PERMIS-2007

PerMIS'07 will be the seventh in the series that started in 2000, targeted at defining measures and methodologies of evaluating performance of intelligent systems. The workshop has proved to be an excellent forum for discussions and partnerships, dissemination of ideas, and future collaborations in an informal setting. Attendees usually include researchers, 

graduate students, practitioners from industry, academia, and government agencies.

PerMIS 2007 

PERFORMANCE METRICS

FOR 

INTELLIGENT SYSTEMS WORKSHOP

Courtyard Gaithersburg Washingtonian Center, Gaithersburg, Maryland USA

August 28 - 30, 2007

NIST Special Publication 1073

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.SP.1073
Table of Contents

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Sponsors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Program Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Plenary Addresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Featured Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Workshop Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

PerMIS Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 14

Technical Sessions

TUE-AM1 Mobile Robot Performance Evaluation I

Evaluation of Navigation of an Autonomous Mobile Robot

[N. Muñoz, J. Valencia, N. Londoño]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Assessing the Impact of Bi-directional Information Flow in UGV

[M. Childers, B. Bodt, S. Hill, R. Dean, W.Dodson, L. Sutton ]. . . . . . . . . . . . . . . . . . . 22

A Common Operator Control Unit Color Scheme for Mobile Robots

[M. Shneier, R. Bostelman , J. Albus, W. Shackleford, T. Chang, T. Hong] . . . . . . . . . 29

How DoD's TRA Process Could be Applied to Intelligent Systems Development 

[D. Sparrow, S. Cazares ] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

A Brief History of PRIDE 

[Z. Kootbally, C. Schlenoff, R. Madhavan] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

TUE-AM2 Special Session I: Autonomy Levels for Unmanned Systems

Autonomy Levels for Unmanned Systems (ALFUS) Framework: Safety and Application 

Issues 

[H-M. Huang] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Evaluation of Autonomy in Recent Ground Vehicles Using the Autonomy Levels for 

Unmanned Systems (ALFUS) Framework 

[G. McWilliams, M. Brown, R. Lamm, C. Guerra, P. Avery, K. Kozak, B. Surampudi]. .54
A Methodology for Testing Unmanned Vehicle Behavior and Autonomy

[D. Gertman, C. McFarland, T. Klein, A. Gertman, D. Bruemmer] . . . . . . . . . . . . . . . . 62

Standardizing Measurements of Autonomy in the Artificially Intelligent

[A. Hudson, L. Reeker] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70

TUE-PM1 Mobile Robot Performance Evaluation II

Assessment of Man-portable Robots for Law Enforcement Agencies

[C. Lundberg, H. Christensen] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Performance Metrics and Evaluation of a Path Planner based on Genetic Algorithms

[G. Giardini, T. Kalmar-Nagy]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

The Evolution of Performance Metrics in the RoboCup Rescue Virtual Robot Competition

[S. Balakirsky, C. Scrapper, S. Carpin] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Robot Simulation Physics Validation

C. Pepper, S. Balakirsky, C. Scrapper] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Design and Validation of a Whegs Robot in USARSim

[B. Taylor, S. Balakirsky, E. Messina, R. Quinn] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105

TUE-PM2 Special Session II: Human Robot Interface Issues

Maze Hypothesis Development in Assessing Robot Performance During Teleoperation

[S. Schipani, E. Messina] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Human System Performance Metrics for Evaluation of Mixed-Initiative Heterogeneous

Autonomous Systems

[L. Billman, M. Steinberg ] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

Concepts of Operations for Robot-Assisted Emergency Response and Implications for

Human-Robot Interaction

[J. Scholtz, B. Antonishek, B. Stanton, C. Schlenoff] . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Multimodal Displays to Enhance Human Robot Interaction On-the-Move

[E. Haas, C. Stachowiak,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

WED-AM2 Autonomy Vs Intelligence

Autonomy (What's it Good for?)

[J. Gunderson, L. Gunderson] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Definitions and Measures of Intelligence in Deep Blue and the Army XUV

[J. Evans] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
Automotive Turing Test

[S. Kalik, D. Prokhorov] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Autonomous Robots with Both Body and Behavior Self-Knowledge

[B. Gordon] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .159

A Cognitive-based Agent Architecture for Autonomous Situation Analysis

[G. Berg-Cross, W-T. Fu, A. Kwon] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .162

WED-AM2 Panel Discussion I

Can the Development of Intelligent Robots be Benchmarked? Concepts and Issues from

Epigenetic Robotics (Moderator: Gary Berg-Cross, EM & I) . . . . . . . . . . . . . . . . . . . 168

WED-PM1 Human Machine Interaction

Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the

Loop

[A. Steinfeld, S. Bennett, K. Cunningham, M. Lahut, P-A. Quinones, D. Wexler, D.

Siewiorek, J. Hayes, P. Cohen, J. Fitzgerald, O. Hansson, M. Pool, M.

Drummond] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

Survey Measures for Evaluation of Cognitive Assistants

[A. Steinfeld, P-A. Quinones, J. Zimmerman, S. Bennett, D. Siewiorek] . . . . . . . . . . . .189

Development of Tools for Measuring the Performance of Computer Assisted Orthopaedic

Hip Surgery Systems

[N. Dagalakis, Y. Kim, D. Sawyer, C. Shakarji] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .194

Haptic Feedback System for Robot-Assisted Surgery

[J. Desai, G. Tholey, C. Kennedy] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

WED-PM2 Special Session III: Space/Aerial Robotics

Prototype Rover Field Testing and Planetary Surface Operations

[E. Tunstel] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .210

Planning to Fail - Reliability as a Design Parameter for Planetary Rover Missions

[S. Stancliff, J. Dolan, A. Trebi-Ollennu] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

A Decision Space Compression Approach for Model Based Parallel Computing 

Processes

[R. Bonneau, G. Ramseyer] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Physically-Proximal Human-Robot Collaboration for Air and Space Applications

[E. Atkins] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .230
THU-AM1 Performance Assessment of Algorithms

Analyzing the Performance of Distributed Algorithms

[R. Lass, E. Sultanik, W. Regli]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .238

An Agent Structure for Evaluating MAS Performance

[C. Dimou, A. Symeonidis, P. Mitkas]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Information Management for High Performance Autonomous Intelligent Systems

[S. Spetka, S. Tucker, G. Ramseyer, R. Linderman]. . . . . . . . . . . . . . . . . . . . . . . . . . . .251

Efficient Monte Carlo Computation of Fisher Information Matrix using Prior Information

[S. Das, J. Spall, R. Ghanem] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .256

Performance of 6D LuM and FFS SLAM -- An Example for Comparison using Grid and 

Pose Based Evaluation Methods

[R. Lakaemper, A. Nuchter, N. Adluru, L. Latecki] . . . . . . . . . . . . . . . . . . . . . . . . . . . .264

THU-AM2 Special Session IV: Smart Assembly Systems

Smart Assembly: Industry Needs and Challenges

[J. Slotwinski, R. Tilove] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .271

Science based Information Metrology for Engineering Informatics

[S. Rachuri] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .277

Evaluating Manufacturing Control Language Standards: An Implementer's View

[T. Kramer] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .281

Interoperability Testing for Shop-Floor Inspection

[F. Proctor, W. Rippey, J. Horst, J. Falco, T. Kramer] . . . . . . . . . . . . . . . . . . . . . . . . . 289

A Virtual Environment-Based Training Systems for Mechanical Assembly Operations

[M. Schwartz, S. Gupta, D. Anand, R. Kavetsky] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294

THU-PM Panel Discussion II

(Re-)Establishing or Increasing Collaborative Links Between Artificial Intelligence and 

Intelligent Systems (Moderator: Brent Gordon, NASA-Goddard) . . . . . . . . . . . . . . . . .302
FOREWORD

The 2007 Performance Metrics for Intelligent Systems (PerMIS) Workshop was held at the Courtyard 

Gaithersburg Washingtonian Center from August 28–30. This seventh installment of PerMIS started in 2000 

targeted at defining measures and methodologies of evaluating performance of intelligent systems, and focused on applications of performance measures to practical problems in commercial, industrial, homeland 

security, and military applications. The cardinal theme of this year’s workshop was the interplay between 

autonomy and intelligence, i.e. how does autonomy influence intelligence and vice versa. 

Defining and measuring aspects of a system:

• The level of autonomy

• Human-robot interaction

• Collaboration

Evaluating components within intelligent system

• Sensing and perception

• Knowledge representation, world models, ontologies

• Planning and control

• Learning and adaption

• Reasoning

Infrastructural support for performance evaluation

• Testbeds and competitions for intercomparisons

• Instrumentation and other measurement tools

• Simulation and modeling support

Technology readiness measures for intelligent systems

Applied performance measures in various domains, e.g.,

• Intelligent transportation systems

• Emergency response robots (search and rescue, bomb disposal)

• Homeland security systems

• De-mining robots

• Defense robotics

• Hazardous environments (e.g., nuclear remediation)

• Industrial and manufacturing systems

• Space/Aerial robotics

• Medical Robotics & Assistive devices

This year’s exciting program consisted of four plenary addresses, one featured presentation, four special sessions, and two panel discussions. In addition to these, there were five general technical sessions. All of these 

presentations addressed, in one way or another, performance metrics, evaluation, and analysis of intelligent 

systems in diverse domains ranging from space robotics to manufacturing, from mobile robotic systems to 

human machine interaction, to name a few.

PerMIS 2007

1
PerMIS’07 was sponsored by NIST 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). We also acknowledge the financial support of the 

IEEE Washington Section. 

There were several firsts to this year’s workshop. The proceedings of PerMIS’07 are being indexed by INSPEC, Ei Compendex, ACM’s Digital Library, and are released, as in previous years, as a NIST Special Publication. These indexing services will enable the presented work to reach a wider audience for increased references and citations. Springer Publishers hosted a booth on the last two days of the workshop during which 

time some of the displayed books were raffled off. We thank Springer for their participation and hope that this 

is the beginning of many years of their support.

We would like to thank all members of the PerMIS′07 Program Committee, and the reviewers for contributing 

to the success of the workshop. Most importantly, we thank all authors for their valuable submissions and the 

attendees for their participation. We sincerely hope that you enjoyed the presentations and ensuing discussions, while forging new relationships and renewing old ones. It was our great pleasure to host all the attendees.

See you next year!

Raj Madhavan Elena Messina

Program Chair General Chair

SPONSORS

 

 

PerMIS 2007

2
PROGRAMCOMMITTEE

General Chair:

Elena Messina (Intelligent Systems Division, NIST, USA) 

Program Chair:

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

R. Bonneau (AFRL USA)

S. Balakirsky (NIST USA)

G. Berg-Cross (EM & I USA) 

J. Bornstein (Army Res. Lab. USA)

S. Carpin (UC Merced USA)

J. Evans (USA)

D. Gage (XPM Tech. USA)

J. Gunderson (Gamma Two, Inc. USA)

L. Gunderson (Gamma Two, Inc. USA)

A. Jacoff (NIST USA)

S. Julier (Univ. College London UK)

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

R. Lakaemper (Temple Univ. USA)

L. Latecki (Temple Univ. USA)

M. Lewis (Univ. of Pittsburgh USA)

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

L. Reeker (NIST USA)

C. Schlenoff (NIST USA)

A. Schultz (Navy Res. Lab. USA) 

M. Shneier (NIST USA)

R. Smith (OSD USA)

R. Tilove (General Motors USA)

E. Tunstel (Jet Propulsion Lab. USA)

PerMIS 2007

3
ABSTRACT

Fully repeatable and controllable 

experiments are essential to enable 

a precise comparison of multi-robot 

systems. Using different case 

studies, we describe a general 

methodology for conducting 

experimental activities for multirobot systems. This is a first step 

toward the goal of fostering the 

practice of replicating experiments 

in order to compare different 

methods and assess their strengths 

and weaknesses.

In the first case study, we examine 

the problem of building a 

geometrical map of an indoor 

environment using multiple robots. 

The map is built by integrating 

partial maps made of segments 

without using any odometry 

information. We show how to 

improve the repeatability and 

controllability of the experimental 

results and how to compare different mapping systems.

We then present a case study of auction-based methods for the 

allocation of tasks to a group of robots. The robots operate in a 

2D environment for which they each have a map. Tasks are 

locations in the map that must be visited by one robot. Robots 

bid to obtain tasks, but unexpected obstacles and other delays 

may prevent a robot from completing its allocated tasks. We 

show how to compare our experimental results with other 

published auction-based methods.

BIOGRAPHY

Maria Gini is a Professor at the Department of Computer 

Science and Engineering of the University of Minnesota. Before 

joining the University of Minnesota, she was a Research 

Associate at the Politecnico of Milan, Italy, and a Visiting 

Research Associate at Stanford University. Her work has 

included motion planning for robot arms, navigation of mobile 

robots around moving obstacles, unsupervised learning of 

complex behaviors, coordinated behaviors among multiple 

robots, and autonomous economic agents. She has coauthored 

over 200 technical papers. She is currently the chair of ACM 

Special Interest Group on Artificial Intelligence (SIGART), a 

member of the Association for the Advancement of Artificial 

Intelligence (AAAI) Executive Council and of the board of the 

International Foundation of Autonomous Agents and Multi-Agent 

Systems. She is on the editorial board of numerous journals, 

including Autonomous Robots, the Journal of Autonomous 

Agents & Multi-Agent Systems, Electronic Commerce Research 

and Applications, Integrated Computer-Aided Engineering, and 

Web Intelligence and Agent Systems.

ABSTRACT

This talk first describes several 

approaches to measure the 

performance of ground robots. It is 

easy enough to measure quantities 

such as speed and reliability. It is 

more challenging to define metrics 

for perception, planning, and 

autonomy. The talk then presents 

selected results of applying the 

approaches to systems developed 

by several Government programs.

BIOGRAPHY

Dr. Krotkov is the President of Griffin 

Technologies, a consulting and software firm specializing in robotics and 

machine perception. Before founding 

Griffin, he worked in industry as an 

executive in a medical imaging technology start-up, in government as a 

program manager at DARPA, and in 

academia as a faculty member of the 

Robotics Institute at Carnegie Mellon 

University. Dr. Krotkov earned his Ph.D. degree in Computer and 

Information Science in 1987 from the University of Pennsylvania, 

for pioneering work in active computer vision.

 ABSTRACT 

Designing human-robot collaboration 

systems is an inherently multidisciplinary endeavor aimed at providing 

humans with rich, effective and satisfying interactions. Over the past ten 

years, my laboratory has focused on 

educational collaboration, wherein 

the purpose of the interaction is to 

provide measurable learning for humans through exploration and discovery. We propose that the creation 

of a successful human-robot collaboration system requires innovation in 

several areas: robot morphology;

robot behavior; social perception;

interaction design; human cognitive 

models and evaluation of educational 

effectiveness. Our iterative process 

for collaboration design extends 

evaluation techniques from the informal learning field together with underlying technical advances in robotics. This talk describes our research methodology, technical contributions and experimental outcomes for three fielded robot systems that push on developing a generalizable, formal approach 

to educational human-robot collaboration. For the past several 

months, our group has been laying the groundwork for largescale dissemination of our technology and curricular instruments. 

PLENARY SPEAKER

Prof. Illah 

Nourbakhsh

Carnegie Mellon

University, USA

Formalizing 

Educational 

Human-Robot

Collaboration

Wed. 08:30

PerMIS 2007 

PLENARY SPEAKER

Prof. Maria Gini

University of 

Minnesota, USA

Methodology 

for 

Experimental 

Research in 

Multi-robot 

Systems with 

Case Studies

Tue. 08:30

PLENARY SPEAKER

Dr. Eric Krotkov 

Griffin 

Technologies, 

USA

Measuring 

Ground 

Robot 

Performance

Tue. 14:00

4
I will describe the robot “community” we wish to help spawn, 

and the ingredients that may help to catalyze a broad form of 

technologically empowered community, including the Telepresence Robot Kit and the Global Connection Project. 

BIOGRAPHY

Illah R. Nourbakhsh is an Associate Professor of Robotics and 

head of the Robotics Masters Program in The Robotics Institute 

at Carnegie Mellon University. He was on leave for the 2004 

calendar year and was at NASA/Ames Research Center serving 

as Robotics Group lead. He received his Ph.D. in computer 

science from Stanford University in 1996. He is co-founder of the 

Toy Robots Initiative at The Robotics Institute, director of the 

Center for Innovative Robotics and director of the Community 

Robotics, Education and Technology Empowerment (CREATE) 

lab. He is also co-PI of the Global Connection Project, home of 

the Gigapan project. He is also co-PI of the Robot 250 city-wide 

art+robotics fusion program in Pittsburgh. His current research 

projects include educational and social robotics and community 

robotics. His past research has included protein structure 

prediction under the GENOME project, software reuse, 

interleaving planning and execution and planning and scheduling 

algorithms, as well as mobile robot navigation. At the Jet 

Propulsion Laboratory he was a member of the New Millenium 

Rapid Prototyping Team for the design of autonomous 

spacecraft. He is a founder and chief scientist of Blue Pumpkin 

Software, Inc., which was acquired by Witness Systems, Inc. Illah 

recently co-authored the MIT Press textbook, Introduction to 

Autonomous Mobile Robots.

ABSTRACT

Commercial applications for the 

everyday deployment of autonomous 

systems based on robotic and 

intelligent systems technologies 

require the highest levels of 

performance, reliability and integrity. 

The general public expects intelligent 

machines to be fully operational 

100% of the time. People expect 

autonomous technologies to operate 

at higher levels of performance and 

safety than people themselves 

exhibit. For example smart car 

technologies are expected to cause 

ZERO accidents while human errors 

kill more 150,000 people on our roads 

every year! This talk will describe the 

design principles that have been 

developed over of the last 10 years 

through exhaustive trial and error 

testing to underpin autonomous 

systems that are suitable for realworld deployment. Currently, it is not 

yet possible to realise an autonomous 

system that doesn't fail periodically. Even if the mean rate 

between failures is days or weeks, a single failure could have 

catastrophic consequences. The approach we have adopted to 

address this situation has been to build-in monitoring systems 

that continually check all key system parameters and variables. If 

the monitored parameters move outside tightly defined bounds 

the system will safely shutdown, and alert the human supervisor. 

The failure conditions are logged and then further testing and 

debugging is performed. The value and appropriateness of our 

approach will be shown by a number of real-world studies. We 

will show that how it is possible to design computer vision 

systems for human-machine applications can operate with over 

99% reliability, in all lighting conditions, for all types of users 

irrespective of age, race or visual appearance. These systems 

have been used in automotive and sports applications. We have 

also show how this approach has been used to design field 

robotic systems that have deployed in automobile safety 

systems and 24/7 mining applications.

BIOGRAPHY

Dr. Alex Zelinsky is a well-known scientist, specialising in 

robotics and computer vision and is widely recognised as an 

innovator in human-machine interaction. Dr. Zelinsky is currently 

Group Executive, Information and Communication Sciences and 

Technology, and Director, CSIRO Information Communication 

Technology (ICT) Centre. Before joining CSIRO in July 2004, Dr. 

Zelinsky was CEO of Seeing Machines, a company dedicated to 

the commercialisation of computer vision systems. Dr. Zelinsky 

co-founded Seeing Machines in June 2000, the company is now 

publicly listed on the London Stock Exchange. The technology 

commercialised by Seeing Machines was developed at the 

Australian National University where Dr. Zelinsky was Professor 

and Head of the Department of Systems Engineering (1996

-2000). Prior to joining the Australian National University, Dr. 

Zelinsky worked as an academic at the University Wollongong 

(1984-1991) and as a research scientist in the Electrotechnical 

Laboratory, Japan (1992-1995). Dr. Zelinsky is an active member 

of the robotics community and has served on the editorial boards 

of the International Journal of Robotics Research and IEEE 

Robotics and Automation Magazine, he also founded the Field & 

Services Robotics conference series. Dr. Zelinsky's contributions 

have been recognised by awards in Australia and internationally. 

These include the Australian Engineering Excellence Awards, US 

R&D magazine Top 100 Award and Technology Pioneer at the 

World Economic Forum.

PerMIS 2007

PLENARY SPEAKER

Dr. Alex Zelinsky

CSIRO ICT

Centre, 

Australia

Building 

Autonomous 

Systems of 

High 

Performance,

Reliability 

and Integrity

Thu. 08:30 

5
 ABSTRACT

After spectacular successes, in 

1970s-1980s, in the use of robotics in

highly structured environments - e.g. 

automotive assembly, welding, and 

painting lines - the penetration of 

"serious" robots (those large and 

powerful enough to be harmful) into 

new applications has slowed down 

markedly. User manuals of most robot arm manipulators warn that under no circumstance can people enter the workspace of an operating 

robot. The reason is simple - due to 

intended use these robots are strong 

enough to endanger a human, yet 

their sensing and intelligence is "too 

dumb" to be trusted for human 

safety. In the roboticists' parlance, 

today's robots are not designed to 

operate in unstructured environments, that is settings not created 

specifically for the robot's operation. 

It is not the function the robot is built 

for that is the problem - it is the robot's interaction with its environment.

The problem is lesser with robot rovers 

but quite pronounced with arm manipulators.

The way to break this barrier is to design robots fully capable of 

operating in an unstructured environment, in places where things

are unpredictable and must be perceived and decided upon on 

the fly. This is a new terrain - the required hardware and intelligence are to be more complex and sophisticated than what we 

know today. In this talk we will review related technical and scientific issues.

BIOGRAPHY 

Dr. Vladimir Lumelsky is the head of the Laboratory of Robotics 

for Unstructured Environments at NASA-Goddard Space Center, 

and is Adjunct Professor of Computer Science at the University 

of Maryland-College Park. The long-term goal of the laboratory is

to develop robots capable of operating in the uncertain and 

changing settings likely to arise in future NASA missions. This 

work builds upon Dr. Lumelsky's work on large sensitive robot 

skin systems prior to joining NASA in 2004, as a professor at Yale

University and later at the University of Wisconsin-Madison 

(where he was The Consolidated Papers Professor of Engineering). Dr. Lumelsky is the author of three books and over 200 professional papers covering the areas of robotics, computational 

intelligence, human-machine interaction, human spatial reasoning, massive sensor arrays, bio-engineering, control theory, 

kinematics, pattern recognition, and industrial automation. He 

has held a variety of positions in both the public and private sectors: he was Program Director at the National Science Foundation, and has led large technical projects, including development 

of a universal industrial robot controller at General Electric (GE 

FEATURED 

PRESENTATION

Dr. Vladimir 

Lumelsky

NASA-Goddard 

Space Center, 

USA

Human-Robot

Interaction in 

Physical 

Proximity: 

Issues and 

Prospects

Wed. 14:00

Research Center), and a joint robot skin development effort with 

Hitachi Corporation. Dr. Lumelsky also has held temporary positions at the Science University of Tokyo (Japan), Weizmann Institute (Israel) and US South Pole Station, Antarctica. He is the 

founding Editor-in-Chief of the IEEE Sensors Journal, and has 

served on editorial boards of other professional journals. He has 

been guest editor of special issues at professional journals; 

served on the Administrative Committees of IEEE Robotics Society and Sensors Council; chaired technical committees and 

working groups; and chaired and co-chaired major international 

conferences, workshops and special sessions. Dr. Lumelsky has 

served as a technical expert in legal cases, including multinational litigation. He frequently gives talks at US and foreign 

universities, government groups, think tanks, and in industry. He 

is a member of several professional societies, and is a Fellow of 

IEEE. 

PerMIS 2007

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08:15 Welcome & Overview

08:30 Plenary Presentation:

Maria Gini 

Methodology for Experimental Research in Multi-robot 

Systems with Case Studies

09:30 Coffee Break

10:00 TUE-AM1 Mobile Robot Performance Evaluation I

Chairs: C. Schlenoff & M. Childers

• Evaluation of Navigation of an Autonomous Mobile Robot

[N. Muñoz, J. Valencia, N. Londoño]

• Assessing the Impact of Bi-directional Information Flow in UGV 

Operation: A Pilot Study

[M. Childers, B. Bodt, S. Hill, R. Dean, W. Dodson, L. Sutton]

• A Common Operator Control Unit Color Scheme for Mobile 

Robots [M. Shneier, R. Bostelman, J. Albus, W. Shackleford, 

T. Chang, T. Hong]

• How DoD's TRA Process Could be Applied to Intelligent Systems 

Development [D. Sparrow, S. Cazares]

• A Brief History of PRIDE [Z. Kootbally, C. Schlenoff, R. Madhavan]

12:30 Lunch on your own

14:00 Plenary Presentation:

Eric Krotkov

Measuring Ground Robot Performance

15:00 Coffee Break

15:30 TUE-PM1 Mobile Robot Performance Evaluation II

Chairs: S. Balakirsky & C. Lundberg

• Assessment of Man-portable Robots for Law Enforcement 

Agencies [C. Lundberg, H. Christensen]

• Performance Metrics and Evaluation of a Path Planner based on 

Genetic Algorithms [G. Giardini, T. Kalmar-Nagy]

• The Evolution of Performance Metrics in the RoboCup Rescue 

Virtual Robot Competition [S. Balakirsky, C. Scrapper, S. Carpin]

• Robot Simulation Physics Validation 

[C. Pepper, S. Balakirsky, C. Scrapper]

• Design and Validation of a Whegs Robot in USARSim

[B. Taylor, S. Balakirsky, E. Messina, R. Quinn]

18:30 Reception

PROGRAM PERMIS

7
PerMIS 2007 

August

08:15 Welcome & Overview

08:30 Plenary Presentation: 

Maria Gini 

Methodology for Experimental Research in Multi-robot 

Systems with Case Studies

09:30 Coffee Break

10:00 TUE-AM2 Special Session I: Autonomy Levels for Unmanned Systems

Organizer: Hui-Min Huang (NIST)

• Autonomy Levels for Unmanned Systems (ALFUS) Framework: 

Safety and Application Issues [H-M. Huang]

• Evaluation of Autonomy in Recent Ground Vehicles Using the 

Autonomy Levels for Unmanned Systems (ALFUS) Framework

[G. McWilliams, M. Brown, R. Lamm, C. Guerra, P. Avery,

K. Kozak, B. Surampudi]

• A Methodology for Testing Unmanned Vehicle Behavior and 

Autonomy 

[D. Gertman, C. McFarland, T. Klein, A. Gertman, D. Bruemmer]

• Standardizing Measurements of Autonomy in the Artificially 

Intelligent [A. Hudson, L. Reeker]

12:30 Lunch on your own

14:00 Plenary Presentation:

Eric Krotkov

Measuring Ground Robot Performance

15:00 Coffee Break

15:30 TUE-PM2 Special Session II: Human Robot Interface Issues

Organizers: Salvatore Schipani & Brian Antonishek (NIST)

• Maze Hypothesis Development in Assessing Robot Performance 

During Teleoperation [S. Schipani, E. Messina]

• Human System Performance Metrics for Evaluation of

Mixed-Initiative Heterogeneous Autonomous Systems

[L. Billman, M. Steinberg]

• Concepts of Operations for Robot-Assisted Emergency Response

and Implications for Human-Robot Interaction

[J. Scholtz, B. Antonishek, B. Stanton, C. Schlenoff]

• Multimodal Displays to Enhance Human Robot Interaction

On-the-Move* [E. Haas, C. Stachowiak] 

18:30 Reception

PROGRAM PERMIS

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08:15 Overview

08:30 Plenary Presentation:

Illah Nourbakhsh

Formalizing Educational Human-Robot Collaboration

09:30 Coffee Break

10:00 WED-AM1 Autonomy Vs Intelligence

Chairs: J. Gunderson & J. Evans

• Autonomy (What's it Good for?) [J. Gunderson, L. Gunderson]

• Definitions and Measures of Intelligence in Deep Blue and the 

Army XUV [J. Evans]

• Automotive Turing Test [S. Kalik, D. Prokhorov]

• Autonomous Robots with Both Body and Behavior 

Self-Knowledge [B. Gordon]

• A Cognitive-based Agent Architecture for Autonomous Situation 

Analysis [G. Berg-Cross, W-T. Fu, A. Kwon]

12:30 Lunch on your own

14:00 Featured Presentation:

Vladimir Lumelsky

Human-Robot Interaction in Physical Proximity: 

Issues and Prospects

15:00 Coffee Break

15:30 WED-PM1 Human Machine Interaction

Chairs: N. Dagalakis & A. Steinfeld

• Evaluation of an Integrated Multi-Task Machine Learning System 

with Humans in the Loop 

[A. Steinfeld, S. Bennett, K. Cunningham, M. Lahut, 

P-A. Quinones, D. Wexler, D. Siewiorek, J. Hayes, P. Cohen, 

J. Fitzgerald, O. Hansson, M. Pool, M. Drummond]

• Survey Measures for Evaluation of Cognitive Assistants 

[A. Steinfeld, P-A. Quinones, J. Zimmerman, S. Bennett, 

D. Siewiorek]

• Development of Tools for Measuring the Performance of Computer

Assisted Orthopaedic Hip Surgery Systems

[N. Dagalakis, Y. Kim, D. Sawyer, C. Shakarji]

• Haptic Feedback System for Robot-Assisted Surgery 

[J. Desai, G. Tholey, C. Kennedy]

19:30 Banquet

PROGRAM PERMIS

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PerMIS 2007 

August

08:15 Overview

08:30 Plenary Presentation:

Illah Nourbakhsh

Formalizing Educational Human-Robot Collaboration

09:30 Coffee Break

10:00 WED-AM2 Panel Discussion I: Can the Development of Intelligent 

Robots be Benchmarked? Concepts and Issues from Epigenetic 

Robotics (Moderator: Gary Berg-Cross, EM & I)

• Douglas Blank, Bryn Mawr College

• James Marshall, Sarah Lawrence College

• Lisa Meeden, Swarthmore College

• Charles Kemp, Georgia Tech.

• Chad Jenkins, Brown University

12:30 Lunch on your own

14:00 Featured Presentation:

Vladimir Lumelsky

Human-Robot Interaction in Physical Proximity: 

Issues and Prospects

15:00 Coffee Break

15:30 WED-PM2 Special Session III: Space/Aerial Robotics

Organizer: Edward Tunstel (JPL)

• Prototype Rover Field Testing and Planetary Surface Operations 

[E. Tunstel]

• Planning to Fail - Reliability as a Design Parameter for Planetary 

Rover Missions [S. Stancliff, J. Dolan, A. Trebi-Ollennu]

• A Decision Space Compression Approach for Model Based 

Parallel Computing Processes [R. Bonneau, G. Ramseyer]

• Physically-Proximal Human-Robot Collaboration for Air and Space

Applications [E. Atkins]

19:30 Banquet (Adam Jacoff, NIST)

PROGRAM PERMIS

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08:15 Overview

08:30 Plenary Presentation:

Alex Zelinsky

Building Autonomous Systems of High Performance, 

Reliability and Integrity

09:30 Coffee Break

10:00 THU-AM1 Performance Assessment of Algorithms

Chairs: R. Lakaemper & S. Spetka

• Analyzing the Performance of Distributed Algorithms

[R. Lass, E. Sultanik, W. Regli]

• An Agent Structure for Evaluating MAS Performance 

[C. Dimou, A. Symeonidis, P. Mitkas]

• Information Management for High Performance Autonomous 

Intelligent Systems [S. Spetka, S. Tucker, G. Ramseyer, 

R. Linderman]

• Efficient Monte Carlo Computation of Fisher Information Matrix 

using Prior Information [S. Das, J. Spall, R. Ghanem] 

• Performance of 6D LuM and FFS SLAM -- An Example for 

Comparison using Grid and Pose Based Evaluation Methods 

[R. Lakaemper, A. Nuchter, N. Adluru, L. Latecki]

12:30 Lunch on your own

14:00 THU-PM Panel Discussion II: (Re-)Establishing or Increasing Collaborative Links Between Artificial Intelligence and Intelligent Systems

(Moderator: Brent Gordon, NASA-Goddard)

• James Albus, Senior Fellow, Intelligent Systems Division, NIST

• Ella Atkins, Associate Professor, University of Michigan

• Henrik Christensen, Director, Center for Robotics and Intelligent 

Machines, Georgia Tech.

• Larry Reeker, Computer Scientist, Information Technology 

Laboratory, NIST

15:30 Coffee Break

16:00 Adjourn

PROGRAM PERMIS

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PerMIS 2007 

August

08:15 Overview

08:30 Plenary Presentation:

Alex Zelinsky 

Building Autonomous Systems of High Performance,

Reliability and Integrity

09:30 Coffee Break

10:00 THU-AM2 Special Session IV: Smart Assembly Systems

Organizers: Robert Tilove (GM) & John Slotwinski (NIST)

• Smart Assembly: Industry Needs and Challenges

[J. Slotwinski, R. Tilove]

• Science based Information Metrology for Engineering Informatics 

[S. Rachuri]

• Evaluating Manufacturing Control Language Standards: An 

Implementer's View [T. Kramer]

• Interoperability Testing for Shop-Floor Inspection

[F. Proctor, W. Rippey, J. Horst, J. Falco, T. Kramer]

• A Virtual Environment-Based Training Systems for Mechanical 

Assembly Operations

[M. Schwartz, S. Gupta, D. Anand, R. Kavetsky]

12:30 Lunch on your own

14:00 THU-PM Panel Discussion II: (Re-)Establishing or Increasing Collaborative Links Between Artificial Intelligence and Intelligent Systems

(Moderator: Brent Gordon, NASA-Goddard)

• James Albus, Senior Fellow, Intelligent Systems Division, NIST

• Ella Atkins, Associate Professor, University of Michigan

• Henrik Christensen, Director, Center for Robotics and Intelligent 

Machines, Georgia Tech.

• Larry Reeker, Computer Scientist, Information Technology 

Laboratory, NIST

15:30 Coffee Break

16:00 Adjourn

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PERMIS

AUTHOR INDEX

Adluru, N. ................THU-AM1

Albus, J. ..................TUE-AM1

Anand, D. ................THU-AM2

Antonishek, B. .........TUE-PM2

Atkins, E. ................WED-PM2

Avery, P. ...................TUE-AM2

Balakirsky, S. ...........TUE-PM1

.................................TUE-PM1

.................................TUE-PM1

Bennett, S. .............WED-PM1

................................WED-PM1

Berg-Cross, G. .......WED-AM1

................................WED-AM2

Billman, L. ...............TUE-PM2

Bodt, B. ...................TUE-AM1

Bonneau, R. ...........WED-PM2

Bostelman, R. ..........TUE-AM1

Brown, M. ................TUE-AM2

Bruemmer, D. ..........TUE-AM2

Carpin, S. ................TUE-PM1

Cazares, S. ..............TUE-AM1

Chang, T. .................TUE-AM1

Childers, M. .............TUE-AM1

Christensen, H. .......TUE-PM1

Cohen, P. ................WED-PM1

Cunningham, K. .....WED-PM1

Dagalakis, N. ..........WED-PM1

Das, S. ....................THU-AM1

Dean, R. ..................TUE-AM1

Desai, J. .................WED-PM1

Dimou, C. ................THU-AM1

Dodson, W. ..............TUE-AM1

Dolan, J. .................WED-PM2

Drummond, M. .......WED-PM1

Evans, J. ................WED-AM1

Falco, J. ..................THU-AM2

Fitzgerald, J. ..........WED-PM1

Fu, W-T. ..................WED-AM1

Gertman, A. .............TUE-AM2

Gertman, D. .............TUE-AM2

Ghanem, R. .............THU-AM1

Giardini, G. ..............TUE-PM1

Gordon, B. .............WED-AM1

.................................THU-PM1

Guerra, C. ................TUE-AM2

Gunderson, J. ........WED-AM1

Gunderson, L. ........WED-AM1

Gupta, S. .................THU-AM2

Haas, E. ...................TUE-PM2

Hansson, O. ...........WED-PM1

Hayes, J. ................WED-PM1

Hill, S. ......................TUE-AM1

Hong, T. ...................TUE-AM1

Horst, J. ..................THU-AM2

Huang, H-M. ............TUE-AM2

Hudson, A. ..............TUE-AM2

Kalik, S. ..................WED-AM1

Kalmar-Nagy, T. .......TUE-PM1

Kavetsky, R. ............THU-AM2

Kennedy, C. ............WED-PM1

Kim, Y. ....................WED-PM1

Klein, T. ....................TUE-AM2

Kootbally, Z. ............TUE-AM1

Kozak, K. .................TUE-AM2

Kramer, T. ................THU-AM2

.................................THU-AM2

Kwon, A. .................WED-AM1

Lahut, M. ................WED-PM1

Lakaemper, R. .........THU-AM1

Lamm, R. .................TUE-AM2

Lass, R. ...................THU-AM1

Latecki, L. ...............THU-AM1

Linderman, R. .........THU-AM1

Londoño, N. ............TUE-AM1

Lundberg, C. ...........TUE-PM1

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

McFarland, C. ..........TUE-AM2

McWilliams, G. ........TUE-AM2

Messina, E. ..............TUE-PM1

.................................TUE-PM2

Mitkas, P. ................THU-AM1

Muñoz, N. ................TUE-AM1

Nuchter, A. ..............THU-AM1

Pepper, C. ...............TUE-PM1

Pool, M. ..................WED-PM1

Proctor, F. ................THU-AM2

Prokhorov, D. .........WED-AM1

Quinn, R. .................TUE-PM1

Quinones, P-A. .......WED-PM1

................................WED-PM1

Rachuri, S. ..............THU-AM2

Ramseyer, G. ..........THU-AM1

................................WED-PM2

Reeker, L. ................TUE-AM2

Regli, W. ..................THU-AM1

Rippey, W. ...............THU-AM2

Sawyer, D. ..............WED-PM1

Schipani, S. .............TUE-PM2

Schlenoff, C. ............TUE-AM1

.................................TUE-PM2

Scholtz, J. ...............TUE-PM2

Schwartz, M. ...........THU-AM2

Scrapper, C. ............TUE-PM1

.................................TUE-PM1

Shackleford, W. .......TUE-AM1

Shakarji, C. ............WED-PM1

Shneier, M. ..............TUE-AM1

Siewiorek, D. ..........WED-PM1

................................WED-PM1

Slotwinski, J. ...........THU-AM2

Spall, J. ...................THU-AM1

Sparrow, D. .............TUE-AM1

Spetka, S. ...............THU-AM1

Stachowiak, C. ........TUE-PM2

Stancliff, S. .............WED-PM2

Stanton, B. ..............TUE-PM2

Steinberg, M. ...........TUE-PM2

Steinfeld, A. ............WED-PM1

................................WED-PM1

Sultanik, E. ..............THU-AM1

Surampudi, B. .........TUE-AM2

Sutton, L. .................TUE-AM1

Symeonidis, A. ........THU-AM1

Taylor, B. ..................TUE-PM1

Tholey, G. ...............WED-PM1

Tilove, R. .................THU-AM2

Trebi-Ollennu, A. ....WED-PM2

Tucker, S. ................THU-AM1

Tunstel, E. ..............WED-PM2

Valencia, J. ..............TUE-AM1

Wexler, D. ...............WED-PM1

Zimmerman, J. .......WED-PM1

PerMIS 2007 13
ACKNOWLEDGMENTS

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

ideas and efforts are very much appreciated.

Website and Proceedings

Debbie Russell (Chair)

Local Arrangements

Jeanenne Salvermoser (Chair)

Jennifer Peyton

Conference and Registration

Kathy Kilmer (Chair)

Teresa Vicente

Mary Lou Norris

Angela Ellis

Finance

Betty Mandel (Chair)

PerMIS 2007 

Intelligent Systems Division 

Manufacturing Engineering Laboratory 

National Institute of Standards and Technology

100 Bureau Drive, MS-8230

Gaithersburg, MD 20899 

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

Thank you 

PerMIS 

attendees!

14
Evaluation of Navigation of an Autonomous 

Mobile Robot 

N. D. Muñoz, J. A. Valencia, N. Londoño 

Polytechnic Jaime Isaza Cadavid, 

University of Antioquia 

 Medellín-Colombia 

giraa@udea.edu.co

Abstract— In this paper, the navigation of an 

autonomous mobile robot is evaluated; Several metrics 

are described; These metrics, collectively, provide an 

indication of the quality of the navigation, useful for 

comparing and analyzing control architectures of 

mobile robots. Two control architectures are simulated 

and compared in an autonomous navigation mission. 

Keywords: Performance Metrics, Mobile Robots, Control 

Architectures.

I. INTRODUCTION

An autonomous mobile robot has to combine mission 

execution with fast reaction to unexpected situations. 

To overcome this problem, various types of control 

architectures for mobile robot have been designed, 

treating to improve performance of the navigation 

system of a mobile robot for the execution of the 

mission. 

Despite the wide variety of studies and research on

robot navigation systems, quality metrics are not often 

examined, which makes it difficult to make an 

objective comparison of performance [11]; in general, 

use of quality metrics is limited to measuring and 

analyzing the length of the path or the time needed by 

the robot to complete the task. Additionally, the lack of 

consensus on how to define or measure these systems

impedes rigor and prevents evaluation of progress in 

this field and compare its different capabilities [5]. 

However, by applying navigation comparison metrics 

of a mobile robot, such as the trajectory (path) length, 

collision risk and smoothness of trajectory, using a 

protocol, that is in a systematic and ordered way, 

experimental works on mobile robots navigation 

control algorithms can be systematized, and this will 

help researchers to decide which architecture should 

be implemented in the vehicle. 

This paper presents the methodology used for the 

evaluation of the experiment. First, various 

performance metrics used in the navigation of mobile 

robots are described, then the protocol to be followed 

in the evaluation of the experiment is defined, and

finally the obtained results are presented with the aid of 

a simulation software. 

II. QUALITY INDEXES ON TRAJECTORIES

There are various metrics that can be used to evaluate 

the performance of a navigation system, but none of

them is able to indicate the quality of the whole system. 

Therefore it is necessary to use a combination of 

different indexes that quantify different aspects of the 

system. Having a good range of performance 

measurements is useful for: Optimizing algorithm 

parameters, testing navigation performance within a

variety of work environments, making a quantitative

comparison between algorithms, supporting algorithm

development and helping with decisions about the 

adjustments required for a variety of aspects involved 

in system performance [3]. 

In general terms, navigation performance metrics can 

be classified in the following order of importance:

Security in the trajectory indexes or proximity to 

obstacles, metrics that consider the trajectory towards 

the goal and metrics that evaluate the smoothness of 

the trajectory. 

A. Security metrics 

These metrics express the relationship between the 

security with which the robot travels through a 

trajectory, taking into account the distance between the 

vehicle and the obstacles in its path [2]. 

 Security Metric-1 (SM1): Mean distance between 

vehicle and the obstacles through the entire mission 

measured by all of the sensors; the maximum value 

15
will be produced in an environment free of obstacles. If 

the deviation of the index from its maximum value is 

low, it means that the route taken took fewer obstacles. 

Security Metric-2 (SM2): Minimum means distance 

to the obstacles. This is taken from the average of the 

lowest value of the n sensors. This index gives an idea 

of the risk taken through the entire mission, in terms of 

the proximity to an obstacle. In an environment free of 

obstacles SM1 = SM2 is satisfied.

Minimum Distance (Min): Minimum distance 

between any sensor and any obstacle through the entire 

trajectory. This index measures the maximum risk 

taken throughout the entire mission. 

B. Dimensional metrics 

The trajectory towards the goal is considered in its 

spatial and temporal dimensions. In general, it is 

assumed that an optimal trajectory towards the goal is, 

whenever possible, a line with minimum length and 

zero curvature between the initial point (xi

,yi) and the 

finishing point (xn,yn), covered in the minimum time. 

Length of the Trajectory Covered (PL) is the length of 

the entire trajectory covered by the vehicle from the 

initial point to the goal. For a trajectory in the x-y 

plane, composed of n points, and assuming the initial 

point as (x1, f(x1)) and the goal as (xn, f(xn)), PL can 

be calculated as: 

∑

−

=

= + − + + −

1

1

2

1

2

1

( ) ( ( ) ( ))

n

i

L i i i i P x x f x f x

 (1) 

Where (xi

, f (xi)), i = 1, 2. . . n are the n points of the 

trajectory in Cartesian coordinates [6]. 

The length of a trajectory given by y = f(x), in the x-y 

plane between the points (a, f(a)) and (b, f(b)), can also 

be calculated as (Selekwa,2004) 

∫

≅ + ′

b

a

PLaprox f xi dx 21 ( ( ))

 (2) 

Mean distance to the goal (Mgd): This metric can be

applied to robots capable of following reference 

trajectories. An important aspect when determining the 

quality of the navigation system of a robot is the ability 

to follow a trajectory that aims to reach a goal; so, to 

evaluate the quality of the execution of the trajectory, 

the mean distance between the vehicle and goal is 

analyzed. The difference becomes more significant if 

the distance covered is shorter [9]. The mean distance 

to the goal is defined by the square of the proximity to 

the goal distance ln, integrated across the length of the 

trajectory and normalized by the total number of points 

n: ( ( ))

2 2 min ( ) ( ( ) ( )) n i n i n

l = ∀n x − x + f x − f x

 (3) 

n

l ds

Mgd

l

∫ n

=

0

2

 (4) 

Control Periods (LeM): It is the amount of control 

periods. This metric relates to the number of decisions 

taken by the planner to reach the goal, if the robot 

moves with lineal and constant speed (v). This gives an 

idea of the time needed to complete the mission [2]. 

C. Smoothness metrics 

The smoothness of a trajectory shows the consistency 

between the decision-action relationship taken by the 

navigation system, and also, the ability to anticipate 

and to respond to events with sufficient speed [9]. The 

smoothness in the way a trajectory is generated is a 

measure of the energy and time requirements for the

movement; a smooth trajectory allows translates into 

energy and time savings [4]. Additionally a smooth 

trajectory is also beneficial to the mechanical structure 

of the vehicle. 

Bending Energy (BE): This is a function of the 

curvature, k, used to evaluate the smoothness of the 

robot’s movement. For curves in the x-y plane, the 

curvature, k, at any point (xi,f(xi)) across a trajectory is 

given by: 

2

3

2

1( ( ( )) )

( )

( , ( ))

i

i

i i

f x

f x

k x f x

+ ′

′′

= (5) 

The bending energy can be understood as the energy 

needed to bend a rod to the desired shape [1]. BE can 

be calculated as the sum of the squares of the curvature 

at each point of the line k(xi

,yi), along the length of the 

line L. So, the bending energy of the trajectory of a 

robot is given by: 

∑=

=

n

i

E i i

k x f x

n

B

1

2

( , ( )) 1

 (6) 

Where k(xi, yi) is the curvature at each point of the 

trajectory of the robot and n is the number of points in 

the trajectory. 

The value of BE is an average and does not show with 

sufficient clarity that some trajectories are longer than 

others. Therefore, TBE can be used instead; this metric 

takes into account the smoothness and length of the

trajectory simultaneously. 

TBE is defined by 

∫

=

b

a

TBEk (x)dx 2

 (7) 

And numerically, 

∑=

=

n

i

E i i TB k x f x

1

2

( , ( ))

 (8) 

16
The straighter the trajectory, the lower BE and TBE 

values will be, which is desirable since the energy

requirement is increased according to the increase in 

the curvature of the trajectory. 

Smoothness of Curvature (Smoo) is defined by the 

square of the change in the curvature k of the trajectory 

of a vehicle with respect to the time, integrating along 

the length of the trajectory and normalized by the total 

time t [9]. 

t

ds

dt

dk

Smoo

l

∫













=

0

2

 (9) 

 

III. DEFINITION OF THE EVALUATION PROTOCOL FOR 

THE EXPERIMENTS

The control architectures under analysis provide 

basic capabilities for the mobile robot, such as the 

ability to evade obstacles and to generate a trajectory 

towards a goal. 

The control architecture algorithms were simulated 

according to the characteristics of the mobile robot 

platform Giraa_02 used in the laboratory, which has a 

cylindrical structure of 30cm diameter and 

approximately 20cm height; figure 1. It has 8 infrared 

sensors distributed equally around the robot’s 

circumference, and these have a range of 26.5cm and a 

15 degree detection cone; the vehicle has a differential 

locomotion system, and its position is provided by an 

optical encoder and a magnetic compass [8]. 

Fig. 1: Mobile Robot Giraa_02 

Data acquisition in the mobile robot, which occurs 

during each control period, consists of the current 

position of the robot and its orientation (xi

,yi,θi). The 

eight (8) proximity sensors are also read, the maximum 

reading being 26.5 cm, so that, if the robot spends n 

control periods reaching the goal, there is an array of n 

x 11, and n sampling points per 11 pieces of data (3 

coordinates and 8 sensors). 

Taking into account that the objective is to execute a 

navigation mission from a starting point to a final point 

(navigation mission towards a goal), an order of 

importance can be established for evaluating the 

navigation characteristics, as follows: 

1. The mean distance between the vehicle and 

the obstacles during the trajectory 

2. The distance covered by the vehicle between 

the starting point and the goal 

3. The time needed to complete the mission 

4. The smoothness of the trajectory 

The first point considers the security of the trajectory 

and measures the risk taken by the robot in its 

movement towards the goal. The second and third 

points measure aspects related to the planning of the 

trajectory, and the fourth point considers the quality of 

the trajectory according to the energy and time 

required for the movement. 

These characteristics can be analyzed using the 

following set of performance metrics: 

1. SM1, SM2 and Min are proposed for 

evaluating security. 

2. PL and LeM are proposed for evaluating the 

trajectory 

3. TBE is proposed for evaluating the 

smoothness of the trajectory. 

For general purposes, only one metric is required for 

each one of the 3 categories described in section 2, but 

the use of various metrics helps to improve the analysis. 

In our case, the indexes were selected according to the 

abilities of the mobile robot GIRAA_02, considering

the information provided by its data acquisition 

system; the readings from all the sensors are available, 

for each point of the path, allowing the calculation of 

SM1, SM2, and Min. The Mgd index does not apply in 

this navigation mission since it applies when a 

trajectory is followed; TBE is proposed because it 

analyses the smoothness and length of the path. Also, 

this metric is numerically simpler and more precise, 

making it easier to calculate than the other metrics. 

IV. DEVELOPMENT OF THE TEST AND RESULTS

A. Control Architecture 1 

This is a reactive architecture based on a potential 

field method, which produces two different behaviors: 

first, attraction to the goal, and second, repulsion of the 

obstacles. The planning of the movement consists in

the proper combination of both behaviors in such a 

17
way that the robot reaches the goal without collisions. 

This combination is achieved using a vector sum [7]. 

B. Control Architecture 2 

This control architecture is based on behaviors 

denominated AFREB “adaptive fusion of reactive 

behaviors” [12]. By using a neural net, an appropriate 

combination of the behaviors can be achieved, so that 

the system is able to realize more complex tasks, such 

as navigation towards a goal, while evading obstacles 

in its path. 

TABLE I 

ROBOT PERFORMANCE RESULTS 

Performance 

Index 

Control 

Architecture 

1 

Control 

Architecture 

2 

SM1 [cm] 26.1630 25.6276 

SM2 [cm] 18.3750 17.3750 

Min [cm] 11 7 

PL [cm] 562.7810 581.9479 

LeM 283 292 

TBE 0.8535 0.0846 

Maximum SM1= 26.5cm 

C. Simulations 

The first stage of the work focused on evaluating the 

metrics using conventional simulators that permit the 

validation of the effectiveness and the limitations of 

the algorithms. SRM simulator was used on the Matlab 

platform. A 6m x 4m flat, structured environment with 

static obstacles was created for the execution of a

navigation mission between two points (towards a 

goal);The starting position was 50,50 and the position 

of the goal was 500,300 (scenario 1); The paths 

generated by the architectures are shown in figures 2 

and 3. Table 1 summarizes the results obtained from

the simulation using both control architectures 

according to the quality metrics described. 

In an obstacle-free path, maximum SM1 would be 

26.5cm for the Giraa_02, and a similar result occurred 

with SM2 and Min. 

D. Analysis of results 

In scenario 1, Architecture 1 uses less control periods, 

and consequently takes less time to complete the 

mission, and covers a safer and shorter path. 

Architecture 2 covers a smoother path, figures 4 and 5 

show a smaller change in the orientation during each 

control period, with consequent energy saving and less 

structural stress on the robot. 

Fig. 2: Path generated by architecture 1 

Fig.3: Path generated by architecture 2 

From table 1 it can be deduced that the difference 

between both architectures in the trajectory and time 

taken is only 3.3% and 3.1% respectively. The robot

equipped with architecture 2 passed a minimum 7 cm 

from any obstacle, which is acceptable for a 30cm 

diameter robot; also, it showed approximately 65% 

less bending energy than architecture 1. For these 

reasons, architecture 2 is considered the best choice. 

In the other scenarios (figures 6), Architecture 1 

tends to generate safer trajectories, because the robot 

normally transits through zones that are farther from 

the obstacles. This is because the closer the robot is to 

the obstacles, the higher the repulsion potential. 

Although Architecture 2 is governed by the same 

repulsion principle, the command that finally guides 

the robot is a combination of 5 different behaviors, and 

this means that the role of the repulsion potential is less 

important, and collisions are less likely than with

Architecture 1. The Main difference in all the 

simulations is that Architecture 2 generates smoother 

trajectories than Architecture 1. Table II summarizes 

the results obtained in all scenarios. 

18
 

Fig. 4 Smoothness of the trajectory, change in the robot heading each control period, generated by architecture 1 

Fig. 5 Smoothness of the trajectory, change in the robot heading each control period, generated by architecture 2 

TABLE II 

ROBOT PERFORMANCE RESULTS (ALL SCENARIOS) 

Metric SM1 

[cm] 

SM2 

[cm] 

Min 

[cm] 

PL 

[cm] 

LeM TBE

Scenario Arc. 1 Arc. 2 Arc. 1 Arc. 2 Arc. 1 Arc. 2 Arc. 1 Arc. 2 Arc. 1 Arc. 2 Arc. 1 Arc. 2

1 26.1 25.6 18.3 17.3 11 7 562.7 581.9 283 292 0.2463 0.0846

2 25.9 25.7 13.0 14.0 3 7 441.8 429.9 222 216 0.2810 0.0718

3 25.4 23.9 10.0 8.9 3 3 456.7 462.9 234 235 0.5873 0.0120

4 25.0 24.4 13.0 12.4 7 3 395.7 359.9 199 181 0.4007 0.0140

5 25.9 24.9 19.4 16.4 15 3 275.8 259.9 139 131 0.1626 0.0394

6 26.0 25.9 19.7 22.6 7 11 229.9 229.9 116 116 0.1722 0.0469

SM1 maximum = 26.5cm 

Angle 

in 

degrees

Angular Velocity, W

Control Period 

Control Period 

Angle 

in 

degrees

Angular Velocity, W

19
Simulation Architecture 1 Architecture 2 

Scenario 2 

start point 

(100,170) 

Goal 

(470,350) 

Scenario 3 

start point 

(50,350) 

Goal 

(400,205) 

Scenario 4 

start point 

(50,350) 

Goal 

(195,175) 

Scenario 5 

start point 

(350,50) 

Goal 

(530,210) 

Scenario 6 

start point 

(300,350) 

Goal 

(500,340) 

Fig. 6 Paths generated by the control architectures in other scenarios 

20
 The wall-following behaviors CW and CCW in 

Architecture 2, enable the robot to transit through narrow 

zones like corridors, while keeping a safe distance from the 

obstacles and also generating smooth trajectories; this does 

not happen with Architecture 1, as shown in sceneries 3 and 4. 

