measurement sensor, is still fairly 

new to the vehicle industry and requires further experimentation 

in real or simulated manufacturing environments to ensure safe 

vehicle operations. The sensor used for these experiments 

measures range to 7.5 m for each of its (176 x 144) pixels with 

an internal modulating frequency of 20 MHz. The distance of 

an object is measured by determining the phase-shift between a 

continuously modulated sine wave that is emitted and the one 

that is received after having been reflected by the measured 

scene. The Flash LIDAR sensor that was tested emits a short 

pulse of light at 870 nm into the environment and senses 

returned illumination within its 0.26 rad x 0.22 rad (47.5o x 

39.6o) field of view (FOV). The following subsections briefly 

discuss 3D Flash LIDAR experiments performed at NIST for 

use as safety sensors on manufacturing vehicles. 

313
3.1 Detection of Standard Obstacles 

NIST has recently performed measurements with results [4] to 

be used as background information towards changes to the 

ITSDF B56.5 Safety Standard with regard to non-contact 

sensors detecting standard test pieces. The B56.5 standard 

defines safety requirements relating to the elements of design, 

operation, and maintenance of powered, not mechanically 

restrained, unmanned automatic guided industrial vehicles and 

automated functions of manned industrial vehicles. 

 

Figure 3 – (top) Top view of an AGV with multiple 3D imaging sensors 

surrounding the vehicle and measuring to and beyond the ideal sensing 

region, (bottom) side view of a forklift with multiple 3D imagers 

showing sensing volumes required to reach the ideal sensing capability. 

The forklift is shown carrying a double-height palletized load and the 

green arm carries a 3D imager to look over, and in front of, the load. 

Optical and acoustic sensors were tested in these experiments on 

B56.5 standard test piece sizes, as well as a large flat metal 

plate, cinder block, and other test pieces and test piece 

coverings. Over 120 data sets from 21 different tests using a 

variety of test piece configurations, coverings, layouts, and 

sensors (sonar, color camera, 2D scanning LADAR, and 3D 

Flash LIDAR) were collected in a NIST laboratory. For this 

paper, our focus is mainly on the optical, 3D Flash LIDAR 

sensor experiments and results. 

The ITSDF B56.5-2005 Safety Standard section on non-contact 

sensing devices states that if the sensor is used as the primary 

emergency device, the sensor shall be fail-safe in its operation 

and mounting and shall stop the vehicle travel prior to contact 

between the vehicle structure and the object detected. Test 

pieces are to be detected in the main direction of travel and are 

to be: a 600 mm cylinder with a 200 mm diameter lying at any 

angle to, and anywhere on, the path of the vehicle and a second, 

400 mm cylinder with a 70 mm diameter set vertically anywhere 

fully within the path of the vehicle. The test pieces described in 

the standard are of specific size, originally based on the British 

EN1525 standard. [5] Because the standard is based on contact 

sensors, however, there are currently no restrictions on test 

piece coverings. Requirements for covering test pieces are 

necessary because non-contact sensors may react differently to 

various materials to be detected. A sensor may or may not detect 

a particular material and a failure to detect could cause a safety 

hazard. An example might be that a person wearing dark 

clothes may not be detected by some optical sensors. Also, only 

cylindrical test pieces are listed in the standard and perhaps 

provide better performance than flat test pieces might when 

positioned at specific angles with respect to the sensors. The 

experiments were designed to evaluate these additional 

problems, with the goal of suggesting new language to add to 

the standard. The experimental setup for each test included 

positioning the test piece (see Figure 4) at approximately 1 m, 2 

m, 3 m, and 4 m distances away from the sensors as data was 

collected. 3D imager FOV 

Movable arm 

with sensor end 

Forklift 

(a) Horizontal Cylinders: 200 mm diameter x 600 mm long 

(b) Vertical Cylinders: 70 mm diameter x 400 mm tall 

(c) Suggested Flat Plate: 500 mm x 500 mm 

Figure 4 – Standard and suggested test pieces for the B56.5 standard 

measured by a (left) color camera, (middle) 2D scanning LADAR, and 

(right) 3D Flash LIDAR. The suggested flat plate images are marked 

with a rectangle showing the 500 mm square suggested test piece. 

Various coverings over the test pieces, including cotton cloth, 

paint, known density color patches, and clear glass were used to 

314
evaluate how well the sensors could detect the pieces under 

different conditions. The coverings used were representative of 

different colored clothing and of manufactured or other 

industrial materials that may be near vehicles. Figure 4 shows 

the three test pieces suggested for the B56.5 standard and a 

snapshot of data collected by the 2D scanning LADAR and the 

3D Flash LIDAR. 

Figure 4a shows two cylinders, the left one is painted with flat 

black paint and the right one is partially covered with three 

reflectance paper patches (6 % (density of 1.22D-black), 50 % 

(density of 0.30D-gray) and white). Figure 4b shows in the 

color camera image the right most cylinder covered with the 6% 

density (black) patch. Figure 4c shows a suggested flat plate 

test piece to be added to the B56.5 standard. 