In general terms, Architecture 2 exhibits better performance, 

and the bending energy index is always lower than in 

Architecture 1, even in scenarios 2, 4 and 5, generating 

shorter trajectories and using less time to complete the 

mission. 

V. CONCLUSION

The results obtained demonstrate the need to establish a 

procedure that can be used to analyze and compare control 

architectures using various performance metrics, since, as we 

have shown, the shortest path or the algorithm that enables 

the robot to reach the goal most quickly is not necessarily the 

most appropriate. 

One of the most important contributions of the project is the 

definition of a comparison protocol for control architectures, 

based on various quality indexes of the trajectories. The 

protocol can be used as a tool in the analysis of the control 

algorithms for autonomous navigation missions. Although 

some of the metrics are intuitive, it was demonstrated that 

together they are useful for systematizing experimental 

control algorithms for the navigation of mobile robots. The 

results show the importance of setting up an ordered and 

methodical procedure in order to analyze and compare the 

various performance metrics of the different control 

architectures. As was seen, the algorithm that produces the 

shortest or fastest route to the goal is not always the most 

energy efficient. 

The results show better performance for architecture 2 in 

both simple and complex circumstances. These scenarios 

demonstrates that architecture 2 performs satisfactorily where 

architecture 1 is unable to perform, or performs less 

efficiently. In the simulations, the robot Giraa_02

programmed with architecture 2, was able to navigate 

satisfactorily while reaching the goal, even through complex 

and narrow trajectories; generally producing non-oscillatory 

and smooth movements. This architecture demonstrates that 

combining behaviors using a neural net is a good option for 

avoiding conflicts between behaviors. In the same way, the 

fusion of behaviors through a lineal combination is a simple 

and efficient method; such a method is necessary given the 

limited computational capabilities of the robot. For these 

reasons Architecture 2 was implemented in the Giraa_02 

robot in a real environment. 

ACKNOWLEDGEMENT

This work was partial result of the FPPI06-1-04, 

FPPI06-1-07, MC02-1-09 and POLI 2061080185 projects, 

financed by the University of Antioquia and Polytechnic 

Jaime Isaza Cadavid. The authors wish to thanks to ICARO, 

GIMEL, Engineering & Software research Groups. 

http://ingenieria.udea.edu.co/giraa. 

REFERENCES

[1] E. Aguirre and A. Gonzalez, “Fuzzy Behaviors for mobile robot 

navigation, design, coordination and fusion”, International Journal of 

Approximate Reasoning, vol. 25, 2000, pp. 255-289. 

[2] J. Álvarez, Planificación del movimiento de vehículos autónomos basada 

en sensores. Tesis doctoral, Universidad de Oviedo, Oviedo, Spain, 1998, pp. 

178. 

[3] G. Cielniak, A. Treptow and T. Duckett, “Quantitative Performance 

Evaluation of a People Tracking System on a Mobile Robot”, Proceedings of 

the European Conference on Mobile Robots (ECMR), Ancona, Italy, 2005. 

[4] S. Dongqing, “Aerial robot navigation in cluttered urban environments”, 

PhD Thesis, The Florida State University, Florida, USA, 2006, pp. 87. 

[5] J. Evans and E. Messina, “Performance Metrics for Intelligent Systems”, 

Proceeding of the Performance Metrics for intelligent Systems Workshop,

Gaithersburg, MD, August 14-16 of 2000. 

[6] Y. Guo and J. Wang, “A new performance based motion planner for 

nonholonomic mobile robots”, Proceedings of the 3rd performance metrics for 

the Intelligent Systems Workshop (PerMIS’03) NIST, Gaithersburg, MD, 

September 2003. 

[7] J.C Latombe, “Robot Motion Planning”, Kluwer Academic Publishers, 4th

Edition, Boston, 1996. 

[8] N. Muñoz, C. Andrade, N. Londoño. “Diseño y construcción de un robot 

móvil orientado a la enseñanza e investigación”, Ingenieria & Desarrollo Ed. 

9, 2006. 

[9] J. Rosenblatt, “DAMN: Distributed Architecture for Mobile Navigation. 

PhD. Thesis”, Carnegie Mellon University Robotics Institute, Pittsburg, PA, 

1997. 

[10] M. Selekwa, E. Collins and J. Combey, “Multivalued Verus univalued 

Reactive Fuzzy Behavior Systems for Navigation Control of Autonomous 

Ground Vehicles”, Proceedings from the 17th annual Florida Conference on 

the Recent Advances in Robotics FCRAR2004, May 20, 2004. 

[11] S. Wong, L. Middleton and B. MacDonald, “Performance metrics for 

robot coverage task”, Proceedings Australasian Conference on Robotics and

Automation ACRA, Auckland, New Zealand, 2002, pp. 7-12. 

[12] A. Zalzala and A. Morris, Neural Networks for Robotic Control, Ellis 

Horwood, 1996, pp.278.

21
Assessing the Impact of Bi-directional Information 

Flow in UGV Operation: A Pilot Study 

Marshal A. Childers, Barry A. Bodt, Ph.D., Susan G. Hill, Ph.D. 

U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 

mchilders@arl.army.mil, babodt@arl.army.mil, sghill@arl.army.mil 

Robert M. Dean, William F. Dodson, Lyle G. Sutton 

General Dynamics Robotics Systems, Westminster, MD 

rdean@gdrs.com, wdodson@gdrs.com, lsutton@gdrs.com 

Abstract—In June 2007, the Robotics Program Office of the U.S. 

Army Research Laboratory and General Dynamics Robotics 

Systems (GDRS) engaged in an exploratory assessment of how bidirectional information flow impacts Unmanned Ground Vehicle 

(UGV) operation. The purposes of the pilot study were to frame 

scenarios, protocol, infrastructure, and metrics for a more formal 

experiment planned for the fall of 2007 while providing current 

data feedback for the architecture developers. The study was 

conducted at Fort Indiantown Gap, PA over two distinct areas of 

rolling vegetated terrain using the eXperimental Unmanned 

Vehicle (XUV). In this paper, we will share the preliminary 

findings of the impact of bi-directional information flow on 

observed robotic behavior, discuss the associated impact on the 

operator, and relate lessons learned to the planning of our fall 2007 

experiment. 

Keywords: bi-directional information flow, perceptive planning, 

deliberative planning, pilot study, unmanned ground vehicles

I. INTRODUCTION 

 A brief background helps motivate the current study. In 

FY 2003, the RPO and GDRS conducted, with testing 

oversight by the National Institute of Standards and 

Technology, an extensive three-site experiment of an 

autonomous navigation system (ANS) [1]. The ANS relied 

on perceptive level planning to achieve a manually predetermined route of way points in rolling desert, rolling 

vegetated and urban terrain. The ANS was given a 

Technology Readiness Level 6 designation by Future 

Combat Systems in part due to this study. Interim advances 

in the Soldier Machine Interface (SMI) greatly simplified 

manual route planning, while perception algorithms and 

hardware continued to mature. More recent developments in 

the architecture allow for deliberative planning in a move 

toward tactically intelligent behaviors. 

 Higher level deliberative planning draws on the 

objective of the operation and the global map of a priori

information (elevation and feature data). Deliberative 

planning consists of separate layers to independently assess 

costs for traversing terrain; the current configuration 

considers costs associated with mobility, time, coverage, 

exposure, and threat. Those layers are combined using a 

weighted heuristic into a single planning layer for use by the 

route planning algorithm. Different weight combinations 

map into various tactical concepts, which allows the SMI to 

provide explicit choices to the user such as “prefer roads” or 

“stealth”; weights can be individually set during 

experimentation. Deliberative and perceptive level planning 

are integrated through the field cost interface (FCI) and best 

information planning (BIP). Local perception provides costs 

at ~5 Hz rate for local paths finishing along the arc formed 

by the sensor range. FCI is a feature that provides a bridge 

between deliberative layer planning and local planning by 

assigning costs at ~1 Hz rate along the perimeter of the 

sensor range representing the entry points for continued 

routes to the objective way point. BIP uses the sensed data 

flowing up from the perceptive level to update the 

deliberative planning map. BIP uses the sensed data flowing 

up from the perceptive level to update the deliberative 

planning map. Using this updated information may be 

especially useful with imperfect a priori knowledge of the 

terrain. It is this bi-directional flow of information that is 

the focus of the study. 

II. DATA COLLECTION 

 The data collection involves a series of informal 

comparative tests in which a simple route, with a few 

widely separated way points, is traversed by the robot. The 

first condition isolates performance of the perceptive layer 

planning as a baseline. The second condition makes the 

global map available for revising the route between predetermined way points in consideration of mobility. Under 

this condition, BIP is available to assist perceptive level 

decisions. A third condition again uses the global map for 

revising the route between way points but in addition 

exercises the FCI, taking into consideration the current 

robot location on the map while the run is in progress. A 

fourth condition allows the global map information in 

22
establishing the initial route and enables the mobility 

deliberative planning layer through the FCI with BIP 

assisting perceptive level decisions. Subsequent conditions 

exercise other deliberative planning layers and vary weights 

in the aforementioned heuristic. 

 These planning configurations were exercised over two 

distinct course areas in situations intended to highlight the 

value added by the deliberative planning layer. Routes were 

selected in both locations to provide a stiff challenge for 

perceptive level planning that may benefit from bidirectional information flow. For example, a vegetationformed cul-de-sac provides such a challenge. Once in the 

cul-de-sac, the perceptive level is unlikely to be able to 

determine an exit path. However, when the BIP feature 

augments the global map, an exit path may appear. Some 

runs focused on impacting the route based on these layers. 

 The first phase of data collection was performed at 

Area B12 at Fort Indiantown Gap, PA and the second phase 

at Area A1. Area B12 (Figure 1) is consistent with rolling 

vegetated terrain and is mostly cross country over open 

fields with high vegetation, but also contains woods, thick 

brush, large rocks, gravel road, unimproved trails, and mild 

changes in elevation. The areas wherein a priori terrain 

feature data was made available for route planning are 

highlighted by light green shading; for areas not shaded the 

only a priori data used for planning was elevation data. An 

initial pre-planned route consisting of three way points 

incorporated a cul-de-sac located in the vicinity of the 

second way point. The XUV traversed the route using an 

onboard Laser Detection and Ranging (LADAR) sensor and 

corresponding algorithms to detect and avoid encountered 

obstacles that were unknown a priori. A trail, high brush 

and trees were encountered along the way to an elevated 

position. In figure 1, the route begins on the left in a 

clearing and continues toward an area of trees and brush not 

present on the global map. After achieving the second way 

point, the robot turned toward the final way point marked to 

the far right. The exact path the robot traveled appears in 

red. 

 Area A1 is characterized by relatively flat terrain, with 

open ground being more grassland; trees and brush occur in 

patches and dense woods and marshy areas are present. In 

Figure 2, the planned route in yellow begins on a trail in the 

lower left portion of the figure and proceeds through woods, 

which are present in the area but were intentionally removed 

from the global map to encourage interaction with the 

feature. After achieving the second way point beyond the 

woods, this path exhibits the planner response to a defined 

exposure point located further to the right beyond the figure 

view. The exposure deliberative layer attempts to minimize 

the line of sight to a position. The path allows the robot to 

achieve the second way point, but then directs the robot to 

the tree line, the best option in consideration of exposure, as 

it proceeds to the third way point. The route sends the robot 

toward an opening in the tree line, followed by a small 

meadow, and finally leads it through some tree clusters 

where the third way point is located on the way to the fourth 

and final way point. Notice the robot icon and red track on 

this run has not achieved the final way point in the upper 

right. This path to a marsh resulted in an emergency stop (estop) which was initiated by the safety operator in order to 

prevent the vehicle from entering the marsh. 

Fig. 1. Area B12 

Fig. 2. Area A1 

 Data collection protocol evolved over the two-week 

study as performance of technology was observed and as 

new situations occurred. Initially, rules were established for 

end of mission, administrative stops, and e-stops, similar to 

past experimentation. To accommodate BIP, the protocol 

was refined during the first week at Area B12 to specify 

when to allow the global map to be updated through BIP. 

This feature was not automated for the study. Rather, an 

23
action on part of the operator to execute a re-plan was 

required. This re-plan was permitted, when the XUV called 

for help, usually after three back-ups from the XUV failed 

to provide a clear path (successive back-ups are part of the 

ANS and are used to provide better perspective when the 

robot otherwise does not see a clear path ahead). Replanning was executed after instructing the XUV to 

backtrack to the location it occupied after the final, 15m 

back-up and before the XUV called for help. Another 

modification was made to this procedure when testing 

moved to Area A1. There it was determined that when the 

re-plan was to be executed, the robot should first be 

repositioned, heading along the re-planned path. Other 

protocol adjustments responded to a recurrent “off-course” 

message and allowed aborted runs if the operator was 

unable to use the cameras for teleoperation. 

III. DATA INTERPRETATION 

A. Measurement 

 Measures of performance are elusive. Previous 

experiments focused on progressing along a pre-determined 

route safely, as fast as possible, and with minimum operator 

interventions. Consequently, success or failure, speed, 

operator intervention frequency and duration were natural 

metrics for performance. Tactical intelligence, however, 

provides a far greater challenge due to the qualitative flavor 

of “how well” the robot has progressed over the route. 

“How well” must be assessed in consideration of standard 

tactical considerations that are not crisply defined for this 

technology and may require trade-off decision making. 

Measurement of operator workload perspective comes from 

surveys administered after each run and live observations 

made during each run, augmented by video record. The 

operator must oversee the progress of the robot during 

execution of the selected route (intervening as necessary) 

and will likely strive for an understanding of the indirectly 

observed planning decisions made along the way; this 

presents new cognitive challenges. Quantitative measures 

for robot behavior would normally include the number and 

duration of operator interventions, the frequency for 

required re-planning using BIP, time to complete the route, 

and exposure time (when the exposure layer is activated); 

time aspects are not addressed here. In addition, we use 

observations made by data collectors and plot the 

differences between pre-planned routes and the alternative 

routes developed during each run. 

B. Descriptive Measures 

 Simple descriptive statistics appear in the following 

tables. We recognize they are at best rough and indirect 

measures of performance but are worth reporting for 

completeness. Table 1 reports the outcome of each run 

performed in Area B12. The baseline autonomous mobility 

(AM) perception runs both resulted in e-stops. BIP alone 

resulted in the normal end of mission “halt” message to the 

operator. FCI alone resulted in one normal halt and one end 

of mission in which the robot traveled far away from the 

intended final way point. With both BIP and FCI, three 

runs resulted in two halts and another end of mission where 

the robot was off course. 

TABLE 1 

 

RUN OUTCOME FREQUENCY BY 

EXPERIMENTAL CONDITION (AREA B12) 

 Outcome 

Condition Halt Off 

Course 

E-Stop All 

AM 0 0 2 2 

BIP 2 0 0 2 

FCI 1 1 0 2 

BIP+FCI 2 1 0 3 

All 5 2 2 9 

 

 Table 2 reports the outcomes for conditions run in Area 

A1, focusing on the mobility benefits of BIP and the FCI. 

Three runs were aborted due to teleoperation camera failure. 

The FCI weights were varied, with the larger values 

yielding more control to the perceptive layer (i.e. FCI=2 

means that the weighting of the perceptive layer costs was 

twice that of the deliberative layer). Results are mixed; FCI 

alone results in five halts in six attempts, whereas the 

combined BIP and FCI result in only one halt in seven 

attempts, one abort and five e-stops. Most e-stops occurred 

when the XUV tried to cross the marsh, but some were 

called due to excessive wander of the robot. 

 

TABLE 2 

RUN OUTCOME FREQUENCY BY 

EXPERIMENTAL CONDITION (AREA A1) 

 Outcome 

Condition Halt Abort E-Stop All 

AM 2 1 2 5 

BIP 0 1 1 2 

FCI=1 2 0 0 2 

FCI=2 1 0 1 2 

FCI=4 2 0 0 2 

BIP+FCI=1 0 1 2 3 

BIP+FCI=2 1 0 1 2 

BIP+FCI=4 0 0 2 2 

All 8 3 9 20 

 Table 3 shows the remaining conditions that were run 

and corresponding results. Toward the end of the second 

week, these runs were attempted to explore the impact of 

24
additional layers being considered in the deliberative 

planning. Mobility and FCI weights were also adjusted 

based on observation from earlier in the week. The weights 

for the Mobility, Time, and Exposure layers had a possible 

value of 0 to 1. Five of eight runs resulted in a normal halt. 

Two runs resulted in an e-stop and one in an abort. 

TABLE 3 

RUN OUTCOME FREQUENCY AND 

DELIBERATIVE LAYER WEIGHTS (AREA A1) 

 Outcome 

Condition Abort EStop Halt All 

BIP+Mob=0.5 1 0 0 1 

BIP+Mob=0.5+Exp=1 0 0 1 1 

BIP+Mob=0.5+FCI=2 0 1 0 1 

BIP+Mob=0.5+Exp=1+ 

FCI=2 

0 0 1 1 

BIP+Time=1 0 0 1 1 

BIP+Time=1+FCI=4 0 0 1 1 

Mob=0.5+FCI=2 0 0 1 1 

Mob=0.5+FCI=1+ 

Exp=1 

0 1 0 1 

All 1 2 5 8 

 Table 4 lists the various measures collected during the 

runs at Area B12. The FCI condition results in fewer backups, because the effect of the FCI routed the XUV, 

unintentionally, away from the cul-de-sac. Teleoperation 

repositioning was an important measure at Area A1, 

because it was used after a re-plan that was based on BIP. 

Repositioning was performed to orient the robot along the 

re-planned path. A similar summary for Area A1 was 

produced but is not presented. 

TABLE 4 

EVENT FREQUENCIES (AREA B12) 

 Conditions 

 AM BIP FCI BIP+FCI 

Runs 2 2 2 3 

Teleop_Obstacle 1 0 1 0 

Teleop_Reposition 0 0 0 0 

Back-up 5m 9 7 3 6 

Back-up 10m 3 4 2 1 

Back-up 15m 0 2 2 1 

Back-up Total 12 13 7 8 

Oper_Max BUs 0 2 1 0 

Oper_Off Course 1 1 3 5 

Resume_Only 0 1 4 5 

Backtrack_15m 0 2 0 0 

BU_Stuck 1 0 0 0 

C. Path Analysis 

 Plots of the route traveled were made for each of the 

runs in the study. The plot, together with summary statistics 

and narratives collected during the run allow us to interpret 

events along the run. Figure 3 shows the outcome for a run 

in Area B12. The yellow line represents the original route 

plan to visit a second way point prior to traveling to the end 

way point at the helicopter pad. The global map feature data 

included for route planning is dated by several years and 

inconsistent with current vegetation. The red line indicates 

the path of the robot. The XUV traveled into the high brush 

near the second waypoint. Several backups were executed 

by the ANS in the vegetated cul-de-sac. A first re-plan 

based on BIP appears in blue. Subsequent attempts by the 

robot still failed to find a path. The operator teleoperated the 

XUV away from the trees at the end of the cul-de-sac and 

executed a new re-plan (orange) based on the updated 

terrain feature data in the global map. This route led the 

XUV successfully to the goal. Figure 1 in Section II 

provides a second example of the impact of BIP in which 

the run required three re-plans (blue, orange, and green) but 

no teleoperations to successfully reach the objective. 

Fig. 3. Area B12 (Mobility =1, BIP) 

 Area A1 produced several interesting examples. Figure 

4 shows one run in which the mobility and exposure 

deliberative layers were turned on along with BIP and the 

FCI. The positions from which to limit exposure were to the 

Southeast (North is up) of the operator control unit (OCU) 

position, denoted by the blue symbol in the lower right hand 

corner of the image, and to the Southeast of the final 

waypoint. The yellow line represents the original plan, 

25
passing through an area with dense woods that were 

intentionally not included on the global map. An early offcourse message resulted in the first re-plan (blue); little 

change in path occurred. Both show the interest in achieving 

the second way point before retreating to the tree line in 

consideration of exposure. Although a definitive reason for 

the XUV traveling wide of that second re-plan before 

turning to the second way-point is not possible, a plausible 

reason may be that the deliberative planning layer was 

attempting to use the tree line along the South edge (not 

visible on the figure) or subtle changes in elevation to 

reduce the silhouette of the robot. Past the second way 

point, another off-course was issued because of the distance 

between the actual and planned routes. A re-plan (orange) 

provided a path to the last two way points. After passing 

through a gap in the trees and progressing through a 

meadow, the robot traveled to a location, which often 

produced an off-course message. At that point a final replan (green) was provided to guide the XUV to the final 

way point. 

Fig. 4. Area A1 (BIP+Mobility=0.5+FCI=2+Exposure=1) 

 Figure 5 shows a situation with the deliberative layers 

for mobility and exposure turned on but only BIP operating 

during the run. This run also shows three re-plans. During 

the run, the robot does not appear to wander to the extent 

apparent in the run depicted in Figure 4. 

Fig. 5. Area A1 (BIP+Mobility=0.5+Exposure=1) 

 Not all runs were successful, clearly since we report a 

total of 11 e-stops in this area. Figure 6 illustrates one such 

run where the XUV appears lost after 6 re-plan attempts. An 

e-stop was called for safety reasons when the robot came 

too close to the OCU position. Figure 7 shows a typical path 

leading to an e-stop due to the XUV proceeding down a culde-sac to an impassable marsh. Actually, this particular run 

had to be aborted due to inclement weather. 

Fig. 6. Area A1 (BIP+Mobility=0.5+FCI=2) 

26
Fig. 7. Area A1 (BIP+Mobility=0.5) 

 Additionally, two real time displays (not shown) aid 

interpretation. One shows how the terrain around the robot 

is being assessed by the dynamic planner in terms of safe 

progression and another illustrates how the global map is 

being updated with local terrain features that the robot 

encounters during route traversal. 

D. Operator Workload 

 Although the pilot study focused primarily on the 

integration of the bi-directional flow of information into 

UGV operation, the potential impacts on operator 

performance were considered as well. Three GDRS 

software developers (co-authors of this paper) acted as 

operators of the XUV during all the pilot study runs. 

Certainly these operators were not intended as 

representative of Soldier users; however, it was decided that 

useful operator feedback on workload and situation 

awareness (SA), as well as methods to measure the 

workload and SA, could be obtained from the pilot test 

operators during this early integration assessment. 

 Operator tasks included set-up for each run, execution 

of initial route plan, monitoring of XUV status, and 

intervention where required. There were some required 

teleoperation interventions. There were also re-plans during 

runs that implemented BIP. In these cases, the recalculation 

of routes was automated using the bi-directional flow of 

data, however, the call for, and execution of, the new plan 

were operator tasks (the objective is for this to be an 

automated process in the future). The operators were asked 

to make ratings of their overall workload (on a scale from 0-

10), adapted from the Overall Workload Scale in [2] and 

[3]. Ratings (on scales of 1-7) of situation awareness (SA) 

were obtained for the three areas of 1) ability to perceive 

information, 2) ability to understand information, and 3) 

ability to predict what would happen. These situation 

awareness questions were drawn from the definition of SA 

in [4]. A final question on expectations (“Did the XUV do 

what you expected...?”) was asked, also. In addition to the 

subjective ratings, video recordings of the OCU, over the 

shoulder of the operator, were also obtained. 

 Ratings from 26 trials were collected across all 

operators. In general, workload ratings were relatively low 

(mean= 3 (out of 10)) and situation awareness ratings were 

relatively high (mean=6 (out of 7)). When asked if the 

XUV behaved as expected, operators responded “yes” for 

twenty of the 26 trials. It should be noted, however, that 

some of these “yes” responses had qualifiers attached. For 

example, five of the “yes” responses also said something 

like “except for this one part of the run which was 

unexpected.” Interestingly, one operator said that the XUV 

behaved as expected because “I had different expectations 

based on what it did for the last [similar] runs.” Based on 

the ratings and additional responses, then, it seems as if the 

robot behavior at times was unexpected, puzzling the 

operators, and some expectations were changed based on 

observed behaviors. This question of expectations and 

changing expectations needs to be explored further for its 

implications on information and decision support for 

operators. 

 The issues that are highlighted here include the ability 

of the operator to perceive and understand information 

relevant to intelligent behavior by the robot, and then know 

what will happen in the future (as shown by prediction and 

expectation). What does the operator need to know? How 

involved does the operator need to be (or want to be) in 

planning decisions made automatically by the robot; when 

is permission to execute and proceed needed? Issues of 

trust in automation and complacency arise. Workload 

associated with these tasks, for single and multiple robots, 

as well as all other operational tasks being performed, are 

important to consider. 

IV. CONCLUSION 

 As a result of the June 2007 exercise, the developers 

learned a great deal about the technology performance, 

necessitating changes for later releases. The Army sponsors 

recognized many issues to be addressed in further testing. 

With regard to changes, the FCI could benefit from an 

investigation of the balance of deliberative planning layers 

and perceptive planning. It was suspected that weighting 

played a role in some of the unexpected behaviors observed. 

A desirable change would enable, at the end of the mission, 

the field cost planner to yield to the original planner so that 

the end way point can be more consistently achieved. Some 

runs, especially in Area B12, resulted in end of missions 

being called when the XUV was far removed from its 

destination. Further, improved tracking is sought to keep 

synchronized the robot and the OCU. It is suspected that the 

run illustrated in Figure 6 was probably due to the 

27
instructions coming to the robot at an inappropriate time for 

where the robot actually was at that time. Improved 

handling of off-course messages is also being worked; 

during the experiment, this message resulted in “resume 

mission” or a “re-plan” and execute. 

 Several improvements related to the BIP are also 

ongoing. The range of local sensing is being increased from 

20 m to 60 m. This should increase the potential path 

options seen by BIP. A planned improvement is to increase 

the scan sweep upon robot back-up and when the robot 

slows, the latter taking advantage of the opportunity not to 

compete with processing that supports the ANS. During the 

June 2007 exercise, we observed instances when the XUV 

missed opportune paths in very close proximity, most likely 

because the scan was not wide enough to see them. When 

the opportunity called for using BIP to suggest a re-plan for 

execution, current protocol requires the operator to reorient 

the robot to the new route; this process is being automated. 

Efficiencies in sensed data logging are also being pursued. 

 With regard to the design of the study, several issues 

were recognized. The test protocol must be sufficiently 

robust to handle the new situations created by enhanced 

robot behaviors. Data consistency depends on a tight, 

strictly adhered to protocol. Communications continue to 

present a challenge; to work this problem more time is 

warranted prior to the experiment to evaluate base station 

locations. Scripts will be developed to automatically set 

configurations for each run. In the present study, this work 

was done anew each time, sometimes at both the OCU and 

at the XUV, taking more time and introducing more 

opportunity for set-up error. 

 The terrain and mission context of runs must also be 

revisited. In the present study, we relied on one classic case, 

the cul-de-sac, to exercise the new technology. Other 

interesting cases must be determined to highlight “problem 

solving” over an array of challenges. Further, the terrain 

must be expanded and more varied for the coming 

experiment. Elevation data, for example, changed very little 

in either area, minimizing the technology’s ability to 

leverage it in consideration of, for example, an exposure 

layer. And by restricting the length of the run in the present 

exercise (run lengths for Area B12 and Area A1 were less 

than 1 kilometer), we limited the FCI in leveraging mobility 

and time layers as well. An expanded area is available for 

testing in the fall. Finally, to provide a richer environment 

for workload assessment and to experiment with a maturing 

Reconnaissance Surveillance, and Target Acquisition 

(RSTA) capability, RSTA mission elements should be 

rolled into the subsequent experiment. 

 The glaring issue remains of how to measure the 

success or failure of this intelligent system. Our approach is 

merely to identify elemental challenges for the system to 

overcome and then to determine whether or not they were 

overcome. But the question of exactly how to determine 

success and to what degree remains elusive. In a large scale 

study, changes in the frequency of e-stops, teleoperation, 

etc. would be revealing. Elements of time to complete the 

mission and the duration required to overcome a course 

obstacle would also serve as a basis for comparison. A 

decision tree developed post hoc to be evaluated using 

subjective utilities from a Soldier scout has been considered. 

The authors welcome input on measurement that could be 

helpful in subsequent evaluations. 

REFERENCES 

[1] R. Camden, B. Bodt, S. Schipani, J. Bornstein, T. 

Runyon, F. French, C. Shoemaker, A. Jacoff, A. Lytle, 

“Autonomous Mobility Technology Assessment Final 

Report,” ARL-TR-3471, 2005. 

[2] M. Vidulich and P. Tsang, “Absolute Magnitude 

Estimation and Relative Judgment Approaches to 

Subjective Workload Assessment,” in Proceedings of the 

Human Factors Society 31st Annual Meeting, 1987, pp. 

1057-1061. 

[3] S. Hill, H. Iavecchia, J. Byers, A. Bittner, A. Zaklad, and 

R. Christ, “Comparison of Four Subjective Workload 

Rating Scales,” Human Factors, 34(4), pp. 429-439, 1992. 

[4] M. Endsley, “Design and Evaluation for Situation 

Awareness Enhancement,” in Proceedings of the Human 

Factors society 32nd Annual Meeting, 1988, pp. 97-101. 

[5] M. Endsley, “Towards a Theory of Situational 

Awareness in Dynamic Systems,” Human Factors 37(1), 

pp. 32-64, 1995. 

28
A Common Operator Control Unit Color Scheme for Mobile Robots 

M. Shneier, R. Bostelman, J. S. Albus, W. Shackleford, T. Chang, T. Hong 

Intelligent Systems Division 

National Institute of Standards and Technology 

Gaithersburg, MD 

Abstract 

The Intelligent Systems Division at the National 

Institute of Standards and Technology (NIST) has participated in the Defense Advanced Research Project Agency 

(DARPA) Learning Applied to Ground Robots (LAGR) 

project for the past 2 1⁄2 years. In Phase 2 of the LAGR program, NIST was asked to provide a common operator control unit (OCU) color scheme for all LAGR teams to use. 

The color scheme simplifies the task of LAGR’s evaluation 

team by providing a straightforward way to compare the 

performance of each of the teams using the different OCUs. 

During Phase 1, LAGR performers applied their own standards to the OCU color scheme and DARPA and other performers had a very difficult experience evaluating what the 

robot was computing based on stereo image, instrumented 

bumper, and inertial data. 

NIST developed the color scheme based on realworld conventions and on the desire to accommodate as 

much of the teams’ existing color schemes as possible. For 

example, typically red lights mean stop and green lights 

mean go for automobiles. This scheme was adopted by 

coloring obstacles red and traversable ground green in the 

new common color scheme. Red, green, blue (RGB) colors 

were produced for a variety of necessary parameters including: unknown regions, lethals, bumper hits, road/path, 

planned and traversed paths, goal, and waypoints. Also, 

vehicle modes were expressed such as: Normal Control, 

Aggressive, Backing, Stopped, and Manual modes. 

The paper discusses the color scheme for ground 

robots developed for the LAGR Program. 

I Introduction 

The Operator Control Unit (OCU) for a mobile robot needs to display a lot of complex information about the 

state and planned actions of the vehicle. This includes displays from the robot’s sensors, maps of what it knows about 

the world around it, traces of the path it has already traveled 

and predictions of the path it is planning to take, and information about obstacles, clear ground, and unseen regions. 

The information needs to be easy to understand even by 

people who have no understanding of the way the control 

system of the robot works, and should enable them to halt 

the vehicle only if it is about to take an action that will 

cause damage. 

In order to display all the information in an understandable way, it is necessary to use color to represent the 

different types of region. There are no existing conventions 

on what colors to use and each OCU developer must decide 

how best to assign the colors. This is fine if the color 

scheme chosen is indeed easy to understand and if the people using the OCU are only dealing with a single robot. 

However, there is a growing need for the ability to control 

more than one robot, especially in the areas of urban search 

and rescue and bomb disposal robots, where a damaged or 

destroyed robot needs to be replaced as soon as possible. 

There has been at least one effort to create a multirobot OCU[1], called MOCU. The goal is to create an OCU 

that can control multiple vehicles of different types and 

from different manufacturers from a single OCU. The OCU 

is based on a core set of capabilities enhanced by modules 

that provide specific capabilities and communication protocols needed by each vehicle. The capabilities are defined at 

run time through configuration files. An impressive set of 

vehicles can be monitored simultaneously using MOCU. 

Unfortunately for the purposes of this paper, no standard 

color scheme is described for MOCU, so it was not possible 

to benefit from this approach. 

Another paper does describe the color scheme used 

in its multi-vehicle OCU [2]. They describe the following 

assignments of colors, some of which are similar to the 

choices discussed below. The display a two-dimensional 

scrollable map of the search area marked with icons to indicate the relative positions of the robots, obstacles, target 

munitions and terrain features. Icons for robots change color 

to indicate the current status of the robot: green indicates 

operation within normal limits and red indicates a fault or a 

lack of progress. In the work described in this paper, multiple robot modes are described with different colors. Robot 

icons also indicate the heading of the robot and its position 

relative to domain objects and other robots. An obstacle 

detected by the robot or manually entered by the operator is 

displayed as a gray box. Areas that are indicated clear by 

the robot's IR sensors can optionally be colored blue on the 

operator's display, to distinguish those areas from unswept 

areas. If the robot detects steep parts of the terrain, the OCU 

can mark those areas yellow or orange, depending on the 

sensed inclination. As the robot moves, the clear areas under 

the munitions detector are shown in green, similar to what is 

used in our OCU. When a robot detects unexploded ordinance, its location is marked with a red circle on the OCU 

display. In the OCU described here, critical obstacles are 

also displayed in red. 

29
The OCU developed in this work was oriented towards a somewhat different purpose. As part of the DARPA 

LAGR program [3], a large number of teams were tasked 

with developing learning algorithms to try to improve their 

performance in a series of field trial, held once a month. 

After the first phase of the program, NIST was asked to 

develop a common color scheme for the OCUs used by each 

team so that the evaluation team could reduce the need to 

learn the meaning of colors used by each team. 

II The DARPA LAGR Program 

The DARPA LAGR program [4] aims to develop 

algorithms that will enable a robotic vehicle to travel 

through complex terrain without having to rely on handtuned algorithms that only apply in limited environments. 

The goal is to enable the control system of the vehicle to 

learn which areas are traversable and how to avoid areas 

that are impassable or that limit the mobility of the vehicle. 

To accomplish this goal, the program provided small robotic 

vehicles to each of the participants (Figure 1). The vehicles 

are used by the teams to develop software. A separate 

LAGR Government Team, with an identical vehicle, conducts tests of the software each month. 

The vehicle provided by DARPA is a small but 

very capable robot with substantial on-board processing 

capacity and a rich set of sensors. The sensors include two 

pairs of color cameras mounted on a turret on the front of 

the vehicle, a pair of infra-red range sensors (non-contact 

bumpers) on the front of the vehicle, and a physical bumper 

centered on the front wheels of the vehicle. For position 

sensing, the vehicle has a Global Positioning System (GPS) 

receiver, wheel encoders, and an inertial navigation system 

(INS). In addition, there are sensors for motor current, battery level, and temperature. There are four single-board 

computers on the vehicle, one for low-level vehicle control, 

one for each of the stereo camera pairs, and one for overall 

control of the vehicle. All processors use the Linux operating system. The vehicle has an internal Ethernet network 

connecting the processors, and a wireless Ethernet link to 

external processors. 

The availability of range information from stereo 

vision enables the robot to navigate largely using the geometry of the scene. Sensor processing is aimed at determining where the vehicle is and what parts of the world 

around it are traversable. The robot can then plan a path 

over the traversable region to get to its goal. 

When the vehicles were delivered, they came with 

a baseline control system and a baseline OCU. While some 

of the teams stuck with the baseline OCU, many developed 

their own OCU to provide better information for debugging 

and monitoring the robot. Since there were no standards for 

how the OCU should look, each of the teams developed 

dissimilar appearance models. As a result, when the Government evaluation team ran the monthly tests, they had to 

learn the conventions used by each OCU in order to monitor 

performance of the associated vehicle controller. This led 

them to request that NIST develop a common color scheme 

for Phase II of the program. 

Figure 1. The robot used in the DARPA LAGR program. 

III The Color Scheme 

There is a large amount of literature on color and 

how to select color schemes for human-computer interfaces. 

Reference [5] provides a brief overview and links to more 

comprehensive resources. Many of these approaches make 

use of the color wheel and recommend selecting complementary or analogous colors. There are many tools to help 

in this selection [6]. This approach breaks down, however, 

when more than a small number of colors have to be selected and when certain colors have an accepted meaning in 

the application. 

The way the color scheme for the LAGR OCUs 

was developed was to start out with a straw man proposal, 

which was sent out to the teams and to the DARPA Program 

Manager. Comments were received from a number of 

teams, which resulted in a revised scheme. This process was 

iterated until a scheme acceptable to all was developed. This 

may not be the best way to assign colors, but the intention 

was not to find the most pleasing scheme, but one that 

would be easy to interpret. Note that the color scheme was 

not intended to change the individual teams’ OCU layout or 

content, only to ensure that the appearance of similar information in different OCUs would be consistent in meaning. 

For the OCU, a large amount of information had to 

be represented simultaneously. Most of the teams displayed 

similar information, but allowance had to be made for extra 

features if they were required by even one of the teams. The 

information to be displayed included maps, which often 

included a low resolution long-range map and a higher resolution close-up map. These displays contain most of the 

information the color scheme needs to represent. Also displayed is vehicle mode (normal, aggressive, backing up, 

etc.). A third set of displays includes one or more images of 

what the vehicle is currently seeing. While there are often 

30
overlays on the images, so far the colors for these have not 

been standardized. 

Maps need to display a wide range of information 

about the terrain and the planned and traversed paths. The 

colors assigned to the various features are shown in Figure 2 

and 3. The same color scheme is used for both the low resolution and high resolution maps. Most colors are fixed, and 

refer to a single type of feature, but traversability ranges 

across a green-to-black spectrum based on how expensive it 

is to cover the associated terrain. Green means the vehicle 

can easily drive, while black means it is very difficult to 

drive. Note that in the Hue, Saturation, Luminance (HSL) 

color space, this requires changing only the Luminance 

component of the color, making for an easy mapping from 

cost to color. These costs, in conjunction with obstacles and 

bumper hits, are used by the planners to determine the optimal path to the goal. Comparing our scheme for representing steep slopes with that of [2], they have two fixed colors, 

orange and yellow for different degrees of steepness, while 

our scheme would assign smoothly-varying colors starting 

as green at the base of the slope and becoming darker as the 

slope gets steeper. This, we believe, gives more information 

about the true nature of the traversability of the slope.

 Traversability cost ranges from low = 

green (0,255,0) to high = black (0, 0, 0) 

Unknown regions = blue (0, 0, 255 

Lethal obstacles = red (255, 0, 0) 

Bumper hits = dark red (170, 0, 0) 

Road/path = light gray (215, 215, 215) 

 High-level planned path = yellow 

(255,255,0) 

 Low-level planned path = orange (255, 

150, 0) 

 Traversed path = dashed purple (255, 0, 

255) 

Goal direction = white (255, 255, 255) 

Goal = white square (255, 255, 255) 

 Waypoints = yellow squares on high level 

path (255,255,0) 

 Camera FOV border = white (255, 255, 

255) 

Figure 2. The map colors and their interpretations. 

The vehicle itself is displayed on the map, together 

with its field of view (FOV), planned path, and the path 

traversed so far. The planned path includes waypoints and 

an indication of the location of the goal. The straight-line 

path to the goal is also indicated. If the vehicle controller 

determines that it is traversing a road or path, this is displayed in gray. 

The display of the vehicle changes color to indicate 

the current mode (Figure 4). Modes represent the status of 

the vehicle. Not all teams make use of all the modes, but 

simple ones, such as normal autonomous driving, stopped, 

and backing up are universal. 

Figure 3. A schematic showing the meaning of the colors on 

the map. 

Normal Control = aqua (40, 200, 200) 

Aggressive = dark purple (130, 0, 130) 

Backing up = purple (255, 0, 255) 

Stopped = brown (150, 100, 50) 

Manual = light blue (120, 120, 255) 

Figure 4. Vehicle modes displayed by coloring the vehicle 

on the map. 

IV Implementation by LAGR Teams 

Each of the LAGR teams was required to implement the scheme by the February 2007 test date. The common OCU Color Scheme code was sent to all teams in December, 2006. Implementation was only partially achieved, 

in that some teams did not have their OCUs ready in time, 

and some implemented only some aspects of the color 

scheme. NIST implemented the scheme (Figure 5 and 

Figure 6), showing both a high-resolution near-range map 

and a low-resolution, longer range map. In these figures, the 

vehicle believes that it is traversing a path, so the planned 

trajectory remains on the path until it ends and the vehicle 

has to enter unknown terrain to reach the goal. 

SRI International originally developed their own 

color scheme, shown in Figure 7, which included only some 

of the new scheme features shown in Figure 2. There is no 

representation of the vehicle, and no waypoints, and the 

31
traversable terrain is shown in a single color. While the 

items represented have not changed, SRI implemented the 

full color scheme (Figure 8, by permission of SRI) so that 

the terrain is now shown with variation in traversal cost, 

obstacles are in red, and unknown regions in blue. 

Figure 5. The NIST high resolution, short-range map.

Figure 6. The NIST low resolution, long-range map. 

Figure 7. The old SRI OCU color scheme. 

Another of the teams, Netscale Technologies, Inc., 

converted from their old, rather attractive, color scheme shown 

in Figure 9 for the high-level (low resolution) map and 

Figure 10 for the high resolution map. Their implementation 

of the common color scheme is shown in Figure 11 (low 

resolution) and Figure 12 (high resolution). Note that Netscale made other changes to what is represented in the 

OCU, so the maps are not directly comparable.

An interesting variant of the color scheme was implemented by the University of Pennsylvania. While it uses 

similar colors to the common color scheme, it uses pastel versions. For their scheme, the color correspondence to the map 

cost from low to high is shown in the color bar on the right 

of Figure 13 (by permission of U. Penn.). Green means safe 

to drive over, red marks obstacles, blue areas are unknown, 

and white indicates the planned path. This is in contrast to 

their old color scheme, shown in Figure 14. That color 

scheme was based on shades of gray. The higher the map 

cost, the darker the grayscale, and the lower the map cost, 

the whiter the color. In this scheme, white meant safe to 

drive over, dark gray (black) meant an obstacle, midgray 

was unknown, and blue indicated the planned path. Clearly, 

the new color scheme is closer to the standard, but it is not 

fully compliant. 

Figure 8. The new SRI OCU using the common color 

scheme. 

Figure 9. The old Netscale low resolution map color 

scheme (with permission from Netscale Technologies). 

32
Figure 10. Old Netscale high-resolution maps. 

Figure 11. New Color Scheme for Netscale's low resolution 

maps. 

Figure 12. New color scheme applied to Netscale high resolution map. 

Figure 13. U. Penn's variant of the color scheme.

V Discussion and Conclusions 

The color scheme was distributed to the teams in December, 2006. They were expected to try to have it in place by 

the January, 2007 test, and were mandated to have it in place 

by the February, 2007 test. While a few of the teams made the 

deadline, many did not. NIST provided a function to map cost 

and identification of a pixel or region to color, and now all 

teams have either adopted the color scheme as is, or, like U. 

Penn, make use of a variant based on the common color 

scheme. 

Use of the color scheme has had the desired effect. 

The Government evaluation team has expressed satisfaction in 

the results. They can more easily understand the OCUs of different teams, although the fact that teams do not have to use a 

standardized OCU layout makes the benefit smaller than it 

could have been. There is a lot of resistance in the teams to 

changing their OCU displays. The displays were developed for 

debugging and for monitoring the vehicles’ progress. Given the 

very large variation in approaches to the LAGR problems, it 

would be difficult and very time-consuming to come up with a 

universal OCU that captured all the possible information to be 

displayed and also supported the debugging approaches of all 

the teams. 

33
Figure 14. U. Penn's prior color scheme.

It is interesting that there are few, if any, standard 

color schemes for computer applications. Large vendors, such 

as Microsoft and Apple, develop and publish in-house styles 

for their products that cover layout, color, and menus, amongst 

other things. Typically, colors are chosen from “themes” or 

groups of compatible colors Thus, all Microsoft Office products have the same look and feel on a single machine. By 

choosing different themes, different users keep consistency 

between different tools, but the tools may appear different than 

those on another machine. This is not a standard, however, 

although other vendors are encouraged to adopt the color 

schemes. Other tools for accomplishing similar tasks often use 

different color schemes to differentiate their products and avoid 

potential legal issues. 

There are many tools to help choose colors that go together [6], but they work primarily for small numbers of colors. 

For larger numbers, people sometimes use a “natural” color 

scheme in which a photograph of a natural scene is used to pick 

the colors. The assumption is that nature is harmonious, so the 

resulting color scheme will be as well. This approach works 

well, but doesn’t suit our purposes. We have certain colors that 

by convention in the program have predefined meanings (e.g., 

red for obstacles). Some of the colors should stand out from 

the others, so not all the colors should be harmonious. We also 

had to accommodate strong feelings on the part of different 

teams about use of certain colors. All this led us to a consistent, 

understandable color scheme that breaks many of the rules for 

picking colors. 

It was a useful, if somewhat tedious process to develop the common color scheme. Work needs to be done more 

broadly to develop standard color schemes for different application areas, such as medical images, geographic information 

systems, and other complex visual displays that require substantial effort to understand. 

Bibliography 

 [1] D. N. Powell, G. Gilbreath, and M. H. Bruch, "Multirobot operator control unit," SPIE Proc. 6230: Unmanned Systems Technology VIII, Defense Security 

Symposium ed Orlando, FL: 2006. 

 [2] P. K. Pook, J. A. Frazier II, T. Ohm, R. Robert, and 

G. Whittinghill, "A Testbed for Evaluating Robot 

Team Interaction under Operator Supervision," Proceedings of UXO Forum '98, 

http://citeseer.ist.psu.edu/267793.html 1998. 

 [3] L. D. Jackel, E. Krotkov, M. Perschbacher, J. Pippine, and C. Sullivan, "The DARPA LAGR Program: 

Goals, Challenges, Methodology, and Phase I Results," Journal of Field Robotics, Special Issue on 

Learning in Unstructured Environments, vol. 23, no. 

11/12, pp. 945-973, 2006. 

 [4] T. Wagner, "Learning Applied to Ground Robots 

(LAGR)," 

http://www.darpa.mil/ipto/programs/lagr/index.htm, 

2005. 

 [5] Wikipedia, "Color Theory," 

http://en.wikipedia.org/wiki/Color_theory, 2007. 

 [6] P. Lyons and G. Moretti, "Nine Tools for Generating 

Harmonious Colour Schemes,", The 6th Asian Pacific Conference on Computer Human Interaction 

(APCHI 2004) ed Rotorua, New Zealand: 2004. 

34
How DoD’s TRA Process Could Be Applied To 

Intelligent Systems Development

D.A. Sparrow 

Institute for Defense Analyses 

4850 Mark Center Drive 

Alexandria, VA 22311, USA

dsparrow@ida.org

S. Cazares 

Institute for Defense Analyses 

4850 Mark Center Drive 

Alexandria, VA 22311, USA

scazares@ida.org

Abstract—Over the past several years, the Department of 

Defense (DoD) has instituted a Technology Readiness Assessment 

(TRA) process based on NASA’s Technology Readiness Levels 

(TRLs). The motivation was to ensure that technology development was complete and that performance was understood before 

entering into the System Development and Demonstration (SDD) 

phase of a program. Such a disciplined approach may aid in 

Intelligent Systems development. However, NASA’s TRLs were 

derived in a context of hardware systems, and the hardware 

TRLs needed modification to treat software and software-intensive systems. This paper will examine under what conditions 

additional modifications might be necessary to treat Intelligent 

Systems. 

Technology development can only be “complete” in the context 

of a specific program with known performance requirements. 

Thus, the TRA’s focus on critical technology elements (CTEs)—

those technologies used in a new or novel way that are essential 

to system performance. These CTEs are assessed for their performance in a relevant environment, as determined from a consideration of the system’s requirements. For Intelligent Systems, 

this focus on CTE’s and relevant environment may provide a 

disciplined approach to ensuring technology maturity before 

system development. 

The algorithms that make decisions will often be the distinctive 

CTEs, unlike the CTEs of hardware systems. However, the 

major differences between Intelligent and hardware systems are 

likely to be in the “relevant environment”. Intelligent Systems 

that develop and execute a course of action will, by their nature, 

present challenges in the definition of the “relevant environment”. We will explore the effect of various degrees of “intelligence” on CTEs and the relevant environment in this paper. 

I. INTRODUCTION

Over the past several years, the Department of Defense 

(DoD) has incorporated a Technology Readiness Assessment 

(TRA) process into the formal systems acquisition process. 

This is a data-driven, metrics-based approach that uses 

NASA’s Technology Readiness Levels (TRLs). The motivation behind instituting this process was to ensure that technology development was complete and that performance was 

understood before entering into the System Development and 

Demonstration (SDD) phase of a program. Such a disciplined 

approach may aid in Intelligent Systems development. However, NASA’s TRLs were derived in a context of hardware 

systems. The hardware TRLs needed modification to treat 

software and software-intensive systems. This paper examines 

under what conditions additional modifications might be necessary to treat Intelligent Systems, and even though no modifications were identified, some subtle differences between 

Intelligent and hardware systems were found. 

One difference between Intelligent and hardware systems is 

in the nature of the “relevant environment”. An Intelligent 

System with enough capacity for adaptation may be able to 

function in environments outside the design space. Intelligent 

Systems that develop and execute a course of action will, by 

their nature, operate in a variety of environments and require 

attention to a comprehensive set of possible environments 

early in development. A second major difference is the need 

for early identification of metrics to properly train an Intelligent System, since Intelligent Systems frequently require 

training to optimize the values of their many parameters for a 

given purpose. We find the need for early attention to environments and metrics to be distinguishing features of Intelligent Systems. 

Technology development can only be “complete” in the 

context of a specific program with known performance 

requirements. Thus, the TRAs evaluate the readiness of Critical Technology Elements (CTEs)—those technologies that are 

used in a new or novel way and are essential to system performance. These CTEs are assessed for their performance in a 

relevant environment, as determined from a consideration of 

the system’s requirements. For Intelligent Systems, this focus 

on CTEs and the metrics needed to assess performance in a 

relevant environment may provide a disciplined approach to 

ensuring technology maturity before system development. 

II. THE TECHNOLOGY READINESS ASSESSMENT (TRA) 

A. Motivation 

The purpose of a TRA is to ensure that programs entering 

the SDD phase of development are based upon mature technologies. Experience has shown that programs that enter SDD 

with immature technologies are frequently plagued with additional and, hence, expensive design cycles [1]. Current DoD 

regulations require a TRA before Milestone B or Milestone C 

approval (Key Decision Points B and C for space programs). 

These are the decision points for beginning SDD and Low 

Rate Initial Production (LRIP), respectively. For ships, a TRA 

is also required at program initiation [2]. 

35
B. The TRA: Critical Technology Elements (CTEs) 

A TRA is a metrics-based process that assesses the maturity 

of the CTEs of a system design. The CTEs must be 

• Essential for system performance and 

• New or novel technologies or technologies used in a 

new or novel way. 

An example here may be instructive. The Crusader SelfPropelled Howitzer system was envisioned to have a much 

faster rate of fire and much improved cross-country mobility 

than those of earlier systems. The faster rate of fire was a 

capability that depended upon CTEs. This capability required 

use of an autoloader, which, in turn, required a propellant that 

could be handled by a machine. Two candidates emerged: 

(1) A new liquid propellant that could be regeneratively 

pumped into the combustion chamber (i.e., pumped using the 

pressure in the combustion chamber rather than an external 

force) and (2) an established solid propellant, packaged in a 

combustible case form that could be handled by a machine. 

The first candidate was clearly a new or novel technology. 

The second candidate was an established technology used in a 

new or novel way. Both are examples of CTEs, and the 

maturity of these CTEs was essential for proceeding forward 

with the Howitzer development program. In contrast, crosscountry mobility was a capability that did not depend upon 

CTEs. Cross-country mobility for a vehicle of this weight 

class required tracks, pads, shoes, and the like. However, 

since there was nothing new or novel in this aspect of the 

mobility system, none of these components qualified as CTEs. 

C. Technology Readiness Levels (TRLs) 

The CTEs, once identified, are assessed against a set of predefined metrics as part of the TRA process. The DoD has 

adopted NASA’s TRLs as metrics for assessing the maturity 

of hardware-based systems: 

1. Basic principles observed and reported 

2. Technology concept and/or application formulated 

3. Analytical and experimental critical function and/or 

characteristic proof of concept 

4. Component and/or breadboard validation in a laboratory environment 

5. Component and/or breadboard validation in a relevant 

environment 

6. System/subsystem model or prototype demonstration in 

a relevant environment 

7. System prototype demonstration in an operational 

environment 

8. Actual system completed and qualified through test and 

demonstration 

9. Actual system proven through successful mission 

operations. 

For software systems, some changes in TRLs 4–7 were 

required, reflecting the differences in subsystems and software 

modules and the need for end-to-end demonstrations of software systems. The software TRLs used by the DoD are 

1. Basic principles observed and reported 

2. Technology concept and/or application formulated 

3. Analytical and experimental critical function and/or 

characteristic proof of concept 

4. Module and/or subsystem validation in a laboratory 

environment 

5. Module and/or subsystem validation in a relevant 

environment 

6. Module and/or subsystem validation in a relevant endto-end environment 

7. System prototype demonstration in an operational high 

fidelity environment 

8. Actual system completed and qualified through test and 

demonstration in an operational environment 

9. Actual system proven through successful missionproven operational capabilities. 

TRLs 1–3 apply to invention through proof of concept 

(PoC) and are clearly in the realm of basic science and technology (S&T). Achieving TRL 4 depends upon component or 

module development, usually with some application in mind. 

TRLs 5–7 exist in the context of a program with requirements. 

Requirements are a prerequisite for defining the relevant and 

operational environments in which the CTEs are envisioned to 

perform. Requirements are also needed to determine some 

minimum threshold above which the performance of a technology is deemed acceptable in a relevant or operational environment. Finally, requirements are frequently needed to 

determine whether the application of a technology is new or 

novel. 

At this stage, in addition to formal requirements, the expert 

judgment of the technical community is needed for a variety 

of reasons. First, expertise is needed in identifying CTEs. In 

addition, expertise is needed in deducing the relevant environment from the operational environment. Although satisfactory 

performance is usually demonstrated earlier in a relevant environment (at TRL 6) than in an operational environment (at 

TRL 7), the operational environment is typically defined first, 

with the definition of a relevant environment derived thereafter. Expert technical judgment is key to this derivation. 

Considering satellite systems at this point is instructive. One 

does not want to have to launch a satellite into the space 

environment in order to proceed with the design and demonstration of the satellite. The relevant environment for the 

technologies on the satellite will depend upon what aspect of 

the environment (e.g., thermal load, the radiation environment 

of space, or g-forces during launch) is causing stress in the 

satellite. Any of these stressors and the effects of the stressors 

upon the satellite can be readily tested and demonstrated in 

the lab. In general, technical expertise is needed to ensure that 

while the performance of a system’s CTEs is demonstrated in 

the face of environmental stressors, an expensive, exhaustive 

demonstration program is not applied to noncritical elements. 