Overall results from using the 3D Flash LIDAR sensor included 

the following: 

 The sensors used in the tests show a noticeable difference 

between highly reflective versus relatively low reflective 

targets. 

 In a horizontal cylinder test, two cylinders placed side by 

side were difficult to detect at 2 m range and undetected 

beyond 2 m with the flat black painted cardboard cylinder 

being much more difficult to detect than the metal cylinder. 

The cylinder appears to blend in with the floor (see Figure 

5 range data). The cylinders are detected only when they 

are in front of a background obstacle or wall. 

 In a flat plate detection test, the 1 m, 0o (perpendicular to 

the sensor) test produced poor results. The obstacles at this 

distance and angle were difficult to discover by the 

researcher in the range image, although the obstacles were 

detected in the intensity image. However, at 1m and tilted 

at a 45o angle with either the horizontal or vertical axis, the 

plate was detected in the range and intensity data. Beyond 

1m, all flat plates were detected except when the plates 

were covered with reflective foil. There were no problems 

detecting painted or unpainted cinder blocks. 

 In the flat plate glass test, the glass was never detected 

because the sensor saw through the glass. The frame 

holding the glass was detected. 

 

Figure 5 – (top) Close-up of the test piece covered with reflectance 

patches. (bottom) 3D Flash LIDAR intensity (left) and range (right) data 

from the horizontal cylinder. 

3.2 Effects of Highly Reflective or 

Emissive Surfaces 

Experiments were conducted to better understand how 3D Flash 

LIDAR reacts to highly reflective objects in their fields of view. 

Such objects are typical of AGV laser positioning system 

reflectors. Highly reflective surfaces may cause distortions in 

the data which could affect how the vehicle sensors perceive 

their surroundings, potentially causing them to miss obstacles in 

the vehicle path. Here we briefly explain the experiment and 

results. Full details can be reviewed in [6]. Two experiments 

were completed: 1) highly reflective object test and 2) sensor 

passing by a light source. 

The 3D Flash LIDAR was fixed to the front edge of a small 

table on wheels at a height of approximately 1 m above the 

floor. We consulted with an AGV manufacturer to establish the 

typical size and mounting height at which AGV positioning 

reflectors (cylinders) were typically mounted. We set up a 0.75 

m x 0.1 m diameter reflector so that the sensor beam hits the 

center of the reflector at 2.5 m above the floor, as well as at 2 m 

(called 0 height), 1.5 m (called -0.5 m height) and 1 m (called 

-1 m height) above the floor and at ranges of 7 m to 3.5 m from 

the 3D sensor. The bottom of the reflector cylinder was placed 

on these surface heights. Figure 6 (top) shows a top view 

drawing of the experimental layout. Figure 6 (middle) shows 

the experimental setup showing several obstacles in the sensor’s 

FOV, including a reflector brightly illuminated by the camera’s 

flash, and the data capture computer laptop (lower right). 

Figure 6 (bottom) shows the data captured from the scene using 

a 3D Flash LIDAR. The yellow arrows show the chairs in the 

left and right in the photo and range data. Note that the side 

view (bottom-right) of the data is skewed (i.e., vertical surfaces 

appear angled back) as a result of the highly reflective surface 

from the reflector. 

Results show that when the 3D Flash LIDAR was mounted low 

so as to not receive returns from highly reflective surfaces, the 

received data was not distorted. When the reflector was 

detected within the scene, the image was distorted in that region. 

Masking out upper rows of the sensor’s light emitting diodes 

helped to remove some distortion of the scene for high mounted 

reflectors. Further, we determined that two options are possible 

to alleviate the problem: (a) algorithm A, to adjust the threshold 

of the 3D imaging sensor and/or (b) algorithm B to remove the 

high intensity measurements – this process is done off-line or 

post-processed. For algorithm A, the 3D Flash LIDAR can be 

adjusted to remove high intensity data directly from the received 

camera data. We added a simple software slide “adjuster” tool 

for simplicity. This can be run as a constant image adjuster in 

real time. 

Thresholding is performed within the camera so the host 

computer is not burdened by this extra task. For the second 

option, we developed an algorithm built into our display tool 

that finds and counts reflectors in the scene and masks out a 

variable-size region around the reflector based on the area of 

high intensity returns. Both the threshold and the masking 

algorithms can run in real-time. Additionally, algorithm B can 

be run as image post-processing because the thresholding is 

carried out in the sensor. Neither algorithm corrects the distorted 

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data. They only detect the region of the image where distortion 

is likely and data should not be used. Whether this is useful 

depends on factors outside the sensor processing system. For a 

particular application it may be acceptable for the AGV to run 

slowly enough while near a reflector to rely only on a physical 

bumper or other safety sensor instead of a 3D flash imager. 