Despite the usefulness of the TRA process, an important 

point to note is that TRAs are confined in scope. They are not 

risk assessments, design reviews, or a method to address system integration. Yet, a well-performed TRA should result in 

36
the use of mature technologies, which, in turn, should reduce 

risk and enable efficient system integration. 

D. Regulatory Aspects 

The TRLs were developed and first used by NASA in the 

1970s. In the late 1990s and early 2000s, DoD became interested in TRLs as a means of managing the tendency for programs to enter SDD with immature technologies. Use of TRLs 

as part of a formal TRA was first required in 2003. TRL 6 was 

the standard for Milestone B (entry into SDD), and TRL 7 

was the standard for LRIP. In 2006, a statutory requirement 

was enacted for certification that “the technology in the program has been demonstrated in a relevant environment” prior 

to Milestone B. This language is taken from the definition of 

TRL 6. 

The Government Accountability Office (GAO) has also 

begun to look at Department of Energy (DOE) projects from a 

technology readiness perspective [3]. DOE has responded by 

reassessing their own processes in this area. One can reasonably expect increased attention to technology readiness in 

many areas. For instance, any fielding of Intelligent Systems 

is likely to trigger regulatory interest. The Federal Aviation 

Administration (FAA) has already issued rulings on 

unmanned air vehicles in commercial airspace, even though 

these unmanned systems have little onboard intelligence or 

autonomy. As Dr. Zelinsky points out in his abstract “People 

expect autonomous technologies to operate at higher levels of 

performance and safety than people themselves exhibit” [4]. 

E. Summary 

The purpose of the DoD’s TRA process is to ensure that all 

new or novel technologies essential for meeting system 

requirements have been identified and that the performance of 

these technologies has been demonstrated in the appropriate 

environment(s). The motivation behind the TRA process is to 

prevent the extended deadlines and high costs that immature 

technologies often cause in SDD. As discussed in the next 

section, applying the TRA process to Intelligent Systems will 

present some challenges. However, development of Intelligent 

Systems will also benefit from the TRA process. 

III. INTELLIGENT SYSTEMS

A. Definitions 

To consider what may distinguish Intelligent Systems from 

other “ordinary” hardware or software systems in the DoD 

acquisition process, we will use the following working definition: An Intelligent System is a system that makes complex 

decisions or recommendations in a complex environment in 

place of a human. The system may use data provided externally or obtained by the Intelligent System itself. The decisions may result in actions in the physical world. 

Many autonomous systems make and execute complex decisions in constrained environments or simple decisions in 

complex environments. Either of these cases can be handled 

as “ordinary” development. The case of interest is one in 

which the data stream and output options are complex and the 

field of controlled action is extensive and complex. 

Some specific examples may be illustrative at this point. 

Deep Blue, the chess-playing computer, is a pure example of 

an autonomous system that makes complex decisions but acts 

within a constrained environment. There are no consequences 

to chess playing in the real world that require special attention. Factory robots exhibit complex behaviors but, much like 

Deep Blue, operate in a constrained environment—including 

an environment that is constrained even in the case of failure. 

In another example, the Global Hawk Unmanned Aerial 

Vehicle (UAV) has some capability to select an airfield and 

land when certain failures occur. This is an impressive 

accomplishment but, again, relies on the constrained environment of an airfield landing strip. On the opposite end of the 

spectrum, the automotive industry has produced several 

autonomous systems that must operate in the full range of 

automotive environments. These systems have been successful in large measure because of their simple input data streams 

and simple basis for decision making. Anti-lock brake (ALB) 

systems and air bags are two examples. 

In contrast to these examples, an example of the case of 

interest for an Intelligent System would be a “robo-medic”: an 

autonomous robotic vehicle capable of operating in collapsed 

buildings or mines, locating injured people, performing triage, 

and perhaps diagnosing or even treating injuries. (Think of 

St. Bernards dispensing brandy.) A more topical example 

might be the robot-assisted surgery discussed in the Human 

Robot Interface and Human Machine Interaction sessions of 

this conference.[5] 

In summary, this paper proposes the following description 

of what distinguishes an Intelligent System from a system that 

is merely autonomous: 

• An autonomous system is one that makes and executes 

a decision to achieve a goal without full, direct human 

control. 

• An Intelligent System is one that performs autonomously in complex and/or new environments. 

B. Proof of Concept (PoC) for Intelligent Systems 

As discussed previously, the TRLs initially proposed by 

NASA were intended for hardware systems. Modification of 

some TRLs was needed for software systems. Upon initiating 

the line of research discussed in this paper, we anticipated that 

further modification to the TRLs would be needed for 

Intelligent Systems development, particularly those TRLs 

addressing the concepts of “relevant and operational environments”. At this point in our investigation, however, we 

believe the TRL language is suitable (i.e., without modification) for Intelligent Systems, but we also believe that more 

and earlier attention must be paid to the concepts of “relevant 

environment” and “metrics to assess performance”. Specifically, in the development of ordinary hardware and software 

systems, precise definition of performance requirements 

(including the performance metrics on which they are based 

and the environments in which they are tested) are not needed 

37
until TRL 5. We believe that Intelligent System development 

requires at least an initial definition of environments and performance metrics at a stage earlier than TRL 5, such as during 

the PoC stages of TRLs 2 and 3. 

In a typical PoC demonstration of an “ordinary” hardware or 

software system, the definition of environments is often based 

upon convenience, and little attention is given to size, weight, 

and power. Furthermore, the performance of the system is 

often defined in several vague and qualitative ways because 

one specific, quantitative set of metrics to define performance 

is often not yet agreed upon. It is only after PoC is demonstrated that performance requirements are defined, and this 

often does not occur until TRL 5. These requirements define 

(1) the range and type of environments in which the system 

must perform, (2) the quantitative metrics used to measure 

how well the system performs in those environments, and (3) 

the thresholds placed upon the metrics, showing the minimum 

performance level the system must meet. 

In contrast, during a PoC demonstration of an Intelligent 

System, the metrics used to define performance and the environments in which the performance is judged must already be 

defined because an Intelligent System often requires training. 

As we noted earlier, the purpose of an Intelligent System is to 

make a decision or recommendation in a manner that is in 

some fashion better than or as good as a human. To achieve 

this ability, training is often essential. As is discussed in the 

following sections, the training requirement of Intelligent 

Systems necessitates the definition of “relevant environment” 

and “performance metrics” at an earlier stage in the TRA 

process than would be necessary for “ordinary” hardware or 

software systems. 

i) Operational and Relevant Environments 

In the development of “ordinary” hardware and software 

systems, environments considered during PoC are often too 

narrow, which leads to a “point solution” without the growth 

potential we associate with intelligence. This is problematic 

for Intelligent System development for two reasons: 

1. By definition, an Intelligent System must perform in 

complex environments. Furthermore, the ability to 

know these environments in advance may not be possible. Defining an environment that is broad enough to 

encompass all types of environments in which the 

Intelligent System may later have to function can be a 

challenge. 

2. An Intelligent System often requires training using a 

set of data labeled with ground truth information, and 

consolidating such data into a training set can often be 

a challenge. To avoid a point solution or other suboptimal outcome, the training data must be drawn from 

the comprehensive set of environments in which the 

Intelligent System may later perform. Thus, the environments must be defined before the training data is 

collated and input to the Intelligent System training 

module. If a trained Intelligent System is required to 

demonstrate PoC at TRL 3, then the environments that 

dictate the characteristics of the training data are 

required before TRL 3.

ii) Metrics to Assess Performance 

During the development of “ordinary” hardware and software systems, metrics to assess system performance (and the 

thresholds placed upon them) may not be defined until as late 

as TRLs 5 and 6. Intelligent System development, however, 

requires earlier attention to performance metrics since these 

metrics define the quantitative criteria that must be minimized 

(or maximized) with the learning rule of the training process. 

Since training of some sort may be required to show PoC at 

TRL 3, these performance metrics must be defined before 

TRL 3.

These early defined metrics can, and should, later evolve 

into some of the more rigorous system requirements put into 

place at TRLs 5 and 6. At that point, thresholds can be 

assigned to each of the metrics to show the minimum level of 

performance above which the system must demonstrate in 

order for the program to proceed further. Thus, while the 

technology developer uses the performance metrics at TRLs 2 

and 3 to probe “how well” the technology can be envisioned 

to perform, the system developer uses the evolved metrics 

(and related thresholds) at TRLs 5 and 6 to determine if the 

technology performs “good enough”. 

C. Summary 

An Intelligent System makes complex decisions or recommendations in a complex environment in place of a human. 

With little modification, the TRA process can be applied to 

the development of an Intelligent System. However, special 

attention must be paid in TRLs 2 and 3 during the PoC phase 

of Intelligent System development. Specifically, technology 

developers must formulate at an initial definition of the “environment” and “performance metrics” at TRLs 2 and 3 rather 

than waiting until TRLs 5 and 6, as is possible in the development of ordinary hardware and software systems. 

Intelligent Systems require this early attention to environments and performance metrics because training is often 

essential for Intelligent System functionality. While the considered environments define the breadth and scope of the 

labeled data input to the training module of the Intelligent 

System, the performance metrics define the quantitative measure that is minimized (or maximized) by the learning rule 

during the training process. 

IV. CONCLUSION

Both complex data and a complex sphere of action are 

required for an Intelligent System to be substantively different 

from an ordinary system. For a given mission, one can sometimes collapse either the data or the sphere of action, resulting 

in a much simpler development. Even in the fully complex 

case, the current language of the TRL definitions appear to us 

adequate to ensure sufficient technical maturity as developments pass from the technology to the product phase. How38
ever, for these fully complex cases, successful Intelligent 

Systems development requires early attention to the breadth 

and unpredictability of the environments. In addition, early 

identification of performance metrics will be a prerequisite for 

the training aspects of Intelligent Systems. 

REFERENCES

[1] GAO/NSIAD-00-137, Defense Acquisition: Employing Best Practices 

Can Shape Better Weapon System Decisions, April 26, 2000. Available online: http://www.gao.gov.new.items/ns00137t.pdf

[2] Department of Defense, Technology Readiness Assessment (TRA) Deskbook, May 2005. Available on-line: 

http://www.defenselink.mil/ddre/doc/tra_deskbook_2005.pdf

[3] GAO-07-336, Major Construction Projects Need a Consistent Approach 

for Assessing Technology Readiness To Help Avoid Cost Increases and 

Delays, March 2007. Available on-line: 

http://www.gao.gov/new.items/d07336.pdf

[4] Dr. Alex Zelinsky, “Building Autonomous Systems of High Performance 

Reliability and Integrity,” Invited talk, PerMIS 2007. 

[5] S. Schipani and E. Messina, “Maze Hypothesis Development in Assessing 

Robot Performance During Teleoperation,” Tue-PM2 PerMIS 2007, and 

N. Dagalakis, Y. Kim, D. Sawyer, and C. Shakarji, “Development of Tools 

for Measuring the Performance of Computer Assisted Orthopaedic Hip 

Surgery Systems,” Wed PM1 PerMIS 2007. 

39
A Brief History of PRIDE

Z. Kootbally, C. Schlenoff and R. Madhavan

National Institute of Standards and Technology

100 Bureau Drive

Gaithersburg, MD, USA

Email: {zeid.kootbally, craig.schlenoff, raj.madhavan}@nist.gov

Abstract — PRIDE (PRediction In Dynamic Environments) is a

framework that provides an autonomous vehicle’s planning system

with information that it needs to perform path planning in the

presence of moving objects. The underlying concept is based upon

a multi-resolutional, hierarchical approach that incorporates multiple

prediction algorithms into a single, unifying framework. This framework supports the prediction of the future location of moving objects

at various levels of resolution, thus providing prediction information

at the frequency and level of abstraction necessary for planners at

different levels within the hierarchy.

This paper presents the chronology of the development of the PRIDE

framework. We describe the different prediction algorithms developed

for moving object predictions. We provide details on different work

performed specifically for each prediction algorithm and how these

algorithms are used together to give better predictions. The chronology also relates the successive simulation packages and testbeds1

used in each step of the development of the PRIDE framework.

Keywords: 4D/RCS, aggressivity, autonomous vehicles, critical time points, long-term prediction, moving object prediction, PRIDE, short-term prediction, integration methodology.

I. INTRODUCTION

The field of autonomous ground vehicles has made prominent strides during the last decade. Advancements have been

made in methods for autonomous navigation of autonomous

vehicles in dynamic environments. Funding for research in

this area has continued to grow over the past few years, and

recent high profile funding opportunities have started to push

theoretical research efforts into practical use. Autonomous

systems in this context refer to embodied intelligent systems

that can operate fairly independently from human supervision.

Many believe that the DEMO III Experimental Unmanned

Vehicle (XUV) effort represents the state of the art in autonomous off-road driving [17]. This effort seeks to develop

and demonstrate new and evolving autonomous vehicle technology, emphasizing perception, navigation, intelligent system

architecture, and planning. It should be noted that the DEMO

III XUV has only been tested in highly static environments.

It has not been tested in on-road driving situations, which

include pedestrians and oncoming traffic. There have also been

experiments performed with autonomous vehicles during onroad navigation. Perhaps the most successful has been that of

1Commercial equipment and materials are identified in this paper in order

to adequately specify certain procedures. Such identification does not imply

recommendation or endorsement by the National Institute of Standards and

Technology, nor does it imply that the materials or equipment identified are

necessarily the best available for the purpose.

Dickmanns [2] as part of the European Prometheus project in

which the autonomous vehicle performed a trip from Munich

to Odense (> 1600 kilometers) at a maximum velocity of

180 km/h. Although the vehicle was able to identify and

track other moving vehicles in the environment, it could only

make basic predictions of where those vehicles were expected

to be at points in the near future, considering the vehicle’s

current velocity and acceleration. The agent architecture AUTODRIVE [19] simulates the generation and execution of a

driver’s plan to reach a destination safely while taking account

of other road users and obeying traffic signs and signals. The

selection of appropriate goals is made through a process of

“dynamic goal creation” that causes the continual run-time

creation and modification of sub-goals.

Most of the work in the literature dealing with drivers’

actions and predicted behavior has been performed by psychologists in an attempt to explain drivers’ behaviors and to

identify the reason for certain dysfunctions [1], [3], [7]. Our

research interest bears upon a level of situation awareness

of how other vehicles in the environment are expected to

behave considering the situation in which they find themselves.

When humans drive, they often have expectations of how

each object in the environment is expected to move according

to the situation they find themselves in. When a vehicle is

approaching an object that is stopped in the road, we expect

it to slow down behind the object or try to pass it. When

we see a vehicle with its blinker on, we expect it to turn or

change lanes. When we see a vehicle traveling behind another

vehicle at a constant speed, we expect it to continue traveling

at that speed. The decisions that we make in our vehicle are

largely based on these assumptions about the behavior of other

vehicles.

To address this need, we have developed a multiresolutional, hierarchical framework, called PRIDE (PRediction in Dynamic Environments) that provides an autonomous

vehicle’s planning system with information that it needs to

perform path planning in the presence of moving objects [12],

[15]. This framework supports the prediction of the future

location of moving objects at various levels of resolution, thus

providing prediction information at the frequency and level of

abstraction necessary for planners at different levels within the

hierarchy.

This paper presents the chronology of the development

of the PRIDE framework, starting back in 2003 when the

40
initial concept called Moving Object Representation, Prediction, and Planning System (MORPPS) was first introduced

using a Kalman filter-based prediction approach. In 2004,

we started using the AutoSim simulation package to provide

higher resolution simulations of moving objects and on-road

driving. We also introduced a second set of prediction algorithms that predicted at longer timeframes (seconds into the

future as opposed to tenths of seconds). The term PRIDE

appeared in 2005 and looked at using the outputs of the

two prediction approaches to strengthen/weaken the results

of the other. PRIDE was also applied to simulate realistic

traffic patterns during on-road driving by using the longerterm prediction algorithms to control individual vehicles on a

crowded roadway. More recently, in 2006 and 2007, work has

been performed to determine the future time horizons when the

different prediction algorithms give the best results. We also

started incorporating driver aggressivity into the longer-term

algorithms, and determined how the perceived aggressivity

of a driver in the environment affected the future position

of the vehicle they were driving. During this same time,

we ported the PRIDE algorithms over to the Mobility Open

Architecture Simulation and Tools (MOAST) and the Urban

Search and Rescue Simulation (USARSim) framework [16],

which provided a higher-fidelity simulation platform with a

physic-based engine.

This paper is organized as follows: Section II presents

the initial concept called Moving Object Representation, Prediction, and Planning System (MORPPS), which explored

logic-based motion prediction while using different prediction

algorithms for different environments. Section III provides an

overview of the PRIDE framework. Section IV gives details on

the short-term prediction approach along with the description

of LAser Detection And Ranging (LADAR) noise models.

Section V describes the second prediction approach, the longterm, cost-based, probabilistic moving object prediction algorithms. Section VI provides information on different works

performed on the integration of the long-term and shortterm predicted estimates. Section VII discusses the role of

aggressivity in PRIDE and describes how it is addressed

using MOAST and the USARSim simulation environment.

Section VIII concludes the paper and gives an overview on

future work.

II. THE DAYS OF MORPPS

The initial moving object framework called MORPPS

(Moving Object Representation, Prediction, and Planning System) [14] was developed in 2003. This framework provides

a mechanism to apply appropriate prediction algorithms and

representational approaches in order to fully capture the information needed to navigate in the presence of moving objects.

A. Logic-Based Motion Predictions in Constrained Environments

The framework explores logic-based prediction algorithms

for use in constrained environments. The purpose of these

algorithms is to predict the probability that an object will

occupy a given location in space at a given time by taking

into account: a) the constraints that are placed on the object’s

motion and b) the influencing factors that would cause it to

take a given action over another at specific times.

In the case of on-road driving, vehicles must stay on the road

and as such, the road network provides the constraints dictating

the bounds in which a vehicle may travel. A database structure [4] has been developed to capture detailed information

about the road network, which includes information about the

curvature of lanes, road interconnectivity, signage and traffic

control, lane marking, etc.

The rule-based prediction approach requires that one discretizes the possible actions that a moving object may take. In

the case of a vehicle driving on-road, we limit the actions of

the vehicle to be: remain at a constant velocity in the current

lane, slowly accelerate in the current lane, rapidly accelerate in

the current lane, slowly decelerate in the current lane, rapidly

decelerate in the current lane, change to a lane on the left,

change to a lane on the right, turn to a lane on the left (at an

intersection), turn to a lane on the right (at an intersection),

make a U-Turn (at an intersection).

B. Constraints on Motion and Influencing Factors

Different factors can affect the probabilities associated with

the possible actions that a vehicle may take while driving onroad. There are two classes of factors that we must consider.

The first are factors that limit the possibilities of where the

vehicle is able to reach. In other words, by considering these

factors, we can eliminate certain portions on the maps that

are not reachable by the vehicle. We call these constraints

on motion. An example of a constraint on motion is a priori

road network information, where the road network limits the

possible locations that the vehicle can possibly attain.

The second are factors that influence which of the possible

actions the vehicle is likely to perform out of those that are

available to it. We call these influencing factors. An example of

an influencing factor can be the weather and the environmental

conditions. Weather and environmental conditions affect the

visibility and slickness of the road surfaces. As the weather

and environmental conditions worsen, the probability often

increases that the vehicle’s velocity will decrease.

III. THE PRIDE FRAMEWORK

Many efforts on the framework led to the second generation

of MORPPS called PRIDE (PRediction In Dynamic Environments) that was conceived in 2004. From this time, we consider PRIDE as a multi-resolutional, hierarchical framework

that provides an autonomous vehicle’s planning system with

information required to perform path planning in the presence

of moving objects. This framework supports the prediction

of the future location of moving objects at various levels

of resolution, thus providing prediction information at the

frequency and level of abstraction necessary for planners at

different levels within the hierarchy. To understand the way

that PRIDE was developed and the functionality that it is

intended to provide, it is important to understand the 4D/RCS

41
architecture [5] on which it was based. 4D refers to the four

dimensions (three dimensions of space and one dimension

of time), and RCS stands for Real-time Control Systems.

The 4D/RCS architecture provides a reference model for

unmanned vehicles on how their software components should

be identified and organized. It defines ways of interacting to

ensure that high-level objectives can be met. To achieve this,

the 4D/RCS reference model provides well defined and highly

coordinated sensory processing, world modeling, knowledge

management, cost/benefit analysis, behavior generation, and

messaging functions, as well as the associated interfaces.

The 4D/RCS conceptual framework spans the entire range

of operations that affect intelligent vehicles, from those that

take place over time periods of milliseconds and distances

of millimeters to those that take place over time periods of

months and distances of thousands of kilometers. The 4D/RCS

model is intended to allow for the representation of activities

that range from detailed dynamic analysis of a single actuator

in a single vehicle subsystem to the combined activity of

planning and control for hundreds of vehicles and human

beings in full dimensional operations covering an entire theater

of battle. In order to span the wide range of activities included

within the conceptual framework, 4D/RCS adopts a multilevel

hierarchical architecture with different range and resolution

in time and space at each level, as shown for a military

environment in Figure 1 [5] and described below.

Fig. 1. A high level block diagram of a typical 4D/RCS reference model

architecture.

At the Servo level, commands to actuator groups are decomposed into control signals to individual actuators. Outputs to

actuators are generated every 5 milliseconds (ms). Plans that

look ahead 50 ms are regenerated for each actuator every 5 ms.

Plans of individual actuators are synchronized so that coordinated motion can be achieved for multiple actuators within an

actuator group. At the Primitive level, multiple actuator groups

are coordinated and dynamical interactions between actuator

groups are taken into account. Plans look ahead 500 ms and are

recomputed every 50 ms. At the Autonomous Mobility level,

all the components within an entire subsystem are coordinated,

and planning takes into consideration issues such as obstacle

avoidance and gaze control. Plans look ahead 5 seconds (s)

and replanning occurs every 500 ms. At the Vehicle level,

all the subsystems within an entire vehicle are coordinated to

generate tactical behaviors. Plans look ahead 1 minute (min)

and replanning occurs every 5 s. At the Section level, multiple

vehicles are coordinated to generate joint tactical behaviors.

Plans look ahead 10 min and replanning occurs about every

minute. At the Platoon level, multiple sections containing a

total of 10 or more vehicles of different types are coordinated

to generate platoon tactics. Plans look ahead an hour (h) and

replanning occurs every 5 min. At the Company level, multiple

platoons containing a total of 40 or more vehicles of different

types are coordinated to generate company tactics. Plans look

ahead 5 h and replanning occurs every 25 min. At the Battalion

level, multiple companies containing a total of 160 or more

vehicles of different types are coordinated to generate battalion

tactics. Plans look ahead 24 h and replanning occurs at least

every 2 h.

The PRIDE framework was developed to provide moving

object predictions to planners running at any level of the

4D/RCS hierarchy at an appropriate scale and resolution. The

underlying concept of the PRIDE framework is based on

a multi-resolutional, hierarchical approach that incorporates

multiple prediction algorithms into a single, unifying framework. At the higher levels of the framework (Vehicle level

and above, as shown in Figure 1), moving object prediction

needs to occur at a much lower frequency and a greater level

of inaccuracy is tolerable. At these levels, moving objects are

identified as far as the sensors can detect, and a determination

is made as to which objects should be classified as “objects

of interest”. Once objects of interest are identified, we use

the long-term prediction approach presented in section V to

predict where those objects will be at various time steps

into the future. At the lower levels (Autonomous Mobility

level and below, as shown in Figure 1), we utilize estimation

theoretic short-term predictions using sensor data as described

in section IV to predict the future location of moving objects

with an associated confidence measure.

IV. IMPLEMENTING THE SHORT-TERM PREDICTION

ALGORITHM

Details on the development of a combined probabilistic

object classification and estimation theoretic framework to

predict the future location of moving objects, along with an associated uncertainty measure can be found in [11]. The framework proposed adopts a more generalized view of moving

object representation and prediction in concurrently integrating

multiple knowledge representation approaches from disparate

sources to completely model the information necessary for

dynamic planning.

A. The OneSAF Testbed (OTBSAF)

In this approach, the prediction algorithms are tested using

the OneSAF (OTBSAF) testbed as the virtual sensor. OTBSAF

is a simulation package used for integrating, testing and

user feedback of technology developments into the OneSAF

Objective System. It provides operational environments useful

for identifying, developing, prototyping, demonstrating, and

testing of enabling technologies and entity behaviors. As a

42
simulated environment, OTBSAF is able to represent moving

objects. By querying OTBSAF, we can retrieve an object’s

location and velocity at the current time. To validate the

testbed and prediction algorithms, we are initially using this

retrieved data to serve as our processed sensor data.

B. LADAR Noise Model

In this work, the LADAR sensor is the primary source

of sensor data. The data retrieved from OTBSAF is perfect

sensor data. In other words, when we ask for the location

or dimensions of the object, we are presented with the exact

location and the exact dimensions without any associated

uncertainty. Although convenient, this does not represent the

information that we expect to get from sensors on the actual

vehicle. To compensate for this, we have introduced a noise

model into the data retrieved from OTBSAF [11].

C. Prediction of Moving Objects

An Extended Kalman Filter (EKF) is employed to predict

(estimate) the position and velocity of the moving object at

a future time instant. Kalman’s prediction theory allows the

computation of the best estimate of a future system state by

using the most recent estimates of system state along with

the system dynamic model. With appropriate interpretation,

covariance analysis inherent in the Kalman filtering techniques

serves as a confidence measure indicative of the uncertainty in

the predicted system states. The EKF thus provides a convenient measure of prediction accuracy through the covariance

matrix. The EKF employs a nonlinear model derived from

equations based on the kinematics of the moving objects

(vehicles) to be predicted.

The EKF is a well established recursive state estimation

technique where estimates the states of a nonlinear system are

obtained by linearization of the nonlinear state and observation

equations. Within the PRIDE framework, short-term prediction

of objects moving at variable speeds and at given look-ahead

time instants (every one-tenth of a second) are predicted using

the EKF. It should be noted here that, in contrast to the longterm predictions, the estimation-theoretic short-term prediction

algorithm does not incorporate a priori knowledge such as

road networks and traffic signage and assumes uninfluenced

constant trajectory. More information on the short-term prediction algorithm can be found in [10].

V. IMPLEMENTING THE LONG-TERM PREDICTION

ALGORITHM

The long-term (LT) situation-based probabilistic prediction

approach was implemented in AutoSim in 2004 [12]. Autosim

was developed by the Advanced Technology Research Corporation and was used to provide higher resolution visualizations

of moving objects and on-road driving. AutoSim is a highfidelity visualization tool which models details about road

networks, including individual lanes, lane markings, intersections, legal intersection traversibility, etc. Using this package,

we have simulated typical traffic situations (e.g., multiple cars

negotiating around obstacles in the roadway, bi-directional

opposing traffic, etc.) and have predicted the future location of

individual vehicles on the roadway based upon the prediction

of where other vehicles are expected to be.

The LT prediction approach is used to predict the future

location of moving objects for longer time horizons. Figure 2

graphically shows the overall process flow.

Fig. 2. The situation-based probabilistic (long-term) prediction process.

The output of this loop is a list of locations with associated

probabilities showing where a vehicle is expected to be at

specific times in the future. Using these probabilities, we can

create traffic patterns in one of two ways:

• Control the vehicle to move to the location with the

highest probability. For example, if the vehicle has a 40

% chance of being at location A, a 30 % chance of being

at location B, a 20 % chance of being at location C, and

a 10 % chance of being at location D, the vehicle will

always be commanded to move to location A.

• Control the vehicle to move to a location whose likelihood is proportional to the probability that it is expected

to be there. One approach would be to use a random

number generator. In this way, a vehicle’s movement

would be closely tied to the probabilities coming out of

the moving object predictor, as opposed to always moving

to the location with the highest probability.

Independent of the approach used to control the vehicles, the

output of these algorithms result in realistic traffic patterns

involving one to many vehicles that can be used as a basis

to evaluate the performance of autonomous vehicle within

simulated on-road driving scenarios.

A. Possible Vehicle Actions

The process of predicting several time steps into the future

consists of a series of continuous actions which constitute a

driving procedure. Each action is accomplished in one time

step, thus, for a time of prediction n, n actions will be

completed. The long-term prediction algorithms use different

types of actions. The first type of actions consists of a set of

speed profiles: Quick Acceleration (QA), Slow Acceleration

(SA), Keep the same Speed (KS), Quick Deceleration (QD),

Slow Deceleration (SD). The second type of actions concerns

43
the changing of lanes: a vehicle has the possibilities of staying

in its lane (SL), changing to the right lane (CR), changing

to the left lane (CL). The last type of action pertains to

intersections, a vehicle has the possibility to turn left, to turn

right or to go straight through an intersection.

At this step, for each vehicle on the road, the algorithm

computes all possible sequences of actions, regarding the

current velocity and location. Some actions may not be possible due to the vehicle’s current velocity (for example, a

vehicle moving slowly cannot change lanes in one second

during a deceleration). In this case, those actions are not

considered. Each sequence of actions is generated in a realistic

way using rules. Presently, a single rule is applied to all of

the possible action sequences to generate the most realistic

ones. To evaluate these rules, we associate a value to each

‘acceleration profile’: 2 for QA, 1 for SA, 0 for KS, -1 for

SD, and -2 for QD. The rule states that a vehicle can only

switch from an action to another action if their values differ

at most by one. An example of action sequences and their

associated validity is shown in Table I.

TABLE I

EXAMPLE OF VALID AND INVALID SEQUENCES OF ACTIONS.

Actions Validity Description

SD SD SD SD Valid

QD QD QA QA Invalid QD to QA illegal

B. Cost Model

The sequences of actions are deemed finite, and the probabilistic LT prediction algorithms use an underlying cost

model that simulates the danger that a driver would incur by

performing an action or occupying a state [15]. These costs

are being used by multiple efforts within the program that

this effort is a part of. Thus, there is value of building the

probabilities directly from these costs to allow for synergy

with other efforts. These costs can be separated in two different

categories:

1) The cost representing the vehicle’s actions: This cost

represents the penalties for performing an action as a

function of the amount of attention needed. For example, the changing lane action needs more concentration

than going straight in the same lane, thus the cost for

changing lane is greater.

2) The cost representing the vehicle’s state on the road:

The proximity to other static and dynamic objects on

the road is assigned to a cost of collision with these

objects. Examples of static objects on the road are road

blocks, debris, etc. Examples of dynamic objects on

the road are other vehicles. The costs associated with

static or moving objects is proportional to the danger

and imminence of collision. For example, a road block

at one kilometer ahead is less dangerous than another

vehicle passing at three meters ahead.

Examples of costs are shown in Table II.

TABLE II

EXAMPLE OF ACTIONS WITH THEIR CORRESPONDING COSTS.

Action Cost

Quick Acceleration (QA) 5

Quick Deceleration (QD) 5

Changing lane (CL, CR) 20

Opposite direction 500

Collision (CO) 1000

Being under the speed limit (US) 5

Being over the speed limit (OS) 5

C. Predicted Vehicle Trajectory

Costs of collision between vehicles are computed using

Predicted Vehicle Trajectories (PVTs) which represent the

possible movements of vehicle throughout the time period of

prediction being analyzed. A PVT is a vector whose origin

represents the current position of the vehicle (xIP , yIP , tIP =

0) at time = 0 and its extremity represents the predicted

position (xP P , yP P , tP P = tpred) where tpred is the predetermined time in the future for the prediction process.

Also contained within the PVT is the action-cost and actionprobability information.

A collision is detected when PVTs cross each other, the

location and time of the collision is determined using a

parametrization of each PVT. This information can be obtained

by using a parametrization of each PVT as represented in the

following equations.

1⁄2

x1(t1) = xP P1t1 + xIP1(1 − t1)

y1(t1) = yP P1t1 + yIP1(1 − t1); t1 ∈ [0, 1] (1)

1⁄2

x1(t2) = xP P2t2 + xIP2(1 − t2)

y1(t2) = yP P2t2 + yIP2(1 − t2); t2 ∈ [0, 1] (2)

where t1 and t2 are the parameters for each PVT. Equations

(1) and (2) create a linear system where t1 and t2 can be

solved using Cramer’s rule:

t1 =

xIP2 − xIP1 xIP2 − xP P2

yIP2 − yIP1yIP2 − yP P2

xP P1 − xIP1 xIP2 − xP P2

yP P1 − yIP1yIP2 − yP P2

t2 =

xP P1 − xIP1 xIP2 − xIP1

yP P1 − yIP1yIP2 − yIP1

xP P1 − xIP1 xIP2 − xP P2

yP P1 − yIP1yIP2 − yP P2

The two vehicles will cross each other at two different

times, (t1, tpred) for the first vehicle, (t2, tpred) for the second vehicle. For a small difference between the two times,

the collision is probable or certain. Conversely, for a large

difference, the collision is improbable. Thus if the PVTs cross

and the difference of time is less than a predetermined time

(τ ), we use Equation (3) to determine the collision cost:

44
Collision Cost = CO (τ − (tpred|t1 − t2|)) (3)

where CO is the predetermined maximum cost than can

occur when colliding with a specific object (Table II) and τ is

the predetermined time difference in which a cost for collision

will be incurred.

D. From Cost to Probability

As discussed previously, the PRIDE algorithms compute n

realistic sequences of actions with an associated cost. Based

on this cost, we can determine the probability that the vehicle

will perform that sequence of actions in the following way.

The first step is to create a ratio of the cost for performing a

given sequence of actions to the sum of all of the costs for

performing n sequences of actions:

ratioi =

Xn

j=1

costj

costi

, ∀ i ∈ [1, n]

We then normalize the ratio of each sequence of actions by

dividing it by the sum of all of the ratios, as shown in Equation

(4):

probai =

ratioi

Xn

j=1

ratioj

, ∀ i ∈ [1, n] (4)

Equation (4) computes the normalized probability of a given

sequence of actions occurring as compared to all sequences of

actions that are possible at that time.

VI. INTEGRATION OF THE LONG-TERM AND SHORT-TERM

PREDICTIONS

One key component of the PRIDE framework is the ability

to integrate the predictions from the two algorithms described

in Sections IV and V. With this integration, we are able to

increase or decrease the confidence of the results of each

of these prediction algorithms based upon how well the

predictions align. The methodology used to integrate the longterm prediction estimates with those provided by the shortterm prediction algorithm is detailed in [15].

A. Significance of Critical Time Points

We define critical time points as those that lie between time

periods when both ST and LT provide useful estimates. This

is important as it provides opportunities for leveraging the

predictions when both prediction algorithms provide valuable

estimates during these times. To facilitate discussion, we define

tbp as the break-off point beyond which the ST estimates are

of little value.

When an exteroceptive sensor observation becomes available, the innovation and the innovation covariance (which is

a 2 × 2 matrix as we are considering xv and yv), are checked

to determine if the EKF updates are to be performed with

that observation. The following two conditions are checked to

determine if the observation falls within 2σ (95 %) bounds:

 ̄

 ̄

 ̄

 ̄

 ̄

Ã

ν(1)

p

S(1, 1)! ̄

 ̄

 ̄

 ̄

 ̄

< 2.0 and

 ̄

 ̄

 ̄

 ̄

 ̄

Ã

ν(2)

p

S(2, 2)! ̄

 ̄

 ̄

 ̄

 ̄

< 2.0

If the above conditions are not satisfied, the ST estimates

will no longer be bounded (the covariances of the position

estimates grow without bounds) and accordingly their consistency cannot be guaranteed. The time instant at which this

occurs is termed the break-off point, tbp.

B. Experimental Results

The integration of the predictions from the two algorithms

has been performed in different ways through important efforts.

In 2005, work was performed to apply the integration

methodology on a straight line with obstacle avoidance [15].

The resulting prediction estimates showed that while the

ST predictions provide accurate position estimates within a

shorter time horizon, the quality of the predictions degrade

considerably as the time horizons get longer. Conversely, the

LT prediction algorithms specifically address this shortcoming

by providing realistic estimates at longer time horizons that are

amenable for autonomous on-road driving. The probabilistic

scaling methodology was used to integrate the two prediction

algorithms more tightly, such that the results of the ST

prediction can help to validate those of the LT prediction and

vice-versa.

In 2006, a new way to apply the integration methodology

was implemented [10]. To analyze the performance of the

prediction algorithms and to determine the window in which

both the ST and LT algorithms provide reasonable results, we

let the vehicle traverse the track until the first break-off point

occurs. As mentioned earlier, the break-point occurs when

the ST estimates are no longer consistent. The integration

methodology is used on the ST and LT estimates belonging to

the time period [0 − tbp] by varying the speed of the vehicle

and the time of prediction.

During the same year, in our last effort using the integration

methodology, we have tested the performance of the ST

and the LT prediction algorithms with several data sets of

varying data rates, speeds and prediction intervals on a closedtrack [8]. The results have consistently demonstrated that ST

estimates are superior to LT estimates in the time period

[t0 − 0.25tbp] and the LT estimates are to be preferred in

the time period immediately after tbp until 2tbp especially

when no external corrections are available for ST prediction.

Subsequently, [0.25tbp − tbp] is the most desired time period

for the integration of the ST and LT estimates. We compare

the results of the integration methodology performed in the

two mentioned time periods along a closed-track. We use the

last LT estimates from the previous integration to find the next

break-off point, and we repeat the same process until the last

break-off point of the track.

VII. DRIVER AGGRESSIVITY

The addition of aggressivities is the latest enhancement to

the PRIDE framework. The term aggressivity in this context

refers to the following description [18]:

45
A driving behaviour is aggressive if it is deliberate,

likely to increase the risk of collision and is motivated by impatience, annoyance, hostility and/or an

attempt to save time.

The aggressivity feature was developed after the integration of

the PRIDE framework with the Open Architecture Simulation

and Tools (MOAST) and the Urban Search and Rescue Simulation (USARSim) simulation environment [16]. This effort

provides predictions incorporating the physics, kinematics and

dynamics of vehicles involved in traffic scenarios.

A. Mobility Open Architecture Simulation and Tools (MOAST)

MOAST is a framework that provides a baseline infrastructure for the development, testing, and analysis of autonomous

systems that is guided by three principles: 1) Creation of a

multi-agent simulation environment and tool set that enables

developers to focus their efforts on their area of expertise,

2) Creation of a baseline control system which can be used

for the performance evaluation of the new algorithms and

subsystems, and 3) Creation of a mechanism that provides

a smooth gradient to migrate a system from a purely virtual

world to an entirely real implementation.

MOAST implements a control technique which decomposes the control problem into a hierarchy of controllers

with each echelon (or level) of control, adding more capabilities to the system. Module-to-module communications

in MOAST is accomplished through the Neutral Message

Language (NML) [6], based on a message buffer model.

B. Urban Search And Rescue Simulation (USARSim)

USARSim is a high-fidelity physics-based simulation system that provides the embodiment and environment for the

development and testing of autonomous systems. This is an

open source simulation environment that is based on Epic

Games Unreal Tournament 2004. Originally developed to

study human robotic interactions in multi-agent environment

in an Urban Search And Rescue (USAR) environment [9],

USARSim is expanding its capabilities to provide realistic

simulation environments to assist in the development and

testing of cognitive systems, autonomous nautical vessels, and

autonomous road driving vehicles.

USARSim utilizes the Karma Physics engine and highquality 3D rendering facilities of the Unreal game engine

to create a realistic simulation environment that provides the

embodiment of a robotic system.

C. System architecture of the MOAST/USARSim and PRIDE

Frameworks

The embedded client-server architecture (Figure 3) of the

Unreal game engine enables USARSim to provide individualized control over multiple robotic systems through discrete

socket interfaces. The interfaces provide a generalized representation language that enables the user to query and control

the robots’ subsystems. All the communications between the

clients (Unreal client and the Controller) and the server are

performed through the network. The Unreal Server includes

Fig. 3. System architecture of USARSim, MOAST and PRIDE.

the Unreal Engine, Gamebots to bridge the Unreal Engine with

outside applications, the maps and the models (robot models,

victims, etc). MOAST first connects to the Unreal Server, then

it sends commands to USARSim to spawn a robot. At this

step, MOAST listens to the sensor data and sends commands

to control the robot.

As depicted in Figure 3, PRIDE uses a Road Network

Database [4] to retrieve the information about road networks

for the moving object prediction process. The purpose of

the Road Network Database 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. The PRIDE framework assumes

knowledge of the current position and the velocity of the

vehicles on the road to predict their future locations. The

PRIDE algorithms retrieve the status (position and velocity)

of every vehicle by querying their corresponding navigation

channel. At this step, the information from the Road Network

Database is used to compute the future positions of the moving

objects. The data commands are sent to MOAST through the

Primitive level.

D. Modeling Aggressivity within PRIDE

Unlike other approaches that use an underlying static cost

model for activities such as path planning, this approach

introduces the concept of a dynamic cost model, where the

costs are vehicle specific and are a function of what is

perceived in the environment. As explained in section V, we

associate underlying costs to various actions and states. We

then sum the costs that are associated with a specific driving

maneuver and use that overall cost to determine the probability

that a vehicle will perform that maneuver; the higher the cost

to perform the maneuver, the lower the probability that it

will occur. However, different drivers have different driving

behaviors, and thus have different underlying costs model. One

driver may be very conservative, only changing lanes when

absolutely necessary, never exceeding the speed limit, etc. On

the other hand, another driver may drive very aggressively,

weaving in and out of lanes, greatly exceeding the speed

limit, and tailgating other drivers. In most cases, one would

experience both kinds of drivers on any trip (along with many

drivers that fall somewhere in the middle), and a moving object

46
prediction framework needs a mechanism to account for all

such circumstances.

When a driver is first encountered, it is extremely rare that

one can instantaneously determine the perceived aggressivity

of the driver. This information is often determined after observing the driver for a certain amount of time, characterizing

their driving behaviors, and assigning an aggressivity. The aggressivity that is assigned greatly impacts PRIDE’s predictions

as to where that driver will be at times in the future. For

example, we would likely assume that a conservative driver

will remain in their lanes whenever possible and stay a safe

distance behind the vehicle in front of him. An aggressive

driver would have a higher probability of changing lanes. We

may also find that the aggressivity of the driver may change

over times. There are times when one can observe a driver for

many seconds at a time. In this case, the driver’s aggressivity

may change, perhaps they are very aggressively trying to get

to a certain lane but become more passive when they get there.

The PRIDE framework addresses all of these driver types

and all of the situations mentioned above. Experiments and

corresponding results performed on the aggressivity can be

found in [13].

VIII. CONCLUSIONS AND FUTURE WORK

The utility of algorithms of predictions has proved to

be particularly important with emphasis on complex path

planning for autonomous vehicles in dynamic environments.

This paper presented the chronology of the development

of the PRIDE framework, a hierarchical, multi-resolutional

approach for moving object prediction during autonomous onroad driving. We discussed the different concepts used during

each step of the development of PRIDE. We described the

different prediction algorithms, how they can be used to predict

the future location of moving objects. We then showed the

features within PRIDE and how they individually make the

strength of each algorithm. We also detailed how the shortterm and long-term algorithms can be unified to provide better

predictions and we gave an overview of different efforts using

the integration methodology. We provided an overview of

the successive simulation packages used to accomplish more

complex traffic situations and used to implement the set of

features that constitute this framework today.

Although substantial progress has been made in designing and

implementing the PRIDE framework, there is still much to

be done. In order to have more complicated traffic situations,

we plan on using multiple vehicles in more complex road

networks, even though PRIDE is not limited algorithmically

to deal with multiple vehicles. In future papers we will tape a

real traffic scenario and compare the results to those provided

by PRIDE, in this way we can analyze how well PRIDE

predicts the future location of the vehicles. PRIDE aims to

integrate fuzzy logic for traffic negotiation at intersections and

for identification of object of interests. We also plan to upload

a release of PRIDE on sourceforge once a stable version is

available.

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47
Autonomy Levels for Unmanned Systems (ALFUS) 

Framework: 

Safety and Application Issues 

Hui-Min Huang 

National Institute of Standards and Technology 

Gaithersburg, MD 20899, U.S.A. 

hui-min.huang@nist.gov 

Abstract--The Autonomy Levels for Unmanned Systems (ALFUS) 

framework is generic and applicable to multiple unmanned system 

(UMS) domains. The key component of the Framework is metrics 

along the three established axes or aspects. This paper attempts to 

examine how the metrics might be applied to selected domains that 

include homeland security, manufacturing, and defense. In 

particular, the paper attempts to lay out how the critical UMS 

concerns, including requirements specification, performance 

measures, safety, and risks might be established from the 

Framework. 

Key words: autonomy, contextual autonomous capability (CAC), 

environment, human independence (HI), human robot interaction 

(HRI), metrics, mission, task, unmanned system (UMS)

I. INTRODUCTION 

The ALFUS Ad Hoc Working Group has been developing 

the ALFUS Framework aiming at providing standard terms, 

definitions, metrics, and tools to facilitate UMS lifecycle 

practices. Participants from various Government 

organizations and their contractors, including U.S. 

Departments of Commerce, Defense, Energy, and 

Transportation and from industry have been volunteering 

their efforts. The current results include a terms and 

definitions document [1], which has begun to be adopted by 

or referenced in various documents [2]. The Framework 

document is expected to be published soon. 

ALFUS is an ongoing effort. As such, this paper highlights 

some key accomplishments of ALFUS, discusses current 

issues, as well as points out future directions. 

II. FRAMEWORK 

ALFUS is highlighted with a three-aspect model, as shown 

in Figure 1. The aspects of mission complexity (MC), 

environmental complexity (EC), and human independence 

(HI) characterize the autonomy of UMSs. The objective for 

a UMS autonomous operation is to achieve the missions as 

assigned by its human operator(s) through the designed 

human-robot interface (HRI) or assigned by another system 

that the UMS interacts with. Each of the aspects is further 

elaborated with a set of metrics, as described in the earlier 

papers, including [3, 4, 5]. 

Figure 1: The Three Aspects for ALFUS 

A Potential Benefits 

Autonomy offers many benefits to human life. The ALFUS 

framework helps characterizing the autonomy. This 

characterization process would, in turn, help the design and 

evaluation of the UMS. 

(1) Enhance safety: Human safety is the utmost 

concern in the modern society. However, there are 

tasks not suited for humans, particularly, those that 

must be performed in environments that may be 

• dangerous—where heavy machinery may be 

running, a building may be collapsing, or chemical, 

biological, radioactive, nuclear, and explosive 

material might exist 

• extreme—where it may be too hot, too cold, or too 

tight 

• hostile—where enemy may be firing. 

UMSs are suited for these tasks. The ALFUS 

Framework employs sets of definitions to facilitate 

communication of the issues and sets of metrics to 

facilitate the analysis of the issues. For example, in a 

dangerous environment, certain types of HRI may be 

needed at certain portions of the mission. The difficulty 

of the task may not exceed certain levels. These are just 

some examples for ALFUS application. 

48
(2) Enhance outcome: By enhancing outcome, we 

mean achieving: 

• mission/task/order goals 

• accuracy and repeatability, in time and space 

• savings in time, space, and material 

For example, it has been well recognized that those tasks 

that are repetitive and boring to humans and those 

beyond human physical abilities could be easily 

achieved by UMSs. Also, appropriately equipped UMSs 

enhance the outcome. A UMS with high sensing and 

perception capability has a better chance of achieving a 

task requiring high precision. 

We attempt to explore applying ALFUS from these 

perspectives. 

III. KEY CONCEPTS IN ALFUS 

A key definition in ALFUS is Autonomy: 

“A UMS’s own ability of integrated sensing, 

perceiving, analyzing, communicating, 

planning, decision-making, and acting, to 

achieve its goals as assigned by its human 

operator(s) through designed human-robot 

interface (HRI)” 

The autonomy is based on the UMS’s internal capability 

of performing all the identified autonomy enabling 

functions in an integrated manner. This integrated 

function set forms a complete control cycle. The 

autonomy is further elaborated into the second key 

concept in ALFUS, which is called Contextual 

Autonomous Capability (CAC): 

 “An unmanned system’s contextual autonomous 

capability is characterized by the missions that the 

system is capable of performing, the environments 

within which the missions are performed, and 

human independence that can be allowed in the 

performance of the missions. 

Each of the aspects, or axes, namely, mission 

complexity, environmental complexity, and human 

independence is further attributed with a set of 

metrics to facilitate the specification, analysis, 

evaluation, and measurement of the contextual 

autonomous capability of particular UMSs” 

This CAC model facilitates the characterization of 

UMSs from the perspectives of requirements, capability, 

and levels of difficulty, complexity, or sophistication. 

The model also provides ways to characterize UMS’s 

autonomous operating modes. The three axes can also 

be applied independently to assess the levels of mission 

complexity, environmental complexity, and autonomy 

for a UMS. 

The HI axis is also referred to as the axis for level of 

autonomy (LOA) [6]. 

As defined, the CAC model encompasses multiple layers of 

abstraction. The following are the two essential layers: 

• The Metric Model for ALFUS: UMS is characterized 

with defined sets of metric, including the percentage of a 

mission that is planned and executed by the UMS 

onboard processors, the levels of task decomposition, the 

solution ratio in the physical environment, etc. 

• The Executive Model for ALFUS: a UMS is 

characterized with the three aspects or axes, namely, 

MC, EC, and HI. These axes are summaries of the 

individual metrics. Particularly, the weighted averages 

of metric scores form the axis scores. The HI scores 

correspond to levels of autonomy, similarly for the levels 

of MC and EC. 

Additional layers of abstraction are allowed. For example, 

the human interaction time metric along the HI axis might be 

further decomposed to actuation time, monitoring time, 

sensory data acquisition time, etc. Earlier concepts even 

involve another, even higher layer, single CAC score that is a 

weighted average of the three axis scores. The CAC index is 

a combination of the metric scores of the three axes and the 

result can come from many combinations of the three axes. 

Figure 2 provides an illustration. However, participants are 

feeling that this might be an oversimplified index. Further 

investigation of this issue is planned. 

The higher layers facilitate requirements specification and 

communication purposes, whereas the lower levels facilitate 

implementation and testing and evaluation. 

In the research community, the term autonomy level may be 

used in different contexts. Bruemmer, D.J., et al., in [7], uses 

the term dynamic autonomy. Barynov and Hexmoor used 

terms including preference autonomy, choice autonomy, and 

decision autonomy [8]. In practices, autonomy levels are 

often used to indicate only the degrees of human 

independence. They are all consistent with and can be 

facilitated by the ALFUS CAC model. 

The CAC index, including the autonomy level may be used in 

a nominal sense while the specific level values are dynamic 

or are adjusting, to the extent of the system design, along the 

course of mission execution depending on the changes of the 

environmental and operating conditions. 

49
Figure 2: Illustrative Combinations of CAC 

IV. ALFUS MODELS FOR UMS SAFETY, RISK AND 

MISSION SUCCESS 

We postulate the following new, key concepts for the 

purpose of expanding the applicability of ALFUS for UMS. 

(L(CACR − L(CAC)) ∝ L(risk)

Where 

CACR: CAC Requirements 

L: level; 

∝ : proportional to or positively corresponds to 

Note that, 

L(risk) <=0 

indicates minimal or no risk. 

This leads to the following: 

L(safety) ∝ (1− L(risk))

where: 

: normalization 

These, themselves, lead to the following observations: 

Level of safety may be contributed by the following 

factors: 

a. insufficient capability in any of the root autonomous 

capabilities [9] 

b. insufficient scores in any of the metrics/axes. 

In addition, safety may be considered as a subset of 

complexity, either mission or environmental. 

V. SIMULATION TO FACILITATE AND EVALUATE 

THE BENEFITS OF AUTONOMY 

It is well understood that the application of simulation can 

save UMS development costs. We investigated how ALFUS 

could facilitate the UMS simulation. 

A Non-Physical Entities 

To explore the benefits of UMS simulation, we need to define 

a set of new ALFUS terms and concepts. Although ALFUS 

stresses physical UMSs to distinguish itself from the general 

information technology (IT) world, there are situations when 

ALFUS is applied to non-physical entities, such as the 

following: 

• Logical UMS (LUMS) are those inherently non-physical 

entities that interact with UMSs, such as a computer 

control and management software system like a flexible 

manufacturing system when it is treated as an 

independent entity. In a hierarchical control system, a 

high level control node that coordinates low-levels 

UMSs may be a LUMS. 

• Virtual UMS (VUMS) or Soft UMS (SUMS) refers to 

UMSs in simulation. 

LUMS, VUMS, and SMUS interact with UMSs using 

established communication channels. 

B ALFUS to Facilitate Simulation 

A UMS must be specified before development, so does a 

UMS simulator. There might be two approaches to the 

development of the simulator, one that is geared toward the 

specific UMS, and the other toward a generic simulation 

environment. 

For a specific simulator, the ALFUS-based UMS 

specification is used as design criteria for the simulator. The 

VUMS should be developed to be able to perform at the level 

of CAC as the to-be-developed UMS. 

In a generic UMS simulation environment, the simulated 

operating environment might be adjusted, per the design, to 

the desired difficulty level. For example, the friction of the 

roads, the slopes of the hills, the density of the traffic, etc., 

could all be designed as adjustable. The autonomous 

capabilities of the simulated UMSs can be measured and 

characterized. These are all benefits facilitated by ALFUS. 

The mission could be scripted to the desired level of 

complexity, as well. Attributes such as the number of 

VUMSs in a team and the commanding structure, the sensory 

capabilities, the accuracy of the goals, etc., could be designed 

as adjustable to reflect the desired level of MC. 

Similarly, an ALFUS enabling HRI in a simulator could be 

designed such that the levels of human interaction time could 

be adjusted, the types of interactions that the VUMS could 

initiate could be pre-set, the HRI displays could be adjusted 

to simulate different levels of stress that might be caused, etc. 

50
VI. APPLYING ALFUS TO MANUFACTURING 

DOMAINS 

A Rationale 

Automation is a key to manufacturing efficiency and safety. 

The challenge is that a manufacturing process could be very 

complex and dynamic. It could involve operators in a semiautomated facility. It receives work orders for different 

products and different quantities. It may need to generate 

various kinds of reports that contain different kinds of 

information for different purposes such as production 

control, quality control, or maintenance analysis. The 

process may also need to adjust its schedules to 

accommodate storage or shipping constraints. Therefore, a 

framework for performance measure and capability 

characterization like ALFUS should be beneficial. The 

following lists some features that ALFUS could apply: 

a. A flexible manufacturing system (FMS), in its 

entirety, could be considered a UMS. If required, 

the highest level system software could be 

considered a LUMS. 

b. Equivalent missions/tasks include production orders 

and inspection orders at a high level, machine a part 

and inspect a part at a middle level, or drill a hole of 

X diameter and or inspect the hole at a low level. 

Correspondingly, the manufacturing system 

capability could be characterized as number of parts 

per day. 

c. Autonomous capabilities could help the many 

factors that a manufacturing process may encounter, 

such as raw material composition/sizes/weights, 

equipment breakdown, etc. The variation in the raw 

material could cause adjustments for the equipment, 

including its settings, workload, and process flow. 