Figure 7 shows color camera (left) and 3D Flash LIDAR 

intensity and range data (right) of (a) no reflector in the scene, 

(b) a reflector laying on the chair, (c) a reflector in the chair 

where the threshold algorithm A has removed it from the data, 

(d) a reflector in the chair and the masking algorithm B has 

removed the region of high reflectivity. 

 

 

Figure 6: (top) Graphic showing the top view of the experimental setup 

of the highly reflective surface test; (middle) photo of the experimental 

set-up showing several obstacles in sensor view, a reflector illuminated 

by the camera’s flash, and the data capture computer laptop (lower right 

in the photo); (bottom-left) captured data showing the front view and 

(bottom-right) side view of the scene using a 3D Flash LIDAR sensor. 

The yellow arrows point to the same chairs and reflector in the photo and 

data. 

An engineering solution to the reflector problem would be to 

mount the 3D sensor so that it is less likely to see the reflectors 

as shown in Figure 8. The floor could be flagged so that the 

sensor does not detect it as an obstacle, given the known sensor 

height. Unfortunately, this would leave the AGV laser 

positioning system’s sensor above the field of view of the 

obstacle detection sensor, and thus unprotected by the sensor. 

(a) reflector locations 

(b) 

(c) 

(d) 

Figure 7: Color camera (left) and 3D Flash LIDAR intensity and range 

data (right) of (a) no reflector in the scene, (b) a reflector lying on the 

chair, (c) a reflector on the chair where the threshold algorithm A has 

removed the highly reflective region from the data, (d) a reflector in the 

chair where the masking algorithm B has removed the region of high 

reflectivity. 

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The results of the reflectance experiments were: 

 Using sensor software drivers programmed to automatically threshold out highly reflective objects could improve 

bad range data issues. 

 Masking the upper sensor LED’s removed some image 

distortion. A better solution is to use a non-reflective surface just above the camera lens to block the upper LED’s. 

And perhaps even better is not to use the data by masking it 

using software. 

 Mounting the 3D sensor specifically to detect obstacles 

below absolute positioning reflector heights could eliminate or greatly reduce position sensor reflector interference 

with the 3D sensor. 

Figure 8: Possible mounting scheme for the 3D Flash LIDAR sensor so 

as to not detect absolute positioning system reflectors. 

For a second part of this experiment, we used a second 3D Flash 

LIDAR with a similar light source and pointed it directly at the 

first LIDAR. This experiment provided information about how 

well the sensor would function when a passing vehicle had 

similar sensors onboard. The results were that the sensor 

demonstrated no visually detectable change in range 

measurements when an LED array light source from a similar 

sensor passed by. This is probably due to the extreme 

unlikelihood that one sensor would send out its illumination 

flash at the same time as the other sensor was in receive mode. 

3.3 Detection of Forklift Tines 

As suggested by an AGV manufacturer, NIST recently 

measured forklift tines using the 3D Flash LIDAR. Full details 

of this experiment, including time results, can be reviewed in 

[7]. The issue is that forklift tines and other obstacles can 

overhang the path of automated guided vehicles or other 

forklifts and go undetected when using only a 2D line scanning 

LADAR mounted to the vehicle so that the scan line is just 

above and parallel to the floor. We overlaid the 3D Flash 

LIDAR data on an image from a color camera to provide a clear 

view of the tines or other obstacles detected. All measurements 

were taken dynamically while moving the sensor towards the 

forklift tines. 

The 3D Flash LIDAR sensor and a color camera were mounted 

together with the camera lens just behind the flash sensor (see 

Figure 9. The camera FOV is slightly larger than that of the 3D 

Flash LIDAR. The two sensors were angled so that the 3D Flash 

LIDAR sensor detected the floor at a maximum distance of 6 m 

in front of the vehicle. This setting allowed a known sensor-tofloor distance to be used in the data processing algorithm, 

eliminated detection of the highly-reflective objects above the 

FOV, and eliminated detection of the cluttered background. 

The forklift tines were set at heights of 0.25 m and 0.5 m. No 

preparation of the tines (e.g., paint, sand, etc.) was done. The 

cart was pushed towards the forklift tines at approximately 0.09 

m/sec during most data captures and for one experiment the cart 

was pushed at 0.53 m/s. Figure 10 shows the range, intensity, 

and overlaid obstacle detection on a color image when viewing 

the forklift tines from the front and side. 

After reviewing the results from the unprepared tines and floor 

experiments, the researchers decided to conduct three additional 

tests, including: paint the tines with fluorescent paint, cover the 

floor and combine the painted tines with the covered floor to see 

what improvements, if any, would result. For each of the 

additional tests, the above experimental procedure was repeated. 

3D Flash LIDAR 

and color camera 3D flash LIDAR 

 

Cart

cable 

cover 

Figure 9: Experimental setup of a cart with 3D Flash LIDAR and color 

camera sensors (background) and raised forklift tines (foreground). The 

forklift tines were measured from the side (left) and from the front 

(right). The metal cable cover appears similar to the fork tines. 

Painted Tines: Only the sides of the tines were painted. The 

reason being: 1) this surface is the smallest, yet still visible to 

the sensor, and 2) this surface is the least likely to have paint 

removed when the tines are in use (i.e., wear against pallets is 

minimal for this surface). Only slight paint overspray covered 

the top surface of the tines. 

Covered Floor: Another experiment included covering the floor 

with either white poster boards or with gray paint. This created 

a bright, uniform surface that was less detectable than the 

unprepared floor. 

Combined Painted Tines with Covered Floor: A third 

experiment included both painted tines and covered floor. 

Figure 11 shows collected data from the 3D imager overlaid 

onto a photo of painted tines over painted floor where the floor 

in the foreground remains unpainted. As shown in the figure, 

there is little difference in this case between the painted and 

unpainted floors creating noise displayed as obstacles. 

However, the forklift and its tines were clearly detected as 

obstacles. 

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Each data set (video) was reviewed and a human interpretation 

that the tines were detected required that a significant number of 

3D Flash LIDAR pixels be clustered on the forklift tines. The 

percentage of time and the distances from the sensor at which 

the necessary pixel clustering on the tines appeared was noted. 

Table 1 shows the percentage detection distance moving the 

sensor from its start position to the tines, at 1 m and at 0.5 m for 

both front and side views of the tines. 

 

(a) 

 (b) 

Figure 10: Data from the 3D Flash LIDAR showing range and intensity 

and obstacle detection overlaid on a color camera image of forklift tines 

as viewed from the (a) side and (b) front. 

A key issue is that the 3D Flash LIDAR sensor processing 

program uses a height threshold to remove pixels beneath the 

forklift tines. Without this threshold, data from the floor and the 

tines may not appear different, and the tines would not be 

detected even by a human observer. Pixels with heights below 4 

cm were removed, leading to two false negative results (Side 1, 

4 cm and Front, 4 cm). It may be preferable to develop an 

adaptive filtering algorithm that would allow the threshold to be 

lowered. 

In some data sets, we saw ‘bleeding’ of obstacle detect data 

between, behind and in front of the tines. Figure 12 shows the 

tines being detected in the intensity and range images and also 

shows ‘bleeding’ of data perhaps from the left wall onto the 

floor in the bottom image. The painted tines joined with the 

floor covered with white poster boards where another 

unexpected phenomenon was detected as the front and rear tines 

were combined as if they were one large obstacle. Since the 

tines were clearly detected as shown in the intensity and range 

images, during our evaluation of fork tine detection, we 

determined that this phenomenon did not change our forklift 

tines detection results. Therefore, our results show that during 

this test the 3D Flash LIDAR did detect the front tine. 

Table 1: Percentage of Successful Forklift Tines Detection 

Detect Percentage 

tine view (1 

=from right, 2 

= from left), 

tine height 

above the floor 

at the full 

distance 

along the last 1 

m 

along the last 

0.5 m 

Side 1, 4 cm 0 % 0 % 0 % 

Side 2, 4 cm 17 % 57 % 100 % 

Front, 4 cm 0 % 0 % 0 % 

Side 1, 8 cm 50 % 100 % 100 % 

Side 2, 8 cm 50 % 100 % 100 % 

Front, 8 cm 27 % 85 % 100 % 

cable 

cover 

Obstacle detection 

overlaid on a color 

camera image 

A snapshot of data from the additional tests is shown in Figure 

11 showing painted and unpainted floors and painted tines. 

Painted gray 

floor 

Painted fork 

tines 

Painted gray 

floor 

Unprepared 

floor cable 

cover 

Obstacle detection 

overlaid on a color 

camera image Figure 11: 3D Flash LIDAR sensor data overlaid onto a color camera 

image of painted forklift tines above a painted floor and beside 

unpainted floors. 

 We also showed that a 3D imager can be adjusted so as not to 

flag obstacles outside of a chosen area. This is useful for when 

the sensor is attached to a vehicle and the vehicle is driving 

along a narrow path and/or approaches a turn and the wall or 

obstacle in front of the vehicle prior to the turn is detected as an 

obstacle and in turn, stops or slows the vehicle. 

Figure 13 shows blue areas on the right and left sides of the 

vehicle path that have been excluded from processing. 

Although the forklift tines appear in the image to be beyond the 

right edge (threshold) between light and dark colors, they are 

not. Some of the tines are shown as blue (grayscale black) and 

some are red (grayscale gray). The exclusion regions can be set 

for any side or range from the sensor and can be varied for 

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complex paths and volumes if needed. Slow or stop regions can 

be set simultaneously in the same manner. 

Figure 12 – Range, intensity (top) and obstacle detection overlaid on a 

color camera image (bottom) of forklift tines. “Bleeding” detect data 

phenomenon is shown in the bottom image of detected forklift tines 

above a uniform poster-board floor covering. 