It should be beneficial that this could be done in a 

human-machine coordinated way. 

d. It is desirable that a manufacturing process’s 

performance be measured. ALFUS might serve this 

purpose. 

e. The EC needs to be characterized for such 

conditions as a new operator could inadvertently 

interfere in a work volume, a part might fall off a 

UMS carrying the lot along the route, or a machine 

breaks down. In other words, a manufacturing 

environment could be highly unstructured. 

f. The low level machining instructions correspond to 

the low level skills as identified in the military UMS 

domain. Skills have different levels of difficulty, so 

are the machining instructions. For example, for 

inspecting holes, tolerances make differences in 

terms of difficulty. 

B Toward ALFUS Measures and Indices 

Autonomous capability related measures, derived from 

ALFUS, could help characterizing a manufacturing process in 

the following possible ways: 

a. Highly autonomous manufacturing UMS might 

correspond to higher initial equipment cost but 

lower overall lifecycle cost as well as higher 

capability for complex “missions,” i.e., products. 

│initial cost│ ∝ L(CAC)) 

 

│lifecycle cost│ ∝ (1 - L(CAC)) 

b. Lower complexity products might mean that they 

are suitable for mass production on low CAC 

manufacturing UMSs. 

C Examine a Safety Model for a Industrial Process 

The following is a multiple layers safety model for a 

manufacturing process plant, listed from narrow to broad 

scopes or from the low to high levels, i.e., item #a is the 

lowest level and item #h the highest. The higher level safety 

design activates when the low level design fails [10]: 

a. to design the equipment, turning, milling, drilling, 

forging, die-casting, rolling, etc., and process plant 

to be inherently safe 

b. process control to be designed with safety functions 

c. procedure for and activation of alarms and operator 

intervention 

d. safety shut down and interlock of affected entities 

e. response mechanisms for fire and gas 

f. containment system for the hazard 

g. plant emergency response evacuation system 

h. community emergency response evacuation system. 

Each step contains an independent safety design, yet they are 

integrated to produce a coordinated safety operation. 

We observe that system configuration is expanded and 

system complexity increases from the lower to higher levels. 

As a result, 

a. Safety related missions or tasks become more 

complex. 

b. The operating environments become broader and 

involve more entities. They may tend to be more 

dynamic and unstructured. 

c. Higher levels of CAC provides for higher capability 

for safety. 

VII. ADDITIONAL DOMAINS 

A. Defense 

UMSs are well suited for military types of operations. UMSs 

can replace soldiers in harm’s way and can get themselves in 

51
extreme operational and environmental conditions. War 

fighting, surveillance, medical assistance in the field, logistic 

support, etc., are just a few of the fruitful areas for UMS 

deployment. These are also rich issues warranting the 

application of ALFUS. 

B. Search and Rescue (SAR) 

One of the major concerns in SAR would be the 

environment. Would it be accessible? Would it be safe for 

responders to approach? How is an environment or an 

environmental condition be described and conveyed to the 

decision maker so that the following issues, among 

additional, others, can be revolved: an appropriately 

composed and equipped Emergency Response Team [2] be 

dispatched, at a certain Point of Arrival, whether and what 

kind of Incident Support Team might be needed, and, 

possibly, under the command of a certain Federal 

Coordinating Officer. 

EC levels might be used to identify particular environments 

used for robotic certification. 

Efforts have also begun at NIST to establish the performance 

metrics for SAR robots by developing test arenas with 

various, adjustable levels of difficulty [11, 12]. 

C. Border Security 

Variety in terrain and lengths in distance are among the 

challenges of securing the National borders. For the portions 

of the border that is difficult to traverse, ALFUS could be 

applied to characterize the levels of complexity, which could 

facilitate deploying UMSs with appropriate CACs. For the 

busy crossing ports, UMSs could help the safety related tasks 

such as baggage checking and identity verification. ALFUS 

could help characterizing the task complexity specifying HRI 

requirements. 

D. Bomb Disposal 

The ultimate concern for bomb disposal would be safety of 

human. Therefore, this is the type of task for UMS. ALFUS 

could help analyze the complexity of such an operation. The 

results could help optimize human assistance, including a 

safe operating condition and environment for the involved 

operator. 

E. Standard Mission/Task Ratings 

Skill ratings for human tasks are used [13, 14, 15]. It would 

be interesting to explore similar ratings for UMS. For a 

particular domain, a collection of tasks or a collection of 

typical scenarios that involves the combination of task, 

environment, and HRI can be rated for CAC. The 

information would be maintained in a database. When a 

situation arises that calls for the deployment of a UMS, the 

situation could be analyzed and the matching tasks or 

scenarios could be identified. The information could be used 

to efficiently deploy a capable UMS to handle the situation. 

VIII. SUMMARY 

Key concepts for the ALFUS Framework are introduced, 

with particular focus on the safety issue. A selected set of 

domains are analyzed for the applicability of ALFUS, 

including manufacturing, military, and homeland security. 

We discovered that ALFUS CAC should be helpful for 

indicating a robot’s ability to conduct certain missions. We 

also discovered that each application domain may be unique 

that warrants expansion of the existent metric sets in ALFUS. 

Safety concerns also warrants expansion of the existent 

metric sets. 

ACKNOWLEDGEMENT 

ALFUS participants contribute to the Framework contents 

through workshop participation. Funding sources for this 

effort, throughout various stages of the Framework 

development efforts, include Department of Homeland 

Security, Science and Technology Directorate, Army 

Research Laboratory, and NIST. 

REFERENCES 

 

[1] Autonomy Levels for Unmanned Systems Framework, 

Volume I: Terminology, Version 1.1, Huang, H. Ed., NIST 

Special Publication 1011, National Institute of Standards and 

Technology, Gaithersburg, MD, September 2004 

[2] ASTM International Standards E2521-07a and F 2541-06; 

http://astm.org 

[3] Huang, H., et al., “Characterizing Unmanned System 

Autonomy: Contextual Autonomous Capability and Level of 

Autonomy Analyses,” Proceedings of the SPIE Defense and 

Security Symposium 2007, Orlando, Florida, March 2007 

[4] Huang, H., “The Autonomy Levels for Unmanned 

Systems (ALFUS) Framework--Interim Results,” 

Proceedings of the Performance Metrics for Intelligent 

Systems (PerMIS) Workshop, Gaithersburg, MD, August 

2006 

[5] Huang, H., et al., “Autonomy Measures for Robots,” 

Proceedings of the 2004 ASME International Mechanical 

Engineering Congress & Exposition, Anaheim, California, 

November 2004 

[6] Ragon, M. and Jones, J., Decomposition of Mobility & 

Allocation of Functions to Autonomy Levels, Future Combat 

System Program Lead System Integrator Slide Presentation 

for ALFUS #14 and #15 Workshops, September 2006 and 

January 2007 

[7] Bruemmer, D., et al., "Shared understanding for 

collaborative control," IEEE Transactions on Systems, Man 

and Cybernetics, Part A 35(4), pp. 494-504, July 2005 

[8] Barynov, S. and Hexmoor, H., “Quantifying Relative 

Autonomy in Multiagent Interaction,” Book Chapter in Agent 

52
 

Autonomy, Hexmoor, H., et al., Ed., Kluwer Academic 

Publishers, The Netherlands, 2003 

[9] Autonomy Levels for Unmanned Systems Framework, 

Volume I: Terminology, Version 1.1, Huang, H. Ed., NIST 

Special Publication 1011, National Institute of Standards and 

Technology, Gaithersburg, MD, September 2004 

[10] Process/Industrial Instruments and Control Handbook, 

McGraw-Hill Handbooks Series, McGraw-Hill Professional 

Publishing, NY, NY, 1999 

[11] Jacoff, A., Weiss, B, Messina, E., “Evolution of a 

Performance Metric for Urban Search and Rescue Robots 

(2003),” Proceedings of the 2003 Performance Metrics for 

Intelligent Systems (PerMIS) Workshop, Gaithersburg, MD, 

August 16 - 18, 2003 

[12] Messina, E. and Jacoff, A., “Performance Standards for 

Urban Search and Rescue Robots,” Proceedings of the SPIE 

Defense and Security Symposium, Orlando, FL, April 17-21, 

2006 

[13] DTIC Accession Number AD0645054: Comparison Of 

Merited Grade And Skill Level Ratings Of Airman Jobs, 

http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPref

ix=html&identifier=AD0645054, Defense Technology 

Information Center, Fort Belvoir, VA 

[14] 

http://www.trainingfinder.org/competencies/list_levels.htm 

[15] http://www.us-army-info.com/pages/mos/skills.html 

53
Evaluation of Autonomy in Recent Ground 

Vehicles Using the Autonomy Levels for 

Unmanned Systems (ALFUS) Framework

George T. McWilliams, Michael A. Brown, Ryan D. Lamm, Christopher J. Guerra, 

Paul A. Avery, Kristopher C. Kozak, Bapiraju Surampudi 

Southwest Research Institute 

6220 Culebra Road 

San Antonio, TX, USA 

george.mcwilliams@swri.org 

 

Abstract— Over the last few decades, the field of unmanned 

systems (UMSs) has begun to emerge into a variety of markets. 

The military has already deployed unmanned air, sea, and ground 

vehicles. Universities and other research institutions have 

developed semi-autonomous passenger vehicles that have driven on 

highways in the U.S. and abroad. The National Aeronautics and 

Space Administrations (NASA) has developed unmanned rovers that 

have been navigating the planet Mars for several years. Even the 

transit and commercial freight market has developed programs for 

unmanned vehicles research to help solve complex logistics issues. 

In order to compare the capabilities of unmanned systems in such a 

wide variety of markets, the Autonomy Levels for Unmanned 

Systems (ALFUS) framework has been established in a series of 

workshops. While this framework is still under some development, 

it can be used in its current state to compare unmanned systems. In 

this paper, we highlight some of the major accomplishments made in 

the field of ground vehicle autonomy in particular. We then map 

the capabilities of these ground vehicles to the ALFUS framework 

and summarize the resulting trends that occur from this mapping. 

Keywords: autonomy, unmanned systems, environment, human 

independence, mission

I. INTRODUCTION

Over the last few decades, the field of unmanned systems 

(UMSs) has grown into a hot research topic, which has led to 

the deployment of a variety of UMS types in several different 

markets. In the U.S., the Department of Defense (DoD) has 

been the main catalyst in this research since a Congressional 

mandate requires that one-third of the nation’s combat 

vehicles are to be unmanned by 2015. The DoD has also 

benefited the most from this research by already deploying 

unmanned air, sea, and ground vehicles to military operations. 

The area of ground vehicle autonomy has been a particularly 

interesting research field due to the fact that ground vehicles 

are used in so many different markets. The military uses 

unmanned ground vehicles (UGVs) for dangerous operations 

such as explosive ordnance disposal (EOD). Universities 

and other research institutions have developed 

semi-autonomous passenger vehicles that have driven on 

highways in the U.S. and abroad. The National Aeronautics 

and Space Administrations (NASA) has developed unmanned 

rovers that have been navigating the planet Mars for several 

years. Even the transit and commercial freight market has 

developed programs for unmanned vehicles research to help 

solve complex logistics issues. 

Because of the complexity involved in comparing such 

unmanned and autonomous systems, the Autonomy Levels for 

Unmanned Systems (ALFUS) framework has been developed 

through a series of workshops. The levels of autonomy are 

decomposed into categories of mission complexity (MC), 

environmental difficulty (ED), and human independence (HI) 

[1]. The ALFUS framework includes terms and definitions, 

a detailed model and summary model for autonomy levels, 

and the guidelines and processes needed to apply the generic 

framework [2]. Although the ALFUS framework is still 

under development during the writing of this paper (Summer 

2007), the current state of the framework can be used to 

compare the autonomy levels in the ground vehicle markets 

discussed above. 

In this paper, we highlight some of the major 

accomplishments made in the field of ground vehicle 

autonomy by selecting and evaluating individual vehicles and 

programs. We then map the capabilities of these ground 

vehicles to the ALFUS framework and summarize the 

resulting trends that occur from this mapping. 

II. EVALUATION PROCESS

A. Vehicle Selection 

Because of the amount of research done in ground vehicle 

autonomy and the number of vehicles developed, it is 

challenging to choose which vehicles to highlight. For 

54
example, the military already has numerous UGVs, such as 

iRobot’s Packbot, Remotec’s Andros, and the Army Future 

Combat Systems’ MULE, as well as several others. 

However, there have been a number of demonstration vehicles 

and programs that have captured the state-of-the-art at their 

respective times in history. These vehicles and programs 

were developed by a variety of researchers for a variety of 

different markets. This paper intends to divide these 

vehicles and programs into categories based on their use and 

their market. These categories will be discussed in general 

in the following sections. Then at least one case study of a 

vehicle or program will be described and that vehicle or 

program will be evaluated using the ALFUS Summary Model. 

The categories used in this paper include On-Road Passenger 

Vehicles, Vehicles used for Transit and Freight, 

Extraterrestrial Rovers, Off-Road Military Vehicles, and 

DARPA Grand Challenge 2005 Vehicles. The DARPA 

Grand Challenge vehicles could have been placed under the 

Off-Road Military Vehicles category, but it is given its own 

category since so many vehicles competed in the event and 

since it is the most recent program to take place. It should be 

noted that not all of the vehicles selected are UGVs by its 

strictest definition since some of them transport humans. 

However, they are all unpiloted when they are in 

“autonomous mode”, and their autonomous capabilities can 

still be evaluated through the ALFUS framework. 

B. Metrics and Tools 

A spreadsheet-based software tool has been developed to 

automatically compute the autonomy levels based on the 

weights and the metric scores that users input [3]. However, 

since the tool has been developed, the ALFUS framework has 

continued to evolve and the metrics have continued to change. 

The method of evaluation used in this paper is based on the 

most recent tables of metrics. The MC, ED, and HI of these 

vehicles and programs under evaluation are matched up 

against these metrics and placed in the appropriate bin without 

using a tool to analyze the lowest level subtasks of these 

categories. 

C. Disclaimer 

The capabilities mapped to the ALFUS framework for the 

following vehicles and programs was subjective and was 

provided for illustrative purposes only. It is not the intent of 

the authors to conduct a detailed analysis of the systems 

discussed in the case studies and therefore there may be 

variability or inaccuracies in the analysis of the autonomy 

levels presented.

III. ON-ROAD PASSENGER VEHICLES

A. Analysis of Autonomy Levels in On-Road Passenger 

Vehicles 

Many movies have been made that feature fully autonomous 

On-Road Passenger Vehicles and illustrate the dreams of a 

technological society that can move people at high speeds in 

vehicles that drive themselves. Researchers have taken on 

this challenge by attempting to solve parts of the problem 

over time. This section will analyze the application of the 

ALFUS autonomy levels for On-Road Passenger Vehicles 

and attempt to apply this framework to two vehicles that 

accomplished autonomous steering capabilities: the 

ARGO Autonomous vehicle and the NavLab 5 “No Hands 

Across America” vehicle. 

1) Mission Complexity: The mission complexity 

associated with On-Road Passenger Vehicle autonomy 

is relatively simple: Move people from point A to point 

B. Mission complexity can increase if route 

optimization is desired or cooperative maneuvers with 

other vehicles (e.g. a fleet) are required. So far in 

On-Road Passenger Vehicles, high mission complexity 

has not been realized. 

2) Environment Difficulty: The environmental difficulty of 

the mission associated with On-Road Passenger 

Vehicles can vary significantly based upon the ultimate 

application. In this case, the environment can range 

from closed-track, or protected lane navigation, to 

negotiation of complex urban roads with significant 

potential for interference by other vehicles and 

pedestrians. 

3) Human Independence: Like the previous two 

perspectives, the HI metric has wide variability ranging 

from partial human control such as human throttle 

control to fully autonomous passenger vehicles.

While not yet realized through full implementation, the 

ALFUS framework can be used in the evaluation of UGVs 

used for On-Road Passenger Vehicles. The following 

describe two systems and an analysis as to their autonomy 

level determination. 

B. Case Study: NavLab – “No Hands Across America” 

The Carnegie Mellon University (CMU) NavLab 5 vehicle 

was a 1990 Pontiac Trans Sport that achieved 98.2% 

autonomous driving on a 3000 mile tour from Pittsburgh, 

PA to San Diego, CA called “No Hands Across America” 

[4]. The vehicle used the Rapidly Adapting Lateral 

Position Handler (RALPH) computer program. RALPH uses 

video images to determine the location of the road ahead 

and the appropriate steering direction to keep the vehicle on 

the road. The researchers actuated the throttle and brake 

manually. High level processing was performed on a 

Sparc LX class portable workstation. Low level steering 

motor control and safety monitoring was performed using an 

HC11 microcontroller. A color camera was used along 

with a differential GPS system to determine the vehicle’s 

position and upcoming trajectory. Table I depicts the 

NavLab 5 vehicle aligned within the ALFUS framework. 

55
TABLE I 

ALFUS SUMMARY MODEL OF NAVLAB 5 

MC ED HI 

10 

9 

8 

7 X 

6 X 

5 

4 X 

3 

2 

1 

0 

C. Case Study: ARGO Autonomous Vehicle 

The ARGO Autonomous Vehicle was demonstrated in 1998 

on a 2000 km Italian tour and used only a stereoscopic 

vision system to perform lane-following and obstacle 

avoidance behaviors [5]. The vision system acquired pairs 

of 768x288 pixel grey level images at 25 Hz using a PCI 

Matrox graphics board. A 200 MHz MMX Pentium 

processor was utilized to process the images and perform 

autonomous steering capability by controlling an actuator on 

the steering wheel. 

Acoustic and Visual warnings were given to the driver via 

onboard devices and displays. These warnings alerted the 

driver of unsafe distances to the leading vehicle or unsafe 

vehicle positions in the lane. The vehicle finished with an 

average speed of 90 km/h. 94% of the time the car was in 

fully autonomous mode, with the longest autonomous 

stretch being 54 km. Table II depicts the ARGO 

Autonomous Vehicle aligned within the ALFUS framework. 

TABLE II 

ALFUS SUMMARY MODEL OF ARGO AUTONOMOUS VEHICLE

MC ED HI 

10 

9 

8 X 

7 X 

6 

5 

4 X 

3 

2 

1 

0 

IV. TRANSIT AND FREIGHT

A. Analysis of Autonomy Levels in Transit and Freight 

Autonomy in transit and commercial freight has long been 

viewed as the panacea to complex logistics issues associated 

with the movement of people and goods. Numerous 

projects have been undertaken to evaluate the effectiveness 

of the application of autonomy in this domain; however, 

none have been widely deployed. This section will analyze 

the application of the ALFUS autonomy levels to transit and 

freight systems and attempt to apply this framework to two 

recent programs: the Carnegie Mellon University (CMU) 

Houston-Metro Automated Bus Project and the CityMobil – 

CyberCar Project. 

1) Mission Complexity: The mission complexity 

associated with unmanned logistics and transit systems 

can vary depending upon the tasks to be performed. 

The transit of people or goods via an UGV between two 

points can be classified as the lowest level of mission 

complexity next to teleoperation. Should the mission 

include route optimization, end operations such as 

loading or unloading of freight, or multi-modal transit, 

then the autonomy level could increase significantly. 

Thus far in transit and freight, a high degree of mission 

complexity has not been realized. 

2) Environment Difficulty: The environmental difficulty of 

the mission associated with logistics and transit can also 

vary significantly based upon the ultimate application. 

In unmanned systems, this can range from closed-track, 

or protected lane navigation, to negotiation of complex 

urban environments with significant potential for 

interference by other vehicles and pedestrians. 

Additional constraints for various forms of hazardous 

material transport can make the environment 

challenging. 

3) Human Independence: Like the previous two 

perspectives, the HI metric has wide variability ranging 

from partial human control such as with automated 

throttle – human steering bus systems through fully 

automated people movers at theme parks, airports, and 

congested city centers. 

The ALFUS framework can be used in the evaluation of 

UGVs used for transit and freight mobility systems. The 

following describe two systems and an analysis as to their 

autonomy level determination. 

B. Case Study: CMU Houston-Metro Automated Bus 

The Automated Highway Systems (AHS) demonstration in 

August of 1997 on a 12 km segment of Interstate 15 near 

San Diego demonstrated the feasibility of automated transit 

when CMU outfitted two Houston Metro buses with 

56
automated throttle, brake, and steering capability [6]. The 

RALPH software developed for the “No Hands Across 

America” vehicle was also used here. These buses were 

envisioned to traverse the Houston area High Occupancy 

Vehicle (HOV) dedicated lanes and were not intended to 

coexist with normally piloted manned vehicles. Table III 

depicts the CMU Houston-Metro Automated Bus aligned 

within the ALFUS framework. 

TABLE III 

ALFUS SUMMARY MODEL OF CMU HOUSTON-METRO AUTOMATED BUS

MC ED HI 

10 X 

9 

8 

7 

6 

5 X 

4 

3 X 

2 

1 

0 

C. Case Study: CityMobil – CyberCars 

The CityMobil Project is a European Commission sponsored 

mobility solutions program with urban demonstration 

deployments at Heathrow, Castellon and Rome. The 

CyberCar vision is to provide door-to-door on-demand 

service for the delivery of people and goods in congested 

urban areas [7]. Ultimately, the technology necessary for 

full vehicle autonomy negotiating urban pedestrian-rich 

environments will be coupled with fleet management 

systems to optimize routes and vehicle distribution. 

Currently, the operating environment is restricted to private 

property or dedicated/restricted lanes. Mission complexity 

does contain elements of vehicle-to-vehicle and 

vehicle-to-infrastructure communication with limited to no 

human interaction and no teleoperation. Table IV depicts 

the CyberCar aligned within the ALFUS framework. 

TABLE IV 

ALFUS SUMMARY MODEL OF CITYMOBIL CYBERCAR

MC ED HI 

10 X 

9 

8 

7 

6 X 

5 X 

4 

3 

2 

1 

0 

V. EXTRATERRESTRIAL ROVERS

A. Analysis of Autonomy Levels in Extraterrestrial Rovers 

Atmospheric and space vehicles have wide varieties of 

autonomous capabilities. Various organizations have 

deployed unmanned air vehicles (UAVs) to perform 

activities including monitoring, surveillance, 

communication relay, and military strike. In space travel, 

autonomy is critical where human performance is unreliable 

or in situations where communication latencies prohibit 

remote operation. This section will analyze the application 

of the ALFUS autonomy levels to planetary rovers: NASA’s 

Spirit and Opportunity [8] [9], which are currently exploring 

the Martian surface. 

1) Mission Complexity: The mission complexity 

associated with planetary rovers depends on constraints 

resulting from the rover’s payload, the requirements of 

the science goal, and communication latency issues. 

These rovers must make decisions such as avoiding 

obstacles and traversing hazardous terrain. Poor 

judgments regarding hazardous terrain can jeopardize 

the whole science mission of the rover if a maneuver 

results in damage to the payload or loss of the vehicle. 

2) Environment Difficulty: The environmental difficulty 

for planetary rovers results from terrain variations, soil 

composition, and weather. Obstacles consist of terrain 

features such as craters, rocks, and geologic formations. 

Steep grades and cliffs present additional hazards. 

Daylight patterns have an affect on the rovers’ ability to 

maintain battery charge. Inclement weather can 

constrain a rover’s operational capability. 

3) Human Independence: Early rovers depended on 

significant human intervention. As the technology has 

progressed, the amount of human intervention has 

decreased. Activity planners depend on human input 

for the current rovers, but the route planners function 

mostly autonomously. Communication latency has 

forced the development of improved route planning.

The ALFUS framework can be used in the evaluation of 

UGVs used as planetary rovers. The following sections 

describe Spirit and Opportunity’s systems. 

B. Case Study: NASA Rovers: Spirit and Opportunity 

In January 2004, Spirit and Opportunity landed on the 

Martian surface and began operations. They have 

exceeded their designed life. Part of the mission 

management includes software called Mixed Initiative 

Activity Plan Generator (MAPGEN), which depends on 

operator analysis and intervention. The route and path 

planning software consists of two major components: 

AutoNav and guarded moves. The first of these uses 

57
decision making algorithms to traverse between a start and 

goal point. The guarded moves uses manually specified 

maneuvers, but can prohibit actions deemed dangerous. 

The rover has VisOdom or visual odometry which uses 

stereoptical images to compute odometry for fusion with an 

Inertial Navigation System (INS). These capabilities 

allowed Spirit to traverse over 4.5 km with slopes of less 

than 20 deg. 

TABLE V 

ALFUS SUMMARY MODEL OF NASA’S SPIRIT

MC ED HI 

10 

9 

8 

7 X 

6 X X 

5 

4 

3 

2 

1 

0 

VI. OFF-ROAD MILITARY VEHICLES

A. Analysis of Autonomy Levels in Off-road Military Vehicles 

Autonomy in off-road military vehicles has enjoyed a 

financial and technical focus that has only recently begun to 

be matched in other sectors. A number of successful 

projects in this area have demonstrated the effectiveness and 

usefulness of autonomous vehicle technology in the domains 

of force protection, reconnaissance, search-and-destroy, as 

well as others. This section will analyze the application of 

the ALFUS framework to two of these recent programs: 

DEMO III, and The Crusher. 

1) Mission Complexity: The mission complexity 

associated with UGV off-road military systems can vary 

depending on the specific tasks the vehicle must 

perform, and the nature of its environment both in terms 

of terrain and hostilities. For military applications, 

both the tasks and environment can be unpredictably 

dynamic, particularly when one is affected by the other, 

such as when the mission task switches from 

reconnaissance to force protection due to a change in 

the environmental hostilities that are present. 

2) Environment Difficulty: The environmental difficulty of 

the mission in a military application can be extreme due 

to both terrain, and hostilities aspects. The nature of a 

hostile environment also requires a UGV to distinguish 

friend from foe, and to make appropriate cost/benefit 

judgments from this information. For example, a 

UGV should not consider hostile action against an 

enemy target if that action will endanger members of its 

group, and conversely, should perhaps sacrifice itself if 

doing so would maintain the integrity of a human 

member of its own group. 

3) Human Independence: Like the previous two 

perspectives, the HI metric has wide variability ranging 

from teleoperation to full autonomy. Military UGV 

programs exist within this full spectrum, with many of 

the vehicles able to operate under varying degrees of 

autonomy depending on the requirements of the 

mission. 

The ALFUS framework can be used in the evaluation of 

UGVs used for off-road military mobility systems. The 

following briefly describe two such systems, and provide a 

high-level analysis of their autonomy levels within the 

ALFUS framework. 

B. Case Study: XUV DEMO III 

The Experimental Unmanned Vehicle (XUV) DEMO III 

project included participants such as the Army Research 

Laboratory (ARL), the National Institute of Standards and 

Technology (NIST), and NASA, as well as others. The 

XUV Demo III [10] is a vehicle designed primarily for 

mission support roles like reconnaissance, route trafficability, 

enemy detection, etc. With a powerful suite of sensors, 

including rear- and forward-facing vision, FLIR, RADAR, 

and LADAR, the vehicle is able to create a sophisticated 

world model of its immediate surroundings. 

Computational processing power and communications 

antennas enable the vehicle to fuse its sensor data to perform 

path planning in difficult terrain, locate and identify other 

machines and warm-body elements, and share this 

information with other members of its group. Vehicle to 

vehicle communications enable tactical behaviors such as 

platooning, and cooperative search. The platform can also 

be fitted with various weapons systems for direct action 

missions. 

TABLE VI 

ALFUS SUMMARY MODEL OF XUV DEMO III 

MC ED HI 

10 

9 X 

8 

7 

6 X X 

5 

4 

3 

2 

1 

0 

58
C. Case Study: The CRUSHER 

The Crusher [11] is a heavy duty autonomous vehicle that is 

able to travel at high speeds across difficult terrain thanks in 

part to its six large wheels and sophisticated suspension 

system. The vehicle is also symmetric, and if flipped 

upside-down, it will autonomously reconfigure its control 

and continue on its mission with a delay of little over a 

minute. The Crusher’s suite of sensors is used to create 

terrain maps while traveling at high speeds. Telescoping 

sensors also provide wide-area terrain data while remaining 

largely hidden. The vehicle’s controls also employ 

machine learning techniques. As the vehicle’s strategy for 

avoiding obstacles is often to not avoid them, the design 

focus has been on increasing ruggedness of the platform to 

perform high-speed navigation on rugged terrain. 

TABLE VII 

ALFUS SUMMARY MODEL OF CRUSHER

MC ED HI 

10 

9 

8 

7 X X X 

6 

5 

4 

3 

2 

1 

0 

VII. DARPA GRAND CHALLENGE 2005 VEHICLES

A. Analysis of Autonomy Levels in DARPA Grand Challenge 

Vehicles 

While other UGVs may be said to legitimately score higher 

on one or even two of the axes, probably the best composite 

representation of UGV autonomy that is currently available 

(as of the summer 2007) is the set of vehicles that completed 

the 2005 DARPA Grand Challenge. Five vehicles finished 

the race that year and can be considered the pinnacle of 

UGVs, at least until DARPA’s next challenge is completed 

later this year. This section will analyze the application of 

the ALFUS autonomy levels to the DARPA urban challenge 

vehicles (generically first) and then apply this framework to 

the winner of the challenge: Stanford’s Stanley. 

4) Mission Complexity: The Grand Challenge provided a 

straightforward mission to the competitors: drive over 

varying terrain along a specified course in the 

California desert in the shortest amount of time possible. 

Three issues drove the complexity in this mission: 

course length, uncertainty in terrain, and the 

competition element. The length of the course 

required substantial endurance from the hardware and 

software components in the vehicle. Varying terrain had 

to be accommodated as it was specified as part of the 

challenge. Finally, as part of a race, the mission 

required some consideration of the potential 

performance of other competitors. 

5) Environment Difficulty: Since the race course was 

situated in the California desert, and therefore not trivial, 

it can be said to have been a milestone in UGV 

environmental difficulty. The general ruggedness of 

the terrain increased the challenge but commercially 

available, off-road-capable vehicles were sufficient to 

master the terrain. However, the drivers of the 

human-driven trail vehicles for each autonomous 

vehicle said it was oftentimes difficult to keep up with 

the autonomous vehicle on the rugged terrain. The 

2005 course contained a mixture of well-maintained 

roads, dirt and gravel tracks, washboard and washouts, 

rutted tracks, poorly defined berms, open areas, choke 

points, obstacles, winding mountain roads, and steep 

drop-offs. Each of these poses challenges for UGVs 

but in all cases the environment and the obstacles in the 

environment were assumed to be static. 

6) Human Independence: The vehicles were required to 

demonstrate substantial human independence to 

complete the challenge. Beyond the most obvious 

aspects of human independence, namely automatic 

route following and obstacle avoidance, the vehicles 

were required to automatically meet multiple objectives 

and resolve conflicts (e.g. simultaneous waypoint 

corridor adherence and obstacle avoidance), adapt to 

GPS dropout, and recover from any hardware or 

software failures that might otherwise prevent the 

vehicle from completing the race. 

The ALFUS framework can be used in the evaluation of 

UGVs that participated in the 2005 DARPA Grand 

Challenge. The following describes the winning vehicle, 

Stanford’s Stanley, and provides a brief analysis as to its 

autonomy level determination. 

B. Case Study: Stanley 

As the winner of the Grand Challenge, Stanford’s Stanley 

represents a significant milestone on the path to full vehicle 

autonomy. Stanley completed the 132-mile course through 

the California desert in 6 hours and 53 minutes, and aside 

from two instances when DARPA race officials manually 

“paused” the vehicle, it traversed the entire course with no 

human intervention. The route for the course was specified in 

advance, so the primary challenge faced by Stanley was to 

identify the drivable terrain along that route. Five SICK laser 

scanners were used successfully to that end. In addition to the 

laser scanners, Stanley utilized a GPS and IMU to follow the 

specified route and estimate vehicle pose, and a color camera 

59
to identify drivable terrain beyond the range of the laser 

scanners. Because the global route was provided by DARPA, 

the path planning problem for Stanley was one of local 

obstacle avoidance rather than global route generation [12]. 

Table VIII shows where Stanley is estimated to fall within the 

ALFUS framework. 

TABLE VIII 

ALFUS SUMMARY MODEL OF STANLEY

MC ED HI 

10 X 

9 

8 

7 

6 X 

5 

4 X 

3 

2 

1 

0 

IV. CONCLUSION

The field of autonomous vehicles or unmanned ground 

vehicles has evolved over the last 2 decades, primarily driven 

by government research funding. While the military and space 

programs pioneered early development, interest from the 

commercial transportation sector is just beginning. The three 

axes used in the ALFUS setting allow a comparison of current 

state of the art in autonomous vehicles and perhaps show 

areas of opportunity for planners. The assessments presented 

are subjective, although all bias has been consciously avoided. 

It is worth noting that it was very challenging to decouple the 

three axes without using an evaluation tool to analyze the 

lowest level subtasks of these categories. This challenge 

reached its height when evaluating vehicles that have not been 

directly involved in a public demonstration. For example, 

the lack of literature on these vehicles made it difficult to 

determine what type of mission it is able to complete, and 

what level of human dependence it would need to complete 

that mission. It was most challenging to decouple mission 

complexity and environment difficulty. There is significant 

research opportunity to further the state-of-the-art in both of 

these areas. The human independence accomplishments have 

been excellent across all markets such as military, space, mass 

transit and hobbyists. However, this is most likely a result of 

lower mission complexity and environmental difficulty. 

The three metrics of MC, ED and HI are summarized in 

Figures 1, 2 and 3. The military is leading the research front 

in mission complexity while the hobbyist/academic 

community achieved a high degree of human independence. 

0

1

2

3

4

5

6

7

8

9

10

NAVLAB5

ARGO

AHS DEMO

CYBERCAR

SPIRIT

XUV DEMOIII

CRUSHER

STANLEY

UGV Name

ALFUS Mission Complexity

Fig. 1 Average ALFUS Mission Complexity has been low in the UGV 

research arena 

0

1

2

3

4

5

6

7

8

9

10

NAVLAB5

ARGO

AHS DEMO

CYBERCAR

SPIRIT

XUV DEMOIII

CRUSHER

STANLEY

UGV Name

ALFUS Environment Difficulty

Fig. 2 ALFUS Environment Difficulty has remained steady 

0

1

2

3

4

5

6

7

8

9

10

NAVLAB5

ARGO

AHS DEMO

CYBERCAR

SPIRIT

XUV DEMOIII

CRUSHER

STANLEY

UGV Name

ALFUS Human Independence

Fig. 3 UGV research community has excelled in ALFUS Human 

Independence – the most visible norm of autonomy 

60
0

5

10

15

20

25

30

NAVLAB5

ARGO

AHS DEMO

CYBERCAR

SPIRIT

XUV DEMOIII

CRUSHER

STANLEY

UGV Name

ALFUS MC+ED+HI

Fig. 4 The sum of ED, MC and HI is very close for all vehicles 

 

The sum of the capabilities of every vehicle is very close as 

shown in Figure 4. While the three axes can vary based on 

application area needs, it is worth noting challenges in one 

direction usually necessitate simplification on other directions. 

An ideal vehicle with rating of 30 may perhaps be needed for 

autonomous military vehicles carrying human cargo in a 

hostile urban setting. An urban autonomous vehicle in civilian 

applications may never need a mission complexity level of 10. 

ACKNOWLEDGEMENT

The authors would like to thank the ALFUS Working Group 

participants. In particular, the following participants offered 

guidance during the completion of this paper: 

Hui-Min Huang, NIST 

Elena Messina, NIST 

Charles Bishop, Boeing 

 

REFERENCES

[1] Huang, H., Albus, J., Messina, E. "Toward a Generic Model for Autonomy 

Levels for Unmanned Systems (ALFUS)," Proceedings of the Performance 

Metrics for Intelligent Systems (PerMIS) Workshop, 2003. 

[2] Huang, H., Pavek, K., Novak, B., Albus, J., Messina, E. "A Framework 

For Autonomy Levels For Unmanned Systems (ALFUS)," Proceedings of the 

AUVSI's Unmanned Systems North America, 2005. 

[3] Huang, H., Pavek, K., Albus, J., Messina, E. "Autonomy Levels for 

Unmanned Systems (ALFUS) Framework: An Update," Proceedings of the 

2005 SPIE Defense and Security Symposium, 2005. 

[4] Carnegie Mellon University (July 1995), Carnegie Mellon Researchers 

Will Prove Autonomous Driving Technologies During a 3,000 Mile, 

Hands-off-the-Wheel Trip from Pittsburgh to San Diego, Press Release. 

[5] Broggi, A., Bertozzi, M., Conte, G., Fascioli, A. Automatic Vehicle 

Guidance: The Experience of the ARGO Autonomous Vehicle. World 

Scientific 1999, ISBN 981-02-3720-0. 

[6] Thorpe, C., Jochem, T., Pomerleau, D. “The 1997 automated highway free 

agent demonstration,” IEEE Conference on Intelligent Transportation 

Systems, pp. 496-501. 1997. 

[7] www.cybercars.org 

[8] Leger, P.C. et al. “Mars Exploration Rover Surface Operations: Driving 

Spirit at Gusev Crater.” 2005. 

[9] Ai-Chang, M. et al. “MAPGEN: Mixed Initiative Planning and 

Scheduling for the Mars Exploration Rover Mission,” IEEE Intelligent 

Systems. 2004. 

[10] Shoemaker, M.C., Bornstein J. “Demo III Program: A Testbed for 

Unmanned Ground Vehicle Autonomous Navigation,” IEEE Symposium on 

Intelligent Control, 1998. 

[11] Carnegie Mellon University (April 28, 2006). Carnegie Mellon’s 

National Robotics Engineering Center Unveils Futuristic Unmanned Ground 

Combat Vehicles. Press Release. 

[12] Thrun, S., Montemerlo, M. et al. “Stanley: The Robot that Won the 

DARPA Grand Challenge,” Journal of Field Robotics, vol. 23, no. 9, pp. 

661–692, 2006. 

61
A Methodology for Testing Unmanned Vehicle 

Behavior and Autonomy 

Gertman, D. I.1

, McFarland, C.2, Klein, T. A.3, Gertman, A. E.4, and Bruemmer, D. J.1

1

Robotic and Human Systems Department, Idaho National Laboratory (INL) 

2

Johns Hopkins University, Department of Engineering, 3Oregon State University, College of Engineering 

4

Albertson College of Idaho, Department of Mathematics 

Contact: david.gertman@inl.gov, david.bruemer@inl.gov 

Abstract—This paper discusses approaches developed at the 

Idaho National Laboratory (INL) for quantifying and analyzing the 

performance of human-robot teams across different domains. 

These methods reflect experience and insights gained from 

previous INL experiments that have focused on landmine detection 

and marking; mapping and localization for robot positioning, 

mobile manipulation for explosive ordinance disposal (EOD), 

radiation characterization , and urban search and rescue operations. 

An overarching goal of this work has been to enhance our 

understanding of how the robot, the control and display interface, 

the task context, and the human contribute to or hinder mission 

success. Our approach to performance measurement was 

developed in concert with the iterative design cycle of our 

intelligent robotic control system, the robot intelligence kernel 

(RIK).[1] In extending and refining the RIK for various 

applications, three factors key to holistic human-robot performance 

assessment were identified: comprehensive planning; the inclusion 

of end users in the design and performance evaluation phases of 

the study; and combining automated data collection with subjective 

measures. The paper discusses lessons learned in developing and 

applying performance metrics and provides a brief overview of 

measures that we are currently using to support the assessment of 

autonomy. In particular, the paper emphasizes the application of 

these metrics to behaviors for complex and potentially dangerous 

missions. 

Keywords: Idaho National Laboratory, Unmanned Vehicle, 

Performance Metrics , Autonomy, Data Logging 

I. INTRODUCTION AND BACKGROUND

During the last few years, the Idaho National Laboratory 

(INL) has been conducting experiments to iteratively assess 

and improve a suite of intelligent behaviors called the robot 

intelligence kernel (RIK). The RIK is a collection of robot 

behaviors and interface display methods which allow the 

user to choose the appropriate level of autonomy and 

interface perspective for effective performance of the task at 

hand. Throughout these studies, an approach to 

experimental design and data analysis has evolved that 

emphasizes the need to facilitate, understand and predict 

human robotic interaction (HRI).[1,2] 

Although these studies vary in terms of users and task 

domains, the functional allocation of tasks between robot 

and human has always been a focus. By using on-board 

robot intelligence it is possible to free up the operator to 

attend to other duties; however, the goal is not to create fullautonomy, but rather to understand how the human and 

robot work together to produce optimal performance. In the 

course of HRI laboratory and field studies conducted at the 

INL with a variety of robot platforms and missions [2,3], 

three key factors were determined to be important: 

comprehensive planning including experimental design, 

selection and inclusion of end users, and an approach to 

data collection that recognizes combined contribution of 

objective and subjective data. One of the outputs of the 

planning and experimentation cycle is the development of 

performance metrics. Some of these metrics such as 

operator cognitive workload, error, and frustration focus on 

the user. On the other hand, others metrics such as the 

number of collisions, the distance traveled or sensor 

performance in terms of detections and false positives focus 

on the robot. Finally, some of these metrics apply to the 

interface including communication bandwidth, joystick 

usage, and automated logging of a wide variety of control 

inputs. In addition, we have developed and used 

application-specific performance metrics for urban search 

and rescue (USAR); urban reconnaissance; robot mapping 

and positioning; landmine detection; and radiation source 

detection and localization. In performing these studies, we 

have reached the conclusion that neither objective nor 

subjective data alone are sufficient to provide an in-depth 

understanding of performance and performance issues. 

Under the auspices of the Office of the Secretary of 

Defense (OSD), the Joint Ground Robotics Enterprise 

(JGRE) Technology Transfer Program the INL and the 

Space and Naval Warfare Systems Center in San Diego, the 

INL has developed components of perception, behavior, 

communication, and world modeling. The RIK uses these 

components to support five separate control modes: fully 

teleoperated, safe, shared, collaborative tasking mode and 

fully autonomous. In the teleoperated condition the human 

user has navigational control over the robot using a high 

bandwidth video feed. In safe condition the robot is able to 

protect itself from running into an obstacle while the user is 

still under full navigational control. In shared condition the 

robot is responsible for driving and the operator may 

62
provide directional cues intermittently. In the collaborative 

tasking mode, a variety of map-based “drive by intent” tools 

are used such as a visual hotspot which tells the robot to 

provide video of a region or the target icon which tells the 

robot to path plan and drive to a given location. In 

autonomous condition the robot has an end-to-end mission 

and accepts no user input. This paper discusses how these 

different modes of control have been developed and 

evaluated in a spiral cycle. Of particular interest is the way 

in which the experimental design and use of metrics has 

changed throughout this process. These changes have been 

due to the increasing capabilities and reliability of the 

robotic system as well the lessons of experience. More to 

the point, this paper indicates that different experimental 

methods and metrics must be used depending on the level of 

technology readiness, robustness and reliability. 

II. BACKGROUND RESEARCH

Before rigorous testing with human participants, it was 

necessary to measure the effectiveness of the actual robot 

performance. Across all modes of control, the RIK uses a 

positioning system that incorporates a Simultaneous 

Localization and Mapping (SLAM) method developed by 

the Stanford Research Institute [4, 5]. A measure of the 

overall positioning accuracy and reliability was a necessary 

prerequisite to other more in depth experimental questions. 

This testing and verification was accomplished by the 

Remote Sensing Lab in Nevada which used a laser 

theodolite system to track actual robotic movement in three 

dimensions. This testing showed that the robot was able to 

localize to within + / - 5 cm while moving for long periods 

of time within an open area of approximately 1500 square 

feet. In addition, an innovative ground truth system was 

used to test the ability of the robot to follow preset waypoint 

paths within a dynamic environment. This method was to 

place a black tape strip on the ground that could be sensed 

by a downward facing light sensor in the center of the robot. 

This sensor monitored when the robot’s center was 

positioned over this two inch wide tape. The black tape was 

in place in order to match a “virtual rail” created as an 

AutoCAD drawing. The INL GUI software used this to 

create a waypoint path plan which was then transmitted to 

the robot. The robot then used its behaviors to follow this 

plan. The experiment was used to metric the percent of time 

that the robot was able to stay over the black tape when the 

robot traveled at various commanded speeds. Although the 

robot could not visibly be perceived to be off of the “virtual 

track,” the experiment showed that the robot remained on 

the black tape approximately 75% of the time.[6] 

With issues of positioning addressed, the next step was 

to assess the performance of the robot behaviors for obstacle 

avoidance and the value of shared control. In one of our 

early studies, 107 participants drawn at random from 

attendees of the INL annual community exposition in 2004 

were given the task of locating sources (a simulation for 

finding people) [7]. In this study, each of these novice 

participants had 60 seconds to locate as many of five 

different items as they could. This USAR study focused 

jointly on the usability of the interface, the performance of 

the users and the performance of the robot behaviors. This 

was a preliminary study focused on understanding the 

benefits of robot behaviors and fundamental issues 

surrounding use of autonomy. The study allowed us to 

demonstrate that the use of behaviors was valuable in terms 

of task efficiency, providing a statistically significant 

increase in the number of objects found across ages and 

gender. However, despite these objective performance 

results, one of the insights from this study was that the 

combined use of subject feedback in conjunction with 

performance metrics provided the best overall 

understanding regarding performance during the 

experiment. 

Another study, performed in 2004 was focused on 

measuring the benefits of mapping. The experiment 

introduces a virtual three-dimensional (3-D) map 

representation that supports collaborative understanding of 

the task and environment. The goal of the 3-D display is to 

provide a workspace for collaborative understanding 

between the human and robot. The virtual 3-D component 

has been developed by melding technologies from the INL 

[8], Brigham Young University [9], and Stanford Research 

Institute (SRI) International [4,5]. When used in place of 

video, the 3-D map reduced operator workload and 

navigational error. By lowering bandwidth requirements, 

use of the virtual 3-D interface was also shown to enable 

long-range, non-line-of-sight communication [10]. 

Finally, we consider a previous study focused on a 

sensor payload. In particular, this study was focused on 

detection and marking of landmines. In 2005, the INL, the 

US Army Maneuver Support Center (MANSCEN), and the 

US Army Test and Evaluation Command (TECO) 

conducted an experiment to test the INL Autonomous 

Robotic Countermine System (ARCS). The robot that the 

test used was developed at Carnegie Mellon University 

(CMU). The robot system employed ground marking 

equipment developed by the SPAWAR. In this study as 

well, the planning was comprehensive involving many 

entities. The study was performed out at the INL unmanned 

aerial vehicle (UAV) airstrip on an unimproved dirt road. 

To test the system, six anti -tank mines were buried in the 

road at depths between 6 and 8 inches. For this experiment, 

four elements were used to metric the ARCS including: 

finding the landmines, marking the landmines, reporting the 

landmines to the control interface, and marking the proofed 

lanes for dismounted troops to move through. To measure 

the performance of the robot for the experiment, the 

Autonomy Levels for Unmanned Systems (ALFUS) ratings 

were used as an additional metric. The ALFUS ranking was 

developed by the National Institute of Standards and 

Technology (NIST). In this ranking there are three scales, 

human intervention, environment complexity, and mission 

63
complexity to indicate the overall level of autonomy 

exhibited by an unmanned system. The higher the average 

ALFUS score the greater the autonomous performance of 

the robot [11]. The use of ALFUS was an important recent 

milestone in our approach to applying metrics. Figure 1 

presents an interface screen shot available to operators 

during the experiment that show fusion of GPS, map and 

real time aerial imaging. The results of the real-world 

experiment showed that the proposed autonomous robot 

countermine system accurately marked, both physically and 

digitally, 124 out of 131 buried mines in an average time of 

less than six minutes. 

Figure 1. Interface screen shot for countermines 

Across each of these experiments, determining what to 

measure, who to include as part of the subject pool, how to 

properly balance laboratory and field issues in experimental 

design, the level of robot autonomy and the design of the 

display and control interfaces were large issues. In the 

remainder of this paper we address these factors while 

reviewing performance metrics. We develop further the 

argument that the approach to performance metric 

development and usage should be a flexible one, where 

meaningful measures depend on the level of technology 

readiness. Moreover, we believe that studies and the metrics 

that accompany them can build systematically on one 

another. Lessons learned from each experiment can be used 

to guide the development and execution of the next phase of 

study. 

III. APPROACH

The goal of understanding the performance of the robot and 

human; the usefulness of the control and display interface 

within the mission context; and subtleties regarding the 

linkage between preference and performance can be 

identified to provide insights for fielding systems, training, 

and future collaborative design. The following sections 

present our approach regarding the conduct of field studies. 

This includes: subject selection; comprehensive planning 

and study design, data logging, and employing subjective 

and objective measures to assist in tabulating, interpreting, 

and characterizing human-robot performance. 

IV. SUBJECT SELECTION

Pedahazur and Schmelkin (1991) describe many of the 

pitfalls in research related to selection and handling of study 

participants. [12] Although the main thrust of their 

arguments center around complications and threats to 

validity arising from differences in assumptions between 

subject and researcher including perceptions regarding the 

immediate environment, their admonishment to select the 

right subjects is aligned with our way of thinking. Novices 

and experts do not perform alike. As with Pedhazur and 

Schmelkin (Ibid), we also have been concerned with how 

subjects perceive the task, understand the context for task 

performance, and harbor assumptions different than those of 

the experimenters. Anecdotally, years ago, Israeli students 

were given a Rorschach to respond to. The experimenters 

did not know that the students had mistakenly believed that 

this was a test of imagination and that the best strategy for 

doing well was to produce as many divergent and obscure 

answers as they could within the time permitted. It wasn’t 

until a subject debrief was held that the experimenters 

learned of the students’ assumptions. Had they not 

participated in a debriefing session, the students would have 

been diagnosed with severe personality disorders. 

Also, we have found that end users can help during the 

planning process to make scenarios more relevant and to 

highlight important task behaviors; they also participate in 

and provide feedback during the experiment. An important 

issue is that the experiment design and debriefing must 

carefully distinguish between the various kinds of end users 

and the different levels of user experience. All so called 

“subject area experts” are not created equally. In fact, in a 

recent study with radiological hazard detection, consider 

that three different kinds of subjects were involved 

including personnel with robot operation and dirty bomb 

training; those with only dirty bomb experience and those 

subjects with general training with radiation detection. In 

the experiment, treating these users as if they were all the 

same would have been an unfortunate mistake since certain 

features of the robot behaviors and interface were used very 

differently and, in fact, user during analysis experience 

turned out to be a statistically significant factor. 

V. PLANNING AND STUDY DESIGN

In terms of evolving a comprehensive planning process, user 

insights and operational experience are key to determining 

the appropriate level of realism, difficulty, and task 

conditions found in the field. Every successful study 

requires a multidisciplinary team effort. In our recent dirty 

bomb experiment [in publication] different types of experts 

including radiation health and facility planning experts were 

brought into the planning process. Chemical, biological, 

64
nuclear, radiological and explosives (CBRNE) trained 

military explosive ordinance disposal (EOD) personnel were 

included in the early planning stages of the experiment to 

develop experimental goals consistent with systems that are 

operating in the field. In formulating the study design, we 

have used many of the study design features as practiced by 

the behavior sciences research and development 

communities. We have coupled this to traditional usability 

test paradigms. Even though the technology in robotics is 

dramatically changing, we have attempted to contain the 

complexity that always seems inherent in field studies. 

Realizing that obtaining a large number of subjects from a 

user population is always difficult, we have used 

randomization and counterbalancing in our designs. We 

have also employed computer-based data logging to reduce 

labor requirements and to improve reliability. All subjects 

receive “hands on training” and are encouraged to provide 

feedback during structured sessions. Inherently, fielded 

study designs will always reflect a number of real-world 

challenges including the availability of subjects and 

facilities; technical challenges associated with moving robot 

intelligence from one platform to another, and requirements 

for integrating improvements in sensor technology or to 

implement new behaviors. 

VI. LOGGED DATA

One of the basic goals of any experiment is to generate 

meaningful findings from basic metrics of success. In past 

experiments, the approach to data collection has been to 

attempt to figure out a priori which data would reveal 

significant results. During experiments, various equipment 

problems often ensued with the result that some portion of 

dependent variables were not recorded across all subjects. 

Likewise, because of human error in transcription or 

observation some dependent measures were not available 

for analysis. Lastly, sometimes measures are selected that 

fail to produce significance. Often experimenters know that 

they should be either measuring something differently or 

something different. 

Early on we decided that the answer to these pressing 

problems was to record video and screenshots of the 

experiment that could be retrieved after the experiment was 

finished, but due to human error and inconsistencies, this 

was also often lacking key pieces of information. Even with 

video taping, the general metrics used in these studies were 

often simplified down to broad aspects of performance such 

as the degree to which the task was completed, and the time 

in which the task was completed [2]. Although we believed 

that there were other meaningful data to report, there 

seemed to be no easy way around this predicament. 

Facing the continual problem of the lack of significant 

results that were in stark contrast to subject self-report and 

observations of the experimenters, and believing the cause 

to be directly the result of attempts to draw conclusions 

from a limited data collection approach, we sought to 

determine a means by which to implement a more finely 

tuned approach. Harbour, et al (2006)[13] state that 

performance improvements for high technology systems 

often come in small increments, thus more finely tuned 

performance-based metrics are needed to better analyze the 

differences between systems. We now use “data logging” as 

a way of further measuring smaller aspects of performance, 

such as joystick bandwidth, and joystick vibration and 

others [14]. As a caveat, not every small performance 

measure is going to prove to be successful, thus the 

flexibility provided by data logging described below is key 

to being able to evolve the right measures. 

A) Development of data logging capability 

Data logging stores the communications stream 

between the robot and the user interface automatically. By 

logging the communication stream, researchers are able to 

capture and review the experiment experience from multiple 

perspectives. Being able to extract new metrics and 

variables from the saved communication stream allows 

unforeseen issues and questions to be addressed. In addition 

to helping to create more objective metrics for the system, 

data logging also helps to prevent the human error found in 

earlier experiments. 

B)Data logging as a two edged sword 

Data logging also carries certain potential problems. 

One such problem is that the abundance of data can be 

overwhelming. This problem may be remedied by reducing 

the search space for dependent measures to those things that 

are theoretically meaningful andr allotting sufficient time 

for data analysis. Another potential problem that results 

from data logging is the potential for errors within the 

computer. These errors can be remedied by thoroughly 

testing the system before the actual experiment takes place. 

The data files can be subject to various tests regarding data 

range and standard deviation and any outliers reviewed to 

determine whether problems are present. 

By utilizing data logging, useful variables and metrics 

can be recorded in a more precise manner, and data that was 

previously difficult to capture is now easily retrieved. Some 

useful data that we have identified through data logging 

experience that would be otherwise unavailable or costly to 

determine includes: robot initiative, percent time active, and 

operator confusion measures. Robot initiative reflects the 

number of times that the robot had to protect itself. Operator 

confusion represents the number of times that the operator 

tried to force the robot to continue to follow a path after 

he/she had previously been told that the robot was unable to 

follow that path. Percent time active is indicative of the 

amount of time logged that the operator was using the 

joystick or mouse to control the robot divided by total time. 

Other logged measures include joystick usage in terms of 

back and forth versus side to side movement and average 

time between actions. 

For example, ‘% of time active’ is a relatively new 

metric; in the past we used joystick movements as a way to 

65
record this information. Recently, it was found that since 

there were some instances in which a joystick was not used 

as a preferred strategy, that joystick movement was no 

longer an adequate measure of performance. We hadn’t 

decided to use “time active” a priori. In the past, without 

detailed data logging, we would have been forced to wait 

until the next experiment to evaluate this metric and 

determine whether it was an adequate correlate for workload 

and localization. This “% of time active” measure was 

synthesized from computer generated data logs and showed 

results similar to the video summaries. As it turned out, the 

users spent much less time interfacing with the system in 

target mode than they did in other modes ( F =.0001, df=2, 

Tukey significant at p <0.05, for target versus joystick, and 

target versus joystick and map). Thus, we were able to find 

a “hard measure” for workload only after being informed by 

softer, qualitative measures. Had we limited ourselves to 

simple quantitative measures, these differences would likely 

have not been discovered. 

Data logging is important because it allows for a 

number of hard measures to be collected that can be further 

analyzed, better allowing for a more accurate depiction of 

the many dimensions of an experiment. Thus, in our most 

recent experiment, automated data logging was used to 

develop measures for robot initiative, operator confusion, 

and active time measures. These measures allowed us to 

distinguish between levels of performance in instances in 

which almost all of the subjects completed the task in 

similar periods of time. By generating these measures of 

robot initiative, operator confusion, and active time from 

logged files it was straightforward to prove the usefulness of 

data logging. The art is in determining which of the many 

measures are most useful in terms of predicting user 

performance. Data logging is best served when used in 

conjunction with additional subjective and objective 

measures that constitute a holistic approach to data 

collection described elsewhere in this paper. Later, in 

sections below we present data logging measures and 

discuss their value in aiding human- robot performance 

characterization. 

Robot initiative per square foot was another variable 

generated by data logging. Robot initiative as an absolute 

number was non-significant whereas initiative adjusted for 

the square foot of distance traveled was significant. The 

insight to control for distance first surfaced during subject 

interviews. Figure 2 presents these findings excerpted from 

the dirty bomb study; note that the mean target mode 

condition is associated with lower initiative than the other 

two conditions. 

Robot Initiative per Square Foot

0

0.001

0.002

0.003

0.004

0.005

0.006

2 3 4 5 6 7 13 8 9 10 11 12 14 15 16 17 18 19 20

Subject

Robot Init./Sq Ft

Joystick

Joystick + Map

Target Mode

Figure 2. Robot initiative as a function display mode 

VII. CONSIDERING OBJECTIVE AND SUBJECTIVE 

MEASURES

Good experiment planning provides a structure for data 

analysis by selecting potentially valuable performance 

metrics to record. Finding crucial performance metrics to 

aid in characterizing and understanding the nuances of 

human-robot team performance in relation to mission 

objectives requires an a priori understanding of the humanrobot teaming experience. In the most extreme case, an 

almost infinite number of independent variables with an 

infinite number of interactions can be conceived. This list is 

reduced by review of the mission objectives, task 

procedures, military doctrine, and experience of personnel 

in conducting similar missions. Selection and analysis of a 

limited number of planned metrics reduces the measurement 

challenge to something desirable, however, to effectively 

establish the relationship between important elements and 

performance gains across multiple scenarios the right mix of 

measures must be considered. This means considering 

objective and subjective measures. It is preferred to identify 

a select group of measures that can easily be either 

combined or reduced to cover the problem space. 

 Through statistical analysis, objective data often yield 

information regarding different treatment conditions or 

levels of independent variables thought to be important. To 

some extent, they have proven useful in heightening our 

ability to characterize and predict human-robot 

performance. Notionally, objective data give a systemcentric view of the experience, where as subjective data 

provides a human-centric view of the experience. 

Quantification of subjective data allows researchers to 

perform similar analysis to that which is performed with 

objective data, but from a human centered perspective. No 

matter how well a system performs, it is still possible for the 

subjective experience of the operator to fail to reflect 

objective performance gains. Human-centered analysis 

allows researchers to understand the perspective of 

66
operators and their assessments regarding the system and 

system performance. Thus, in our studies we use subjective 

rating, debrief, and interview data. We have found the 

debrief sessions involving end-users are often the most 

revealing in terms of what robot behaviors are the most 

valuable. 

A) Finding appropriate objective/quantitative metrics with 

help from subjective data 

The appropriate objective data are often not obvious. In 

early INL studies, basic statistical procedures were used on 

the data logged during the experiment. The computer-based 

data logging that we now conduct consists of the computer 

keeping track of subject actions, system status, and 

experimenter input during the administration of the 

experiment. In one instance, using counter balancing 

methods, it was possible to preclude learning effects from 

skewing the data. We also performed qualitative data 

analysis of three data sources: video, researcher journal 

comments, and debriefing interviews, that indicated that 

source location had an effect on subject performance. 

Through interviews it was determined that the measure of 

the area to be searched was an influential variable, this 

analysis determined that the square footage associated with 

different search areas was the primary cause of performance 

discrepancy between runs. Normalization of the a number 

of quantitative data with respect to square footage provided 

much cleaner differentiation between the groups and 

between the conditions while removing the dependence of 

findings on source location. The effect of statistically 

removing the influence of distance from the logged data was 

to indicate a potentially important, i.e., statistically 

significant finding that otherwise would have been lost. 

B) Developing an understanding of a system level result 

 Researcher comments during the experiment and postexperiment observations of videotapes have proven useful 

adjuncts to objective data. For example, during a recent 

radiological localization and mapping experiment, there 

were striking differences between user perceived physical 

workload and stress as measured by the NASA task load 

index (TLX) [15] and researcher’s assessment of that same 

workload using postural differences as an indirect measure 

of workload. The postural differences were sensitive to 

robot autonomy conditions, when users in the “video only” 

mode attempted to accomplish the navigation and 

localization tasks there was visible strain in their posture as 

the users curled their back to place their face as close to the 

monitor as possible. Those same users were seen in a more 

casual posture while operating the system in target mode, 

which indicates a lower level of physical workload and 

stress. This result was used to aid in understanding and 

interpreting other subjective and objective data. 

C) When subjective measures can be misleading 

 Subjective/qualitative data provided additional 

information about the discrepancy between workload ratings 

and actual workload as determined by performance 

measures. During the debrief interviews it was revealed that 

participants subjectively anchored their workload ratings 

based on their own experiences. This had not been a major 

problem in the past, subjects often use their own frame of 

reference. Although variations in rating scale design such 

as the behaviorally anchored rating scales (BARS) exist and 

have been used in a multiple settings such as clinical 

research and a variety of disciplines including management 

science we did not consider them to be necessary. 

However, the frame of reference used by many subjects was 

in-theatre combat!!! In comparison, all of our conditions 

were low in stress, complexity, and time pressure. In our 

review of the rating data, all of the modes were found to be 

very light on workload, which washed out any differences. 

Because during the interview and debrief process we 

learned how subjects were anchoring their responses, we 

were able to find look for more appropriate “Hard 

measures” of workload that were sensitive to levels of 

autonomy and display mode.

D) When the most “objective” measure is not the best 

 In our recent radiological localization, i.e., dirty bomb 

scenario, the ultimate goal was to compile subject scores 

communicating the accuracy of radiological source 

locations during experimental trials. One of our assessment 

tools for task completion required the participants to mark 

those locations on a floor plan of the test facility INL 

researchers decided against a completely quantitative and 

objective measurement strategy, i.e., just measuring the 

distance from the subject’s mark to the actual source, in 

favor of a measure that retained an element of contextual 

content. This measurement strategy took into account not 

only the numeric distance, but combined this with the 

operators’ grasp of both the global and local environment. 

These data were converted to a ten point scale which was 

chosen to match the breath of possible answers and 

uncertainty inherent in the map marking process (the maps 

themselves were not perfect). Interviews and discussions 

with EOD experts suggested that landmarks on the map 

were the key contextual requirement for location 

communication between robot operators and other human 

team members. The global portion of the localization was 

evaluated by subtracting from the score the number of 

landmarks (corners, pillars, walls, doors,) between each true 

source location and the location marked by the subject. The 

local portion of the metric was evaluated by cataloging the 

four most important landmarks at each actual source 

location and subtracting the number of cataloged landmarks 

which were not present at the location marked . This metric 

is more complex, yet retains more of the user definition of 

good localization. 

 The results, as shown in, Table 1 convey the benefits of 

the target mode for the localization task as a function of user 

67
experience. For the civilian support team (CST) and 

explosive ordinance disposal (EOD) subject populations, the 

per cent failure declined dramatically as the level of 

autonomy increased.(The joystick mode is associated with 

the least autonomy followed by joystick and map. The target 

+ map mode represents a nearly fully autonomous 

condition) For nuclear engineers (NE), the group least 

familiar with robots and emergency response procedures, 

the level of autonomy failed to have a positive influence 

upon the per cent failures. 

Table 1: Results of source localization via map analysis 

 Joystick Joystick 

+ Map 

Target 

+ Map 

% 

Excellent 

EOD 57.1% 66.7% 100.0% 

% Fail 28.6% 0.0% 0.0% 

% 

Excellent 

CST 42.9% 50.0% 50.0% 

% Fail 42.9% 16.7% 0.0% 

% 

Excellent 

NE 50% 50% 75.0% 

% Fail 25.0% 50.0% 25.0% 

*Excellent scores range from 90-100 

**Failed scores range from 0-60 

Within the EOD group, the increase in “% excellent” source 

location scores improved with each corresponding increase 

in robot autonomy level. One of the most interesting 

findings is not just the metrics themselves, but the 

realization that across several of the metrics, it seems that 

one of the most important advantages of autonomy is that it 

allows us to place tight bounds on performance. Essentially, 

for a number of different measures, the standard deviation is 

much tighter in instances where a higher level of autonomy 

is utilized. In the dirty bomb study, the chart presented in 

Figure 3 shows this to clearly be the case for the ability to 

localize the source. 

Figure 3. Accuracy scores for ability to localize sources by 

subject 

E) Determining a composite performance measure 

 As we reviewed the various measures we have tried to 

consider which mix of metrics might have the best 

predictive ability. Below is our first attempt to produce a 

composite metric integrating objective and subjective 

measures of robot behavior developed over the course of 

multiple studies. (See Appendix A for this equation). Data 

regarding the ranges, means, variances, and assumptions for 

the measures are the subject of another paper. Also, this 

metric is to be refined over the course of additional planned 

studies. Table 2 presents the scores obtained when applying 

the metric to recent human-robot performance data. 

Table 2. Composite performance scores for group 

experience and robot autonomy conditions 

 Joystick Joystick 

+ Map 

Target 

+ Map 

EOD 0.723 0.728 0.838 

CST 0.623 0.645 0.796 

NE 0.618 0.603 0.650 

Note that when the composite metric is applied that the best 

mean task performance is obtained for the most experienced 

group in the highest. i.e., target + map, autonomy mode. 

When compared with the other less experienced users, the 

EOD user group had the highest mean performance scores. 

Finally, the highly automated target mode was associated 

with better performance than either of the two other 

conditions. 

VIII. SUMMARY AND DISCUSSION. 

Over the past few years INL has developed an integrated 

approach to conducting field studies that emphasizes the 

discovery and development of metrics key to human-robot 

system performance. Comprehensive study planning, 

involving end-users in design and metric development, and 

the use of objective and subjective measures are part of the 

field study process. Laboratory studies can be useful in 

establishing systems performance, debugging equipment, 

and establishing general bounds for performance, however, 

field study experiments have a direct realism and relevance 

for end-users. Ideally, as the technology readiness level of 

the systems increase, a transition is needed from laboratory 

studies which are more controlled and precise to more open 

ended field studies. The evolution of testing methods 

described in this paper illustrates one attempt to accomplish 

this transition over the course of several years. 

In many studies, we have noted large performance and 

preference differences among less expert and more expert 

end users. Although it is more difficult to enlist the 

participation of end-users, particularly those with qualified 

robotic experience, the data, insights, and lessons learned 

are significantly more valuable in guiding decision makers 

Map Analyzation Score for End Users

0.00

5.00

10.00

15.00

20.00

25.00

2 3 4 5 6 7 13 8 9 10 11 12 14 15

Subject #

Score 

Video Only

Video and Map 

Target

68
as well as future developments for control strategies and 

behaviors. The dependent measures identified in the studies 

reviewed in this paper: operator confusion, robot initiative, 

and % time active, would not have been developed without 

the aid of data logging leading dependent measure synthesis 

and the incorporation of end user comments. Further, there 

is some evidence that grouping users based on the level of 

their experience can unearth important trends and insights. 

Simple dichotomies such as user-novice can overstate 

findings, collapsing these groups for analysis purposes may 

prove to be worse. 

Understanding the issues of robot autonomy can not be 

accomplished in a single study, it must be done over a 

period of years where nuances relating to the interplay 

between the human and robot behaviors can be looked at 

singly or in conjunction with one another. Our approach has 

always been to ask “What is the fundamental benefit of 

additional behaviors as opposed to applying our efforts to 

optimize a single, specific application?” 

REFERENCES

[1] D. J. Bruemmer, D. I. Gertman, C. W. Nielsen, D. A. Few, and 

W. D. Smart, Supporting Complex Behaviors with Simple 

Interaction Tools, Advances in Robotic Systems, edited by 

Aleksandar Lazinica, Vienna, , in press, expected December 

2007. 

[2] D. J. Bruemmer, R. L. Boring, D. A. Few, J. L. Marble, and M. 

C. Walton. "I call shotgun!": An evaluation of mixed 

initiative control for novice users of a search and rescue robot.

(2004) Proceedings of the 2004 IEEE conference on Systems, 

Man, and Cybernetics. 

[3] D. J. Bruemmer, D. A. Few, C. W. Nielsen. 2006. "Spatial 

Reasoning for Human-Robot Teams," in Emerging Spatial 

Information Systems and Applications, ed. Brian Hilton, Idea 

Group Inc. Hershey, PA, pp. 350 – 372 

[4] K. Konolige, “Large-scale map-making,” in Proceedings of the 

American Association for Artificial Intelligence (AAAI), San 

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[5] J. S. Gutman and K. Konolige, “Incremental mapping of large 

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[6] D. J. Bruemmer, D.A. Few, S. Sirin, H. Hunting, M. Walton, 

and F. Carney. Autonomous robot for sensor 

characterization. Robotics and Remote Systems for Hazardous 

Environments, Gainesville, Florida, March 28-31, 2004 

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Walton, and C. Nielsen. "Shared Understanding for 

Collaborative Control." IEEE Transactions on Systems, Man, 

and C, 2005ybernetics , Part A. Systems and Humans, vol.35, 

no.4, pp. 505-512, Jul. 2005. . 

[8] D. J. Bruemmer, D. A. Few, R. Boring, M. Walton, J. L. 

Marble, C. Nielsen, and J. Garner, ‘Turn off the television!:’ 

Robotic exploration experiments with a 3-D abstracted map 

interface,” in Proc. 38th Hawaii International. Conference on 

System Sciences, Waikoloa, HI 

[9] D. J. Bruemmer, D.A. Few, R. L. Boring, J. L. Marble, M. 

Walton, and C. Nielsen. "Shared Understanding for 

Collaborative Control." IEEE Transactions on Systems, Man, 

and Cybernetics , Part A. Systems and Humans, vol.35, no.4, 

pp. 505-512, Jul. 2005. 

[10] C. Nielsen, M. Goodrich, and J. Crandall, “Experiments in 

human–robot teams,” in Proc. Int. Workshop Multi-Robot 

Syst., 2003, pp. 241–252. 

[11] D. J. Bruemmer, and M. O. Anderson. "Intelligent Autonomy 

for Remote Characterization of Hazardous Environments", in 

Proceedings of the IEEE International Symposium on 

Intelligent Control, Houston, TX. October 2003 

[12] E. J. Pedhazur and L. P. Schmelkin Measurement, Design, and 

Analysis: An Integrated Approach, Lawrence Erlbaum & 

Associates, Hillside NJ. 822 pgs. 1991. 

[13] J. L. Harbour, D. J., Bruemmer, and D. A. Few "Measuring 

Unmanned Vehicle System Performance: Challenges and 

opportunities." AUVSI Unmanned Systems North America, 

Orlando, FL. August 29-31, 2006 

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United Kingdom, September 6-8 2006. — 193kB PDF 

[15] S. Hart, “NASA Task Load Index (TLX),” Human 

Performance Research Group, NASA Ames Research Center, 

Moffett Field, CA. Version 1.0 

APPENDIX A - COMPOSITE PERFORMANCE MEASURE 

DISCLAIMER 

This paper was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government 

nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility 

for any third party’s use, or the results of such use, of any information, apparatus, product, or process disclosed in this paper, or represents that 

its use by such third party would not infringe privately owned rights. 

[ ]

[ ]

[ ]

12

(1 ( ))

(1 ( )) (1 ( ))

(1 ( )) (1 ( )) (1 ( )) (1 ( )) (1 ( ))

20

10

2

10

1

tan 10

tan

/

/

/

/

/

/

AverageSA AverageWorkload

StartSA Source SA Source SA

MaxDis ce

EndDis ceFromStartLocation

MasRI Time

RI Time

MaxRI

RI

MaxOpCon SqFt

OpCon SqFt

MaxOpCon

OperatorConfusion

MaxTime SqFt

Time SqFt

MaxTimeTaken

TotalTimeTaken

+ − +

+ − + − + + +

− + − + − + − + −

69
Standardizing Measurements of Autonomy in the 

Artificially Intelligent

Amy R. Hudson

National Institute of Standards and Technology 

University of Maryland College Park 

Gaithersburg, MD 

ahudson5@umd.edu 

Abstract— The amount of control that an intelligent system has 

over their actions, whether they are able to act independently from 

their creator, plays a major factor in describing systems and in 

distinguishing them from each other. Different levels of autonomy 

reflect the different abilities of the machines as well as where and 

how they can play a part in our daily lives. We may begin to 

comprehend these abilities and possible applications into human 

society once we can classify the levels of autonomy. The goal of 

this project is to set a framework for establishing a standard of 

autonomy in the scientific community by examining past and 

current methods of measurement as well as exploring different 

levels of autonomy's ethics and implications. Once this framework 

is made available to the scientific community, more tests and 

experiments will be conducted to refine and further ingrain the 

framework so that classifications of artificial intelligent agents are 

available universally. If we are to continue improving upon our 

machines, developing them to be more adept at communicating 

and accomplishing tasks, then a set of standards must be 

established for the safety and convenience of mankind.

Keywords: autonomy, artificial intelligence, standards 

Artificial Intelligence (AI) is a relatively new area 

of research and study, having been around for only fifty 

years. Its waters are uncharted, and as time progresses, there 

are more and more possibilities of research brought to the 

foreground, a multitude of things we may accomplish in 

collaboration with humans and the artificially intelligent. 

DARPA (Defense Advanced Research Projects Agency) is 

extremely interested in employing robots to take over the 

more dangerous and dirty parts of being a soldier, such as 

searching for enemy combatants in a crowd or unknown 

territory, to fetching injured soldiers from the battlefield. 

The threat of quivering in surgery has become obsolete with 

the use of robots to help out in making precise incisions into 

patients, just as countries with aging populations are 

considering the benefit of having robots available to care for 

the elderly. Through communicating when pills should be 

taken, providing or aiding in transport, the burgeoning AI 

generation will care for our elderly, taking our dispositions 

on retirement and our progressing age and making them 

more pleasant and convenient to both the aging and the 

people who have to care for them. This lightening the load, 

Larry H. Reeker 

National Institute of Standards and Technology 

Gaithersburg, MD 

larry.reeker@nist.gov

so to speak, captures the drive behind further investigating 

AI and using it as a source of helping society to cope with 

unpleasant tasks, as well as providing a new curiosity with 

which its citizens are free to explore and philosophize 

about. Now that the importance of AI research and testing 

has been (at least tentatively) established, we can proceed to 

emphasize the importance of having standards of 

measurement for artificially intelligent machines, both 

expediting and simplifying the research process, going 

about in an organized way so that the AI world will be on 

the same page and better prepared to provide insights that 

can easily be jumped off from to reach a new idea and new 

way in which our community may benefit. 

The authors feel that the most pressing part of a 

machines makeup to standardize is their autonomy level. 

Autonomy (or the closeness of it) is THE major factor in 

determining what task a robot is able to complete; knowing 

how much human supervision and maintenance provisions a 

company will have to provide for a machine can help the 

company in either budgeting for these provisions, or 

perhaps investing in developing a program for the machine 

that allows it to conduct its task in its environment with 

much less human intervention, and therefore less costs. If 

autonomy is standardized then companies across the world 

would be able to accurately gauge expenses, and computer 

scientists interested in AI and robotics could test their latest 

exploits in making a machine more independent by using 

the standards both their competition is using, and the same 

standards they themselves have used in the past. There are 

already a wide variety of machines relying different 

amounts on their human creators, from vacuums that sense 

surfaces and clean your house without you pushing them or 

directing them, to cars that are able to navigate across 

deserts. With standard autonomy levels we will have a 

convenient way of measuring improvement in the field of 

AI. 

There are five areas in particular where a human 

may intervene on the behalf of a machine. If these five areas 

summarize autonomy levels, then measurements of these 

areas can then be translated into a general measurement of 

70
autonomy, even as standardizing these measurements would 

standardize measurements of autonomy. To accurately 

standardize these measurements, research must be done on 

the spectrum of abilities our machines have now, matching 

today’s autonomy levels with levels we can deduce after 

learning more about AI’s possibilities in future research. 

This paper provides a framework which may prove useful to 

the scientific community when they are compiling these 

autonomy levels [see Epilogue below]. 

The first three areas where a machine may require 

aid from a human will be relatively simple to measure, 

especially if an ontology of autonomy is created amongst 

robots, so that the same scale and checking box can be used 

for every robot. Whether a robot can replicate, or can 

change programming or physical aspects of itself to better 

suit what it interprets its task to be, reflects its creating 

abilities. A one would be given to the system that can only 

be created with human help, a two to the system that can 

only create software and program or the system can only 

recreate physically, and a three to the system that is able to 

replicate itself completely; these numbers are just 

suggestions. A robot could be dependent on humans or other 

robots for movement to complete its task, being worn or 

carried perhaps. Is the system able to move without human 

interaction? The less independence it has, the lower score. 

Same thing for maintenance; is the system able to fix any 

malfunctions, physical and/or technical? Robots can get 

damaged in the course of working and a company that 

invests in a robot that is able to fix glitches in its system will 

save money. Also, if new programming updates are 

constantly being added by the robot automatically then the 

technology will always be up-to-date, and the robot will be 

able to accomplish more, faster! The machine must continue 

to adapt to its environment in order to be truly autonomous. 

Communication is often an area where a human 

could intervene, maybe always present to translate what the 

robot senses into terms that they are able to, if not 

understand, then at least use to complete their tasks. Or the 

human (or another robot) would have to explain to other 

humans or robots what this machine is trying to show. Is the 

system able to effectively communicate with other machines 

and other humans? For measuring communication 

autonomy, the robots and people that the machine interacts 

with may be polled, with 50% of robots understanding the 

machine all the time, and only 10% of humans 

understanding it (without a “translator”). If the majority of 

the interactants do not understand the machine at anytime, 

the machine has the lowest communication autonomy, 

whereas if the majority of interactants understand all the 

time, then the machine gets the highest score. Surveys and 

questionnaires may aid in providing the researchers with the 

opinions of the surrounding public that has interacted with 

the machine, and would need to be modified as our 

communication trends are constantly being altered. Similar 

to communication, society’s views on learning and testing 

for understanding vary significantly across both time frames 

as well as different cultures. Therefore, standardizing these 

components of autonomy should prove challenging. 

The learning component of autonomy is one of the 

harder facets to measure quantitatively. We have created a 

general scale to organize the learning levels on, shown 

below: 

1. Human solves problem with computational and 

data management 

2. Computer interface adapts to human preference 

interactivity 

3. Human specifies list of items and attributes and 

system clusters them 

4. Supervised Learning: Human gives exemplars and 

computer learns 

5. Reinforcement Learning: human interacts in 

learning by providing evaluation functions for system 

outcomes at various places 

6. Means-ends analysis 

7. Computer learns and transfers knowledge 

8. Computer learns and can deal with new 

environment 

This scale still leaves learning with an open-ended 

definition, one which different readers will interpret to mean 

various things. In the study of artificial intelligence, we are 

inflicting machines with our intelligence. It follows that 

these machines should be able to be tested on their learning 

skills with the same methods we employ on ourselves to 

reflect exactly what the person taking the test is capable of 

learning. Humans often do not completely understand each 

other (syntax and diction getting in the way), making 

analysis of tests shaky as answers need to be interpreted 

accurately. By participating in human discourse and 

grasping a greater understanding of it, we may develop AI 

systems that can communicate with humans and learn from 

them even better than humans can, as a human could adapt 

to the machine and then the machine might respond by 

adapting to the human. 

Many studies have been conducted using the Structure 

of Observed Learning Outcomes (SOLO) taxonomy to 

determine how advanced a person’s ability of learning and 

analysis is. The essay structure section of the analysis in 

SOLO is not applicable to our machine situation, but just as 

71
essays need to be well-ordered and structured, with lots of 

examples and specifics, machines the most independent 

from their creators will have an organized programming 

system and be able to create and interpret programs in that 

same organized fashion. English teachers are trained in 

reading a paper and knowing exactly which grade it 

deserves, due to how they have to sit and grade a multitude 

of papers at once. If English has found a way to change a 

qualitative assessment into a quantitative one, programmers 

should have training of assessing the structure and content 

of programs included in their education repertoire to 

accomplish a similar feat. 

1. Misses the point 

2. Single point 

3. Multiple unrelated points 

4. Intermediate 

5. Logically related answer 

6. Unanticipated extension 

There are also many different types of learning, so that 

reinforcement learning, case-based learning, and others, 

could be assessed and compiled to all aid in reflecting the 

general learning capabilities of the machine. The scientific 

community will have to determine through experimentation 

how many of the different learning techniques should factor 

into the general learning assessment, and which of the many 

techniques even apply to learning in machines. Using only 

one or two of the known methods of testing learning may 

still be enough to obtain the score and level that is needed to 

interpret autonomy. If the accuracy of these tests are also 

recorded, then using just these tests are good enough for our 

purposes...is there really going to be a major difference 

between having an autonomy score of 3 and 3.5? and what 

will it be? Perhaps the standards of autonomy should 

become satisficed, since a more general term of autonomy 

will be more understandable to the public and maybe mean 

more for future calculations. 

Once all of these five aspects of autonomy have 

been measured, they then can be added together to obtain 

another number. Sheridan's Model can then be broken up 

into appropriate number ranges for a 1 level of autonomy 

etc., so that the number you obtain from the five aspects of 

autonomy falls in a range for a specific level of autonomy. 

Sheridan's Model: Levels of Autonomy in Decision 

Making [5] 

100 % Human Control 

1- Human considers decision alternatives, makes and 

implements a decision. 

2- Computer suggests set of decision alternatives; human 

may ignore them in making and implementing decision. 

3- Computer offers restricted set of decision alternatives; 

human decides on one of these and implements it. 

4- Computer offers restricted set of decision alternatives and 

suggests one; human may accept or reject, but decides on 

one and implements it. 

5- Computer offers restricted set of decision alternatives and 

suggests which one it, the computer, will implement if 

human approves. 

6- Computer makes decision; necessarily informs human in 

time to stop its implementation. 

7- Computer makes (implements) decision; necessarily tells 

human after the fact what it did. 

8- Computer makes and implements decision; tells human 

after the fact what it did and only if human asks. 

9- Computer makes and implements decision; tells human 

after the fact what it did only if it, the computer, thinks 

human should be told. 

10- Computer makes and implements decision if it thinks it 

should; tells human after the fact if it thinks it should. 

100 % Computer Control 

The ranges of numbers to assign for the levels of autonomy 

also fall into the category of research and testing that needs 

to be conducted to obtain a solid ground for standardization, 

which may be updated as the years progress and more 

machines are created and not able to be labeled as a specific 

level. Updates to these standardizations will make for a 

much more robust way of measuring autonomy, being able 

to fix qualitative data to quantitative data so that much less 

bias is involved in the measurements. 

One way in which we propose to instill these 

standards of autonomy in the future is to modify intelligent 

systems' ontologies to include an autonomy scale, created 

72
based off of the aforementioned qualities of learning, 

communication, movement, maintenance and creation. If 

the machine is able to rate itself on the scale, it will be able 

to communicate to people how much of a task it can be 

entrusted to perform without human input; how reliable it is. 

With this knowledge, people will be able to specify the best 

purpose the machine would serve, enabling the progression 

of forays into the AI realm of thought to be both organized 

and efficient. 

EPILOGUE: SIX YEARS LATER: AUTONOMY & DEEP 

MEASUREMENTS FOR SCIENTIFIC CONSTRUCTS

We have made some suggestions on measuring 

autonomy in this paper, since it was a theme in this year's 

PerMIS, as well as being widely used today. “Theoretical 

Constructs and Measurement of Performance and 

Intelligence in Intelligent Systems” [Reeker, 2000] had a 

different role of trying to use measures and discover how 

they might help in developing scientific theories (e.g. 

Archimedes finding a way to measure amounts of gold and 

silver in a crown resulting in the study of hydraulics). In the 

2000 paper, we also briefly talked about robustness and 

autonomy in extraction of information from text. 

Our discussions to this point in this paper are more 

about measurements of autonomy in the engineering fields 

rather than a precise measurement leading to a part of a 

scientific theory. In the 2000 paper, engineering (by itself) 

is classified as a science of the artificial (certainly true with 

robotics). But engineering and pure science are linked in 

many areas, as one can see computational and scientific 

theories in the work of Herbert Simon and Allen Newell*. 

Testing for Autonomy by using levels of Autonomy or 

readiness levels and finding out how the system performs 

for each level, in what can be called a suite of performance 

metrics, specifies the machines abilities. Of course it doesn't 

tell whether the system is truly intelligent, and it probably 

would not satisfy Lord Kelvin, for instance. One can have 

multiple suites and multiple requirements for readiness, the 

different uses given in a suite on particular tasks. 

The idea of the 2000 paper was to show that a 

young science like artificial intelligence should be looking 

for measures that are not only useful in developing 

technologies but can help put together a new science. This 

new science will have theories that then can be predictive 

through a calculus - or, as Simon and Newell had advocated 

- a computing program of the sort that is called today 

“computational science”. Such theories tend to be able to 

 

*Many people do not realize the amount of philosophy that both Allan 

Newell and Herbert Simon used in their work. They are thought of as 

renaissance men, mostly as in computer science, mathematics and statistics, 

cognitive science, and more, but they were also both philosophers, 

important in helping to develop the new science of artificial intelligence. 

predict what is going to happen in various situations that 

stand up within empirical experiments, and it was called in 

that paper “deep measurements” (instead of “surface 

measurements”). In fact, the importance of being able to 

develop computational systems as part of a theory is of 

interest to many of us, and computational science continues 

its ascent. 

We most often see computational science used 

in [Bekey 2005] and [Mataric, 2007] and not only in 

robotics, but also in The University of Massachusetts’ 

Autonomous, Learning Laboratory's all types of learning. It 

is often used in discussing systems of agents, which can 

encapsulate information -- for Simon, “the allegory of the 

watchmakers” in [Simon, 1969]. 

So what is in this concept of autonomy? In the first 

part of this paper, we talked about human interaction with 

autonomous systems, for robots and other systems. In the 

spirit of the 2000 paper, we would like to look at aspects of 

autonomy that might be deeper: Robustness (already 

mentioned in the 2000 paper), stability, adaptability, 

capability, and scalability. Stability is associated with 

control theory. Its usefulness was around in physical 

systems long before AI, but some new ideas for the 

information age can be found at e.g. [Reeker, L.H. and 

Jones, A.T., 2001]. 

Adaptability is one of the most interesting of the 

“ility”s and is changing the world because information can 

be sent so quickly, whether it is in fly-by-wire systems in 

airplanes to the world-wide web and cell phones. In AI 

systems, we see lots of these adaptability possibilities. In the 

2000 paper, these ideas have been discussed through 

learning and transfer of learning. The ideas of machine 

learning are getting better, and testing is important to them. 

For example, the ensemble methods have shown how to 

take learning systems and make interchanges between 

variance, noise (e.g. outliers), and bias. They can be tested 

using ROC charts (which are hardly different) that tell likely 

false positives and false negatives. New ideas have emerged 

for finding information in great amounts of text, and it has 

been shown that both ROCs and the standard precision and 

recall measurements used together are better than only one 

of either. 

Ontologies, which were only mentioned in the 

2000 paper, are clearly needed for communication between 

intelligent systems, whether human intelligence is studied 

by cognitive systems or artificial intelligence systems. 

Ontologies need more study and they are getting it through 

different efforts, such as the Semantic Web. They need tests 

and metrics, too; PerMIS is doing an important job in 

developing computer and information science and changing 

the world. 

73
To conclude, there is still a lot of need for metrics 

of all types, both at the surface and in depth. 

REFERENCES

[1] Bekey, G.A., [2005], Autonomous Robotics, MIT Press 

[2] Mataric, M. J. [2007], The Robotics Primer, MIT Press 

[3] Reeker, L.H. [2000], Theoretic Constructs and Measurement of 

Performance and Intelligence in Intelligent Systems, Proceedings of the 

2000 PerMIS Workshop. Available from NIST/MEL 

[4] Reeker, L.H. and A. Jones, [2001], Measuring the Impact of 

Information on Complex Systems, Measuring the Performance and 

Intelligence of Systems: Proceedings of the 2001 PerMIS Workshop. 

<http://www.isd.mel.nist.gov/research_areas/research_engineering/Perform

ance_Metrics/past_wkshp.html> 

[5] R. Parasuraman, T.B. Sheridan, and C.D. Wickens, “A Model for Types 

and Levels of Human Interaction with Automation Transactions on 

Systems, Man, and Cybernetics- Part A, vol.30, pp. 286-297, 2000 

[6] Simon, H.A., [1969], The Sciences of the Artificial, MIT Press 

[7] The Chinese University of Hong Kong, 

<www.cuhk.edu.hk/clear/download/PDC/n23_SOLO_assessmt_grid.doc> 

 

74
Appendix A: 

The SOLO taxonomy as a guide to setting and marking assessment 

 

SOLO category Representation Type of outcome Solution to problem Structure of essay Unanticipated extension 

Create Synthesise Hypothesise Validate Predict Debate Theorise 

Solution to problem which goes beyond anticipated answer. Project or practical report dealing with real world ill-defined topic. 

Well structured essay with clear introduction and conclusion. Issues clearly identified; clear framework for organizing discussion; appropriate material selected. Evidence of wide reading from many sources. Clear evidence of sophisticated analysis or innovative thinking.

Logically related answer 

Apply Outline Distinguish Analyse Classify Contrast Summarise Categorise 

Elegant solution to complex problem requiring identification of variables to be evaluated or hypotheses to be tested. Well structured project or practical report on open task. 

Essay well structured with a clear introduction and conclusion. Framework exists which is well developed. Appropriate material. Content has logical flow, with ideas clearly expressed. Clearly identifiable structure to the argument with discussion of differing views. 

Intermediate Solution to multiple part problem with 

most parts correctly solved but some errors. Reasonably well structured project or practical report on open task. 

Essay fairly well structured. Some issues identified. Attempt at a limited framework. Most of the material selected is appropriate. Introduction and conclusion exists. Logical presentation attempted and successful in a limited way. Some structure to the argument but only limited number of differing views and no new ideas. 

Multiple unrelated points 

Explain Define List Solve Describe Interpret 

Correct solution to multiple part problem requiring substitution of data from one part to the next. Poorly structured project report or practical report on open task. 

Essay poorly structured. A range of material has been selected and most of the material selected is appropriate. Weak introduction and conclusion. Little attempt to provide a clear logical structure. Focus on a large number of facts with little attempt at conceptual explanations. Very little linking of material between sections in the essay or report. 

Single point 

 

State Recognise Recall Quote Note Name 

Correct answer to simple algorithmic problem requiring substitution of data into formula. Correct solution of one part of more complex problem. 

Poor essay structure. One issue identified and this becomes the sole focus; no framework for organizing discussion. Dogmatic presentation of 

a single solution to the set task. This idea may be restated in different ways. Little support from the literature. 

Misses the point 

 Completely incorrect solution. Inappropriate or few issues identified. No framework for discussion and 

little relevant material selected. Poor structure to the essay. Irrelevant detail and some misinterpretation of the question. Little logical relationship to the topic and poor use of examples. 

75
Assessment of Man-portable Robots 

for Law Enforcement Agencies 

Carl Lundberg 

National Defence College 

115 93 Stockholm, Sweden 

carl.lundberg@fhs.se 

Henrik I. Christensen 

Georgia Institute of Technology 

Atlanta, GA 30332, USA 

hic@cc.gatech.edu

Abstract - This project has involved testing a Packbot Scout 

within a SWAT-unit1

 for five months. This was done to explore the 

tactical benefits of the system and to test the robot’s technical 

performance with end users. Another objective was to compare 

earlier results – obtained by investigating military during training – 

with results from deployment during true risk. The SWAT-team, 

equipped with and trained to use the robot, set a standard to bring it 

with them on regular missions. Using the robot during negotiation 

proved to be the most beneficial application. Other uses would be for 

long-term surveillances and deploying non-lethal weapons. Early 

results indicate that the Stockholm SWAT-unit, consisting of 80 

active officers, could deploy the robot at least 20 times a year. 

Keywords: SWAT police, user study, man-portable robot, 

Packbot

I. INTRODUCTION

Robots are already an established tool for high-risk 

applications such as EOD2

. Other applications could benefit 

from the use of robots, although a number of issues must be 

considered to enable deployment on a regular basis. The 

technical design must be adjusted to meet special 

requirements for other applications, requiring detailed 

knowledge about the end users and the tasks they face. 

Relevant niches in which robots can perform successfully 

need to be identified, and methods for deployment have to be 

developed. Robot systems need to be versatile, not only serve 

multiple purposes for one particular user, but also adapt to 

several different professions. Keeping the assorted end users 

in the loop during product development, while simultaneously 

exploring methods for deployment is crucial to achieve 

successful and rapid implementation. 

In previous studies we have investigated man-portable 

robots for Military Operation in Urban Terrain3

 [1]. These 

studies were performed during military training maneuvers 

which in general provided a realistic setting. One aspect, 

however, was not accurately represented during training – the 

relation to mortal danger. As a consequence we decided to 

perform a parallel study involving a user group in actual risk, 

 

1 Special Weapons And Tactics. 

2 Explosive Ordnance Disposal, i.e., removal, disarmament, and destruction 

of explosives. 

3 MOUT 

namely the Stockholm SWAT-unit. SWAT-units do, just as 

MOUT-soldiers, target people rather than artifacts or 

substances such as in EOD, CBRN4

, and USAR5. 

The objectives of the project were to: 

• Investigate if users at real risk render results that 

significantly differ from results obtained during training 

maneuvers. 

• Broaden the scope of knowledge regarding the 

feasibility of robots within another high-risk work 

group. 

• Perform continued user-governed assessment of the 

Packbot Scout6

 in realistic settings. 

• Survey a user group to identify opportunities for 

continued research. 

This paper presents initial findings gained through two sets 

of interviews and one written mission report7

. The first set of 

interviews was performed with the SWAT-unit’s chief and a 

member of their Training and Development team, at the time 

the robot was handed over for test8

. The second set of 

interviews was performed with the two officers selected to 

operate the robot after having had the opportunity to deploy 

the robot for five months9

. The results were verified with the 

respondents. 

This article is organized with related work in section 2, a 

description of the users in section 3, and a description of the 

robot in section 4. Section 5 describes how the robot was dealt 

with during the trial and how it could be deployed in the 

future. Section 6 discusses the results and suggests future 

work. 

 

4 Chemical, Biological, Radiological, and Nuclear detection and 

decontamination. 

5 Urban Search And Rescue. The goal in USAR is to localize humans 

confined in destructed buildings. The victims are considered to be static 

unlike the targets of MOUT or SWAT-missions. 

6 The robot system is described in Section 4. 

7 A one page police report describing a live mission performed with the robot. 8 This interview was performed with both respondents at the same time and 

lasted for 1 hour and 40 minutes. 

9 These interviews were performed with one respondent at the time and lasted 

45 min each. 

76
II. RELATED WORK

Various studies have previously investigated high-risk 

workers deploying field robots. The most common 

application, bomb removal or destruction, has been 

successively refined since the first attempts in Northern 

Ireland in the beginning of the 1970’s [2]. Today this is a well 

established robot niche with several mature systems available 

as demonstrated at the European Land-Robot Trial 2006 [3]. 

Other areas of robot deployment shared by the police and 

military are security, surveillance, reconnaissance, and tactical 

support [4, 5, 6; 7, 8, 9]; these are areas that have received 

substantial investments, although much of the research is not 

published in detail [10]. The task of CBRN contamination 

control seems to be a prominent next step as sensor payloads 

are maturing for deployment on robots that are already in 

daily use [7, 11, 12, 13; 14]. Rescue robotics, and especially 

Urban Search and Rescue, is one of the areas of field robotics 

currently receiving the most attention in academic research. 

Countermeasures against, and preparedness for terrorist 

attacks and earthquakes have invigorated efforts to push robot 

technology into use [15, 16, 17, 18, 19]. 

Human-robot interaction outside the scope of high-risk field 

workers has been targeted for research as well. An early 

example is the integration of the SURBOT [20] for mobile 

surveillance in a nuclear power plant. More recent examples 

consist of testing of the robot seal Paro amongst elderly [21], 

the fetch-and-carry robot CERO by a partially impaired 

person [22], and a number of long-term tests of tour guide 

robots such as the RoboX9 at Expo02 [23]. By now space 

applications have been tested substantially through NASA’s 

deployment of rovers on Mars [24]. 

Robot deployment within SWAT-missions specifically10 is 

performed and occasionally reported in news-media [25, 26, 

27]. Most of these cases seem to be ad hoc solutions in which 

EOD-robots are used for other applications. Although the 

academic community has published little on robotics for 

SWAT-tasks [28, 29, 30], there are commercial products 

aimed at the application [12, 14, 31, 32]. 

III. USER DESCRIPTION

A. Organization, demography, and training 

Sweden has three main SWAT-units: Malmö, Göteborg, 

and Stockholm, who attempt to keep methodology and gear 

aligned since they occasionally perform joint missions. The 

Stockholm unit, 85 members strong and the largest of the 

three, is organized into eight SWAT-teams, each consisting of 

8-9 officers. Each team works four shifts per week. The 

number of teams on service varies with the expected amount 

of crime, with at least one team on duty at any given time11. 

During daytime it is common to have one team on alert, and 

 

10 EOD-robots excluded. 11 SWAT-units are organised in shifts to provide permanent service over time. 

Military units are to more extent deployed the entire unit at once with periods 

of recuperation in-between. 

another scheduled for training acting as backup. Although the 

teams have an appointed leader, most decisions are made 

jointly; only under time-pressure is hierarchal leadership 

enforced. The Stockholm SWAT-unit has four mission 

commanders who handle crime-site command and 

communication with the police chief. There are 22 negotiators 

associated with the SWAT-unit. Most of them are stationed 

elsewhere but are on call. Due to physical demands, the 

members of the SWAT-teams are currently all male12. The 

negotiators on the other hand, always work in a pair of one 

male and one female, for tactical advantage purposes. It is 

moreover attempted to have a diverse ethnical background 

amongst the negotiators. 

The average age within the SWAT-team is 36 years. 

Average time spent with the unit is 8-9 years. A minimum of 

five years of police service is required before being 

considered for the 3-month special SWAT-training13. 20% of 

the working hours are spent on training, which to a large 

extent is handled within the teams. To be able to act swiftly 

and in a synchronized manner, the SWAT-teams use 

predefined and well practiced concepts based on reference 

scenarios14. Despite all teams receiving the same basic 

training and having the same gear, they occasionally develop 

their own behavior depending on experiences encountered; 

individualization is discouraged by management in the 

interest of interoperability. In the past all SWAT-team 

members were encouraged to be able to handle all techniques 

and equipment. Recent increases in technical complexity have 

required the team members to assume specialized roles. 

Keeping the competence for different technical aids high is 

considered a problem; new gear is not always properly 

evaluated. 

B. Tasks 

In contrast to many other police units, whose objective is to 

prevent crime, the SWAT-teams are mainly reactive; although 

they are occasionally deployed proactively to demonstrate 

suspicion and readiness to strike. Their main objective is to 

target dangerous situations. Common tasks include resolving 

hostage situations, arresting potentially aggressive suspects, 

and taking suicidal or violent mentally deranged persons into 

custody. In other cases they are called upon to perform rapid 

arrests or searches to prevent suspects from disposing of 

evidence. The SWAT-teams may also be used for riot control 

or routine missions such as high-risk escorts or searching for 

missing persons. 

Missions are initiated either by alarm of an ongoing crime, 

or by the request of assistance by another unit (response 

respectively planned missions). Responding to an ongoing 

crime is more frequent. Apartments or homes are the most 

 

12 A program to equalize the gender distribution is ongoing. 13 The police officers are older, have more experience, and are allowed to 

have an opinion in larger extent than the soldiers [1]. 

14 This, although, the SWAT-police considers them self to be less oriented 

towards training and relying fixed behaviours than the military. Larger space 

is left to individual solution from one case to another. 

77
frequently targeted environments, but open-air missions occur 

as well. The SWAT-units are equipped and trained to perform 

their duties wearing gas masks. Targeting suspects in possibly 

toxic environments occurs 2-4 times per year15. The 

Stockholm SWAT-unit on average performs close to one 

high-risk mission per day. 600 missions were performed 

during 2006. Of these, half were classified as high-risk 

missions. The most common tasks include dealing with severe 

criminals or organized crime. 

C. Typical scenario 

In advance of planned missions, the requested units usually 

survey the strike scene in detail16. This includes gathering 

evidence, getting to know the suspects, their armament, their 

vehicles, and the layout of the strike area. If the suspects 

reside at different addresses, the arrests are often 

synchronized. Planned missions usually occur before or after 

the crimes are committed, in order to minimize risks to third 

parties. 

During crime response missions, the first objective is to 

locate and confine the suspects to prevent escape or hostage 

taking. Subsequently, the mission commander, the SWATteam commander, and negotiators decide how to address the 

situation. A defensive approach, which entails that the suspect 

surrenders according to conditions stated by the police, is 

preferred. Negotiation makes up a large portion of this 

situation and can be a tedious process17. Long negotiations 

challenge the SWAT-teams’ ability to maintain a high level of 

readiness. Missions lasting longer than 6-9 hours require a 

relief unit. 

Offensive actions are based on forceful confrontation with 

the purpose to shock and overwhelm the suspects. 

Distractions such as teargas, pepper spray, or shock grenades 

might be used. The use of distractions or deliberate weapons 

fire (for other than self defense purposes) has to be sanctioned 

by the police chief. 

The Swedish police are increasing efforts towards nonviolent solutions through negotiation18. Decreasing human 

violence is regarded far more important than avoiding 

material damage. Breaching doors is the most common 

destruction during SWAT-missions. 

D. Limitations 

When asked about the main limiting factor, the robot 

operators responded that the restrictions imposed by the 

commanders19 were the most constraining to their 

performance. Despite proper competence, knowledge, and 

 

15 The Swedish Emergency Management Agency is funding acquisition of 

sealed CBRN-vehicles to provide the police with the capability to operate in 

hazardous environments; robots could play a role in within this. 

16 This was also reported by Jones et al. [28]. The military will, in 

comparison, most likely be less informed [1].

17 On one occasion a negotiation lasted for 44 hours. 18 The ambition to achieve non-violent solutions was pointed out to vary 

greatly between countries. In particular, Australia and United Kingdom were 

mentioned to favour negotiation before violence. 

19 Police chief as well as the mission commander. 

tools to act, the SWAT-teams feel they are held back from 

solving cases. 

Personal risk was not reported to be a very limiting factor; 

mission commanders usually take preventive measures to 

avoid risks to third parties or the suspects, long before the 

SWAT-officers regard themselves endangered20. The most life 

threatening moments were considered to occur during 

emergency vehicle transports or vehicular pursuits. The 

SWAT officers argued that their being aware prepares them 

for dangers, whereas the police in general to greater extent 

encounter high risks by surprise. They also reported that they 

are often able to demonstrate enough superiority to cause the 

suspects to surrender without resistance. 

IV. THE ROBOT SYSTEM

A. The Robot 

The iRobot PackBot Scout is a man-portable robot teleoperated using a video link (Fig. 1). The track propulsion 

system includes articulated tracked arms (flippers) which can 

be rotated 360 degrees. The flippers enable significant offroad abilities considering the small dimensions of the robot; in 

addition they enable recovery from roll-over. The top speed of 

the robot is 3.7 m/s and the Ni-Cd batteries enable an 

operating time of about three hours. The PackBot is equipped 

with fish-eye daylight video camera, IR-camera21, IRilluminator, GPS receiver, electronic compass, and absolute 

orientation sensors (measuring roll and pitch). 

Figure 1. The Packbot Scout with the distraction siren (centered on top of the 

robot). 

 

20 This on the contrary to EOD-technicians or MOUT-soldiers who report 

risks to be a crucial limitation [34]. 

21 Infra Red, in the close to visible spectrum. 

78
Figure 2. The operator control unit. 

The operator control unit consists of an Amrel Rocky 

Patriot rugged laptop fitted with a joystick22 allowing for three 

degrees of freedom, and a keypad for toggling functions 

on/off (Fig. 2). Communication between the robot and the 

user interface is achieved using double IEEE 802.11b radio 

links. 

A carrying system was added to both the robot and the 

operator control unit to enable hands-free portability. Other 

field adaptations included fitting the joystick, keypad, and 

cable connectors with protective covers. A small whiteboard 

was attached to the laptop with Velcro so that it could be 

easily removed and used by the operator to sketch the 

explored region. Extra batteries and chargers, both for wallsocket and vehicle charging were provided, as well as 

protective cases for transport and storage. 

B. The Payload 

During the project the robot was equipped with a distraction 

siren (Fig. 1). The siren is originally an alarm siren for 

intruder deterrence, developed and manufactured in 

Stockholm by Inferno23. The patented siren generates a highpitch noise which is intolerable to the naked ear. Four 

different frequencies are modulated to cognitively overload 

the auditory organ while not causing hearing impairment 

(123-127 db(A)). Wearing hearing protection or plugging 

ones ears blocks the effect. 

V. ROBOT DEPLOYMENT 

A. Deployment during trials 

The joint study was initiated in mid-December 200624 when 

researchers met with representatives from the Development 

and Training group of the unit. The meeting addressed 

working out guidelines and legislation issues for the trials. 

 

22 Sideways, forward/backward, and twisting the knob (to control the 

flippers). 

23 www.inferno.se 24 14 December 2006 

The police also gave a general overview about their work. It 

was decided to perform the testing with one of the eight 

SWAT-teams until May 2007. The appointed team was 

trained in the basics of robot operation a few days later.25 It 

was left up to them to use the robot as they considered 

appropriate, during training and real missions. The one-day 

training session included a brief description of how the 

military had been using the system in urban intervention [1]. 

Two team members were appointed robot operators for the 

duration of the trials. It was declared that real mission 

deployments were of interest to the study, while it was not of 

great concern whether the robot was damaged. The distraction 

siren was added to the robot system by March 200726. The 

interviews with the operators were performed at the beginning 

of May 200727. 

After handover, the two operators continued to train with 

the robot about once per week. In addition, they gave the other 

team members the opportunity to familiarize with the robot’s 

performance and try operating it. Training – performed both 

outdoors and indoors – included passing obstacles and 

operating under different lighting conditions. The most 

frequently trained task was mapping of previously unknown 

premises and locating suspects. During three training sessions, 

the operators first explored a premise before executing a strike 

mission into the investigated area and finally evaluating the 

benefit of previous knowledge. 

The distraction-siren payload was evaluated in a mock 

hostage situation during which one officer acted hostage taker 

and one officer acted hostage; both were previously 

unacquainted with the distraction-siren. The test showed that 

the noise, although extremely annoying, does not completely 

disrupt willpower (Figure 3). 

Figure 3. Tactical test of the distraction-siren. From left to right: the officer 

acting as criminal; the officer acting as hostage; the two SWAT-officers 

attacking. The hostage taker was instructed to shoot at the police, which he 

succeeded in despite the siren. The hostage immediately plugged his ears with 

his fingers. The electronically filtered hearing protection used by the police 

protected them from the noise. 

 

25 19 December 2006 26 18 March 2007 27 8 May 2007. The trials are continuing. 

79
Once the team had familiarized themselves with the robot, 

they decided to include it on missions involving five or more 

police officers. This was the case for about half of all missions 

performed. On missions with fewer than five participants, the 

team in general considered that no one could be spared to 

operate the robot. In addition, the jeep used for transport of a 

small number of people did not have much extra space; 

accommodating the robot was not a problem for large teams 

since they had access to a van. Since only one SWAT-team 

was trained to bring the robot, and did so on half of their 

missions, the robot was available approximately 10% of the 

total time. 

The robot was deployed in one real mission during the fivemonth trial28; it was used to investigate a suspect bomb in a 

staircase outside an apartment29. The robot enabled the police 

to keep the suspicious object, as well as the surroundings, 

under surveillance with standoff. Once the bomb squad 

arrived, the robot was used to gain initial information about 

the object and the surroundings. While the object was targeted 

by a bomb-technician wearing a bomb suit, the robot was used 

by the others to monitor progress. 

The robot was also considered for exploration of a smokefilled shop which was not on fire. After the team broke the 

door of the shop, they intended to use the robot to search for 

victims, but the fire brigade arrived and took over before the 

mission was initiated. 

The operators reported that it is usually possible to find a 

safe spot for the operator30. Handling the robot was not found 

too challenging for field operation, though the control unit 

lacks key-backlight which is required in darkness. The 

operators considered the video feedback to be fairly adequate. 

However, they thought an improvement in resolution would 

be beneficial, as well as the ability to pan/tilt the camera, since 

having to elevate the front of the robot with the flippers to 

view upwards (Fig. 4) proved time consuming. A backwards 

facing camera was suggested to make backing out of narrow 

spaces more convenient. A zoom function was further 

suggested to enable closer inspection31. 

The range of the radio link was considered sufficient to 

cover apartments, which is the type of premise targeted the 

most. Operations were usually performed from a staircase or 

neighboring apartment. Ruggedness and reliability were 

satisfying as well, although the users claimed the operator 

control unit and the robot sometimes failed to synchronize32. 

 

28 18 February 2007 29 The suspected bomb was located outside an apartment used for persons 

being under protection. 

30 The enemy’s location will be less know during MOUT which requires the 

operator to be protected by other soldiers [1]. 

31 Backwards facing camera and zoom are features available on the URBOT 

[7]. 

32 This error might have been caused by the fact that the OCU does not work 

properly after having been put in, and taken out of, the laptop’s standbymode. The standby-mode is activated by hitting the on-button while the ESCkey is used to turn off the lap-top. Making the mistake to attempt a reboot 

using the on-button might have been the cause of the robot comms lost error.

Spiral staircases were the only obstacles said to pose a 

problem. This problem became evident during the live 

mission targeting the suspected bomb. The police vehicles can 

generally approach the mission area fairly close making the 

distance the robot has to be carried not being very far. The 

robot was considered heavy though not a major obstacle33. 