Figure 13 – Data from the 3D imager overlaid onto a color camera image 

after regions have been excluded from processing (blue areas) using an 

algorithm that flags when obstacles are outside the vehicle’s path. 

Results of the tine detection experiments, as detailed in [5], 

were as follows: 

 Within 1 m from the sensor to the tines provides much 

more robust tine detection than longer ranges. 

 The combination of sensors close to the tines and a high 

threshold height above the floor provides excellent tine 

detection. 

 Higher tines are detected more often than lower tines due 

to the use of the height threshold. 

 When the tines sides were painted with reflective yellow 

paint and the floor was covered with white poster board, 

the sensor performed very well. 

 Slightly lower performance results were found when the 

floor was painted with light gray paint. 

 Other color floor paints may provide similar results. 

However, high contrast between the tine and floor paint 

colors is expected to provide the best results. 

4. CONCLUSIONS 

In this paper, current and proposed ideal non-contact sensor 

configurations for manufacturing AGVs and forklift vehicles 

were presented. These concepts showed the need for 

performance measurements of advanced 3D imagers. NIST 

conducted experiments using a 3D Flash LIDAR sensor and a 

color camera on standard sized test pieces, coated and uncoated 

with materials and standard colors. The resulting measurements 

were used as background information to recommend changes to 

the ITSDF B56.5 standard with regard to non-contact sensors 

detecting standard test pieces. Before these experiments, only 

two cylindrical test pieces were considered in this standard. 

Experimental results determined that a flat test piece should be 

added. We determined that the 3D sensor used is not a viable 

safety sensor for vehicles since obstacles near highly reflective 

surfaces, returned skewed data, ‘bleeding data’ occurred, and 

very ‘noisy’ data was returned when viewing non-uniform floor 

surfaces. Suggestions for sensor improvements, as we 

determined from our experiments, may be to support the sensor 

with robust data processing algorithms that detect highly 

reflective surfaces, and turn off or block sensor LED’s. We 

found that the light source from another 3D imager had little 

effect on the 3D sensor data. The percentage of tine detection 

shown in Table 1 provides a measure of the frequency with 

which the tines were detected. It shows that higher percentages 

of detection occur as range decreases. The snapshots and the 

percentage of detected tines data show that the 3D imager is not 

robust enough to detect black forklift tines 100 % of the time. 

However, the detection improves when the tines were painted 

with fluorescent paint and the floor was painted. 

Obstacle detection 

overlaid on a color 

camera image 

5. REFERENCES 

[1] Occupational Safety and Health Administration website, 

http://www.osha.gov/SLTC/poweredindustrialtrucks/ 

[2] Industrial Truck Standards Development Foundation, 

(2005). ITSDF B56.5 Safety Standard for Guided Industrial 

Vehicles and Automated Functions of Manned Industrial 

Vehicles, http://www.itsdf.org. 

[3] Stephen Balakirsky, Fred Proctor, Chris Scrapper, Tom 

Kramer. An Integrated Control and Simulation 

Environment for Mobile Robot Software Development. 8-

3-2008. New York. Proceedings of the ASME Computers 

and Information in Engineering Conference. 8-3-2008. 

[4] Roger Bostelman, Will Shackleford, Time of Flight 

Sensors Experiments Towards Vehicle Safety Standard 

Advancements, submitted to the Computer Vision and 

Image Understanding special issue on Time of Flight 

Sensors, April 2009. 

[5] British Standard Safety of Industrial Trucks (1998). 

Driverless Trucks and their Systems. Technical Report BS 

EN 1525. 

[6] Roger Bostelman, Will Shackleford, Test Report on Highly 

Reflective Objects Near the SR3000 Sensor, NIST Internal 

Report to Consortium CRADA Partners, February 2008. 

[7] Roger Bostelman, Will Shackleford, Test Report of 

Performance Measurements of a 3D Imager and Color 

Camera Viewing Forklift Tines, NIST Internal Report to 

Consortium CRADA Partners, September 24, 2008. 

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White Paper 

Towards Improved Forklift Safety 

Roger Bostelman, Manager 

Mobility and Manipulation Project 

Intelligent Systems Division 

Manufacturing Engineering Laboratory 

National Institute of Standards and Technology 

Department of Commerce 

Gaithersburg, MD 20899 

October 16, 2009 

1 Introduction 

There are over 1 million forklifts in operation in the United States with an estimated 2 million operators (6 million 

including part time operators) [Chugh] and nearly 2 000 automated guided vehicles (AGVs) in use in the US. 

Forklifts are a necessary piece of material handling equipment for many industries. If used properly, they can reduce 

employee injuries. Unfortunately, they can also pose some safety risks to drivers, pedestrians, and other equipment 

and goods. This White Paper summarizes presentations and discussions from the PerMIS 2009 Special Session on 

“Performance Measurements to Improve Forklift Safety.” Papers presented during this special session are listed in 

the references section. 