The size became a problem only during vehicle transportation. 

The users immediately noticed the absence of two-way 

audio, which would make voice communication possible with 

suspects and victims. Missions including negotiations might, 

as mentioned, span for an extended period of time. Battery 

replacement and the possibility to charge batteries, both from 

wall sockets and vehicles, are needed. The operators 

additionally suggested the ability to charge the batteries while 

mounted in the robot, instead of first having to remove them. 

The distraction siren was considered to be of significant 

interests as it is less violent compared to shock grenades or 

chemical agents, and therefore might be less restricted for use. 

Suspects’ and victims’ reaction to the robot is an open issue; 

the robot might appear frightening, increase aggressiveness, 

or be ignored. The trials did not give any opportunity to 

investigate this issue, which can hardly be examined with 

validity during training. 

B. Considerations on future deployment 

Apart from the mission actually performed (inspection), the 

respondents indicated a number of possible applications. The 

most prominent task suggested was to use the robot as a tool 

during negotiation34. In the first phase it could be used to 

establish communication with the suspect either by bringing 

in a cell phone/radio or establishing a two-way audio link on 

the robot35. During negotiation, the robot could be used to 

transport items to and from the suspect (the counter-parts 

often demand food, cigarettes etc.). The robot could 

furthermore be used for retrieving weapons in case of 

surrender. 

Using the robot for the mentioned applications would 

provide the opportunity to observe the suspects’ 

aggressiveness, rationality, armament, the premise, and 

possible hostages. If negotiating with suicidal individuals, the 

robot might be used to monitor their mental state. As 

demonstrated in the live mission, the robot can also be used 

for visual inspection of objects36. A robot equipped with nonlethal weapons could be used for distraction if negotiations 

fail. Adding non-lethal weapons such as tear gas to the robot, 

however, poses a risk, as the weapons could come into the 

offenders’ possession. It was suggested that the robot should 

 

33 Military missions might, on the contrary, include covering significant 

distances on foot which makes weight more important. MOUT trails proved 

the weight of the Packbot to be right on the limit to what can be accepted for 

a man portable system [1]. Something that is verified by work with the 

URBOT that weights 30 kg [30].

34 This was also an application pointed to be of interest in MOUT [1]. 35 Features that have been taken into consideration by Robotic FX [14]. 36 This has also been suggested by the military and would benefit of a snapshot and zoom function in the user interface [1]. 

80
have a self-defense system, such as the ability to administer 

electrical shocks. 

Another suggestion was to use the robot for long term 

surveillance of a door or a passage to relieve police officers37. 

The robot could also enable the police to manifest their 

presence without exposing personnel to risks. Additionally, 

the robot could be used for missions in hazardous 

environments if equipped with appropriate sensors. The 

operators stated that the robot mainly would be used for 

defensive purposes on missions, i.e., to locate suspects and 

initiate negotiations, rather than to target them. The robot was 

not considered to be suitable for offensive deployment as it 

does not have the ability to act against the counterparts and as 

it is too slow. To circulate and map an area holding the 

suspect did not seem to be a likely application38. It was 

pointed out that outdoor operations could come into question, 

although this was not tested to any large extent. Considering 

the restrictions for using violence, the operators did not regard 

equipping the robot with lethal capabilities to be of any 

interest39. 

The main benefits robots could bring to SWAT-deployment 

were as an enabler of a number of new features during 

negotiation, and also some new tactical advantages in case the 

mission had to be solved offensively. The users did not 

consider the system to have a major influence on their 

personal risk40. The police did not consider the robot to have 

imposed any major disadvantages. The only negative issue 

mentioned was that a robot system would entail yet another 

high-tech utility requiring maintenance, training, transport, 

etc. It was not believed that the option of a robot would make 

the police officers decline to perform risky duties 

themselves41. In addition, it was mentioned that the doermentality and high ambition to achieve immediate results 

might prevent the SWAT-police from deploying the robot42. 

C. Acquisition 

The operators were asked to estimate how often the robot 

would be deployed if the suggested improvements were 

included. They felt that their team had encountered unusually 

few opportunities to deploy the robot during the evaluation 

period, but one of the operators estimated that the robot could 

be part of every fifth high risk mission of the Stockholm 

SWAT-unit (about once per week). 

One of the two operators distinctively argued that the tested 

system should be acquired once two-way audio and keybacklight had been incorporated. The other operator was more 

ambiguous. Although he stated that the robot could be 

 

37 This would require a motion detection system as observing a video screen 

is a task that can not be performed with reliability over time [1]. 38 Contrary to MOUT where combat reconnaissance was pointed out to be 

one of the primary applications of the Packbot [1]. 39 Weaponization was considered highly interesting in MOUT [1]. 40 Reduced risks are the prime benefit for robots in EOD and MOUT. In 

MOUT are robots, in addition, believed to reduce weapons deployment [1]. 41 The military entertained apprehension that the robot would delay advance, 

revile presence, and might make the soldiers less willing to take risks [1]. 42 Behaviour commonly observed during the MOUT-trials [1]. 

valuable, he argued that acquisition depends on cost and 

stated the price limit to be about 29,000 USD. The other 

operator projected the price limit to about 43,000-57,000 

USD43. 

Neither of the operators could suggest any alternative 

equipment they currently lack, that would be preferred over 

the robot. On the other hand, they did indicate occasional 

shortage of personnel to be a limiting and risk-increasing 

factor. When asked to compare the benefits of the robot to 

night vision goggles, both operators argued night vision 

goggles to be more useful44. 

Both respondents agreed that one robot would fulfill the 

tactical needs of the entire unit. Having a second system for 

training and for backup would be convenient. It is currently 

being evaluated if the unit should be equipped with a 

designated vehicle for the new technical equipment; it was 

suggested that the robot should be stationed in the techvehicle. Estimating how many robots would be destroyed 

during a year proved difficult as the suspects’ reactions to 

robot encounter had still not been experienced. One operator 

argued that it probably would not be very many while the 

other chose not to speculate. 

VI. DISCUSSION AND FUTURE WORK

Performing tests in a real setting is of benefit to accuracy, 

but can also convey practical difficulties; especially when 

targeting high-risk applications. It has, in this study, not been 

possible to gain data from several parallel methods to verify 

validity through triangulation45. As indirect observations were 

the only source of information, it would have been 

particularly beneficial to have a large data set, i.e., many 

operators with extensive experience; unfortunately, this was 

not possible either. Only two respondents were available and 

their experience was, despite the rather long trial period, 

limited. In addition, there is an obvious risk of bias between 

the respondents since they work in the same team. 

One of the reasons for selecting the SWAT-teams was to 

study a user under real risk. But, according to the two robot 

operators, they did not consider themselves to be highly 

endangered. From that aspect the setting might be considered 

inadequate to meet the objective, even though the risk-defying 

attitude might be the result of SWAT culture. 

Despite limitations in data collection and misalignment with 

one of the objectives, we consider the results to provide a 

general overview and a starting point for continued studies. 

Apart from continuing and widening the ongoing trials, we 

believe that a theoretical analysis of the police-report records 

would provide statistical data useful for estimating the robot’s 

value. Moreover, we consider the socio-technical and 

 

43 200,000 SEK respectively 300,000-400,000 SEK. These amounts 

correspond fairly well with the tolerable price limit of 20,000-30,000 USD, 

reported by Ciccimaro et al. [30]. 

44 Military considered the robot to be as valuable as night vision goggles 

during MOUT [1]. 

45 For example through comparison of results from observations, interviews, 

and numerical data from experiments [33]. 

81
psychological aspects of robot-person interaction to be of 

particular interests. 

Many of the presented findings align well with results from 

previous studies of both the police and military. For example, 

using the robot as a mean for communication is suggested by 

both groups. Considering the robot not to be suited for the 

most offensive and time-constrained tasks is another 

resemblance [1]. This and previous work on SWAT-teams 

result in similar estimations of tolerable price, and the 

anticipated mental, as well as physical, demands that can be 

placed on the robot operator [30]. There are striking 

differences as well46. While the MOUT-users demand longer 

radio range and improved visual feedback, the police officers 

are generally satisfied with the robot’s performance. Military 

users show a significant interest in weaponization, while the 

SWAT-officers do not regard lethal abilities as a realistic 

application. Reduced risk and decreased weapon deployment 

are considered to be the primary benefits in MOUT. In 

SWAT, the system is seen as having the most potential as a 

tool for negotiation and surveillance over time. 

VII. CONCLUSIONS

The question of whether robots should be acquired for 

SWAT-units calls for a comparison between frequency and 

importance of benefits, and the costs of implementation. 

Bringing the robot as an excuse to communicate or deliver 

items, and at the same time observe the surroundings, the 

suspects, and hostages was stated as a primary benefit. Once 

in place the robot could be used to deploy distractions during 

arrests. Long-time surveillance was considered as a suitable 

application as well. Unlike in MOUT and EOD, risk reduction 

was not considered as a main benefit of the robot. Nor was it 

of interest to give the robot lethal abilities such as suggested 

for MOUT. The investigated users were in general satisfied 

with the performance of the robot. Two-way audio, increased 

field of view, motion detection, and the possibility to store 

images for later viewing are desired improvements. 

The interplay between the robot and those encountering it 

stands out as the most significant open issue. Limited 

experience of actual deployment and only two respondents 

with experience of the system are the primary limitations of 

the study. This prevented a reliable estimation of deployment 

frequency; however, if regarding the one mission performed 

during the five months test period as representative, the 

system would be deployed about 20 times per year. It was 

estimated that one robot would fulfill the tactical needs of the 

Stockholm unit. Acquisition is the primary cost connected to 

the introduction of systems like the Packbot. Costs for 

training, basic maintenance, and tactical development can be 

handled through available recourses with a slight expansion. 

 

46 The level of acceptance vs. criticism to new gear might be influenced by 

cultural differences within the two organizations. The police has traditionally 

not had the recourses to finance custom development, but, been obliged to use 

COTS. The military, on the other hand, has a history of technical development 

according to their exact specifications. 

The users estimated a tolerable price limit to be somewhere 

around 30,000-50,000 USD. 

ACKNOWLEDGEMENTS

The participation of the Stockholm SWAT-unit as well as 

financial support from the Swedish Armed Forces is gratefully 

acknowledged. 

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83
Performance Metrics and Evaluation of a Path

Planner based on Genetic Algorithms

Giovanni Giardini

Department of Aerospace Engineering

Politecnico di Milano

Via La Masa 34

20156 Milano, Italy

giardini@aero.polimi.it

Tamas ́ Kalmar ́ -Nagy

Department of Aerospace Engineering

Texas A&M University

College Station

TX 77845, USA

permis@kalmarnagy.com

Abstract —This paper focuses on the analysis of the performance

of an innovative genetic path planner designed for a single agent

exploration. The proposed method is a generalization of the wellknown Traveling Salesman Problem (TSP) that we call Subtour

problem and it can be formulated as finding the shortest possible

path for visiting a subset of n given targets over a known area. The

algorithm is based on a Genetic Algorithm coupled with a heuristic

local search method. To evaluate the proposed planner, an extensive

performance evaluation has been done.

Keywords: Genetic Algorithm, Traveling Salesman Problem

I. INTRODUCTION

The capability of a system to plan and to act autonomously

represents an important direction in the field of autonomy and

artificial intelligence. Many applications, from space exploration [1]–[3] to search and rescue problems [4]–[7], have

underlined the need of autonomous systems that are able

to plan strategies both with or without the human feedback.

Commonly, the main requirement for an autonomous vehicle

is to navigate through an area while avoiding hazards, and,

for search and rescue applications, determine the location and

condition of victims. However, most of these systems can not

be considered totally autonomous, since human instructions

still play a crucial role.

In general, many autonomous vehicles are not provided

with decision or planning capability. They are usually able to

execute given commands (e.g. reaching a particular point or

using a required instrument), but they are not able to decide

by themselves a sequence of tasks or a plan to achieve. In

other words, the mission strategy and the goals to accomplish

are often decided by human operators.

Our goal is to realize a fully ‘autonomous’ system, where

the autonomous navigation is linked with an autonomous planning and scheduling system onboard the vehicle [8], [9]. Our

system should give to the vehicle the capability of deciding

by itself what to do, allocating the goals to accomplish and

generating a feasible strategy for achieving them.

As a first step toward this complete autonomy, we propose

an innovative planner for finding the near-optimal strategy that

allows the vehicle to accomplish a given set of interesting

targets (or mission objectives) in the shortest amount of time.

In this work we consider the mission objectives as interesting locations displaced over a known map, and ‘strategy’

means a path -a sequence of targets- for visiting these locations

(or a subset of these). As a consequence, our planning problem is closely related to the well-known Traveling Salesman

Problem (TSP) [10]–[12], where an agent (the salesman) has

to visit a given set of n targets such that each target is visited

exactly once, with the additional constraint that the salesman

will need to return to its starting locations.

More generally, our planning problem is stated as a variant

of the TSP that we call Subtour problem: for a given set of

n targets, the agent has to find the shortest path (the Subtour)

that visits k targets out of the n possible ones.

Our method is based on an innovative Genetic Algorithm

[13]–[15] coupled with heuristic local search techniques. The

environment (in terms of targets displacement) is represented

using a graph theory approach ( [16] and [17]) and the

proposed Genetic Algorithm is designed for searching optimal

solutions over the resulting graph. Performance metrics of the

algorithm is defined in terms of optimality of the solution and

computational time.

The outline of the paper is as follows. After the introduction

of the basic mathematical notations necessary for formalizing the combinatorial problems of interest (section II), the

proposed Genetic Algorithm is described (section III). For

evaluating the performance of the algorithm, an extensive

campaign of tests have been conducted and the results are

reported and discussed in section IV.

II. MATHEMATICAL FORMULATION

Graph theory ( [16] and [17]) has been widely used to

describe vehicle routing problems [18]–[20] and therefore it

is the natural framework for this study.

Given a set V = {v1, . . . , vm} of m elements referred to

as vertices (or targets) and a set E = {(vi, vj )|vi, vj ∈ V } of

edges, a graph G is defined as the pair (V, E). In particular,

if the vertices in V are connected to each other, the graph

84
is called complete (or fully connected) and it is denoted by

Km(V ), where m = |V |.

If a weight (or cost) w(vi, vj ) is assigned to every edge

(vi, vj ), G is a weighted graph. If w(vi, vj ) = w(vj , vi), G

is also a symmetric graph.

In a graph, a path P is a sequence of edges with each

consecutive pair of edges having a vertex in common. The

length of a path is the number of its edges, and Pk is a path

of length k. Similarly, a cycle C is a closed path, that starts and

ends at the same vertex. The length of a cycle is the number

of its edges and Ck is a cycle of length k.

The total cost W of a path Pk is the sum of the weights of

its edges

W(Pk) =

X

k

i=1

w(xi, xi+1). (1)

Analogously, for a cycle Ck,

W(Ck) =

k

X−1

i=1

w(xi, xi+1) + w(xk, x1). (2)

After having introduced the necessary notation, we are now

in the position to formalize the combinatorial problems of

interest.

Let T = {t1, . . . ,tn} be the set of n possible targets to

be visited. The i-th target tiis an object located in Euclidean

space and its position is specified by the vector r(ti). The

agent is denoted by a and r(a) is its position.

Let us define the weighted and symmetric complete graph

Kn+1(V ) generated by the augmented vertex set V = T ∪

a (see figure 1). The weights associated with the edges are

given by the Euclidean distance between the corresponding

locations, i.e. w(vi, vj ) = w(vj , vi) =k r(vi) − r(vj ) k, with

vi, vj ∈ V .

The Subtour problem is now defined as finding a path Pk ∈

Kn+1(V ) of length k (that is also the number of targets to

be visited), starting at vertex a and having the lowest cost

W(Pk).

The Traveling Salesman Problem (TSP) poses to find a cycle

Cn+1 ∈ Kn+1(V ) of minimal cost starting and ending at

vertex a, that visits all the n targets once.

III. SOLVING COMBINATORIAL PATH PLANNING

PROBLEMS WITH GENETIC ALGORITHMS

The obvious difficulty with the Subtour and the TSP is their

combinatorial nature. In both cases, a brute force approach

is infeasible for large n. A variety of exact algorithms

(e.g. branch-and-bound algorithms and linear programming

[21]–[23]) have been proposed to solve the classic TSP up to

30000 targets, and researchers have developed approximation

methods based on evolutionary algorithms (e.g. Genetic

Algorithms, Simulated Annealing, Ant System) for its

solution [24]–[26]. These latter sacrifice the optimality for a

near-optimal solution obtained in shorter time [27].

Our method is based on a Genetic Algorithm [13]–[15], and

it has been implemented for solving the Subtour problem, as

well as the classic TSP.

Briefly, a Genetic Algorithm (GA) is a search technique

used to find approximate solutions of optimization and search

problems [13]. Genetic Algorithms are a particular class of

evolutionary methods that use techniques inspired by Darwin’s theory of evolution and evolutionary biology, such as

inheritance, mutation, selection, and crossover (also called

recombination). In these systems populations of data compete

and only the fittest survive.

In a GA, every possible solution is represented by a chromosome (also called plan or individual), which is a sequence

of values (called genes). The algorithm works with population

of candidate solutions (set of chromosomes).

A typical GA starts with a random population and at

every generation step some individuals are chosen and mated.

Mating is achieved through the use of genetic operators ([13]

and [28]), described in section III-A. The newly generated

chromosomes, the offsprings, are then inserted into the new

population. Once a new population is created, its individuals

are evaluated by a predefined cost function c(.). The fitness

is simply the reciprocal of the cost function. After this socalled evaluation phase the weakest (least fit) chromosomes

are discarded. The GA tries to minimize the cost of the

chromosomes by repeating the process of combining and

modifying them according to a set of rules until desirable

solutions are found. During this evolution phase, the number

of genes composing the chromosomes could be either fixed

or variable, depending on the analyzed problem.

In this work, a Genetic Algorithm has been designed to

solve the Subtour problem on the complete graph Kn+1(V ),

where V = T ∪a, T = {t1, . . . ,tn} is the set of n targets and

a is the agent. The algorithm looks for the shortest possible

path Pk ∈ Kn+1(V ) between k ≤ n targets starting from

r(a).

Since the solutions of our problem are paths, chromosomes

are easily coded as the sequence of targets of the path

in the order they are visited by the agent (order based

representation). Clearly, the first element of a chromosome is

always a and targets must be visited only once.

A. Genetic Operators

The performance of a GA strongly depends on the recombination (mating) process, where the genetic materials of

the chromosomes are combined. Clearly, depending on the

genetic operators, the algorithm improves or reduces its speed

of convergence (that is the number of generations necessary

to converge toward the final solution) or its goodness (that is

the fitness of the final solution).

Consequently, in order to better evaluate the performance

of the algorithm, we decide to mainly focus on the description

of the genetic operators, referring to [29] for a more detailed

85
(a) Targets and agent. (b) Vertex set V = T ∪ a. (c) Complete graph K5(V ).

Fig. 1. Given the set of targets T = {t1, . . . ,t4} and the agent a, in (b) the augmented vertex set V = {v1, . . . , v5} = T ∪ a is shown (v5 = a), while

(c) shows the complete graph K5(V ) generated by the augmented vertex set V .

and complete description of the whole Genetic Algorithm.

Genetic operators combine existing solutions into new ones

(crossover operators) or introduce random variations (mutation

operators) to maintain genetic diversity. These operators are

applied in a fixed order (shown in figure 2) with a priori

assigned probabilities. In addition to these operators, a heuristic method to directly improve the fitness of the offsprings is

introduced (see section III-A.4).

Fig. 2. Order of Application of the Genetic Operators. Depending on a given

probability, at first the crossover operator is applied and then the mutation

occurs. Note the last operator is the local heuristic boost method.

The crossover operator generates two new offsprings by

combining the genetic materials of two chromosomes (the

parents) opportunely chosen [29].

Different crossover typologies have been developed for

solving the classic TSP, considering the order-based representation and the TSP constraints. The most common are

those described in [13]: Partially Matched Crossover, Order

Crossover and Cycle Crossover operators.

Even though operators are quite different, they are all based

on the constraint that in a TSP the solutions include all the

targets. However, since this is not the case for the Subtour

problem, the application of these operators is inappropriate.

Because of this, a new set of operators has been defined and

two different crossover methods have been developed: the

single cutting point crossover and the double cutting point

crossover, described below.

1) Single cutting point crossover: with the single cutting

point crossover, parents are halved at the same gene (the

cutting point). The cutting point is chosen either randomly

or to break the longest edge in the parents (the probability of

which one of the two methods is applied is specified a priori).

Once the parents have been halved, two offsprings are created

combining the first (second) half of the first parent with the

second (first) half of the second parent, respectively. Care is

taken to avoid duplication of genes (as every target should

only be visited once) and the length of the chromosomes is

kept constant. See [29] for details.

2) Double cutting point crossover: the double cutting point

crossover operator is similar to the previous one, but the

parents could be now divided at two different genes. The

cutting points are again selected in two ways, depending on

pre-assigned probabilities: they are chosen either randomly or

to cut the longest edge in both the parents. The deterministic

selection method introduces an improvement over the single

cutting point operator, where only one parent was cut along

its longest edge and this point was also used for the other

parent. An important consequence of having two different

cutting points is that the halves will in general have different

number of genes. A simple recombination would thus lead

to two offsprings with different lengths. In order to maintain

the original size of the chromosomes (which is necessary for

producing feasible solutions), the offsprings are created in an

appropriate way, described in [29].

3) Mutation operator: after the application of the crossover

operator, the mutation operator is applied to the new chromosomes. The mutation operator generates a new offspring by

randomly swapping genes and/or randomly changing a gene to

another one not already present in the chromosome. Note that

with the simple TSP this second type of mutation would not

be possible, because for that problem a chromosome already

contains all possible genes. The GA selects the method to

apply with a given probability.

4) Improving offsprings: A common approach for improving the TSP solutions is the coupling of the Genetic Algorithm

with a heuristic boosting technique. The local search method

adopted here is the well-known 2-opt method for the TSP [30]–

[32]. This method replaces solutions with better ones from

their ‘neighbourhood’.

Let us consider a set T of n targets and the corresponding

complete and weighted graph Kn+1(V ) (with V = T ∪a and

a the agent). Let us consider a Subtour Pk ∈ Kn+1, with

k ≤ n. The 2-opt method is based on the inequality

w(xi, xi+1)+w(xj , xj+1) > w(xi, xj )+w(xi+1, xj+1), (3)

86
TABLE I

SIMULATION CASES TO TEST EFFICIENCY OF DIFFERENT GENETIC

OPERATORS. THE 2-OPT METHOD IS NOT APPLIED

Crossover Type Mutation Mean fitness Variance of fitness

Double point Applied 1 1

Single point Applied 0.93 1.32

Double point Not applied 0.92 1.47

Single point Not applied 0.69 2.12

where xi, xi+1, xj and xj+1 are four vertices of Pk. If the

inequality is satisfied, edges (xi, xj ) and (xi+1, xj+1) are replaced with the edges (xi

, xi+1) and (xj , xj+1), respectively.

This method provides a shorter path with no intersecting

links. Consequently, the order of genes in the chromosome

changes [33]. The computational cost of this method is proportional to the square of the number of the visited targets

[31].

IV. RESULTS

A large number of simulations have been performed to

test the performance of the implemented Genetic Algorithm.

The tests described here are run for 250 generations with a

population size of 200 chromosomes. The crossover and mutation operators are applied with a 90% and 30% probability,

respectively.

A. Influence of the 2-opt method on the performance of genetic

operators

To evaluate the performance of the different genetic operators and the 2-opt method, various tests have been performed.

A target configuration for n = 100 targets randomly and

uniformly distributed over a unit square map is generated. This

configuration is kept fixed for all tests in this section to make

comparisons meaningful. The Subtour problem is solved with

n = 30 targets to be visited with a different combinations of

the genetic operators and the 2-opt method.

First, to evaluate the importance of the various genetic

operators, their performance are directly compared and tested

without the 2-opt method. For the different cases of table I

100 simulations have been run and the mean values and the

variances of the distribution of the best (highest) fitness values

of the final populations are shown. Since the optimal solution

is not known, the mean fitness values and the variances of

fitness are normalized by the best result (the highest for

the fitness values, the lowest for the variances of fitness).

The comparison of the quantities in table I shows that the

combined application of the double cutting point crossover and

the mutation operator yields the maximum fitness value and

the minimum variance of the solutions. On the other hand, the

worst solutions are obtained with the standalone application of

the single cutting point crossover operator. These results not

only demonstrate the improvement introduced by the double

cutting point crossover, but they clearly highlight the importance of the mutation operator (note the two best solutions

have the mutation applied).

On the other hand, if the 2-opt method is applied, the results

change, as shown in table II. In particular, independently of

the adopted configurations of genetic operators, there is no

difference between the fitness values of the obtained solutions.

From these new results, it is clear that the main effect of the

2-opt method is in an overall improvement of the algorithm,

which returns good solutions independently of the adopted

operators.

TABLE II

SIMULATION CASES TO TEST EFFICIENCY OF DIFFERENT GENETIC

OPERATORS TOGETHER WITH THE 2-OPT METHOD.

Crossover Type Mutation Mean fitness Variance of fitness

Double point Applied 0.993 3.12

Single point Applied 1 1

Double point Not applied 0.994 2.62

Single point Not applied 0.998 1.31

B. Speed of convergence and genetic operators

The results of the previous section clearly demonstrate

the great improvement introduced by coupling the genetic

operators with the 2-opt method. However, even if the 2-

opt method balances the results of the algorithm, its most

important improvement is in the speed of convergence of the

system.

To quantify the speed of convergence with various genetic

operators and the 2-opt method, the required number of

generations for the convergence of the algorithm and the

associated computational time are compared.

For this purpose, the Genetic Algorithm was used to solve

a 100-TSP problem, whose exact solution is known (the

considered problem is the KroA-100 TSP [34]). For each

different combinations of genetic operators, table III reports

the number of generation (with its variance) necessary to reach

within 1% of the known optimal solution. In every case, 500

simulations have been performed and the variances of the final

results are normalized with respect to the minimum obtained

value. From these results we conclude that the double cutting

point crossover coupled with the mutation operator provides

the highest speed of convergence, while a GA with the only

single cutting point crossover needs more generation steps to

reach close to the optimal solution.

For example, when the double cutting point crossover and

the mutation operators are applied together with the 2-opt

method, the converge of the GA is much faster than without

(figure 3).

The improvement in speed is radical: for this example the

local boosting technique yielded a 25-fold increase in computational speed to reach populations with the same fitness!

87
TABLE III

FOR EACH CASE, THE 2-OPT METHOD IS ALWAYS APPLIED AND THE

NUMBER OF GENERATIONS NECESSARY TO CONVERGE TOWARD THE

OPTIMAL SOLUTION IS EVALUATED. THE SOLVED TSP IS THE KROA100

PROBLEM.

Crossover Type Mutation NormalizedVariance

Number of generations

Double Applied 7.7 1

Double Not applied 8.7 1.14

Single Applied 14.3 1.86

Single Not applied 15.8 1.93

0 50 100 150 0

1

2

3

4

5

6

7

8

NUMBER OF GENERATIONS

NORMALIZED MINIMUM COST VALUES

 WITHOUT 2−OPT METHOD

 WITH 2−OPT METHOD

 OPTIMAL VALUE

Fig. 3. The Subtour is solved with and without the implementation of the

2-opt method. With the application of the 2-opt method, the GA converges

faster.

C. Subtour tests

The genetic path planning has been tested in order to

demonstrate its capability to generate near-optimal Subtours.

All the Subtour tests have been conducted considering a unit

square map with a given target configuration and using the cost

function (1). The double cutting point crossover, the mutation

operator and the 2-opt method have been used. Moreover,

to provide reliable averages, for a given configuration 100

simulations have been performed.

Figure 4 shows a sample Subtour for a problem where the

total number of targets is n = 100 and the shortest path is

sought connecting any k = 20 targets.

In order to rigorously evaluate the optimality of the Subtours

generated by the Genetic Algorithm, a comparison with known

optimal solutions is needed. To our knowledge, no benchmark

solutions exist for the Subtour problem, so the simplest brute

force approach was used to compute optimal paths for some

suitably chosen test problems. Figure 5 shows the optimal

7−Subtour for n = 30 targets and the solution by our Genetic

Algorithm. Table IV summarizes the results for two Subtour

problems, comparing the (brute force) optimal solution with

those generated by the GA.

The error in the final solution can be attributed to the

application of the 2-opt method on every chromosome at

every generation step. In fact, the frequent use of this method

0 0.5 1

0 

0.5

1

X

Y

a

Fig. 4. 20-Subtour problem solution with a given set of 100 targets. The

agent position is also shown.

TABLE IV

COMPARISON BETWEEN EXACT AND GA SOLUTIONS FOR DIFFERENT

SUBTOUR PROBLEMS BASED ON 100 SIMULATIONS.

Number of Subtour Optimal GA

Error

Targets length Solution Solution

30 7 71.27 72.42 1%

50 5 39.87 41.85 5%

restricts the random wandering of the Genetic Algorithm

over the search space, thereby severely restricting the set of

reachable solutions. If the 2-opt method is only applied with

a given probability, much like the other operators, the results

greatly improve and the optimal solution was easily found in

almost all simulations.

D. TSP tests

Since the TSP is a limiting case of the Subtour problem

(the agent visits all targets, i.e. k = n, with the restriction on

returning to the starting position) the proposed algorithm can

also be used to solve this classic problem. The double cutting

point crossover, the mutation operator and the 2-opt method

have already been applied. The suitable cost function is

W(TSP) = w(xi+1, x1) +

Xn

i=1

w(xi, xi+1), (4)

where w(xi, xj ) is the Euclidean distance between the corresponding locations.

The algorithm has been tested with different TSPs whose

optimal solutions have been published with the TSPLIB95 library [34]. This library includes different target configurations

for the TSP and many related problems (Hamiltonian Cycle

Problem, Sequential Ordering Problem, etc.) together with the

exact solutions. We note that the TSPs in the TSPLIB95 library

are solved with a cost function based on rounded distance

between targets.

For every problem considered here, 100 simulations have

been performed and the optimal solution was almost always reached. These results strengthen our claim that the

88
0 0.5 1

0 

0.5

1

X COORDINATE

Y COORDINATE

OPTIMAL

SUBTOUR

GA

SUBTOUR

S

Fig. 5. Comparison between the exact 7-Subtour (n = 30 targets) and the

GA solution.

implemented system is an efficient way to find near-optimal

solutions of the proposed hard combinatorial problems.

E. Computational performance of the Genetic Algorithm

Finally, the computational performance of the proposed

Genetic Algorithm is evaluated. With a fixed number of

generations (200), different TSPs have been solved. In each

problem, the targets are randomly distributed over a 100 square

map. Every test has been conducted on a Pentium IV with a

clock frequency of 1.86 GHz and 1 Gb of RAM, running the

GentooLinux Operation System.

For every configuration, 100 simulations have been run and

the time t of each process has been recorded. Table V presents

the mean values t and the standard deviation σt of the obtained

results for the initialization and the evolution phases.

TABLE V

TABLE REPORTS THE MEAN TIME VALUES t, TOGETHER WITH THE

CORRESPONDING STANDARD DEVIATION σt , SPENT FOR THE

INITIALIZATION AND THE EVOLUTION PHASES.

Number of Initialization Phase Evolution Phase

Targets t [s] σt [s] t [s] σt [s]

50 0.23 0.006 4.97 0.01

100 1.02 0.015 14.23 0.07

150 2.46 0.032 29.38 1.36

200 4.57 0.054 46.8 1.05

250 7.39 0.086 77 5.9

300 10.95 0.12 144.22 9.87

500 33.15 0.27 482.54 57

Not surprisingly, the initialization phase takes up less than

10% of the total computational time.

Focusing on the evolution phase, it is possible to evaluate

the performances considering the total time spent during the

200 iterations by each single operation. These results are

shown in table VI. Together with these results, we also report

the performance of the evaluation phase and of all the other

operations [29] required by the genetic evolution (labeled as

‘other operations’).

TABLE VI

THE MAIN STEPS OF THE EVOLUTION PHASE HAVE BEEN EVALUATED AND

THE MEAN TIMES t SPENT BY THE GA FOR EACH ONE ARE HERE

REPORTED.

Number of 100 200 250 300 500

Targets

Others operations 0.3 0.39 0.47 0.54 0.82

Genetic operators 3.48 13.11 29.1 66.88 251.36

2-opt method 5.56 24.4 36.6 63.66 209

Evaluation phase 4.57 8.4 10.3 12.45 20.22

These results show that the most time-expensive part of the

algorithm is the application of the genetic operators and the 2-

opt method. Here the time spent increases exponentially with

the number of targets. On the other hand, the time required by

the evaluation phase and the other operations depend linearly

on the number of targets. Figure 6 shows the log-plot of time

required vs. target number.

50 150 550

1

10

100

NUMBER OF TARGETS

TIME

GENETIC OPERATORS

2−OPT METHOD

Fig. 6. Performance of the GA: the logarithms of the time spent for the

application of the genetic operators and the 2-opt method are plotted with

respect to the logarithm of the number of targets.

V. CONCLUSIONS

This work describes an innovative Genetic Algorithm path

planner for generating a near-optimal multi-agent strategy for

visiting a set of known targets. The method is based on the

solution of an NP-hard combinatorial problem similar to the

classic TSP, the Subtour problem.

The importance of this work is in the ability of the agent

to plan a strategy -a Subtour- by organizing a sequence of

targets autonomously. The proposed system finds application

in problems where there is limited/no human feedback (like

planetary space exploration or search and rescue problems, in

collapsed buildings). For these kind of missions a high level

of autonomy is required and an efficient planner is a crucial

ingredient for autonomous vehicles for ground applications

and space exploration.

The results presented here show the success of the implemented Genetic Algorithm, both for the simple Subtour

problem and the classic TSP.

89
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Springer, 1998, pp. 823–834.

[34] G. Reinelt, “Tsplib: A traveling salesman problem library,” in

ORSA Journal on Computing, vol. 3, no. 4, 1991, pp. 376–384,

http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/.

90
The Evolution of Performance Metrics in the 

RoboCup Rescue Virtual Robot Competition

Stephen Balakirsky & Chris Scrapper 

NIST 

100 Bureau Drive 

Gaithersburg, MD, USA 

Stefano Carpin 

University of Calif. Merced 

5200 North Lake Rd 

Merced, CA, USA 

Abstract—This paper presents an overview of the 2007 RoboCup 

Rescue Virtual Robot Competition and the performance metrics that 

were used to judge the competition. For this competition, great effort 

was placed in bringing together researchers with diverse interests to 

competitively participate. The competition arenas and metrics used 

for scoring were specifically designed to create a “level” playing 

field for the various research disciplines. The specific metrics, how 

they evolved from the prior year’s competition, and the way in which 

the competition was run will be discussed in detail. Defects that were 

noted in the metrics will also be discussed. 

Keywords: robotics, competition, simulation, performance metrics, 

RoboCup

I. INTRODUCTION

July 2007 saw the second annual running of the RoboCup 

Rescue Virtual Robot Competition in Atlanta GA. Robocup 

[1] provides an international forum where researchers meet to 

compete against each other in robotic competitions ranging 

from soccer to dance to urban search and rescue (USAR). 

Underlying these competitions are basic research thrusts 

focusing on core robotic technologies such as mobility, 

multi-agent cooperation, and fine motor control. 

This year’s USAR virtual robot competition consisted of 9 

runs over 7 days and took place in complex indoor and 

outdoor domains. 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. The use of a priori data and carefully 

constructed worlds allowed the researchers to concentrate 

their efforts in one or more research areas while maintaining 

competitiveness among groups performing in different 

research areas. Examples of the indoor and outdoor worlds 

that were used for the competition are shown in Figure 1 and 

Figure 2 respectively. 

Figure 1: Example of the cubicle area from the indoor environment used 

in the RoboCup 07 competition. 

Figure 2: Example of the bridge accident scene from the outdoor 

environment used in the RoboCup07 competition. 

II. BACKGROUND

A. USARSim 

 The current version of Urban Search and Rescue 

Simulation (USARSim)[3] is based on the UnrealEngine21

game engine that was released by Epic Games as part of 

 1 Certain commercial software and tools are identified in this paper in 

order to explain our research. Such identification does not imply 

recommendation or endorsement by the National Institute of Standards and 

Technology, nor does it imply that the software tools identified are 

necessarily the best available for the purpose.

91
Unreal Tournament 2004. This engine may be inexpensively 

obtained by purchasing the Unreal Tournament 2004 game. 

The USARSim extensions may then be freely downloaded 

from sourceforge.net/projects/usarsim. The engine handles 

most of the basic mechanics of simulation and includes 

modules for handling input, output (3D rendering, 2D drawing, 

and sound), networking, physics and dynamics. USARSim 

uses these features to provide controllable camera views and 

the ability to control multiple robots. In addition to the 

simulation, a sophisticated graphical development 

environment and a variety of specialized tools are provided 

with the purchase of Unreal Tournament. 

The USARSim framework builds on this game engine and 

consists of: 

• standards that dictate how agent/game engine interaction 

is to occur, 

• modifications to the game engine that permit this 

interaction, 

• an Application Programmer’s Interface (API) that defines 

how to utilize these modifications to control an embodied 

agent in the environment , 

• 3-D immersive test environments,

• models of several commercial and laboratory robots and 

effectors,

• models of commonly used robotic sensors 

USARSim does not provide a robot controller. However, 

several open source controllers may be freely downloaded. 

These include the community-developed MOAST controller 

(sourceforge.net/projects/moast), the player middleware 

(sourceforge.net/projects/playerstage), and any of the winning 

controllers from previous year’s competitions (2006’s winning 

controllers may be found on the Robocup Rescue wiki at: 

www.robocuprescue.org/wiki/). A description of the winning 

algorithms may be found in [2]. 

B. RoboCup Virtual Robot Competition 

RoboCup is an annual competition that was held in 2007 in 

Atlanta, GA. Nearly 300 teams from 33 countries participated. 

The virtual robot competition (VRC) is part of the RoboCup 

Rescue Simulation League. The VRC, which this year saw its 

second annual running, is designed to foster collaboration and 

competition between research groups conducting research in 

the diverse areas of human-computer interfaces, map building, 

the formation of multi-agent communication networks, 

complex terrain navigation, and victim search and 

identification strategies. The competition was run over 7 days 

and consisted of two preliminary pass/fail rounds followed by 

three main competition rounds, 2 semi-final rounds, and 2 

final rounds. 

The preliminary rounds of the competition were designed to 

verify that teams met a minimum set of competencies. Teams 

needed to control their robots through the use of a provided 

communications server (a new requirement for this year in 

order to mimic the non-line-of-sight nature of a real disaster 

location), generate maps and find victims, and provide the 

judges with maps and victim locations that were in the proper 

format. Eight teams from five different countries participated 

in the preliminary rounds. All of the teams passed and moved 

onto the actual competition. 

The competition rounds consisted of extensive indoor or 

outdoor terrain. The goal of the competition was to find as 

many victims while clearing as much area as possible before 

the batteries of the robot expired. Robots were given a battery 

life of approximately 20 minutes. In order to support a wide 

variety of research interests and lower the competition entry 

barriers by assuring that teams did not need to be experts in all 

fields, a priori data was provided on the difficulty of terrain 

traversal, the difficulty of communicating with the base 

station, and the difficulty of finding victims. Each of these 

areas had three levels of difficulty as defined as: 

Mobility: 

Easy – Flat floors 

Moderate – Sloped floors, rolling areas, narrow 

passageways, small steps 

Difficult – Stairs, rough terrain, drops and holes that can 

damage the robots 

Communications: 

Easy – Use of communications server required 

Moderate – No direct communication between robot and 

base station 

Difficult – No direct communication to base station and 

robot is prevented from reentering moderate or 

easy communication area 

Victim Finding: 

Easy – Static, exposed victims with minimum false 

alarms 

Moderate – Dynamic (moving limbs), partially occluded, 

many false alarms 

Difficult – Dynamic, significant victim occlusion 

(entombed or hidden), many false alarms 

 

Figure 3: Example of a priori data from competition. 

 

To balance the possible points that were awarded, an 

approximately even number of points were available in each 

area. For example, if we form the tuple (mobility difficulty, 

communications difficulty, victim difficulty), then the area 

covered by (Moderate, Easy, Easy) would have the same 

92
points available as the area (Easy, Moderate, Easy), and 

(Difficult, Easy, Easy). This allowed for teams with higher 

levels of competency or multiple competencies to score more 

points. 

An example of the composite a priori data is shown in 

Figure 3 where mobility information is encoded in red, victim 

information is encoded green, and communication 

information is encoded in blue. The color ranges were from 

0-255 with 85 being easy, 170 being moderate and 255 being 

difficult. In the figure, larger values for colors appear more 

saturated. 

III. PERFORMANCE METRIC

The primary goals of the competition are to report the 

location of victims in the environment and to form accurate, 

attributed maps of the explored area. These two distinct areas 

have separate techniques that are used for judging 

competency, and the performance metrics utilized have 

evolved. 

A. Victim finding 

Since one of the primary goals of the competition is to locate 

victims (and in 2006 to determine the victim’s health status), a 

technique for determining a team’s competency needed to be 

developed. However, what does it mean to “locate” a victim? 

How does one autonomously obtain health status? Several 

possible interpretations exist ranging from simply requiring a 

robot to be in proximity of a victim (e.g. drive by the victim) 

to requiring the robot to employ sensor processing to 

recognize that a victim is located nearby (e.g. recognize a 

human form in a camera image) and then examine that victim 

for visually apparent injuries. While recognizing a human 

from a camera image is the solution most readily portable to a 

real hardware, it places an undue burden on both the 

competitors and the evaluation team. For the competitors, a 

robust image processing system would need to be developed 

that could recognize occluded human forms. No matter how 

exceptional the mapping and exploration features of a team 

were, failing to produce the image processing module would 

result in a losing effort. In addition, the evaluation team would 

need to develop an entire family of simulated human forms so 

that teams could not “cheat” by simply template matching on 

a small non-diverse set of victims. 

It was decided that robots should be required to be “aware” 

of the presence of a victim, but that requiring every team to 

have expertise in image processing was against the philosophy 

of lowering entry barriers. Therefore, a new type of sensor: a 

victim sensor, was introduced. To allow for the metrics to be 

portable to real hardware, this new sensor would need to be 

based on existing technology. 

For the 2006 competition, the victim sensor was based on 

Radio Frequency Identification Tag (RFID) technology. False 

alarm tags were scattered strategically in the environment, and 

each victim contained an embedded tag. At long range (10 m), 

a signal from the tag was readable when the tag was in the 

field of view (FOV) of the sensor. At closer range (6 m), the 

sensor would report that a victim or false alarm was present. 

At even closer range (5 m) the ID of the victim would be 

reported. Finally, at the closest range (2 m), the status of the 

victim (e.g. injured, conscious, bleeding, etc.) was available. 

Points were subtracted for reporting false alarms, and were 

awarded for various degrees of information collected from the 

victims. Bonus points were awarded for including an image of 

the victim with the report. This technique worked well for 

scoring the 2006 competition. However, several deficiencies 

were noted with this sensor system: 

• The RFID tag was located in the victim’s torso and 

operated on a line-of-sight basis. Therefore, it was 

impossible to have largely occluded victims. 

• The operation of the sensor encouraged teams to drive 

quickly through the environment and did not require any 

user input or additional behaviors when a victim was 

located. 

• While the sensor was based on existing technology 

(RFID tags), no actual victim locating system works in 

this way. 

To rectify these problems, the victim sensor was 

significantly revamped for the 2007 competition. The new 

sensor is modeled after template based human form detection. 

The sensor performs a line-of-sight calculation to the victim 

and reports which of the 7 body parts identified in Figure 4 

are visible. In the right side of the figure, the points represent 

the possible sensor hit-points. Yellow points are non-victim, 

and green points represent victim hits. The worlds also 

contained false alarms that would be consistent with a 

template matching algorithm. 

Figure 4: New victim sensor based on template matching of body parts. 

The new sensor configuration required teams to attempt to 

gather multiple body parts from a victim (or have user 

involvement) in order to make a victim/false alarm 

determination. This usually required teams to pause upon 

finding a victim location in order to either alert an operator or 

to conduct a scan in an effort to find more body parts. 

B. Map building 

While knowing that a victim is located inside of a structure 

is useful, having a map of where this victim is located adds 

even more utility. Therefore, building a map of the 

environment is a basic requirement of the competition and 

Pelvis Chest

Foot

Leg

Arm Head

Hand 

93
performance metrics were developed to evaluate the maps. A 

major change between the 2006 and 2007 competitions was 

the requirement that all maps be delivered as geo-registered 

images with specific color mappings or vector files. This 

allowed the judges to directly compare competitor’s maps to 

ground truth using geographic information services (GIS) 

software. The map quality score is based on several 

components; most of which have evolved from 2006 to 2007. 

• Feature quality – In 2006, there was no technique 

available to overlay team generated maps with ground 

truth. Therefore, the feature quality of a map was scored 

automatically by examining the reported locations of 

“scoring tags”. Scoring tags were single shot RFID tags 

(they could only be read once). A requirement of the 

competition was for the teams to report the global 

coordinates of these tags at the conclusion of each run. 

The automatic scoring program then analyzed the 

deviation of the perceived locations from the actual 

locations. The use of these tags had the undesirable result 

that errors occurring early in a run were penalized more 

than late errors (the error affected the locations of a 

greater number of tags). In 2007, feature quality was 

evaluated subjectively. As shown in Figure 5, 

geo-registered maps were overlaid on ground truth and 

were examined for the number of discrete errors. For 

example, on some maps it was obvious that a single error 

led to a piece of the map being rotated. False obstacle 

reports (a single wall being reported in multiple 

locations) and scaling issues were also noted. The maps 

were ranked from best to worst and then assigned points 

based on their ranking. 

Figure 5: Competitors map overlaid on 

ground truth from an indoor scenario. 

• Multi-vehicle fusion – Teams were only permitted to turn 

in a single map file. Those teams that included the output 

from multiple robots in that single map were awarded 

bonus points. This metric did not change between 2006 

and 2007. 

• Attribution – One of the reasons to generate a map is to 

convey information. This information is often represented 

as attributes on the map. In 2006, points were awarded 

for including information on the location, name, and 

status of victims, the location of obstacles, the paths that 

the individual robots took, and the location of RFID 

scoring tags. For 2007, teams were required to denote 

areas explored (gray color on map examples), areas 

cleared of victims (green color on map examples), and 

victim locations. The competition definition of cleared 

meant that no undetected victims exist in that area. 

Therefore, teams received penalties for any victims that 

were located in “cleared” areas and that were not 

reported. Teams were free to include any additional map 

attributes that they found useful. The best teams had 

explored space, cleared space, vehicle paths, victim 

locations, geo-registered victim images, names of 

grouped areas, confidence in information, and more. An 

example of an annotated map is shown in Figure 6. Points 

were once again awarded based on a rank ordering of the 

maps. 

 

Figure 6: Annotations on map include area 

explored (gray), area cleared (green), victims 

located (red cross), and robot paths (multi-colored 

lines). 

• Grouping – A higher order mapping task is to recognize 

that discrete elements of a map constitute larger features. 

For example the fact that a set of walls makes up a room, 

or a particular set of obstacles is really a car. Bonus 

points were awarded for annotating such groups on the 

map. An example of such groupings is shown in Figure 7. 

This metric did not change between 2006 and 2007. 

• Skeleton quality – A map skeleton reduces a complex 

map into a set of connected locations. For example, when 

representing a hallway with numerous doorways, a 

skeleton may have a line for the hallway and symbols 

along that line that represent the doors. A map may be 

inaccurate in terms of metric measurements (a hallway 

may be shown to be 20 m long instead of 15 m long), but 

may still present an accurate skeleton (there are three 

doors before the room with the victim). The category 

allowed the judges to award points based on how 

accurately a map skeleton was represented. This metric 

did not change between 2006 and 2007. 

94
Figure 7: Example of fully annotated and group map. 

The colored rectangles are keyed to various groups 

(ambulance, barrier, etc.). 

•Utility – One of the main objectives of providing a map 

was to create the ability for a first responder to utilize the 

map to determine which areas had been cleared, where 

hazards may be located, and where victims were trapped. 

Points were granted by the judges that reflected their 

feelings on this measure. This metric did not change 

between 2006 and 2007. 

The above mentioned elements were numerically combined 

according to Equation 1 for 2006 and Equation 2 for 2007. 

( ) (1)

1

*10 *10 * *50 *5

2

*10

N

V V V t M E C B S ID ST LO

+

+ + + + − + =

 (2) *5 *5 *10 *5 *5 *5

2 N

V V V M E C FA V S ID + P + IP + + − − − M =

 

The meaning of the variables is discussed below. This 

equation represents a schema that took into account merit 

factors that concerned (1) victims discovery, (2) mapping, and 

(3) exploration. The exact point calculations for each factor 

are presented below. 

1. For victims in 2006, 10 points were awarded for each 

reported victim ID (VID). An additional 10 points were 

granted if the victim’s status (VST) was also provided, and 

properly localizing the victim in the map was rewarded 

with an additional 10 points (VLO). Also, at the referee’s 

discretion, up to 20 bonus points were granted for 

additional information produced (B). For example, some 

teams managed to not only identify victims, but to also 

provide pictures taken with the robot’s cameras. For this 

additional information teams were awarded with 15 

bonus points. Taking a picture of a victim seemed like a 

really useful item. Therefore, in 2007, this became a part 

of the scoring metric (VP) that was worth 5 points per 

victim. Correctly geo-referenced victims were worth 5 

points if found using the victim sensor (VID), and 10 

points if found using image processing (VIP). 

2. Maps were awarded up to 50 points based on their quality 

(M), as previously described. For the 2006 competition, 

the obtained score was then scaled by a factor ranging 

between 0 and 1 (t) that measured the map’s feature 

accuracy. This accuracy was determined through the use 

of the RFID scoring tags. 

3. Up to 50 points were available to reward exploration 

efforts (E). During the 2006 competition, as the robots 

were exploring the environment, their poses (on 1 s 

intervals) were logged. Using the logged position of 

every robot, the total amount of explored square meters 

(m2

) was determined and related to the desired amount of 

explored area. This desired amount was determined by 

the referees and was based on the competition 

environment. For example, in a run where 100 m2

 were 

required to be explored, a team exploring 50 m2

 would 

receive 25 points, while a team exploring 250 m2

 would 

receive 50 points, i.e. performances above the required 

value were leveled off. While this metric was easy to 

automatically compute, it seemed to reward teams for 

passing through a location as opposed to actually 

performing any behaviors while in the location. Therefore 

a major change was instituted for the 2007 competition. 

 For 2007, teams needed to declare where they had 

explored and where they had cleared. Any victims that 

existed in a cleared area and were that were not reported 

by the teams were assessed penalties. The idea being that 

a map of the environment is useful to responders 

(therefore award points), and knowing where they do not 

have to look for victims is even more useful (so award 

more points). Points were awarded based on a linear scale 

ranging from 0 – 35 for area cleared and 0 – 15 for area 

explored. The amount of area that received a top score 

was the average of the top performing two teams. 

Exploration above this cutoff was not awarded with 

additional points. The amount of area explored and 

cleared by each team was automatically computed based 

on their maps. 

On the penalization side, 5 points were deducted for each 

collision between a robot and a victim (C). The number of 

collisions was automatically determined. For 2007, false 

alarms reported as victims (FA) and victims missed (VM) in 

the cleared areas also caused point deductions. 

Another parameter that was used to determine the overall 

score was the number of human operators that were needed to 

control the robots. The idea was borrowed from the Rescue 

Robot league with the intent of promoting the deployment of 

fully autonomous robot teams, or the development of 

sophisticated human-robot interfaces that allow a single 

operator to control many agents. In 2006, the overall score 

was divided by (1 + N)2

, where N was the number of 

operators involved. So, completely autonomous teams, i.e. 

N=0, incurred no scaling, while teams with a single operator 

had their score divided by 4. No team used more than one 

operator. However, for 2007 it was decided that there is no 

95
such thing as a truly operator-less team. At a minimum, an 

operator must be available to deploy the robots and provide 

routine maintenance. Therefore, each team was allowed a 

single operator without a scaling factor. 

B. After Action Evaluation 

In addition to the scores that teams received during the 

competition, a large volume of real-time data was logged for 

post analysis. This information included the actual pose of 

every robot on a 1 s interval, and a recording of all of the runs. 

The hope is that teams will be able to combine this 

information with the environment’s ground truth in order to 

learn from the competition experience. 

IV. FUTURE WORK

 The RoboCup rescue virtual robot competition community 

remains very active and plans are already underway for the 

2008 competition which will take place in Suzhou China. 

While further evolution of the metrics is inevitable, the main 

thrust for this year is the automation of the scoring process. 

Currently, robot-victim bumps are automatically computed as 

well as the area explored and the area cleared. However, 

judging the map quality is a manual process. A process that 

compares competitor generated maps to ground truth and 

scores map accuracy and utility is an active area of research. 

References 

 1. Asada, M. and Kitano, H., RoboCup-98: Robot Soccer 

World Cup II, Springer-Verlag, Berlin, 1999. 

 2. Balakirsky, S., Carpin, S., Kleiner, A., Lewis, M., 

Visser, A., Wang.J., and Ziparo, V., "Towards 

Heterogeneous Robot Teams for Disaster Mitigation: 

Results and Performance Metrics from RoboCup 

Rescue," Journal of Field Robotics, Vol. SUBMITTED, 

2007. 

 3. Balakirsky, S., Scrapper, C., Carpin, S., and Lewis, M., 

"USARSim: Providing a Framework for Multi-robot 

Performance Evaluation," Proceedings of the 

Performance Metrics for Intelligent Systems (PerMIS) 

Workshop, 2006. 

96
Robot Simulation Physics Validation

C. Pepper, S. Balakirsky, and C. Scrapper

Intelligent Systems Division, National Institute of Standards and Technology (NIST)

Gaithersburg, MD 20899-8230, USA

Email: {Christopher.Pepper, Stephen.Balakirsky, Christopher.Scrapper} @NIST.Gov

Abstract — Computer simulation of robot performance is an

essential tool for the development of robot software. In order for

simulation results to be valid for implementation on real hardware,

the accuracy of the simulation model must be verified. If developers

use a robot model that is not similar enough to the actual robot, then

their results can be meaningless. To ensure the validity of the robot

models, NIST proposes standardized test methods that can be easily

replicated in both computer simulation and physical form. The actual

robot can be tested, and the computer model can be finely tuned to

replicate similar performances on equivalent tests. To illustrate this,

we have accomplished this task with the Talon Robot

1

 on NIST

standard test methods..

Keywords: performance metrics, physics validation, response

robots, robots, simulation, urban search and rescue, USARSim

I. INTRODUCTION

There is a growing trend in intelligent systems research to

use a simulated environment in the initial phases of

development. As simulations become more integral in the

development process, it is important for them to become more

accurate to protect the validity of experiments. The solution to

this is to develop standard test methods [1] and validate the

performance of the robot on the test methods in both reality

and simulation [2]. 