Attendees of this special session included: 

Attendee Organization 

Roger Bostelman, NIST 

Mark Austin OHSA – Baltimore/Washington Office 

Benny Forsman Danaher Motion/Kollmorgen 

Richard Ungerbuehler SkyTrax, Inc. 

Mike Shneier NIST 

Will Shackleford NIST 

David McCartney US Army Aberdeen Test Center 

Luke Fletcher Massachusetts Institute of Technology 

Garrett Place IFM Efector 

Steve Ruth IFM Efector 

Tim Meyers Toyota Material Handling 

This paper is structured to first summarize information from special session papers presenting statistics and issues 

which define the forklift safety challenge. Following are remedies presented and discussed during the session that 

can improve forklift safety. Last are discussions and recommendations to further improve forklift safety from the 

final discussion period of the special session. Some excerpts are copied directly from the papers and presentations 

from this session. 

2 Forklift Safety Statistics and Issues 

o OSHA estimates that there are 110 000 accidents each year. 

o $135 000 000 immediate costs are incurred due to forklift accidents 

o Each year, an additional 94 750 injuries related to forklift accidents are reported 

o Approximately every 3 days, someone in the US is killed in a forklift related accident 

o Approximately 31 600 employees suffer some type of injury. 

o Losses affect employees through physical and mental suffering. 

o Almost 80 % of forklift accidents involve a pedestrian 

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o 18.8 % of forklift accidents occur when a forklift strikes a pedestrian 

o One in six of all workplace fatalities in this country are forklift related 

o According to OSHA, approximately 70 % of all accidents reported could have been avoided with proper 

safety 

(some of these statistics are courtesy Bircher America, Inc.) 

Forklift operating environments include: pedestrians, blind spots, both indoor and outdoor use, narrow aisles, 

building columns, 24 hour per day operations, and can include tight turning radii. Pedestrians contribute to 

accidents since they sometimes don’t understand forklift stopping distances and try to “beat” forklifts. Many 

incidents involve limited driver field of view (FOV) issues where driver controls are mostly designed to drive facing 

the forks. This forces drivers to see through bars, chains and cables and at times causing their FOV to be completely 

blocked in the travel direction. Drivers are usually forced to sit facing towards the load, yet look backwards to 

drive. Researchers report that 75% of side tip-over’s occur when a forklift is empty, leading them to conclude that 

these incidents are due more to speeding than other causes. Losses that affect employers due to forklift accidents 

include damage to equipment and loss of productivity. Most lost work time reported in 2007 was due to fork truck 

accidents totaling over 11,040 which is: nearly two times higher than cases involving transportation and material 

moving, nearly 7 times more than production worker involvement, and over 8 times higher than office or 

administrative worker incidents. 

3 Current Remedies to Improve Forklift Safety 

Methods used to reduce forklift accidents include: driver training, safety procedures, equipment maintenance, 

restricted/designated areas of operation, and facility design. While these strategies will always be elements of 

workplace safety programs, collision statistics clearly indicate that training, signage, and floor markings for traffic 

control are not enough to assure a safe environment. Real-time monitoring and control can improve both safety and 

efficiency. 

There are a number of safety systems being researched or in use today. These safety systems are briefly mentioned 

here and are discussed in more details in [Ungerbeuhler]. Automatic barrier guards can be installed to prevent fork 

trucks from falling off a vacant receiving dock. These systems prevent forklifts from running off an open dock and 

can stop a 4500 kg (10 000 lb) forklift traveling at up to 0.8 m/s (4 mph). Warning lights can be installed at blind 

corners to warn of oncoming forklifts. Safety system designers now have new technologies to consider for hazard 

control, particularly for detecting collision and speeding hazards. For pedestrian detection, a prototype system 

employs a simple radio frequency (RF)-tag placed in safety vests worn by warehouse workers. An RF receiver was 

installed on each truck alerting drivers to the presence of any workers within the detection radius of the receiver. 

The researchers found this wearable RF tag prototype to be a low cost solution that they recommend be used along 

with other safety measures. One company places a prototype RF transceiver on each vehicle. A similar batterypowered portable transceiver is clipped onto any pedestrian entering the warehouse. The transceiver creates a virtual 

protection zone around the vehicle or person. When the zones intersect, the transceivers energize a warning signal 

for both the pedestrian and the vehicle operator. This approach is a viable solution for workers and pedestrians. 

Driven largely by the need for smart surveillance and security systems, image processing technology for detecting, 

identifying, and tracking people in video images is now used in commercial applications. One pedestrian tracking 

system analyzes the movement of customers in commercial buildings. Processing images from overhead cameras, 

the system determines the number of customers entering a store and the exact paths taken by customers shopping in 

the store. In retail and banking applications, the technology is used to track queues of customers and to signal when 

more check-out lanes need to be opened. While this technology has not yet been applied to collision-avoidance 

systems, it can be expected in the near future. 

Systems based on presence detection sensors indicate that a vehicle is within the detection distance or zone of the 

sensor. In most cases, there is some ability to configure or engineer the detection distance. Inductive or capacitive 

proximity sensors and photoelectric sensors, all of which are familiar to automation engineers, fall into this category. 

An invisible, infrared light beacon mounted on the top of the vehicle is detected by a receiver up to 25 m away and 

can trigger warning lights or audible alarms for pedestrians and other drivers. Microwave sensors work similarly and 

can shape the detection zone to match an area of interest. Some companies offer warehouse intersection warning 

products using microwave sensors. Four sensors and a warning light are hung above an intersection with microwave 

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sensors aimed in all four directions. A vehicle approaching the intersection is detected and triggers the appropriate 

warning light. 

Further complication exists when both automated guided vehicles (AGVs) and forklifts operate in the same space. 

One company provides accurate and reliable tracking of forklifts, AGVs and other industrial vehicles inside 

buildings in real time to an accuracy of 5 cm to 20 cm using onboard vehicle vision to view 2D barcodes mounted to 

the facility ceiling. Important to many safety applications, indoor position systems determine the instantaneous 

speed and orientation (heading or direction of travel) of each tracked vehicle. 

Several sensors, logistical aspects and tasks are needed to bridge between manned and driverless vehicles as shown 

in Figure 1. 

Figure 1 – Drawing showing sensors, logistical aspects and tasks needed to bridge between manned and driverless 

vehicles 

In facilities where autonomous vehicles are used, a different set of safety requirements exists. Autonomous vehicle 

control systems must assure that inter-vehicular collisions are prevented, and the vehicles must be equipped with 

safety devices to prevent collisions with people or equipment. The current ANSI/ITSDF B56.5 standard is being 

improved to include noncontact safety sensors that detect standard-sized objects with specific reflectivity in the path 

of automated and manned industrial vehicles with automated functions. Two dimensional (2D) laser distance and 

ranging (LADAR) sensors are currently being used on some forklifts to assist driver field of view and on many 

AGVs to detect obstacles in the vehicles’ paths. 2D LADAR measures range to obstacles along a plane. These 

sensors work well but are limited by their 2D measurement capabilities. Three dimensional (3D) imaging is needed 

for viewing overhanging obstacles in the vehicle path. 3D light detection and ranging (LIDAR) sensors are an 

upcoming sensor technology being studied and proposed for use on both forklifts and AGVs. Stereo vision is now 

in use on some AGVs to provide 3D viewing. 

4 Discussions and Recommendations to Further Improve Forklift Safety 

Discussions among the session attendees addressed manned forklifts and AGVs, as well as pedestrians near vehicles, 

where all three can occupy the same material handling environment. This section provides a summarized transcript 

of the discussion portion of the session called: “Recommendations Towards Next Generation Forklifts to be Safe” 

followed by group recommendations. Also listed are two additional recommendations supplied after the group 

discussion occurred. 

Group Discussion

The group discussion was spoken, recorded by a secretary and later summarized without regard to quoting 

individual participants. It was captured without attribution to encourage expression of opinions. NIST expresses no 

opinions within the following summarized transcript: 

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Every facility is dramatically different, but the same types of safety steps can still be taken. There is worry 

because of cost that the forklift industry will be forced to install scanners on forklifts. There are things that can 

be done today for using the intelligence of the onboard forklift controls more than how they’re currently being 

used. These things are not being done today because customers are not asking for them. The reason is because 

customers want their forklift drivers to be able to quickly operate forklifts without costing users additional 

money or training. Small progressive steps towards a safe forklift solution are suggested rather than a leap 

forward solution. 

The forklift industry is similar to the automotive industry, where the element not completely being controlled is 

the people around the vehicle. For example, how would a driver know where there is a pedestrian in a 

distribution facility when their view is blocked unless spotters are used? Also, how would the driver know what 

are pedestrian intentions in a facility? Some sensors to track people are very expensive. Should everyone wear 

a sensor like an RFID tag? If so, what happens when that person forgets their tag and then whose fault is it if 

there’s an accident? OSHA says it is the forklift driver’s fault. This points to the need for additional safety 

measures, such as removing pedestrians from the forklift environment or adding safety sensors or better driver 

FOV sensors to the forklift. 

Industry comparison of AGVs versus forklifts, when considering their relative industry sizes, points to AGVs as 

being safer. An AGV may be too expensive to implement in a factory versus a forklift although there is a need 

point of affordable innovations. There is a need for the ability to track both pedestrians and vehicles. The 

challenge with the AGV market is the cost and the safety. 2D LADAR scanners are a great product but very 

costly to implement to view overhanging obstacles and to completely improve the drivers FOV. The issue is 

cost versus safety. 