A. The Benefits of Computer Simulation

There are a myriad of benefits to computer simulation for

a researcher that make it an attractive option during the

development process. An important attribute of simulations to

a developer is repeatability, which allows for simplified

debugging because the same scenario can be precisely

generated to trigger a known error and check the solution. In

addition to this, all vital data can be logged, including ground

truth, to give developers an understanding of inconsistencies

in their algorithm performance. In contrast to an actual

environment, simulation gives developers access to cost

prohibitive or unavailable sensors. Time can also be spent

efficiently since many researchers can work on copies of a

virtual platform simultaneously where physical platforms may

be limited in availability. Additionally, the actual testing

1 Certain commercial equipment, instruments, or materials (or suppliers, or

software...) are identified in this paper to foster understanding. Such

identification does not imply recommendation or endorsement by the National

Institute of Standards and Technology, nor does it imply that the materials or

equipment identified are necessarily the best available for the purpose.

environment may not be accessible, or may only be accessible

at certain times while the simulated environment is always

available. Virtual access to different testing environments

makes virtual testing very cost efficient. Simulation is also

safer for researchers; and allows them to safely refine their

assumptions about the robot and their algorithms. Therefore,

computer simulations allow a development team to be more

effective and efficient.

B. The Need for Standard Performance Metrics

In some cases, there are errors in the robot models that

result in physics inaccuracies with friction, gravity, mass,

force, etc. The consequence of this is that the simulation

results can be unreliable. In some cases, models exhibit

behaviors that are not possible for the actual robots they

represent. Researchers cannot accurately evaluate the

performance of the robot with a faulty model. The challenge is

therefore to develop a method to expose and resolve the

inconsistencies between virtual models and real robotic

systems.

C. The Proposed Solution

Developers can use standardized test methods [2] to

ensure that the model they use behaves as close to the actual

robot as possible. Using the test methods reveals unknown

inconsistencies between simulation and reality, and

researchers can then identify the problem with the physics of

the model. One can resolve the issue systematically with an

understanding of the simulation physics parameters. These

standard test methods can also be used to verify existing

model performance. With the virtual models validated,

researchers can develop their software and confidently

integrate their work onto physical systems.

II. BACKGROUND

A. USARSim Simulation Environment

USARSim is a high-fidelity simulation of urban search

and rescue (USAR) robots and environments, and is intended

as a research tool. It builds upon a commercially available

game engine produced by Epic Games [3] known as the

Unreal Engine 2.0. Today’s games often achieve a high level

of complexity and realism, and the game engines have become

general purpose simulation engines that can be used to

implement multiple games based on the same foundation.

97
They are extremely customizable, and therefore are excellent

candidates to be used to develop robot simulators and perform

scientific investigations [4]. 

While the internal structure of the Unreal Engine is

proprietary, developers can purchase the engine code. For

most uses this has been made unnecessary by the University

of Southern California's Information Sciences Institute

interface known as Gamebots [4] [5] that allows an external

application to exchange bi-directional information with the

engine. This interface was created for research in artificial

intelligence and is an open source project [5]. The Unreal

Engine implements a Virtual Machine, a concept very similar

to the Java Virtual Machine, which allows for external code to

be executed by the engine. The code must be written in the

UnReal host language called UnrealScript, which is an object

oriented language with syntax resembling C++ and JavaScript.

The code may then be compiled into an intermediate platformindependent bytecode that is executed by the Unreal Engine.

Through UnrealScript, a developer has full access to all

environmental variables and full control of the actors in the

world. 

USARSim sits on top of Gamebots and provides a

standardized interface to robot actuators and sensors.

Extensive research of USARSim, in various applications, has

shown the simulation to behave in a predictable manner with

high correspondence to reality. This research is detailed in [6],

[7], [8], and [9]. USARSim has experienced wide community

acceptance with over 17,000 component downloads to date. In

addition, it is the basis for the RoboCup Rescue Simulation

League Virtual Robots Competition [10]. Additional

information on USARSim and related software may be found

in [11].

Fig. 1. USARSim Talon Model2

B. Karma Parameters

KActors are a class of objects that are controlled by the

Karma Physics Engine. Karma is the game engine used by

Unreal Tournament to control the vehicle physics, level

physics, and rag doll physics [12]. Complicated systems, such

as robot manipulators, can be created using Karma joints.

2 Simulation results for particular payload shown in figure 1.

Most objects in the simulation are static during game play, like

static mesh actors3. KActors are dynamic and interactive, and

each KActor has general Karma parameters, referred to as

KParams, which define its own behavior in the simulation.

The KParams that we use in this paper are KFriction,

KangularDamping, and KCOMOffset. KFriction ranges

between zero and one, where the KActor experiences no

friction at a value of zero and total friction at a value of one

[14]. KAngularDamping is the parameter that determines the

magnitude of force to decrease the angular velocity of the

KActor. KCOMOffset is a vector that defines the

displacement of the center of mass from the center of the

KActor. These are the Karma parameters that dictate most of

the actions that we will change in the robot. More information

on the Karma Physics Engine may be found in [12].

C. Talon Robots

Talon robots are robots produced by Foster-Miller, Inc.

that are used for “explosive ordnance disposal (EOD),

reconnaissance, communications, hazmat, security, defense

rescue” [15]. We chose the Talon for this paper because NIST

has access to the robot, allowing us to determine its

capabilities through physical experimentation. It should be

noted that the NIST robot is several years old and that newer,

more capable Talon models exist.

Fig. 2. Foster-Miller Talon Robot4 [15]

III. NIST STANDARD TEST METHODS5

NIST engineers have developed standard test methods

designed to analyze the performance of USAR robots in a

repeatable and objective manner [1]. Each test was designed

to test a specific attribute of a robot that is determinative of

how successful it can be in a range of rescue situations. The

3 Those unfamiliar with static mesh actors should read [13].

4 This picture depicts a robot configuration different than that used by

NIST in testing.

5 Additional information about the NIST Reference Test Arenas for

Autonomous Mobile Robots can be found in [16].

98
test methods are being developed in partnership with first

responders, robot developers, and technical experts. The

following test methods are a few of those created by NIST and

others, several of which have been submitted to and approved

by the Operational Equipment Subcommittee of the ASTM

International E54.08 Homeland Security Committee [17].

A. Directed Perception

The directed perception test is designed to analyze the use

of “robotic manipulators to perform a variety of tasks in

complex environments” [17]. The test artifacts consist of

cardboard boxes of uniform size with cutout holes. Each box

has targets inside that different sensors can identify, such as

lights and hazmat signs. Non-flat flooring also increases the

difficulty of this test.

B. Grasping Dexterity

This test method analyzes the “requirement to retrieve

objects, not necessarily configured for robot manipulators,

within complex environments” [17]. The setup contains

stacked shelves with items for the robot to pick up and place

from one location to another on the shelving. The items are

often blocks, simulated pipe bombs, or water bottles. The

flooring is also often variable in terrain.

C. Stairs

Stairs test the ground mobility of a robot. The robots must

be able to climb any variety of stairs, including stairs enclosed

on the sides, with railings on the sides, with risers, or open

stairs [17]. They can be constructed of different materials and

at different slopes, presenting a difficult mobility task.

D. Step Field Pallet

The step field pallets are “repeatable surface topologies

with different levels of 'aggressiveness'” [17]. A half step field

pallet (also known as orange step fields) is classified as

medium difficulty mobility, and a full step field pallet (also

known as red step fields) is classified as high difficulty

mobility. The computer generated random step field pallets are

an abstracted test of the mobility of a robot. They are easily

recreated and easily reconfigured. The step field pallets

simulate uneven ground such as that seen in a rubble pile.

Fig. 5. Half Step Field Pallets at 2006 RoboCamp Rome, Italy

Fig. 6. NIST 30cm Step Test with Pipes

Fig. 7. NIST 20cm Virtual Step Test with Pipes

Fig. 4. Grasping Dexterity Test at 2006 RoboCamp Rome, Italy

with teleMAX Bomb Disposal Robot

Fig. 3. Directed Perception Test at 2007 Metro Tech Event, NIST 

with teleMAX Bomb Disposal Robot

99
E. Step Test

The step test is designed to analyze the capability of a

robot to climb increasingly higher plateaus. In some

challenges, shelving brackets are used to hold polymer of

vinyl chloride (PVC) piping at the edge, forcing the robot to

not grip onto an edge for leverage. The free-spinning piping

also simulates a slippery surface the robot may need to climb.

F. Mobility and Endurance (Zigzag and Figure 8)

These test methods are based on the step field pallet test.

The formation of the step field pallets is designed to test the

mobility and endurance of the robot. In this task, robots are to

traverse a prescribed course of either a figure 8 shape or

zigzag shape. Robots must be able to travel the length of the

course quickly enough to avoid losing all battery life, and any

field-repairs of the robot are timed. In the figure 8, multiple

laps may be required.

IV. VALIDATING TEST METHODS: THE STEP TEST

Prior to using test methods in simulation, we must first

create the test methods and ensure they perform as expected.

Researchers can determine the value of individual physics

parameters with simple experiments and reasoned

approximation. The model of the step test was created to the

exact dimensions of the actual test. The important physics

parameters in the real and simulated tests are the friction of

the oriented strand board (OSB), the friction of the PVC

piping, and the angular damping of the PVC piping. 

A. Deriving the OSB Friction of the Step Test 

A simple experiment was created to determine the actual

frictional behavior of OSB. The test consisted of timing

various sizes of OSB sliding on a larger OSB sheet at five

different angles. Several trials were performed for each angle.

At an angle of 9.9o, the approximately 35.6cm x 35.6cm (14”

x 14”) board had enough static friction to resist motion when

at rest and enough kinetic friction to slow to a stop quickly

when in motion. At 14.8o, the board took approximately 1.9s

to slide down the approximately 122cm x 122cm (4' x 4')

sheet. This behavior was replicated in a simulation through a

heuristic derivation of the KFriction parameter, the final value

of which was 0.56. Further testing revealed that this value at

the remaining angles produced results with a strong

correspondence to reality. These results are shown in Table 1.

TABLE 1

14”x14” PLYWOOD FRICTION TEST RESULTS

Time for Sheet to Slide Down, seconds

Trial Number

Ramp Angle 1 2 3 4 5 6 7 AVG

14.8° 1.91 / / / / / / 1.91

15.2° 1.48 1.64 2.43 1.77 1.66 / / 1.80

18.5° 1.06 1.27 1.19 1.11 1.06 1.45 1.61 1.25

27.8° .81 .73 .72 .70 .72 .70 .72 .73

In the derivation of this parameter, several interesting

results with the physics engine and its friction were recorded.

The first observation was that the KMass of the object had no

effect on the friction of the object. One would expect that this

parameter was the coefficient of kinetic friction, μk, and that it

would follow the classic relationship,

F=μkN, (1)

where N is the normal force and F is the force of the drag, but

this was not the case. The second observation was that static

meshes, with added KParams or without, do not affect the

actions of a KActor. KActors seem to only be affected by

other KActors. The pallets used in the step test were changed

from static meshes to KActors, to allow the test to affect the

vehicles. Because KActors are movable during simulation, the

translational motion of them must be controlled. The motion

of the pallets was limited by ball and socket joints, a KBSJoint

Karma constraint in UnrealEd. These constraints prevent the

pallets from sliding out from under the robot during the test.

Fig. 8. Zigzag Endurance Test

Fig. 9. Medium Difficulty Figure 8 Test

with teleMAX Bomb Disposal Robot

100
B. PVC Piping Physics Parameters

Analysis of film from previous USAR events with the

step test showed that there was little to no slip between robot

tracks and the PVC piping. KFriction provides full friction,

i.e. no slip, when set to a value of 1.0. A value of 0.9 for

KFriction will allow tracks to mostly grip the pipe with a

small amount of slip. Finally, the value of the angular

damping parameter needs to be determined because the pipe

experiences friction from the shelving bracket in reality. The

value of 1.0 was chosen to prevent the pipe from spinning

endlessly and to allow the robot tracks to easily spin the pipe.

V. IDENTIFYING MODEL INCONSISTENCIES

Based on behavior analysis of the model, the step test in

reality and virtual simulation are now consistent. Testing with

these methods may uncover differences between the actual

robot and the virtual model. To do this, we simply analyzed

data captured on the actual robot as it attempted different tasks

on the test. It is important to note that the difficulty of the tests

must be increased, for example raising the height of the step,

until the physical robot is unable to accomplish the test. This

provides an upper bound for what the simulation should be

capable of performing. After analysis of this information, we

tested the virtual model to determine whether the simulation

behavior was accurate. Rigorous comparison shows how the

virtual model needs to be altered. 

This was the process for analyzing the Talon robot and

model. The first experiment on the step test was driving the

robot in a direct forward approach, the second was at an

angled forward approach (figure 10), the third was a reverse

approach (figure 11), and the final experiment was at an

angled reverse approach6. The same procedure was then

repeated for the model in simulation. The results of these tests

are shown in Table 2, where a "yes" is climbing the step and a

"no" is not doing so. Other observations were recorded, such

as issues the implemetation of the approximation of the track

behavior.

TABLE 2

RESULTS OF 20CM STEP TEST WITH PIPE 

Robot Model Correlates

Direct Forward No No

Angled Forward No Yes

Direct Reverse No Yes

Angled Reverse No Yes

6 All of the real and simulated tests were performed with the manipulator

arm folded on top of the robot to keep a constant center of gravity. This

position is the start pose of the robot arm and can be seen in figure 11.

A. Track Implementation

In the current version of the Unreal Engine, version 2,

tracks on vehicles must be approximated. These tracks are

approximated in one of two ways. The first has a static tread

attached to the robot, and the robot uses the gears (that

normally propel the track in reality) to propel the vehicle by

directly interacting with the world as wheels. A vehicle model

that does this is the teleMAX robot, developed by telerob [18].

Another method used to estimate the behavior of the track is

to have many wheels of different sizes approximate the shape

of the track. 

The second method was used for the Talon, where the

Talon has large front and rear wheels and little wheels in

between. The small wheels can move translationally to

simulate the flexing of the track. Currently, these wheels are

rigid and oppose transltational motion. Testing has shown that

the wheels on a side, which are supposed to behave as a single

track, can spin at different speeds or in different directions,

which is not possible for a track. The individual gears all

contribute to the motion of the track, which is at a uniform

speed at all points on the track. The implementation of the

tracks needs correction to make the wheel motion uniform.

B. Model Climbs 20cm Height with Piping at Angle

The simulation model was able to climb the step test of

20cm with two PVC pipes. To do this, a controller had to

drive the virtual robot such that it approached the piping at an

extreme angle of incidence. The robot would begin to climb

up with one track and turn such that the second track would

also be on the pipe. Then moving forward it was able to

completely pass the step. In testing with the actual robot, the

robot would rotate into the direct forward approach when

attempting the test at angles. With the robot directly

approaching the piping, it spins its tracks and is unable to get

on top of the pipes or step. The actual robot was unable to

climb the same test height that the virtual model could. 

C. Model Climbs 20cm Height with Piping in Reverse

When the controller drove the Talon in reverse to the step,

it was able to rise up the piping and nearly climb the step. The

model in USARSim was able to climb the stairs in reverse

with ease.

Fig. 10. Talon Model Angled Forward Approach

101
VI. CORRECTING THE ROBOT MODEL

Once a difference between the model and the robot is

identified, one can then adjust and fine tune the behavior of

the robot model with an understanding of the Karma

parameters. This is accomplished by changing the physics and

retesting the robot. This process is repeated until a model can

be verified through its performance on the test methods.

A. Track Implementation

For the track to behave properly as a group of many

wheels, the wheels must all have the same angular velocity. As

it is currently, the tires are all spawned by the KDTrack.uc

class. Each track spawned is issued commands by USARSim.

These commands are then directed to each wheel. When the

entire track is issued a command to drive forward, each wheel

attempts to do just that. Because the wheels interact

differently with the simulation environment, the actual

response of the wheel is then calculated on an individual

basis. The individual wheels of the track can respond

differently to a single command. Another issue was the small

wheels needing realistic linear damping to simulate the flex of

the track. The KLinearDamping parameter value must be

lowered in TalonTrack.uc to produce accurate results.

B. Model Climbs 20cm Height with Piping at Angle

The tracks of the Talon model are able to grip onto the

pipe enough to pull the robot on top of the pipe. This is an

unrealistic part of the model that produces the uncharacteristic

behavior. The classes that control the behavior of the track are

the TalonTrackTire.uc and TalonTire.uc. These classes extend

KTire, the Unreal Tournament class that characterizes the tires

of the Unreal vehicles. Because of this, they inherit control of

the friction, slip, and normal properties of the KTire class. In

the Talon track classes, the tire properties must be changed to

correct the model. The lateral friction on the model is too high

if the robot rotates sideways when approaching at high angles

of incidence, and the roll friction must be reduced for the track

to not be able to grip the pipe. Lastly, the motor torque of the

robot must be decreased to lessen its climbing ability. Other

parameters such as tire softness, tire adhesion, and the slip rate

can affect the performance of the track. These changes have

proven to successfully correct the behavior of the robot in

testing.

C. Model Climbs 20cm Height with Piping in Reverse

Some of the above parameters that changed with the

adjustments on the track will help lessen the problem of the

simulated robot climbing the step test in reverse. The class

that defines the behavior of the Talon is Talon.uc. The actual

robot not being able to climb forward but able to in reverse

indicates a center of mass that is not at the center of the robot.

The Karma parameters of an actor are defined within the

KParams of that object. The property that will change the

center of mass is the KCOMOffset, which has not been set in

the current model. The center of mass is defaulted to the

origin of the robot. By measuring the actual robot to find its

center of mass, the KCOMOffset can be accurately changed to

be accurate. Should the robot be unavailable for measurement,

it is reasonable to estimate the center of gravity from the

location of the heavy battery packs in the front of the vehicle.

The offset would be near halfway toward the front of the

vehicle. This assumption proved accurate in final testing of

the behavior of the modified robot model.

VII. MODEL VERIFICATION

The test methods are not only used to alert researchers of

physics problems, but are also used to show that a model is

accurate. With several of the same test methods, testing

revealed that the robot model behaved as the actual robot.

A. Directed Perception

The arm and manipulator control of the Talon are

accurately replicated for the Talon model in USARSim, which

is illustrated by the directed perception test. The Talon

manipulator uses joint level control to move each link

individually. The performance data captured in simulation

shows a close correspondence to data captured on the actual

test method. The range of motion for each joint has been set to

realistic values that may be inaccurate to the actual range of

motion for the Talon manipulator. This can be corrected after a

few tests with the actual robot manipulator.

B. Grasping Dexterity

The manipulator of the Talon was shown to be accurately

modeled in the grasping dexterity test. This test also analyzed

the gripper of the Talon arm. The robot has a gripper with two

fingers, and the model has these at the correct dimensions.

The control of the manipulator and gripper have been

correctly modeled.

Fig. 11. Talon Model Climbing of Pipe in Reverse

102
C. Stairs

At a Department of Homeland Security (DHS) workshop

held in Las Vegas in 2005, the Talon robot was recorded as it

climbed an open stairway with railings. The robot was able to

use rocks at the base of the stairs to get on top of the first stair.

Once on the stair, it was able to climb the remaining stairs

with relative ease. In simulation, the robot had difficulty

getting onto the first stair without a small obstacle. With that

obstacle in place, the model was able to complete the test with

ease7. The stairs used in the simulated test have a slope of

exactly 40°. This is a slope close in value to that of the stairs

on which the Talon was tested, which are estimated at 41°.

Both tests were also performed on open stairs.

D. Step Field Pallet

The step field pallets were also useful in verifying the

robot model. The robot can perform well on half step field

pallets (medium mobility difficulty). The full step field pallets

(difficult mobility) however proved challenging for the robot.

The actual robot is able to eventually complete the difficult

mobility test by reversing and reattempting at different angles,

which is also the case for the model robot in USARSim. The

model completed the medium mobility test with little trouble,

and completed the difficult mobility test with some difficulty.

E. Mobility and Endurance (Zigzag and Figure 8)

Being based on the step field pallets, figure 8s and

zigzags highlight much of the same abilities of the robot.

Because the medium difficulty mobility is not challenging for

the Talon, this test analyzes the endurance of the robot. The

difficult tests focus on the mobility of the robot. The battery

life of the Talon robot is near four hours at typical operational

speed [15]. The battery life of any robot in USARSim is a

configurable variable, which defaults to 20 minutes. The

7 Because the stairs of the test in Las Vegas are unavailable for friction

experimentation, analysis of captured performance data was used to

validate the simulated test method. Creating the test method as a static

mesh produced results with strong correlation to the robot behavior

observed at the DHS Workshop.

battery life is not calculated based on the use of the electric

devices or energy consumption of the motors; however, this

model simplification is acceptable because the difficulty of

implementation outweighs the minimal benefits of battery

accuracy. In addition to this, robots in USARSim cannot be

damaged yet. Robot damages is being researched, and will be

tested with these endurance tests once implemented.

VIII. CONCLUSION

This testing has revealed the test methods to be an

excellent solution to the problem of determining and

increasing simulation accuracy. The simplicity of the tests

makes model fabrication and physical construction easier. The

test methods created for the ASTM standard test specific

characteristics of the robot, making them easy to use for

modifying robot models. In using the test methods, a

researcher is able to identify a specific problem, and can then

improve the model accordingly. Developers can also use these

tests to validate existing models, and show that the behavior is

accurate to reality.

IX. FURTHER RESEARCH

The changes discussed on the Talon model will be

implemented, including forcing the tires of the track to spin at

the same angular velocity. NIST is currently investigating

possible solutions to the issue. The release of the new Unreal

Engine 3 may provide an answer.

Another area of future experimentation is the gripper

behavior. Testing will be performed to determine the accuracy

of the gripper strength. The arm must also be tested to

determine the amount of weight it can lift. A simple test of

lifting increasingly heavier weights with the actual Talon in a

repeated manner will illustrate the behavior the model should

mimic. Repeating the same test in simulation will allow for

precise retuning of the model physics. In addition, other

commercial platforms will be subjected to similar tests and

have their models validated.

Fig. 12. Talon Model Successful Directed Perception Test

Fig. 13. Talon Robot Pass Stair Test at 2005 DHS Workshop, Las Vegas

103
As different waves of first responder requirements are

implemented in the robots and robot models, more test

methods will need to be developed. The individual capabilities

of the robot must be tested to ensure that they were

implemented correctly. 

ACKNOWLEDGEMENTS

The authors would like to thank the developers of

USARSim, Benjamin Balaguer for assisting in Talon model

modifications, and Tony Downs for conducting and assisting

in testing with the Talon robot. 

REFERENCES

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Robots," ASTM International Standardization News, August 2006.

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Number 3, 2002, pp. 259-267.

[3] Epic games, “Unreal engine,” 20 Jul 2007. www.epicgames.com, 2005.

[4] M. Lewis, J. Jacobson, “Game engines in scientific research,”

Communications of the ACM, Volume 45, Number 1, pp. 27–31, 2002.

[5] Gamebots, 20 Jul 2007. http://gamebots.sourceforge.net/.

[6] S. Carpin and M. Lewis and J. Wang and S. Balakirsky and C.

Scrapper, “USARSim: a Robot Simulator for Research and Education”,

Proceedings of the IEEE 2007 International Conference on

Robotics and Automation, October 2007.

[7] S. Carpin and J. Wang and M. Lewis and A. Birk and A. Jacoff, “High

Fidelity Tools for Rescue Robotics: Results and Perspectives”, Robocup

2005: Robot Soccer World Cup X, Springer, LNAI, Volume 4020, 2005, pp.

301-311.

[8] J. Wang and M. Lewis and S. Hughes and M. Koes and S. Carpin,

“Validating USARSim for Use in HRI Research”, Proceedings of the Human

Factors and Ergonomics Society 49th Annual Meeting(HFES05), 2005, pp.

457-461.

[9] M. Zaratti and M. Fratarcangeli and L.Iocchi, “A 3D Simulator of

Multiple Legged Robots based on USARSim”, Robocup 2006: Robot Soccer

World Cup X, Springer, LNAI, 2006.

[10] M. Balakirsky, C. Scrapper, S. Carpin, and M. Lewis, “USARSim: A

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SPIE, 2007.

[11] S. Carpin, M. Lewis, J. Wang, S. Balakirsky, C. Scrapper, “USARSim: A

Robot Simulator for Research and Education”, Proceedings of the IEEE

International Conference on Robotics and Automation, 2007.

[12] “Unreal Developer Network Karma Reference.” Unreal Developer

Network. 19 Jul 2007 http://udn.epicgames.com/Two/KarmaReference.html.

[13] “Static Mesh” Wikipedia, the Free Encyclopedia, July 31, 2007,

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[14] Busby, Jason, and Zak Parrish. Mastering Unreal Technology: The Art of

Level Design. Indianapolis, Indiana: Sams Publishing, 2005.

[15] “Products & Talon Military Robots, EOD, SWORDS, and Hazmat

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Robots and Systems, Las Vegas, NE, October 27-31, 2003.

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Performance Standards: Progress Update,” Proceedings of the 2007 SPIE

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[18] “teleMAX” telerob, July 23, 2007, http://www.telerob.com.

104
Design and Validation of a Whegs Robot in 

USARSim

B.K. Taylor 

Case Western Reserve Univ. 

Cleveland, OH, USA 

brian.k.taylor@case.edu 

S. Balakirsky, E. Messina 

NIST 

Gaithersburg, MD, USA 

stephen.balakirsky@nist.gov, 

elena.messina@nist.gov 

R.D. Quinn 

Case Western Reserve Univ. 

Cleveland, OH, USA 

roger.quinn@case.edu 

Abstract— Simulation of robots and other vehicles in a virtual 

domain has multiple benefits. End users can employ the simulation 

as a training tool to increase their familiarity and skill with the 

vehicle without risking damage to the robot, potential bystanders, or 

the surrounding environment. Simulation allows researchers and 

developers to benchmark the robot’s performance in a range of 

scenarios without needing to physically have the robot and or 

necessary environment(s) present. Beyond benchmarking current 

designs, researchers and developers can use the information gathered 

in the simulation to guide and generate new design concepts. 

USARSim (Urban Search and Rescue Simulation) is a high fidelity 

simulation tool that is being used to accomplish these goals within 

the realm of search and rescue. One particular family of robots that 

can benefit from simulation in the USARSim environment is the 

WhegsTM*

 series of robots developed in the Biologically Inspired 

Robotics Laboratory at Case Western Reserve University. Whegs 

robots are highly mobile ground vehicles that use abstracted 

biological principles to achieve a robust level of terrestrial 

locomotion. This paper describes a Whegs robot model that was 

designed and added to USARSim’s current array of robots. The 

model was configured to exhibit the same kind of behavioral 

characteristics found in the real Whegs vehicles. Once these traits 

were implemented, a preliminary validation study was performed to 

ensure that the robot interacted with its environment in the same way 

that the real-life robot would.

Keywords: USARSim, Biologically Inspired Robotics, Whegs, 

Urban Search and Rescue, Simulation

I. INTRODUCTION

A. Background on USARSim 

Urban Search and Rescue Simulation (USARSim) is a high 

fidelity simulation tool that can be used to simulate robots in 

various environments [11]. USARSim is built on top of Epic 

Games’ Unreal Tournament 2004*

 (UT2004) physics engine 

known as Unreal Engine 2.0. The Karma Physics Engine*

[9] is utilized to simulate physics within the game. Unreal 

Script, the object oriented programming language for UT2004, 

 

*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 NIST, nor does it imply that the materials 

or equipment identified are necessarily the best available for the purpose.

is used to give robots functionality and to define how the 

robot will interact with its environment. Unreal Editor 

(UnrealEd) is used to create virtual worlds, or maps. It is 

also used to create 3D solid models (static meshes) that can be 

used to either construct a robot, or to construct obstacles and 

objects that are placed within a particular map. 

The idea behind USARSim is as follows. A virtual robot is 

built by creating static meshes to represent its individual parts. 

The parts are connected to each other through a configuration 

file that specifies where and how parts are connected to each 

other (motors, hinges, ball-and-socket joints, etc). In 

addition to the robot, a map is created with obstacles that must 

be overcome, and objects and/or victims that need to be found. 

The physics engine handles the dynamics of how the robot 

should interact with the map that it is placed in. By 

adjusting parameters known as Karma Parameters [9,10], the 

performance of the robot in simulation can be changed. For 

example, changing the inertia tensor of a robot will affect its 

ability to rotate about particular body axes within a given 

world. For robots and maps, end users can select from the 

options available in a current release of USARSim [11], or 

design their own. Controller software is used to perform a 

range of tasks such as issuing simple drive commands, 

implementing autonomous features into the vehicle, and 

running multiple vehicles in a given environment [11,12]. 

This kind of setup allows an individual to build and simulate 

robots relatively quickly and inexpensively from both 

computational and monetary standpoints. USARSim 

currently has applications in end-user training for Urban 

Search and Rescue robots, and in the RoboCup Simulation 

League [2,3]. 

A disadvantage of USARSim is that the Karma Physics 

Engine is proprietary. This means that the exact mechanics 

behind how the engine uses the Karma Parameters cannot be 

obtained. Testing has been done to gain a better, more 

quantitative understanding of how the Karma Parameters 

affect the simulation, and how the parameters map to 

real-world quantities. For example, conversion factors 

between the simulation’s length scales (Unreal Units and 

Karma Units) and real length scales (meters) have 

successfully been established and implemented in more recent 

releases of the software. However, there are still parameters 

105
where this kind of understanding has not been reached. 

Ultimately, this means that iterative testing and comparisons 

must be done on a given real and virtual robot to determine 

the set of Karma Parameters that yields the most realistic 

performance of the virtual vehicle. 

B. Background on Whegs 

Whegs robots are highly mobile unmanned ground vehicles 

that were developed in the Biologically Inspired Robotics 

Laboratory at Case Western Reserve University. Their 

locomotion is based on abstracted biological principles 

observed in cockroach locomotion [1]. Unlike RHex which 

is a biologically inspired robot that predates Whegs [7], 

Whegs robots employ an appendage called a wheel-leg, which 

is made up of a hub with spokes equally spaced about the 

hub’s central axis (Fig. 1). 

Fig. 1. A three spoke wheel-leg appendage 

Most wheel-legs have three spokes. Rotating the 

wheel-legs about their central axes at a constant speed allows 

a given Whegs robot to move in the same way that a wheeled 

vehicle would be driven. In addition, the spokes allow the 

robot to obtain discontinuous footholds on irregular terrain, 

similar to legs [7]. Furthermore, the spokes also allow the 

wheel-leg to reach footholds that are taller than the wheel-leg 

radius. These wheel-leg features allow Whegs robots to be 

propelled in a similar manner to wheeled vehicles. They 

also enable Whegs robots to climb over and negotiate terrain 

that may be impassable to wheeled vehicles (Fig. 2). 

Fig. 2. (a) A wheel leg is able to obtain footholds on obstacles that are taller 

than the wheel-leg radius. (b) A wheel is unable to reach footholds of equal 

height 

Cockroaches have six legs and typically walk in a tripod gait, 

meaning that the front and rear legs on one side move in phase 

with the middle leg on the opposite side. Contralateral pairs 

of legs move out of phase with each other (e.g. when the front 

left leg is in swing, the front right leg is in stance) [5]. When 

the animal comes to a large barrier, it moves its contralateral 

legs into phase to aid in surmounting the obstacle [6]. 

Similarly, Whegs robots employ six wheel-legs with 

contralateral pairs being placed out of phase such that the 

vehicle walks in a nominal tripod gait. Each axle features a 

compliant mechanism that allows the robot to passively bring 

its wheel-leg pairs into phase. This feature aids the robot in 

surmounting obstacles, and allows it to passively adapt its gait 

to changing and irregular terrain (Fig. 3). 

 

Fig. 3 Compliant mechanisms in the axles allow wheel-leg spokes to come 

into phase which aids in climbing obstacles 

Also, cockroaches have a body flexion joint. They use the 

joint pitch the front of their bodies down to avoid high 

centering and to allow their front legs to reach the substrate 

[6]. More recent Whegs robots have been outfitted with body 

flexion joints or similar reasons. In addition, the joint also 

allows the vehicle to pitch its body upward to get a foothold 

on an obstacle during a climb (Fig. 4) [8]. 

Fig. 4. Whegs robot with a body flexion joint surmounting a larger step than 

the robot’s body height 

In addition to Whegs robots, a series called Mini-WhegsTM* 

has also been developed (Fig. 5). 

Fig. 5. Mini-Whegs and its relative size as compared to a cockroach 

These are intended to be smaller, more compact versions of 

Whegs robots. They are on the order of 0.09 m long (9 cm), 

and have a top speed of about 10 bodylengths/second (0.9 

m/s). Because of their small size, Mini-Whegs robots only 

possess four wheel-legs instead of six [4]. The four 

wheel-legs move in a diagonal gait. While some work has 

been done with implementing compliance into the axles of 

these robots, for simplicity, Mini-Whegs typically lack both 

torsional compliance and body flexion joints [4]. 

While different types of Whegs robots have been 

constructed and tested, there is formalized method for 

developers to gauge a robot’s performance limits or test the 

viability of design ideas before construction begins. Also, 

the only current way to learn how to operate a Whegs robot is 

to drive a real robot. A Whegs simulation would allow robot 

designers to test their ideas before construction begins, 

allowing them to make design changes that will improve 

performance. The simulation can also be used by developers 

to test robots in environments that are not readily available, or 

potentially damaging. This would allow designers to gauge 

a given robot’s performance limits. For robots in 

development, performance enhancing changes could then be 

106
implemented. In addition to design work, a Whegs robot 

simulation would allow end users to become skilled in 

operating Whegs vehicles in numerous environment(s) 

without having the robot or environment(s) physically present. 

This can reduce the risk of damaging the robot. If a 

simulated robot is incapacitated, the simulation can be 

restarted rather than having to repair or rebuild the vehicle. 

In this paper, a virtual Whegs robot was created and given 

the same behavioral characteristics as a real robot. The 

virtual robot’s performance was then benchmarked against the 

real robot. Section II describes the approach and methods 

used to impart functionality to the virtual robot and 

benchmark its performance. Section III describes the results 

obtained during testing. It also describes some of the 

problems that were encountered during testing and how these 

issues were resolved. The final section summarizes the work 

presented in this study and discusses future work. 

II. METHODS

To perform this study, a generic Whegs robot model was 

first created in USARSim by adding a “Whegs” class. This 

class and its base classes were given functionality to enable 

the virtual robot with the same behavioral characteristics that 

are found in a real Whegs vehicle. After the virtual robot 

had the necessary behaviors, it was run through several tests 

to gain an understanding of how particular Karma Parameters 

affected its performance. Once the effects of these 

parameters were known, the virtual and real robots were 

placed in test scenarios with the same conditions. The 

results of these tests were compared and used to make 

changes to the virtual robot’s Karma Parameters to improve 

its performance. 

For the purposes of this study, the virtual vehicle was 

modeled after a Mini-Whegs robot with torsional compliance. 

This was done in an effort to lay the ground work for creating 

any given Whegs vehicle while still maintaining a degree of 

simplicity during modeling and testing. As stated above, 

Mini-Whegs robots typically lack a body flexion joint and 

only use four wheel-legs. These features make Mini-Whegs 

robots easier to simulate because there are fewer features to 

control and less wheel-legs to monitor during testing. 

Torsional compliance, while not present on all Mini-Whegs 

vehicles, was not a feature present in USARSim. Because 

this feature is found on many of the Whegs vehicles, it was 

felt that successfully modeling and implementing it would aid 

in laying the fundamental groundwork necessary for building 

more specific and accurate Mini-Whegs and full size Whegs 

models. 

A. Developing a WhegsTM Robot Model 

Modeling a Whegs vehicle can be broken down into two 

main steps: creating the appropriate static meshes, and writing 

and modifying classes to give the virtual vehicle the same 

types of behavioral characteristics as the real vehicle. The 

static meshes were created using UnrealEd. For the purposes 

of simplicity, the chassis was modeled as a rectangular block. 

The wheel-legs were modeled as cylinders (the hub of the 

wheel-leg) with three rectangular blocks (the spokes of the 

wheel-leg) attached to them and placed 120o apart (Fig. 6). 

Fig. 6. Wheel-Leg appendage created in UnrealEd 

Two separate wheel-leg meshes were made. One 

resembled a “Y” shape (Fig. 6) while the other was an 

inverted “Y”. The two meshes were used as contralateral leg 

pairs to achieve proper wheel-leg phasing. 

Incorporating the appropriate behavioral characteristics into 

the robot involved adding new functionality into USARSim. 

As mentioned above, there were no native USARSim features 

that allowed for passive torsional compliance. To solve this 

problem, a new class called “KDSpringy” was created. This 

class tells USARSim to make a hinge joint (KHinge) whose 

hinge type is set to a spring (KHingeType=Springy). 

Physically, this is like connecting two objects together with a 

torsional spring that is able to have stiffness and damping 

about a particular axis. The spring attempts to maintain an 

input angle (KDesiredAngle) between two objects placed in 

an Unreal map (Actors). In USARSim, this corresponds to 

maintaining a desired angle between the current part and its 

parent. Once the KDSpringy class was created and the 

appropriate base classes were modified, it was implemented in 

the following way. 

Chassis

Wheel-Leg Joint Spacer Plate

KDSpringy KCarWheelJoint

Fig. 7. Illustration of how passive torsional compliance is implemented 

A static mesh that is used as a spacer plate was created. As 

can be seen in Fig. 7, the chassis was connected to the spacer 

plate via a KCarWheelJoint. A KCarWheelJoint is a joint 

that has a spin axis that is driven by a motor, and a steering 

axis that is driven by a controlled motor that attempts to 

achieve a specified orientation, similar to a servo. The 

spacer plate was connected to the wheel-leg via KDSpringy. 

This setup effectively made the spacer plate the actual 

“wheel” that drove the vehicle. However, because the 

wheel-leg’s parent is the spacer plate, the two parts rotate 

together. Differences in their rotational speeds come from 

the reaction torques and forces that are experienced by the 

wheel-leg from the terrain, and from the parameter used to set 

107
the stiffness of KDSpringy. A large stiffness allows the 

spring to withstand large reaction torques before deflecting, 

thus allowing the wheel-leg and spacer plate to move at more 

closely matching speeds (this corresponds to the wheel-leg 

functioning like a normal wheel). A lower stiffness means 

that the spring is easier to displace and must be deflected to 

the point where it is able to exert a large enough torque to spin 

the wheel-leg. This enables the spacer plate to wind up the 

spring and build torque when a particular wheel-leg is unable 

to move, which allows the contralateral wheel-leg to come 

into phase with it. This is precisely how Whegs robots 

behave in reality when surmounting obstacles. 

A disadvantage to this method is that UT2004 appears to 

only use tire properties for a tire (a KTire in UT2004) that is 

connected to a KCarWheelJoint. KTire properties allow the 

user to control the following properties of the tire: Rolling 

Friction, Lateral Friction, Rolling Slip, Lateral Slip, Minimum 

Slip, Slip Rate, Tire Softness, Tire Restitution, and Tire 

Adhesion. Even though the wheel-legs are defined as KTires, 

since they are connected via KDSpringy, it appears that they 

only have what are known as KActor properties. KActor 

properties allow the user to control the following parameters: 

KFriction and KRestitution. As can be seen, a KTire is the 

ideal case because more control is allowed over how the tire 

will interact with the environment. This problem can be 

rectified by altering the static mesh so that individual 

wheel-leg spokes are added to the hub as tires. However, 

this solution requires a larger number of parts and more class 

functionality to make the robot function properly. Also, this 

approach gave adverse preliminarily results, which are 

discussed in the next section. Because real Mini-Whegs 

robots do not use formal tires, the preliminary validation was 

performed with the wheel-legs as KActors. It was felt that 

this approach would provide a good first approximation of the 

appropriate set of Karma Parameters that would yield realistic 

virtual performance while narrowing the search space at the 

same time. 

B) Virtual Test Maps and Validation Testing 

After the virtual robot was developed, two maps were 

created to test the vehicle’s performance. One of these maps 

was a large empty room to test the vehicle in walking and 

running while minimizing its chances of hitting a wall. The 

other map included basic obstacles such as: ramps, standard 

2x4 boards (3.81 cm by 8.89 cm actual cross sectional 

dimensions) and textbooks for climbing, a straightaway for 

walking/running testing, and stairs and large drops for falling 

and impact testing. These worlds were used to compare the 

virtual robot’s performance to that of the real vehicle. In this 

study, attention was focused on walking/running and basic 

climbing over textbooks. The following metrics were used 

to evaluate the virtual robot’s performance: 

• Top speed of about 0.9 m/s without significant 

end-over-end rotation (~25 rad/s wheel-leg drive speed)

• End-over-end rotation when attempting to climb up a 

wall at higher wheel-leg drive speeds

• The ability to surmount obstacles (textbooks in this 

study) that are 0.04 m tall in head on and oblique angle 

(30o

) approaches (Fig. 8) Obstacle

30o

Fig. 8. Top view of head on and oblique approaches 

Fig. 9 provides an illustration of how robots are currently 

validated 

Karma Physics Engine

Virtual Robot

Karma Parameters

Real Robot Performance

Virtual Performance

Fig. 9. Illustration of how robots are currently validated 

As stated above, the Karma Physics Engine is proprietary, 

which means that the exact way in which the engine uses the 

Karma Parameters to affect the simulation cannot be directly 

obtained. In addition, the engine uses its own unit system 

(e.g. lengths are in Unreal Units or Karma Units depending on 

the context). The mapping between the Unreal Unit System 

and real world quantities is known for some parameters (e.g. 

length and time). However, conversions for other 

parameters (e.g. force and torque) are still under investigation. 

Due to the proprietary nature of the engine, to perform a 

validation, a robot is run through the engine with an initial set 

of Karma Parameters, as illustrated in Fig. 9. This yields the 

robot’s virtual performance, which is then compared with real 

robot performance through the use of video data and any other 

relevant performance metrics for the robot in question. The 

information learned from the comparison is used to modify 

the Karma Parameters. After parameter modification, the 

virtual robot is run through the engine again for comparison 

with the real robot. This cycle is repeated until the virtual 

performance meets a desired level. For actual validation 

testing, this method was combined with the following 

procedure: 

1) Baseline Run: First, the virtual robot was run through a 

range of drive speeds with an initial set of Karma Parameters. 

The goal of this run was to obtain performance data for an 

initial set of parameters for the purposes of comparison. In 

the open room, vehicles were run through the following wheel 

leg drive speeds: {0, 2, 10, 15, 20, 25, 30} rad/s. 

2) Individual Karma Parameter Variation: After the 

baseline performance test, individual Karma Parameters were 

varied through a range of values while leaving all other 

parameters at their initial settings. For each value, the virtual 

robot was run through the same set of drive speeds used in the 

baseline run. The purpose of these runs was to obtain data 

that illustrated how each parameter affected the vehicle’s 

performance. 

108
3) Physical Reasoning: At this stage, physical reasoning 

was used to determine what conditions were required for the 

virtual robot to behave in a particular way in order to explain 

its performance and the effects of individual Karma 

Parameters.

4) Karma Parameter Search: At this point, with an 

understanding of the effects of different Karma Parameters, 

the method illustrated in Fig. 9 was used to improve the 

virtual robot’s performance. 

The virtual robot was compared to physical observation of a 

real Mini-Whegs robot. All simulation trials were recorded 

using FRAPS*

 [13] video capturing software. In addition, 

the vehicle’s instantaneous velocity, position in the world, 

orientation with respect to the world coordinate frame, time, 

and speed change commands were logged to various files. 

The logged parameters were used to plot the vehicle’s speed 

and velocity components against time. Velocities were 

reported in both world (fixed) and vehicle (moving) 

coordinates to help quantify the robot’s behaviors. Steering 

was not used in any of the tests.

III. RESULTS

A. Dimensions Used in the Simulation 

Initial testing (phases 1-3 of the above described procedure), 

was done with a slightly larger vehicle. Phase 4 was 

performed with a smaller vehicle (Table 1). 

 Initial Dimension Final Dimension 

Body Length (m) 0.1143 0.09 

 Width (m) 0.09144 0.068 

 Height (m) 0.01905 0.02 

Wheel-Legs Diameter (m) 0.096 0.0762 

Table 1. Initial and final dimensions used in the simulation 

The latter dimensions were chosen because they more 

accurately reflect the size and performance basis of current 

Mini-Whegs robots. For example, the 10 bodylength/second 

speed listed above corresponds to a 0.09 m bodylength, so for 

this performance metric, the smaller vehicle size is more 

appropriate. The study could have been performed with the 

larger size vehicle since a real vehicle could be created that 

has larger dimensions. The dimensional change is only used 

here for convenience in comparing virtual and real 

performance. 

B. Functionality for Maintaining Proper Wheel-Leg Phasing 

During testing, it became apparent that new functionality 

would need to be added to the “Whegs” class to ensure that 

the virtual robot maintained proper wheel-leg phasing. 

Initially, when the vehicle spawned into a world, it would 

spawn properly with its wheel-legs out of phase, but then 

“fall” due to its mass such that the wheel-legs were in phase 

(Fig. 10). 

Fig. 10. Wheel-Legs are unable to maintain proper phasing without extra 

class functionality 

Occasionally, all of the wheel-legs would stay out of phase 

upon spawning such that the robot could be tested. However, 

if the robot was not stopped with the correct orientation and 

speed, the wheel-legs would “fall” out of phase. This 

behavior appeared to be independent of the torsional stiffness 

that was provided, and even occurred when the wheel-legs 

were connected directly to the chassis via a KCarWheelJoint. 

This was problematic because real Whegs robots maintain 

proper phasing even when stopped. It was determined that 

this problem was due to the nature of the KCarWheelJoint 

class. This class rotates a given Actor about a spin axis by 

applying a torque to overcome external torques. If the motor 

applies no torque, then the actor will be rotated about the 

motor’s spin axis by all other external torques. Physically, a 

KCarWheelJoint is analogous to having an axle that rotates a 

wheel mounted in a perfectly frictionless bearing and motor. 

Real Whegs robots have a drive train that connects each wheel 

leg to a single drive motor and torsional springs that are 

pretensioned which causes them to maintain proper phasing 

even when the motor is not running. To fix this problem, a 

member function was added to the “Whegs” class that forces 

the KCarWheelJoint to achieve zero angular velocity by using 

a preset torque value when the robot is stationary (drive speed 

= 0 rad/s). This solution appeared to solve the problem. 

C. Wheel-Legs Behaving as KActors Vs. Tires 

Initially, the wheel-legs were implemented as KTires. 

However, when attempts were made to run the robot, the 

wheel-legs would rotate but not cause the robot to translate, 

resulting in the wheel-legs spinning in place and the robot 

itself not making any forward progress. It was determined 

that because the wheel-legs were not connected to 

KCarWheelJoints, KActor properties were used instead of 

KTire properties. The default KActor friction value is zero, 

which led to the wheel-legs perfectly sliding on a given 

substrate. An attempt to rectify this problem was made by 

implementing the solution proposed above: making each 

spoke a KTire that is connected to the central hub via a 

KCarWheelJoint. Because of the problems experienced in 

maintaining proper wheel-leg phasing mentioned in the 

section above, a function was added to the “Whegs” class that 

forces the spokes to maintain their initial orientation relative 

to their parent hub. This solution resulted in the wheel-leg 

spokes drifting into altered positions over time, particularly at 

higher drive speeds. An attempt to remedy this problem was 

made by increasing the torque used to maintain the spoke 

orientation. This resulted in the vehicle going through 

seemingly nonphysical end-over-end rotation at higher wheel 

109
leg drive speeds (about 15 rad/s and higher) while still 

translating forward, and did not appear to remove the drifting 

problem. It was found that the only apparent way to 

influence the problem was to increase the vehicle’s inertia 

tensor or angular velocity resistance (KAngularDamping) 

Karma Parameters. These parameters only seemed to slow 

down the rotation. They also had to be raised to levels much 

higher than any other vehicle in the USARSim, including 

vehicles that are more massive such as the Hummer. 

The nonphysical nature of this behavior and its solution 

prompted performing the validation with the wheel-legs 

behaving as KActors instead of KTires. This approach led to 

behavior that appeared to be more physically relevant when 

compared to the real vehicle. Also, as stated above, because 

this approach offers a narrower parameter search and because 

the wheel-legs on Mini-Whegs vehicles are not formal tires, it 

was felt that using the wheel-legs as KActors would provide a 

reasonable approximation that would allow for relatively 

simple but effective preliminary validation. 

D. Effects of Individual Karma Parameters 

The robot’s speed and velocity components were plotted in 

both world and vehicle coordinates. These plots were used 

as a tool to help examine the effects of individual Karma 

Parameters on the robot’s performance. Example plots are 

shown in Fig. 11. 

Fig. 11. Velocity in vehicle coordinates (top) and world coordinates (bottom). 

In vehicle coordinates, x is forward, y is starboard, and z is out the bottom of 

the vehicle. The absolute value of the velocity components is plotted here 

for comparison with the speed. 

The vertical black lines are the times at which speed change 

commands were issued. The vehicle’s initial speed (which 

occurs around 15 s in the plots shown above) is due to it 

spawning into the world. By looking at the body-fixed 

coordinate plot, it can be seen that the X-component of 

velocity in the vehicle coordinates is nearly identical to the 

speed, meaning that the vehicle is making forward progress 

and not translating to its left or right. In addition, there is a 

point around 65 s where the X-velocity and the speed both 

drop while the Z-velocity spikes. Upon comparison with the 

video data, it was observed that this was the location at which 

the robot flipped over, or end-over-ended. This kind of 

feature was present in all cases where the robot flipped over. 

The world coordinate plot illustrates the vehicle’s tendency to 

move in a particular direction within the world. In this plot, 

the robot is initially heading mostly in the Y direction. 

However, after it flips over at around 65 s, it also begins to 

have motion in the X-direction as well. In addition to these 

kinds of observations, the data in the plots can be used to look 

at other phenomena such as the average speed of the vehicle 

for a given time interval and the variability of the data about 

the average. 

The following Karma Parameters were singly modified 

while leaving all other parameters at their initial settings to 

determine their effects on the performance of the vehicle: 

1) ChassisMass: This is the mass of the chassis. With the 

initial parameters that were set, it was found that altering this 

parameter did not appear to have a large impact on the overall 

performance of the vehicle in terms of being able to reach a 

top speed, or end-over-ending. 

2) KMass: This is the mass of the Spacer Plates. This mass 

was varied to determine the effects of raising and lowering 

mass that is not coincident with the vehicle’s center of mass. 

Initally, the mass of the chassis was very small, both 

compared to the spacerplates, and in an absolute sense, so this 

test also revealed how the vehicle’s overall mass affected 

performance. When these masses were lowered to a value of 

0.002 in the Unreal Unit System, the vehicle immediately 

began to end over end at higher drive speeds (10 rad/s and 

over). At mass values of 2 in the Unreal Unit System, the 

vehicle appeared to perform in a relatively predictable manner 

with only occasional end-over-end instances occurring at 

drive speeds between 25 rad/s and 30 rad/s. 

3) KFriction: This is the friction present in the wheel legs. It 

was found that, as one would expect, higher values of friction 

(10 in this study) resulted in the wheel-legs not slipping on the 

substrate as much during walking. Visible slippage occurred 

with lower friction values (0.5 in the Unreal Unit System). 

Both of these friction values yielded roughly the same vehicle 

average speeds for a given drive speed. However, the 

variation about the average was much higher for the larger 

friction value. This was attributed to stronger braking forces 

in the step cycle. The wheel-legs provide both propulsive 

and braking forces, where braking occurs in the beginning of 

the stance phase, and propulsion occurs towards the end. 

Because the friction value is higher, both the brake and 

propulsion forces are increased. Therefore, when a wheel 

leg touches down, it is able to provide better traction to propel 

x z

y

X

Y

Z

110
the vehicle, but also has a greater tendency to retard its motion 

initially. 

4) KRestitution: This parameter is similar in concept to the 

coefficient of restitution used in collision analysis. A value 

of 1 corresponds to an elastic collision between two objects. 

Values less than one result in increasingly inelastic collisions. 

At a KRestitution value of 1 in the Unreal Unit System, as the 

vehicle’s drive speed was increased, it appeared to have 

increasingly continuous elastic collisions with the ground 

while its forward speed appeared to reach a relatively constant 

value that became independent of the input drive speed. As 

a result, the vehicle’s average speed at higher drive speeds 

seems to flat-line when compared to other tests. The average 

speed also had a great deal of variability for each drive speed. 

5) Torsional Stiffness: The torsional stiffness of the rear 

wheel-legs was set to 250 in the Unreal Unit system for all 

runs. This value appeared to make the back wheel-legs 

rotate with the spacer plates under all circumstances. The 

front torsional stiffness of the front wheel-legs was adjusted to 

see how adjusting the stiffness affected their motion. As 

expected, higher values of stiffness led the wheel-legs to 

behave more like conventional wheels, where low stiffness 

values allowed the spring to “wind up” before rotating the 

wheel-legs. Excessively low values of stiffness cause 

wheel-legs to fall out of phase when the vehicle is spawned. 

At these low values, when a drive command was issued, the 

wheel-legs would not rotate at first. However, after the 

springs were deflected sufficiently, they would rotate forward 

to release the tension as one would expect. 

E. Karma Parameter Search 

After the effects of the above mentioned parameters were 

understood from the single parameter variations, testing was 

done to move the virtual robot towards matching real 

performance. Table 2 indicates the parameters that were 

changed. 

 Karma Parameter (Unreal Unit System) 

Initial 

Value 

Current 

Value 

Chassi

s ChassisMass 0.00342 0.75 

 MaxTorque 32000 50 

 MotorTorque 2400 50 

 KCOMOffset (X, Y, Z) 0 0.04464 

 KCOMOffset (X, Y, Z) 0 0 

 KCOMOffset (X, Y, Z) 0 0 

Wheel 

Legs Wheel-Leg Kfriction 1 0.75 

 Wheel-Leg KRestitution 0 0.1 

 Wheel-Leg Kmass 0.0008 0.08 

Spacer 

Plate Spacer Plate Kmass 1 0 

 Spacer Plate KInertiaTensor(0) ~ Ixx 0.0035 0 

 Spacer Plate KInertiaTensor(3) ~ Iyy 0.0066 0 

 Spacer Plate KInertiaTensor(5) ~ Izz 0.0035 0 

Table 2. Initial and Current Karma Parameter Values 

Column 1 is a listing of the initial values of the Karma 

Parameters. Column 2 represents the current values that they 

have been adjusted to. As can be seen from Table 2, the 

following general changes were made. First, because spacer 

plates are not found on the real robot, their mass and inertia 

values were set to zero so that they would have no effect on 

the dynamic characteristics of the vehicle. Based on the 

results obtained from varying individual Karma Parameters, 

lowering the mass of the Spacer Plates caused severe 

end-over-ending of the vehicle. This prompted raising the 

ChassisMass property of the vehicle to 1 in the Unreal Unit 

System, which drastically reduced this problem. Based on 

testing of an actual Whegs robot on tile, it was observed that 

the wheel-legs slip during walking at higher speeds, similar to 

what can occur with lower values of the KFriction parameter. 