Some companies are doing crossover from forklifts to AGVs. Others are converting manned industrial trucks to 

automated vehicles and light trucks. Long term goals are ideal but where is the balance for cost and safety? 

With the high cost of forklift accidents per year being $135M, there is a need to find a balance. Toyota’s focus 

is on training to help with overcoming the safety issues associated with automated facilities by training 

everyone from the administrative person to the forklift drivers. 

Vehicle tracking systems are effective for forklift safety, although customers are more interested in the cost 

versus the safety. So, there is a crossover of taking jobs versus a safe, efficient facility where ultimately safe, 

efficient systems are more cost effective in the long run. Productivity and efficiency are the driving forces. 

Companies are not trying to lay off people or get rid of forklift drivers but produce more goods. Freight 

transport and storage are all cost driven. Companies recognize a safety need, but no one wants to pay for it. 

For automated forklifts that follow workers down aisles for manual order picking, several commands are 

introduced into the system so the order picker can command the robot and the robot will remain safe. However, 

these commands are more for the order picker than the robot. 

Recommendations

The following summarizes the recommendations for improvements to increase forklift safety arising from the 

discussion and presented papers. 

1 Follow the OSHA checklist; enforce the requirement that all drivers wear seatbelts. 

2 Ergonomics of vehicles are currently difficult so change the driver’s seat so that the driver is not required to 

turn his/her head backwards to see in the direction of travel when the forklift is carrying a load 

3 In noisy environments, add rear backup lighting. Currently drivers rely on their hearing to know when a 

pedestrian is in the way. Therefore, there is a need for something to replace acoustics. A suggestion would be 

to use a laser beam that projects 15 m in front of the vehicle through the intersections to tell pedestrians where 

the forklift is intending to go. 

4 Adding sensors and cameras to forklifts to improve the driver’s FOV are suggested and being tested at NIST. 

See Figures 2 and 3. 

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5 Because there are nearly 1 million forklifts in use today in just the US, there needs to be safety equipment that 

retrofits to existing forklifts, as well as being designed into new forklifts. 

6 There is a need for the ability to track both pedestrians and forklifts and provide the information to the driver 

and/or to the pedestrians. 

7 Systems are needed to control forklift speed to prevent tip over. This must be done this without impacting 

productivity. Technology is needed that can provide advance warning of hazards (earlier reaction time) and can 

directly limit forklift speed to assure adequate stopping distance based on location, load, vehicle type, and 

known hazards. 

8 Automatic load weight display is needed for the driver, similar to the speedometer in a vehicle, that would 

continuously show load weight and changes in % of vehicle lifting capacity as the vehicle moves, lifts, etc. 

(post session input from Ted Jurca, Integrated Visual Data Technology, Inc.)

9 Possible forklift improvements may be (post session suggestions by Rusty Smith, McCall Handling): 

 Driver pin-code entry into a keypad or use a card scanner mounted to each forklift to allow that driver to 

operate the forklift with “black box” (similar to aircraft black boxes) information on who last operated the 

forklift. Potential uses of this improvement may be to: 

o Recall which operator was running the forklift after an incident occurs, 

o Allow drivers who caused prior incidents to control the forklift at limited speeds and/or carry 

limited loads. 

 Load sensors in the seat to shutdown and ensure a forklift “park” condition when the operator leaves the 

seat. 

Figure 2 shows an experiment performed by NIST using several 3D LIDAR imagers near the edge of a loading dock 

to detect both positive and negative obstacles. Figure 3 shows a color camera mounted on an extendable boom on a 

forklift to increase driver field of view of B56.5 standard sized obstacles when blocked by loads, bars, and chains. 

Figure 2 – Data showing detection of both positive and negative obstacles using 3D LIDAR mounted to a forklift 

while at the edge of a loading dock. The red points are obstacles detected and the green points are detected ground. 

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Figure 3 – Color camera mounted on an extendable boom to a forklift to increase driver field of view of B56.5 

standard sized obstacles when blocked by loads, bars, and chains. Bottom right shows an onboard monitor 

displaying camera detected obstacles in front of the forklift load and blocked by the drivers field of view. 

5 References 

[Chugh], Kevin, “A PC Based Virtual Reality Simulation for Forklift Safety Training,” SBIR Phase II, 

http://www.cdc.gov/od/science/phresearch/pi_abstract2.htm, 2009. 

Special session papers presented included the following (in order of their presentation):

[Austin], Mark, “Fork Lift Awareness,” Mark Austin, Occupational Safety and Health Administration 

[Forsman], Benny “AGV Forklifts - Current and Future Safety Systems,”, Danaher Motion, Kollmorgen Corp. 

[Ungerbuehler], Richard H. “Where AGV’s and Forklifts Roam: Preserving Operational Safety in a Shared 

Workspace,” Sky-Trax Incorporated 

[Bostelman], Roger; Shackleford, Will, “Performance Measurements Towards Improved Manufacturing Vehicle 

Safety,” NIST Intelligent Systems Division 

 

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