The vehicle also appeared to have a degree of elasticity with 

the ground, similar to when the KRestitution values were 

raised. Accordingly, the KFriction and KRestitution values 

were lowered and raised respectively. The center of mass of 

the vehicle (KCOMOffset) was not varied in the single 

parameter variation study. However, after examining a 

particular Mini-Whegs vehicle, it was found that many of the 

components such as the steering servo, steering mechanism, 

and battery are located towards the front of the vehicle. Also, 

the virtual vehicle still went into end-over-ending behavior 

more than was desired. Therefore, the KCOMOffset value 

was adjusted to bring the center of mass of the vehicle 

forward. This reduced the end-over-ending behavior slightly, 

but did not completely remove the problem. While the 

center of mass is not typically in the forward section of a 

Mini-Whegs vehicle, the change was made here to judge its 

impact on the performance. In addition to these parameters, 

the KCarWheelJoint motor torque and wheel-leg masses were 

also changed. The motor torque was ultimately lowered 

from its initial value of 2400 to 50 in the Unreal Unit System 

to give the vehicle a more realistic level of drive torque (for 

comparison, the Hummer uses a motor torque value of 2400). 

The wheel-leg masses were raised from 0.0008 to 0.08 

(Unreal Unit System). 

F. Performance Results 

The parameter changes that were made appeared to move 

the virtual vehicle towards matching the performance of the 

real vehicle. With the final set of parameters given above, 

the robot was able to walk at near top speeds with occasional 

end-over-ending. It was also only able to end-over-end at 

walls with higher wheel-leg drive speeds (10 rad/s and up), 

which is normal Mini-Whegs behavior. In climbing tests, 

the robot was able to successfully surmount a 0.04 m obstacle 

with head-on and 30o

 approaches. 

IV. DISCUSSION

Basic functionality modifications of existing USARSim 

base classes along with functionality implemented in the 

newly defined “Whegs” class appears to successfully replicate 

111
the general behaviors of torsional compliance and wheel-leg 

phasing found in Whegs robots. Karma Parameter 

modification based on physical reasoning and observation of 

real robots through videos and direct interaction appeared to 

result in improved, more realistic performance of the virtual 

robot in walking, running and basic climbing. In addition, 

the procedure used in the parameter modification yielded 

insight into how each individual parameter contributes to the 

overall performance of the vehicle. The procedure also 

yielded the velocity history of the vehicle in world and body 

coordinates, along with the speed of the vehicle, and the 

average speed for a given time increment. This data was 

used to determine if the vehicle is able to attain a particular 

drive speed, the degree to which the vehicle collides with the 

ground, and the vehicle’s tendency to end-over-end. 

While the accomplishments listed above are significant first 

steps towards creating an accurate representation of a Whegs 

robot in USARSim, there are many steps that can be taken to 

improve the virtual robot’s performance. With respect to the 

real robot, high speed video capture methods can be used to 

obtain data and establish performance metrics that can also be 

measured within the simulation for more accurate 

benchmarking. In terms of the virtual robot, several steps 

can be taken including: making the wheel-legs function as 

actual tires, refining the behavioral characteristics of the 

“Whegs” class and investigating the use of more detailed and 

accurate static meshes. In terms of the Karma Parameters, 

the above tests can be conducted in more depth and expanded 

to better understand the effects of individual parameters on 

robot performance. More testing can also be done to better 

understand how individual Karma Parameters map to real 

world quantities. Also, while individual Karma Parameters 

can be varied, they are not necessarily independent, so the 

coupling between Karma Parameters needs to be understood. 

For the validation, a number of steps can be taken. Several 

map substrate surfaces can be made from KActors and tuned 

to match the performance of real surfaces such as concrete, 

tile, wood, etc. The virtual robot can then be tested and 

compared to the real robot on each of these surfaces, which 

would yield a better representation of the appropriate Karma 

Parameters for the robot. With more rigorous performance 

metrics defined, numerical methods could be employed to 

help determine how well the virtual robot matches the real 

robot’s performance. All of these steps would lead to a more 

reliable and repeatable simulation. 

ACKNOWLEDGEMENTS

We would like to thank Ben Balaguer and Richard 

Bachmann for their contributions in developing this work. 

This research was performed under an appointment to the 

U.S. Department of Homeland Security (DHS) Scholarship 

and Fellowship Program, administrered 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. 

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International Conference on Intelligent Robots and Systems (IROS 2003), Las 

Vegas. 

[5] Quinn, R.D., Kingsley, D.A., Offi, J.T. and Ritzmann, R.E., (2002), 

"Improved Mobility Through Abstracted Biological Principles," IEEE Int. 

Conf. On Intelligent Robots and Systems (IROS'02), Lausanne, Switzerland. 

[6] Quinn, R.D., Nelson, G.M., Ritzmann, R.E., Bachmann, R.J., Kingsley, 

D.A., Offi, J.T. and Allen, T.J. (2003), "Parallel Complimentary Strategies 

For Implementing Biological Principles Into Mobile Robots," Int. Journal of 

Robotics Research, Vol. 22 (3) pp. 169-186. 

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and Highly Mobile Hexapod Robot”. International Journal of Robotics 

Research, 20(7): 616-631. 

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R.E. (2004) "Comparing Cockroach and Whegs Robot Body Motions," IEEE 

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[13] FRAPS (9/25/2007) http://www.fraps.com/ 

112
Maze Hypothesis Development in Assessing 

Robot Performance During Teleoperation 

Salvatore Schipani 

National Institute of 

Standards and Technology 

100 Bureau Drive, MS-8940 

Gaithersburg, Md., U.S.A. 

salvatore.schipani@nist.gov 

Elena Messina 

National Institute of 

Standards and Technology 

100 Bureau Drive, MS-8230 

Gaithersburg, Md., U.S.A. 

elena.messina@nist.gov 

Abstract— National Institute of Standards and Technology (NIST) 

personnel had the opportunity to assess 14 prospective Urban Search 

and Rescue (USAR) robots, for the purposes of developing 

performance standards which currently do not exist. During this 

exercise, a maze configuration – hypothesized as potentially valid 

test methodology – was assessed. Among the findings, resultant 

significant differences in completion and decision making times 

facilitated classifying platforms based on performance. Also 

revealed was that errors in navigation and encounters with walls 

correlated with times taken in making decisions... the longer it took 

to make a decision, the greater the chance this decision was incorrect. 

Results validated the hypothesis of a maze as beneficial in eliciting 

data necessary for human controlled robot performance assessment. 

Keywords: maze, metrics, performance standards; robotics; 

situation awareness; teleoperation

I. INTRODUCTION

Test performance standards for application-specific Urban 

Search and Rescue (USAR) robots providing valid replicable 

assessment measures do not exist, thus little or no guidance 

may be offered to local, state, or federal agencies regarding 

their utilization or procurement. In 2004, the Department of 

Homeland Security (DHS) Science and Technology (S&T) 

Directorate initiated an effort with the National Institute of 

Standards and Technology (NIST) to formulate 

comprehensive criteria related to the development, 

performance testing, and certification of available and 

anticipated robotic technologies, specifically directed toward 

application in USAR scenarios. To encourage collaboration 

between USAR responders and system developers, and in 

hopes of generating standards consensus among those 

interested, a third response robot evaluation exercise was 

conducted by NIST at the Montgomery County Fire Rescue 

Training Academy in Rockville, Maryland, particularly 

targeting the needs of DHS/Federal Emergency Management 

Agency (FEMA) USAR professionals. Operational standards 

deemed necessarily of concern included mobility, sensing, 

navigation, planning, integration into operational caches, and 

consideration of the human factor. 

Individual characteristics of current production robots 

utilized for USAR vary. In light of recent national security 

concerns, this reality brings to the forefront a necessity for 

categorizing the operational capabilities of tools and methods 

used to placate concerns. Any attempt at the organization of 

such information must address the identifiable requirements 

of emergency response professionals, and offer 

recommendations for system attribute improvement as 

discovered. In August of 2006, NIST personnel had the 

opportunity to assess 14 robots with potential for application 

during USAR situations based on visual sensors, mobility, 

logistic cache packaging, radio communications, and human 

factors in operations. This document reports on one proposed 

measure of performance, a subset of the decision making 

process referred to as operator time to acquire situation 

awareness, when attempting to teleoperate a robot within a 

maze, a scenario hypothesized as valid test methodology 

given observed apparatus methods of control and assumed 

tasks. 

A. Background 

 1) Maze Rationale: 

Mazes derived directly from their descendents, ancient 

labyrinth designs. This symbol and its family of derivatives 

may be traced back over 3500 years, however origins remain 

a mystery. As opposed to a maze, labyrinths have no false 

pathways or dead ends, but rather consist of one single 

meandering way leading from entrance to center. Conversely, 

mazes may possess many paths, enticing or impairing anyone 

attempting to maneuver through. These have become 

accepted exercises in direction finding, providing paths to 

follow, some correct and others erroneous. As such, they are 

considered highly respectable tests of navigational skills, and 

attempted by many. 

Correlations between maze performance and traditional 

psychometric measures of spatial ability have affirmed the 

113
relationship [1,2], especially as vestibular information from 

the inner ear as well as kinesthetic feedback from muscles has 

been shown to provide important cues regarding direction of 

heading and distance information [3,4]. The rationale 

becomes particularly acceptable subsequent to reviews of 

factor analytical studies for large spatial batteries yielding 

multiple spatial dimensions [5,6,7]. Optic flow also provides 

motion and movement cues necessary to navigate through 

environments, offering a visual analyses of motion which we 

have come to anticipate and rely on. Unfortunately, during 

teleoperation, such visual cues become the only aid presented 

[8,9], rendering tasks such as remote control especially 

difficult. Given that these cues are often disturbed during 

teleoperation due to issues in transmission, it should be 

expected that maze navigation become increasingly difficult. 

 2) Acquiring Situation Awareness: 

Though several definitions of Situation Awareness (SA) are 

posed in literature [10,11,12], SA is normally defined in terms 

of goals with particular decision tasks directed to a specified 

effort [13,14]. One definition offered, encompassing the 

essence of what most researchers care to relate, is “The 

perception of the elements in the environment within a volume 

of time and space, the comprehension of their meaning and 

the projection of their status in the near future” [15]. Endsley 

and Garland [14] further define levels of SA as: Level 1, the 

perception of cues: Level 2, an extension of cue perception, 

including the integration of multiple pieces of information 

plus the determination of their relevance to goals. Here, 

meaning must be considered as subjective interpretation 

(awareness) and objective significance (situation) [16, page 

3], so that at this level one is able to derive operational 

relevance and significance from prior data, and; Level 3, the 

ability to forecast future events. SA is normally depicted as 

an operator’s internal state model within an environment 

[17,14], causing designers to consistently question how well 

particular systems support one’s ability to acquire necessary 

information. This design concern is exaggerated in dynamic 

situations and under operational constraints, thus observing 

the acquisition and eventual degree of SA has become a 

frequently used measure of performance. 

Time has been shown a critical affecting factor in acquiring 

both Levels 2 (comprehension) and 3 (future event projection) 

SA [18,19,20]. This is particularly the case in teleremote 

operations, as operator SA must be derived from a 

combination of the environment and integrated system’s 

displays, and then interpreted by the operator at afforded 

instances and in short intervals [14]. Here, sufficient 

information must be provided through a remote interface so as 

to compensate for cues once perceived directly [21], an 

unfortunate scenario commonly found deficient. The 

collection of whatever information presented is assumed a 

subset of that derived from the environment and internal 

system parameters, however only a portion may be displayed 

via existing (visual) interfaces. With the majority of 

teleoperated systems currently deployed, operators are given 

minimal control of which information may be collected other 

than that presented via the visual channel, and are often 

restricted in transmitting commands to request further 

knowledge arrived at in such ways as by the autonomous 

selection of directions of traverse or specifying areas of sensor 

coverage [22]. Such deficiencies in data acquisition not only 

lengthen the time required for information collection, but also 

inhibit assimilation. 

In goal driven processing such as that which takes place 

during teleoperation, an operator actively seeks information 

required for attainment of the goal, during which the mental 

model is claimed as existing underlying knowledge therefore 

the basis for SA [23]. Smith and Handcock (1995) support 

this view of SA as behavior directed toward goal achievement, 

describing it as the “...up-to-the minute comprehension of task 

relevant information”. Referred to as cognition-in-action, 

Lave [24] claims “SA fashions behavior in anticipation of the 

task-specific consequences of alternative actions”. Over 

time, a pattern-recognition thus action-selection sequence 

becomes routine, developing to a level of response 

automaticity [25]. Such automaticity can positively affect SA 

by reducing demands on limited attentional resources, but 

only if proper information is retrieved, comprehended, and 

adequately assimilated. When one’s goal is to eventually 

emplace a system (robot) at a specified location, an internal 

model of previously traversed terrain with appropriate 

continued or corrected model for subsequent route direction 

becomes essential. This has been shown difficult when using 

existing teleremote visual displays due to inadequate cueing 

for guidance, and lack of available space for displaying 

previous information, thus SA is compromised. 

II. METHOD 

A. Participants 

Personnel operating robots during this exercise were 

engineering professionals representing their respective 

product. Each had extensive experience not only in robot 

operation, but also in development. Additionally, each 

vendor-operator was made aware that the performance of their 

product would be compared to competitors during the 

exercise, thus it behooved them to offer their best operator for 

the assessment. Personal observations substantiated the fact 

that each participant could be considered proficient in robot 

manipulation, thus the level of expertise was deemed a fixed 

factor. In all, 14 participants were involved, one each from all 

robot vendors appearing for the test. 

B. Materials 

1) Test Course: 

In this particular maze (see Figure 1), there exists one 

possible solution with only a single main branch leading to 

correct termination, having an approximate solution length of 

2,117.29 centimeters (833.58 inches) which consists of 21 

wall segments equating to 21.17 meters (69.47 feet). 

114
Traveling forward, the maze possess three left turns, three 

right turns, three straight-aways, two left curves, no right 

curves, two irregular curves, two ramps, four junctions, no 

crossroads, loops, or roundabout passages, and two deadended isolation points (designated points 1 and 2 in the Figure 

1 diagram). Additionally, two route enticements were 

constructed at which light was visible hinting at clear passage 

however actually blocked, with only short possible deviation 

lengths within the two provided false passages of 115.57 and 

346.71 centimeters (45.5 and 136.5 inches). 

Figure 1. Original Maze Configuration (not to scale) 

2) Robots: 

Following are descriptions of the 14 participating robots, 

accompanied by individual dimensions (taken from the NIST 

draft publication “Response Robots – DHS/NIST Sponsored 

Evaluation Exercises” Pocket Guide, Version 2006.1). To 

provide for anonymity, each has been designated a number in 

place of name. 

o Robot #1: Width 57.15 centimeters (22.5 inches), length 86.36 

centimeters (34 inches), height 38.1 centimeters (15 inches), 

weight 52.16-63.5 kilograms (115-140 pounds), turning 

diameter 0 centimeters (0 inches) (skid-steer, tracks), maximum 

speed 8.369 kilometers per hour (5.2 miles per hour), non 

tethered (tether option), remote teleoperation control, sensor 

include black and white camera (optional biological, chemical, 

and temperature sensors), five degrees-of-freedom 132.08 

centimeters (52 inches) horizontal reach end effector (i.e., 

manipulator); 

o Robot #2: Width 30.988 centimeters (12.2 inches), length 

42.164 centimeters (16.6 inches), height 15.24 centimeters (6 

inches), weight 6.35 kilograms (14 pounds), turning diameter 0 

centimeters (0 inches) (skid-steer), maximum speed 2.286 

meters per second (7.5 feet per second), non tethered, remote 

teleoperation control, sensor include black and white camera 

(with options for thermal, acoustic, infra-red, and visual wideangle sensing), no end effector;

o Robot #3: Width 25.4 centimeters (10 inches), length 35.56 

centimeters (14 inches), height 16.51 centimeters (6.5 inches), 

weight 6.35 kilograms (14 pounds), turning diameter 50.8 

centimeters (20 inches), maximum speed, 1.829 meters per 

second (6 feet per second), non tethered, remote teleoperation 

control, sensors include color and infrared cameras, no end 

effector; 

o Robot #4: (no data available);

o Robot #5: Width 55.88 centimeters (22 inches), length 68.58 

centimeters (27 inches), height 63.5 centimeters (25 inches), 

weight 56.7 kilograms (125 pounds), tracked skid-steer turns on 

center, maximum speed 10.46 kilometers per hour (6.5 miles per 

hour), non tethered, eyes on and remote teleoperation control 

with way-point following and drive intent, sensors color video 

camera and laser range scanner, no end effector;

o Robot #6: Width 57.15 centimeters (22.5 inches), length 86.36 

centimeters (34 inches), height 63.5 centimeters (25 inches), 

weight 52.16-63.5 kilograms (115-140 pounds), turning 

diameter 0 centimeters (0 inches) (skid-steer, tracks), maximum 

speed 8.369 kilometers per hour (5.2 miles per hour), non 

tethered (tether option), remote teleoperation control, sensor 

include black and white camera (optional biological, chemical, 

and temperature sensors), five degrees-of-freedom 132.08 

centimeter (52 inch) horizontal reach end effector;

o Robot #7: (no data available);

o Robot #8: Width 34.29 centimeters (13.5 inches), length 52.07 

centimeters (20.5 inches), height 30.48 centimeters (12 inches), 

weight 11.34 kilograms (25 pounds), turning diameter 0 

centimeters (0 inches) (skid-steer, tracks), maximum speed 

6.437 kilometers per hour (4 miles per hour), non tethered, no 

tether, eyes on and remote teleoperation control, sensor black 

and white camera, no end effector (i.e., manipulator); 

o Robot #9: Width 53.34 centimeters (21 inches), length 76.2-

86.36 centimeters (30-34 inches), height 30.48 centimeters (12 

inches), weight 27.67 kilograms (61 pounds), turning diameter 0 

centimeters (0 inches) (skid-steer, tracks), maximum speed 

3.219 kilometers per hour (2 miles per hour), non tethered, fiber 

optic cable tether (for data, video, and audio), remote 

teleoperation control, sensors include black and white camera 

(optional biological, chemical, and radiological sensors), five 

degrees-of-freedom 111.76 centimeter (44 inch) end effector;

o Robot #10: Width 40.64 centimeters (16 inches), length 63.5 

centimeters (25 inches), height 19.304 centimeters (7.6 inches), 

weight 11.34 kilograms (25 pounds), turns in place, maximum 

speed 1.341 meters per second (4.4 feet per second), non 

tethered, remote teleoperation and telemetry control, sensor 

black and white camera, end effector (i.e., manipulator) six 

degrees-of-freedom with 106.68 centimeter (42 inch) reach; 

o Robot #11: Width 40.64 centimeters (16 inches), length 68.58 

centimeters (27 inches), height 19.05 centimeters (7.5 inches), 

115
weight 21.77 kilograms (48 pounds), turning diameter 0 

centimeters (0 inches), maximum speed 8.047 kilometers per 

hour (5 miles per hour), non tethered, remote teleoperation 

control, sensor black and white camera on short non-extending 

boom, no end effector; 

o Robot #12: Width 40.64 centimeters (16 inches), length 68.58 

centimeters (27 inches), height 19.05 centimeters (7.5 inches), 

weight 21.77 kilograms (48 pounds), turning diameter 0 

centimeters (0 inches), maximum speed 8.047 kilometers per 

hour (5 miles per hour), non tethered, remote teleoperation 

control, sensor black and white camera on three-rod extending 

boom, no end effector; 

o Robot #13: Width 50.8 centimeters (20 inches), length 55.88 

centimeters (22 inches), height 45.72 centimeters (18 inches), 

weight 6.804 kilograms (15 pounds), turning diameter 0 

centimeters (0 inches), maximum speed 5.633 kilometers per 

hour (3.5 miles per hour), non tethered, remote teleoperation 

control, sensor black and white camera, no end effector; 

o Robot #14: Width 27.432 centimeters (10.8 inches), length 

42.672 centimeters (16.8 inches), height 13.97 centimeters (5.5 

inches), weight 6.35-9.072 kilograms (14-20 pounds), turning 

diameter 0 centimeters (0 inches) (skid-steer, tracks), maximum 

speed 0.4572 meters per minute (1.5 foot per minute), 30.48 

meter (100 foot) polyurethane multi-cord tether, remote 

teleoperation and eyes-on control, sensor black and white tilt 

camera, no end effector.

III. PROCEDURE 

Participants were directed – upon the experimenter command 

“begin” – to teleoperate assigned robotic platforms traversing 

pathways through the unfamiliar maze, and do so within the 

shortest time possible. They were further instructed to operate 

carefully enough to limit or avoid encounters with path walls. 

Their informed consent to participate and to allow a video 

record made of their system was agreed upon prior to test 

initiation, at which time operator sightedness was screened. 

Participants were permitted to ask questions concerning test 

methods and purpose prior to testing, or at any time during the 

test. They were instructed that they were to complete four 

iterations, two in forward and two in reverse, until reaching 

their goals which were open doorways located at the 

beginning and end of the maze. 

A. Data Collection 

Time data collection was recorded in seconds and 

performed manually utilizing hand-held stop watches (one 

recording total maze traverse time, the second monitoring 

time spent in decision points), and on digital video in order 

that post-test evaluations of performance could be made. 

Video records were taken via hand held roving camera, with 

camera person consistently positioned behind the robot thus 

completely out of robot camera view to ensure that no visual 

cues were offered to operators. 

IV. EXPERIMENTAL DESIGN 

The experiment was treated as a 2 x 2 x 1 factorial, where two 

levels of traverse exist (forward and reverse), with two 

instances of dead ended isolation points, and this applied 

between the performance of 14 robotic platforms given one 

level of operator proficiency. 

A. Dependent Measures 

Dependent measures were averaged “maze completion 

times” traveling forward and reverse, averaged “decision 

making times” recorded at points specified within the maze, 

“errors” in direction of traverse when exiting aforementioned 

decision points, and observed “encounters” made with maze 

walls. 

Times were recorded for total maze completion in each 

direction (separate forward and reverse recordings), and 

during instances at which robots entered into and lingered in 

designated dead-ended isolation points. For total completion 

time, recording began as test director instructed participants to 

“begin” each trial, and ended once the robot reached the stepsill of exit doors located at either end of the maze. Each 

participant completed two forward and two reverse iterations. 

For instances in which participants entered a dead-ended 

isolation area (i.e., decision eliciting ‘traps’), total time spent 

within was recorded. Time data collection for this began 

when the most forward portion of a robot crossed a horizontal 

imaginary line at the entrance of the isolation area, and ended 

as the most forward portion again crossed this line exiting. 

This data was treated as the time necessary for participants to 

gain situation adequate awareness, sufficient for participants 

to realize that they had entered a dead end in the maze and to 

reach a decision on how to properly exit. 

As participants exited dead-ended decision points, their 

direction of traverse was recorded for correctness. The 

accurate direction could be determined by experimenter 

observation as being the most obvious direction of course 

traverse within which one might successfully complete the 

maze. Finally, robot encounters with walls (e.g., “hits”) were 

recorded as each participant teleoperated through pathways. 

V. RESULTS 

Following (see Table 1) find descriptive statistics for 

averaged Maze Completion Time, Decision Making Time, wall 

Hits (encounters), and Errors in direction traversed. 

 Table 1. Descriptive Statistics 

(Times in seconds, Hits & Errors in unit segments) 

116
Figure 2 presents maze completion times, showing robots 2, 

8, 9, 10 and 12 displaying lowest times to complete the maze 

(averaging 1.14 minutes, or 68.4 seconds), and robots 5, 6, 7 

the highest (averaging 3.23 minutes, or 193.8 seconds). 

Figure 2. Maze Completion Times 

Robots 1, 8 and 9 displayed lowest decision making times 

(i.e., Situation Awareness gaining time) averaging 6.93 

seconds, and robots 6, 11, and 13 the highest averaging 31 

seconds (see Figure 3). 

Figure 3. Decision Making Times 

No statistically significant difference found among robots 

for average maze completion times (p = 0.68), the most 

frequently attained ranging from 2.14 to 2.48 minutes (128.4-

148.8 seconds). It may be assumed that – being a first attempt 

– the current maze configuration did not provide sufficient 

distance to evoke performance anticipated. Future maze 

investigations employing increased areas of traverse should 

resolve this issue. However, three categories may be 

delineated from the data when observing performance 

groupings which ranged from slightly greater or less than 1.0, 

on average 2.2, and slightly greater or less than 3.0 minutes 

(60, 132, and 180 seconds respectively) (see Figure 4). There 

was a significant difference found between forward and 

reverse times to complete the maze (p = 0.003). Times in 

reverse were shorter, obviously an indication that operators 

were becoming familiar with the test course. 

Figure 4. Histogram of Distributed Completion Times 

There was a significant difference found among robots 

concerning averaged decision making times (p = 0.001), as 

individual attributes of particular platforms apparently aided 

or hindered performance during the challenge. There was not 

a significant difference found between times to decide at 

isolation area 1 versus 2 (p = 0.891), revealing the two similar 

in nature. The most frequently attained decision making times 

ranged from 16.6 to 19.25 seconds. Here again, robots could 

be grouped per three categories of performance of from 

slightly greater or less than 7.5, averaged 18.5, or slightly 

greater or less than 30 seconds (see Figure 5). 

Figure 5. Histogram of Distributed Decision Times 

A significant difference was found among robots 

concerning hits (wall encounters) (p = 0.001). In reviewing 

video recordings, it would appear as if particular robots acted 

out-of-control due to inferior or transmission lagged control 

117
response, no or poor methods of halting forward movement, 

or poor camera views provided the operator. 

There was a significant difference found among robots 

concerning errors (p = 0.048). Errors were also found 

correlated with increased times spent in making decisions (r = 

0.67). This would appear to support the notion that the longer 

it took to make a decision as to which direction to move next, 

the more this decision (the direction of traverse selected) was 

found incorrect. No significant correlations were observed 

between averaged completion times and decision making 

times, revealing these entities distinct (r = 0.543). However, 

averaged wall hits data correlated highly with errors made in 

correct direction of traverse (r = 0.864), suggesting confusion 

in the selection of subsequent travel direction due to postcollision trauma. 

For comparative purposes, individual performance is 

displayed in Table 2. 

Robot Comp. Time Decision Time Errors Hits

1 average best best best 

2 average average average best 

3 average average average best 

4 average average average best 

5 poor average average best 

6 poor poor poor poor 

7 poor average poor poor 

8 best best best best 

9 best best best best 

10 best average average best 

11 average poor poor poor 

12 best average average best 

13 average average poor best 

14 average average average best 

Table 2. Performance as a function of Dependent Measures 

VI. DISCUSSION 

At present, performance standards for Urban Search and 

Rescue (USAR) designated robots are nonexistent, thus little 

guidance may be offered to local, state, or federal agencies 

regarding their purchase or use. A precursor to successful 

search and rescue operations if employing a robot is the 

ability to teleoperate the system satisfactorily, attaining 

directional cures from the remote visual display as possible. 

When one is driving, vestibular information and kinesthetic 

feedback provide additional cues regarding direction. 

However, during teleoperation, the only cues available are 

those presented visually, yet sufficient information must be 

attained via a remote interface in order to compensate thus 

discern most advantageous pathways. Intensifying this effort, 

situation awareness in such circumstances must be attempted 

while on-the-move, which becomes defined in terms of goal 

achievement with time the critical factor affecting acquisition. 

This document reports on one scenario hypothesized as valid 

methodology for assessing performance of such platforms, a 

maze test configuration employed as a navigation exercise. 

Data collected included time to complete the maze, and also 

that necessary for gaining situation awareness when entrapped 

in either of two predestinated dead-ended isolation points. 

Data also included recordings of maze wall encounters, and 

errors made in direction of traverse. Digital video recordings 

were taken to enable post hoc analyses. Participants were 

directed to teleoperate their assigned platforms through the 

maze in the shortest time possible, while avoiding encounters 

with walls. Fourteen robots, potential candidates for 

deployment in USAR scenarios, were involved. Participants 

operating the robots were engineering professionals 

representing their respective product, each possessing 

extensive experience both in operation and platform 

development. Results revealed significant differences in time 

to gain situation awareness (p = 0.001), encounters with walls 

(p = 0.001), and errors made in direction of traverse (p = 

0.048). Also uncovered was that increased times spent in 

making decisions correlated with erroneous subsequently 

selected directions of traverse (r = 0.67), supporting the notion 

that the longer it took to make a navigational decision the 

more this decision could be found incorrect. Finally, 

encounters with walls correlated highly with errors made in 

direction of traverse (r = 0.864), revealing confusion as a 

result of post-collision trauma.

Given results of the current exercise, utilization of a maze 

test approach for evaluating robot teleoperation appears 

rational, as the scenario elicited data sufficient to examine 

performance as intended. Forthcoming endeavors are 

expected to include increased maze distances and complexity, 

to ensure that appropriate pragmatic assessments may be 

made. 

Anticipations are to submit the maze hypothesis to tests of 

validity and reliability in the near future. Generally accepted 

validity determinations involve criterion-oriented procedures 

such as predictive and concurrent, or are else-wise considered 

either content or construct [26]. For the test method in 

question, a predictive approach to validation appears most 

logical, as criterion-oriented validity "involves the acceptance 

of a set of operations as an adequate definition of whatever is 

to be measured." [27]. This will be attempted per 

performance criterion found necessary via repeated 

investigation, as well as by exploiting guidance offered from 

emergency response professionals. Reliability assessments 

should establish whether this type examination measures 

consistently. Concurrently, appropriate levels of maze 

complexity will be evaluated, and mathematical formulas 

aiding in maze construction developed for use by those not 

capable of testing at a NIST designated arena. Subsequently, 

results will be submitted through appropriate committee of the 

American Society for Testing and Materials (ASTM) to attain 

consensus as a national standard, as NIST personnel explore 

supplementary measurement methods deemed essential. 

118
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119
Human System Performance Metrics for 

Evaluation of Mixed-Initiative Heterogeneous 

Autonomous Systems 

Lisa Billman Marc Steinberg 

ARINC Engineering Services Naval Air Systems Command/Office of Naval Research 

lbillman@arinc.com marc.steinberg@navy.mil

Abstract-This paper describes a set of human system performance 

metrics and their implementation and use in a series of operator 

experiments for mixed initiative control of multiple heterogeneous 

unmanned systems. The focus of the work is on technologies that 

support the control of five to ten air, sea, and undersea vehicles 

with a common human interface. The individual systems have 

significant differences both physically and with regards to their onboard level and type of autonomy. This includes some ability of 

the operator to modify the autonomy levels relative to particular 

types of autonomous decision-making. This paper will describe 

the set of metrics and experience in applying them including 

implementation factors and their utility. Finally, it will describe 

some lessons learned.

Keywords: UAV, USV, UUV, Mixed-Initiative, Human System 

Performance Metrics

I. INTRODUCTION 

The Naval Intelligent Autonomy program is developing 

and demonstrating autonomous control and human interface 

technologies that support management of five to ten 

heterogeneous unmanned systems by a single operator 

[1-2]. Accomplishing this requires significant increases in 

autonomous control and greatly reducing the need for 

human intervention in the system as compared with many 

current systems that require one or more dedicated and 

skilled operators to control even a single system. However, 

this does not mean that a desired goal of the work is to 

eliminate the need for human interaction with the system. 

The types of applications being looked at are long, complex 

missions with many interdependencies that will sometimes 

require significant human collaboration with the 

autonomous systems in order to coordinate unmanned 

systems planning and execution with changing situations in 

a dynamic operational space. Further, the difficulty of the 

types of missions being examined are such that they cannot 

currently be solved completely with autonomy and will 

require making use of human strengths such as tactical 

understanding, judgment, and decision-making while also 

minimizing the impact of human weaknesses [3]. This can 

be a particular problem because there may be significant 

differences between how human operators and highly 

advanced autonomous systems conceptualize planning and 

execution. In addition, designers of autonomous systems 

are unlikely to have a full understanding of how users in the 

field will want to utilize these systems and there will be 

times when unforeseen problems will arise. As a result, it 

will be important for operators to be able to interact with 

these systems at a variety of different levels within different 

control loops. Thus, there is a strong need to determine how 

best to design the entire autonomous system in a way that 

supports the role of the human in the system and not just 

assume that this can be solved with a good user interface 

design. 

In the past, many approaches to autonomous systems 

metrics that relate to the operator have assumed that the 

ultimate goal of autonomy is to get the operator out of the 

loop. For this program, it is critical to examine the 

performance of the total system including the human in-theloop as well as examine what factors are impacting on the 

ability of the human to effectively collaborate with the 

automation. The particular type of system being examined 

has some complex features that can make it difficult to 

evaluate. First, it deals with highly heterogeneous vehicles 

including air, sea surface, and undersea vehicles. These 

vehicles have significant physical differences, operate in 

very different environments, and have different types of onboard autonomous control systems. Second, there are 

significant differences in communications with each type of 

platform. This can range from a high altitude Unmanned 

Air Vehicle (UAV) that may have relatively good 

communications with an operator to an Unmanned 

Undersea Vehicle (UUV) or small UAV that may have 

extended periods without communications or relatively low 

bandwidth communications. The vehicles also may be 

widely distributed geographically throughout the course of 

the mission rather than all operating within close proximity 

to each other. Finally, this program is focusing on vehicles 

that can operate with a fairly high degree of autonomy. 

Operators can adjust the levels of autonomy for individual 

vehicles or classes of vehicles so that they respond to 

different types of contingencies in different modes such as 

operation by consent, operation by exception, and fully 

autonomous operation. One consequence of this is it may 

be important to examine factors that are difficult to measure 

such as the operator’s trust and mental model of the 

autonomous system. 

120
This paper presents one approach to a set of metrics 

that can be used to evaluate human system performance for 

very complex autonomous systems and also describes 

experience in applying these metrics in a series of naval 

operator evaluations [4]. It is important to note that these 

are not meant to be solely human performance metrics, or 

measures of hardware/software performance. Rather, these 

metrics are intended to assess human-in-the-loop system 

performance, which includes hardware, software, liveware, 

and the environment. Metrics were chosen based on factors 

such as objectivity, repeatability, real-world validity, 

appropriate level of fidelity, and unmanned system/human 

agent independence and intuitiveness. This is not intended 

to be a comprehensive list of all possible metrics for 

human/autonomous system performance. Some more 

general background on metrics for Human-Robotic 

Interaction can be found in refs. 5-6. There are currently a 

number of programs examining human control of multiple 

unmanned systems at higher levels of autonomy. Each 

program has tended to develop their own approach towards 

metrics and test approaches as suited to their problem. 

Ultimately, it will be important that common sets of metrics 

are developed that can be utilized for these types of systems 

and support comparisons across different programs. 

II. SYSTEM DESCRIPTION 

This section will provide an overview of the Intelligent 

Autonomy architecture, the main components, and how an 

operator would interact with them. The different 

components are integrated via a publish/subscribe approach 

with a set of common Extensible Markup Language (XML) 

schema that allows components from different academic, 

industry, and government performers to interact. 

Additional details about the overall Intelligent Autonomy 

(IA) system can be found in refs. 1-2. 

Initially, an operator would begin tasking the system by 

specifying high-level mission tasks, constraints, and 

priorities through a Mixed-Initiative Interaction Module 

(MIIM). There are several different human interface 

concepts that will be evaluated as part of the MIIM [4, 7-8]. 

Tasks range from simple data collection to more complex 

tasks such as searching or maintaining coverage over a 

region. There are a large number of constraints available 

including no-go zones, no communication zones, no 

surfacing zones for UUV’s, and hard time and precedence 

constraints. Because many of the missions are fairly 

complex, the operator can choose to use a Case-Based 

Reasoning (CBR) component that identifies previous 

mission plans that could be used like a template as a starting 

point [9]. The operator defines high-level features of the 

mission and the CBR component ranks the past mission 

plans that are most relevant to that set of criteria. 

Alternatively, the operator can choose to specify the mission 

completely manually. The operator then goes through a risk 

management process to define what types of risks are 

acceptable for the system to take [10]. For each type of 

risk, the operator defines the severity of risk and possible 

risk mitigation approaches. Risk exposure is managed 

hierarchically. For example, the operator can specify that 

all systems should avoid a particular risk in general, but 

then also specify a particular vehicle or mission task for 

which it would be acceptable to be exposed to that risk. 

Next, the operator can specify the level of autonomy of the 

unmanned systems relative to various contingencies [11]. 

This can apply to all vehicles or to specific individual or 

classes of vehicles. The choice of levels includes fully 

autonomous dynamic replanning, management by exception 

with a customizable time delay, and management by 

consent. 

After completing mission specification, the operator 

sends the mission specification to a multi-vehicle planning 

system. This will allocate tasks to vehicles for all tasks 

other than those that have already been designated to a 

particular vehicle by the operator. The planning system will 

order and schedule tasks for each vehicle and provide inputs 

to detailed route and payload planners that may vary by 

vehicle classes. In order to ensure that solutions are both 

computationally feasible and operationally acceptable, the 

planning problem is decomposed and constrained in 

different ways. Several different approaches towards 

optimization have been examined under the program 

including Mixed Integer Linear Programming, a marketbased approach, and a Contract Net Protocol approach. 

Some mission plans will have secondary tasking that is not 

mandatory to complete. After completion of planning for 

the primary mission, the system will attempt to provide as 

much coverage as possible of secondary tasks without 

violating any of the constraints of the primary mission tasks. 

This is optimized using a receding horizon approach to 

support multi-vehicle coverage of an area with both path 

deconfliction and appropriate vehicle trajectories for 

sensing [12]. Individual vehicle plans are then combined 

into a single mission plan that is provided to the operator. 

The operator is provided with a variety of options to 

visualize and analyze the mission plan including 

geographic, timeline, task allocation, and risk assessment 

displays and animation of the mission [7-8, 10-11]. 

After the plan has been generated and the operator 

approves the mission, the autonomous systems begin 

execution. During the course of the mission the MIIM 

provides a variety of options for monitoring the mission 

including different team and individual vehicle mission 

visualization approaches and an alert management system 

[7-8,13]. While the interface is focused around a map-based 

display, there are also display options that show timelines, 

communication networks, vehicle status, and teaming 

issues. If necessary, the operator can work on developing 

121
new plans to re-task the system or modify the plan by 

tabbing to separate windows for new plan development. 

Some of the vehicles operate without communications with 

the operator for significant periods of time and have onboard mapping, sensor processing and sensor fusion 

capabilities that they can utilize to update their 

understanding of the environment and other entities [14]. 

This information is used as part of a replan assessement 

component on-board both the operator’s control station and 

those vehicles that have a replanning capability. This 

determines if new data will impact on the vehicles 

capabilities or their ability to carry out the mission. If there 

is an impact, the replanning component alerts the operator 

or triggers a fully autonomous replan depending on the 

levels of autonomy specified by the operator and whether or 

not the vehicle is currently in communications. When not 

in contact, it will sometimes be necessary for the vehicle to 

make a decision about changing its mission plan without 

operator assistance based on rules of engagement that the 

operator has specified. This has included some 

examination of the ability of unmanned systems to 

reallocate tasks between vehicles fully autonomously in a 

way that is robust to communications limitations [15-16]. 

In some significant events, the system will provide the 

operator with choices about what the vehicle should do next 

in a mixed-initiative way, which may ultimately lead to a 

replan.

III. METRICS 

 Table 1 contains the list of candidate metrics that 

was compiled by a Human Factors Working Group 

(HFWG) under the Intelligent Autonomy program. The 

HFWG consists of government, academic and contractor 

personnel supporting the IA program who have an interest 

in the user interface design including engineers with 

experience in autonomous vehicles, human-factors 

engineers, and psychologists. 

IV. OPERATOR EVALUATIONS 

Several evaluations of different user interfaces and 

autonomous systems components have been conducted to 

elicit unmanned vehicle operator feedback on the systems, 

and to evaluate the human performance metrics from the IA 

Metrics Toolset [4]. Scenario-based user evaluations were 

employed as the experimental paradigm to identify, refine 

and validate IA metrics. During each evaluation, sets of 

proposed metrics were employed and evaluated. Once the 

data were collected and analyzed, the utility of the metrics 

was analyzed. An end state objective is to provide a toolset 

that can be useful on this and other similar programs. 

There was significant focus on three areas of human 

systems integration for the evaluations: usability, 

appropriateness of automation levels, and system/mental 

model compatibility. An assessment of the usability of the 

systems was accomplished through traditional heuristic 

evaluation techniques, including the use of Likert scales to 

rate various aspects of usability. The appropriateness of the 

levels and kinds of automation was assessed by collecting 

situational awareness scores from the operator during the 

simulation. Finally, the software interface was assessed to 

determine the correlation between the users’ mental model 

and the design and functioning of the software using several 

mapping techniques. 

The process for the evaluations consisted of the 

following steps: 

• Signing of the informed consent and receiving the 

pre-briefing on evaluation 

• Explanation of Intelligent Autonomy Program 

• Human Factors Operator Workload Drivers 

Briefing 

• Evaluation Guidelines 

i. Evaluation Method and Metrics 

ii. Heuristic Feedback 

iii. Graphical User Interface (GUI) 

Guidelines 

• Demonstration of software package by contractor 

i. System Overview Brief 

ii. Simulation/Training 

• Freeplay with software 

• Performance with Scripted Scenarios 

Questionnaire completion 

• NASA-TLX (task load index) completion 

• Crew Debriefing 

Three sets of active duty and retired Navy UAV and 

UUV operators worked in teams during the training and free 

play time. During the actual evaluation, operators worked 

alone, and were supervised by a member of the evaluation 

team. Operators were given the background scenario 

information and required to task the UxVs to accomplish 

various reconnaissance and surveillance tasks such as to 

search sections of a coastline. The evaluation team collected 

data during the evaluation, recorded observations, provided 

the Situation Awareness Global Assessment Technique 

(SAGAT) probes, recorded mental workload ratings, and 

answered any general questions for the operators 

5. EVALUATION AND USE OF METRICS 

The following describes some of the major metrics that 

were employed and evaluated for their utility during these 

evaluations. A brief description of the metric, intended 

implementation and utility is included below. 

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Table 1: Candidate Metrics for the IA Toolset. 

Metric Performance Parameter Human Factors Concern/Intent

Cognitive workload Modified Cooper-Harper scale rating Subjective cognitive workload estimates

Command frequency # Commands/event Objective operator cognitive workload

Communication # Interactions/agent/hour Level of collaboration / communication efficiency

Decision accuracy % Correct decisions Human-system performance (Defining decision points 

and appropriateness of outcome will be difficult.)

Error complexity # Steps used to correct error Extent mission goals are diverted or derailed due to 

human error

Error frequency # Errors/hour Human-system performance

Error impact Time to correct error Extent mission goals are diverted or derailed due to 

human error

Error recovery % Errors corrected Human-system's ability to recover from human error

Planning efficiency # Commands/event/time of event How well does the operator's mental model of the 

planning tool match the actual algorithm?

Replanning Time to resolve forced change Human-system adaptability (i.e. ability to respond to 

system failure, environmental influence, new 

direction, etc.)

Reaction Time Time to respond to stimulus Saliency of important stimuli (ex. availability of target 

images)

Situational Awareness % Accuracy as measured by SALIANT 

or SAGAT or SART scale rating

Ability for operator/team to understand and 

communicate past and present events or states and 

predict future ones. Is the information available for 

the operator to maintain 1)general SA and 2) state or 

modality of specific system elements (i.e. vehicle, 

Mental Model Correlation between system state and 

operator mental model

How well does the operator's temporal and spatial 

mental model of the current system state match 

reality?

Task time Time to complete mission/task Time to complete task can be used to measure 

various aspects of human-system performance 

(efficiency)

Automation adaptability Time to complete mission/task Function allocation of automation. Mission 

performance as influenced by level of autonomy 

(chosen or forced)

Temporal workload Time to complete mission/task Objective operator cognitive workload

Tasks accomplished % Missions/tasks completed Use of pre-defined relevant milestones necessary for 

completing a mission measure the productivity of the 

system through the quality of information and 

interfaces

Trust Lee & Moray trust scale rating Subjective measure of trust/faith, perceived 

predictability, and perceived dependability of the 

automated agent

Usability Likert scale rating Subjective ratings for comfort, ease of use, 

consistency, etc. (elements of usability)

Training Time in training before achieving 

proficiency (as defined by another 

metric/combination of metrics, such as 

tasks accomplished and task time)

How much training is required to meet other human 

performance objectives?

System effectiveness Probability of identifying or classifying 

target

Objective joint agent-agent measure of effectiveness

False alarm # incorrect classifications/# 

classifications

Objective joint agent-agent measure of effectiveness

123
Planning Time 

• Metric Definition: The degree to which the 

automated planning tool supports the planning process 

as a measure of time.

• Performance Parameter: Time from presentation 

of mission goals to start of execution of mission. 

• Utility: Response times are generally most useful 

as a baseline for comparisons of multiple systems, or 

assessment of design enhancements. Response times 

can also be useful if the evaluator has knowledge of 

time allotted to perform a task during actual operations. 

For example, if it takes x minutes to replan a route with 

system A, and the naval system requirement is to be 

able to replan a route in y minutes, then the data could 

be used to determine whether system A is in 

compliance with Navy requirements. Therefore, the 

use of response time measures requires extensive 

knowledge regarding the task and the operational 

requirements, typically based on interviews with 

subject matter experts and review of operational 

concept documentation. 

Task Time 

• Metric Definition: Time to complete task can be 

used to measure various aspects of human-system 

performance (efficiency). 

• Performance Parameter: Time to complete the 

mission, task, or task segment. Task time for three 

events was recorded: defining the mission (planning 

time), re-plan by inserting a constraint such as a no-fly 

zone and re-plan by prosecuting a target.

• Utility: Task reaction time events measured are 

generally not very useful in isolation, but can be useful 

for comparisons relative to other similar designs or to 

measure improvements. However, it is important to 

ensure that this metric is applied to time critical tasks. 

Situation Awareness 

• Metric Definition: Ability for operator/team to 

understand and communicate past and present events or 

states and predict future ones. This can address if the 

information is available for the operator to maintain 

both general SA and the state or modality of specific 

system elements (e.g., vehicle, sensors, environment, 

battlespace, mission, level of autonomy, decision 

framework).

• Performance Parameters: 

o The Situation Awareness Global 

Assessment Technique (SAGAT) provides an 

objective measure of situation awareness by 

directly comparing operators’ reported SA to 

reality. With this technique, a human-in-theloop simulation is frozen at randomly selected 

times, the simulation is suspended, and the 

system displays are blanked while the 

operators quickly answers questions about 

their current understanding of the situation. 

Operators’ perceptions are then compared to 

the real situation (based on information drawn 

from the computer or from subject matter 

experts who answer the SAGAT queries while 

looking at the displays). Comparing the data in 

this manner provides an objective, unbiased 

assessment of SA [17]. 

• Utility: Overall, the SAGAT measure appears to 

provide useful information regarding operator situation 

awareness, and the scores tend to correlate with the 

operators’ and human factors analysts’ opinions 

regarding the display. SAGAT data will only be useful, 

though, if appropriate probes are generated. When 

using SAGAT, the HSI analyst must develop a 

thorough understanding of the task and the information 

the operator requires to accomplish that task. This will 

likely require the evaluator to perform a task or work 

analysis, or at least conduct extensive interviews with 

subject matter experts. Therefore, the use of the 

SAGAT requires intimate knowledge of the operator’s 

task and may be cumbersome to implement. 

Furthermore, once an HSI analyst has conducted the 

task analysis, they can recommend design changes for a 

system prior to conducting a human-in-the-loop 

evaluation. 

Workload 

• Metric Definition: Subjective workload estimates. 

• Performance Parameters: 

o An automated version of the NASA-TLX 

was used to measure workload. NASA-TLX 

is a subjective workload assessment tool. 

NASA-TLX is a multi-dimensional rating 

procedure that derives an overall workload 

score based on a weighted average of ratings 

on six subscales. These subscales include 

Mental Demands, Physical Demands, 

Temporal Demands, Own Performance, Effort 

and Frustration. It can be used to assess 

workload in various human-machine 

environments such as aircraft cockpits; 

command, control, and communication (C3) 

workstations; supervisory and process control 

environments; simulations and laboratory tests 

[18]. 

o Workload ratings were also obtained 

coincident with the administration of each set 

124
of SAGAT probes through use of a modified 

Cooper-Harper Workload rating scale.

• Utility: NASA-TLX is a multi-dimensional 

measurement tool and includes six subscales to provide 

a more detailed analysis of the operator’s workload. 

Those subscales analyses were not conducted for this 

evaluation, but could be examined if more detailed 

information regarding the source of the workload is 

required. The Modified Cooper-Harper Workload 

Rating Scale is a flow chart that leads the operator 

through a series of questions that result in a numeric 

rating of the workload associated with the task. 

Although the scale is one-dimensional, the outcome 

may be more objective and has a quantifiable meaning 

(e.g., “3” indicates “fair, some mildly unpleasant 

deficiencies - Minimal sailor effort required for desired 

performance”). For the purposes of this type of operator 

evaluation, it may be better to use a measure such as the 

Modified Cooper-Harper which provides a more 

general measure workload, but allows the evaluator to 

know exactly what the operator meant by the score. A 

comparison of the Modified Cooper-Harper scale vs. 

NASA-TLX workload metrics was conducted during a 

recent evaluation. As expected, similar ratings were 

obtained between the two methods. 

Usability 

• Metric Definition: Subjective ratings for comfort, 

ease of use, consistency, etc. (elements of usability). 

• Performance Parameters: 

o A Likert scale was used to assess various 

software system capabilities and aspects of 

the user interface. 

o Open ended questions were presented to 

the operators. 

o Usability was also measured using the 

System Usability Scale (SUS) 

• Utility: The System Usability Scale literature 

advises not to use individual question scores in the 

analysis, but to only use the composite score. There 

is limited meaning to the composite score since it 

does not identify the factors that were positive or 

negative in determining the score. In addition to the 

SUS, we employed an additional questionnaire to 

address areas of specific interest. Subjective 

questionnaires provide some of the most useful 

information in the assessment of a user interface. 

This method of data collection allows the operator to 

focus their comments on the areas of most 

importance to them. Furthermore, it provides the 

opportunity for the operators to make 

recommendations and suggest enhancements, which 

is the ultimate goal of conducting an evaluation. 

However, there were some situations in which 

operators would focus on relatively minor aspects of 

the interface that could be easily changed as opposed 

to the more advanced technologies that were being 

studied. 

Mental Model Mapping 

• Metric Definition: The degree to which the 

operator's temporal and spatial mental model of the 

current system state matches reality. 

• Performance Parameters: 

• A number of techniques can be employed that 

attempt to correlate between system state and operator 

mental model. 

o Mental model mapping required operators 

to recreate a visual picture of the display when 

the screen was blanked for the SAGAT 

probes. 

o A second approach presented screen shots 

to the operators and asked them to label icons 

and explain the purpose of various features 

identified on the screen shots. 

• Utility: The mental model mapping was difficult 

to score and did not seem to provide a good indication 

of the operator’s understanding of the system state. The 

second approach provided more detail regarding the 

operator’s understanding of the features on the display, 

but did not successfully address the underlying degree 

of understanding regarding the system state. 

6. CONCLUSIONS 

The metrics described in this paper have been useful in 

understanding important aspects of the cooperation and 

coordination of human/system interaction and collaboration. 

However, additional work will be required to examine other 

metrics and continue refining the implementation of the 

ones already employed. SAGAT has been relatively 

successful as a measure of situation awareness, but the 

probes will need to continue to be refined to improve 

relevance to the operational tasks. Both NASA TLX and 

Cooper-Harper Rating (CHR) were also relatively 

successful as a measure of workload. While TLX provides 

a broader workload scope, Cooper-Harper provides faster 

and more specific design feedback, such as defining the 

severity of the workload problem. The use of task times 

and response times to compare systems or versions of 

systems was also helpful, but could be improved with future 

software versions including embedded recording of times 

within the software packages. Additional refinement of the 

time metrics will require refining the tasks selected for 

measurement to ensure that they are operationally relevant, 

and that the operators are aware that time to complete is an 

important aspect of that task. Some areas that require more 

significant metric development are measures of the 

operator’s mental model and trust of the autonomous 

system. To date, the mental model metrics have focused on 

125
the use of the map to measure the operator’s spatial mental 

model of the task environment and understanding of 

interface features. Another possible approach is to explore 

the temporal aspects of the task perhaps by requesting that 

the operators complete a simplified version of a timeline 

display or to test the operator on their understanding of how 

the autonomy will react in specific contingencies. 

ACKNOWLEDGEMENTS 

The authors would like to acknowledge all of the 

intelligent autonomy team members who helped develop 

and apply the set of intelligent autonomy metrics including 

Cris Cristina, Naval Surface Warfare Center, Missy 

Cummings, MIT, Karen Harper, CRA, John Lee, University 

of Iowa, Mike Linegang, formerly of Aptima, Vera Zaychik 

Moffitt, Lockheed-Martin ATL, Danielle Moulds, 

NAVAIR, Tom Moulds, DTI, and Norman Warner, 

NAVAIR. The work described in this paper was funded by 

the Office of Naval Research. 

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126
Concepts of Operations for Robot-Assisted

Emergency Response and Implications for

Human-robot Interaction

Jean Scholtz

1

Pacific Northwest Laboratories

P.O. Box 999

Richland, WA 99352

jean.scholtz@pnl.gov

Brian Antonishek, Brian Stanton, and

Craig Schlenoff

National Institute of Standards and Technology

(NIST)

100 Bureau Drive

Gaithersburg, MD, 20899, USA

{brian.antonishek, brian.stanton,

craig.schlenoff}@nist.gov

Abstract—In this paper we discuss a field study at Disaster City,

Texas in March 2006. First Responders and robot developers tried

out various concepts of operations in a number of disaster scenarios.

Observations, video data, and questionnaire data were analyzed and

based on these results, we propose some guidelines as well as some

future research areas for human-robot interaction. In addition to the

guidelines proposed as a result of our observations in this study, we

include design implications from other literature, both laboratory

and field studies.

Keywords: Human-robot interaction, rescue robots.

I. INTRODUCTION

The exercise at Disaster City is one of a series in a National

Institute of Standards and Technology (NIST) program

sponsored by the Department of Homeland Security (DHS).

The goal of this program is to develop metrics and evaluation

methodologies for Urban Search and Rescue (USAR) robots.

In initial workshops with the first responder community,

NIST developed a number of requirements for USAR robots

[http://www.isd.mel.nist.gov/US&R_Robot_Standards/

accessed August 31, 2006]. These requirements were

prioritized and

1

several work items are now being developed

with the ASTM standards group for emergency response

[ASTM E.54.08, http://www.astm.org accessed August 31,

2006]. In order to refine the requirements initially developed,

NIST is running a number of “responder meets robots”

exercises.

II. DISASTERCITY

Disaster City is a Texas Task Force One (TX-TF1) training

facility located at Texas A&M University, College Station,

Texas

[http://www.teex.com/teex.cfm?pageid=USARprog&area=US

AR&templateid=1117 accessed August 31, 2006]. It is part

of the Texas Engineering Extension Service (TEEX) at Texas

A&M. The TX-TF1 training site features full-sized

collapsible structures, including a strip mall, office building,

industrial complex, assembly hall/theater, single family

dwelling, train derailments, and three rubble piles.

The event took place over three days. There were “scenarios”

scheduled for 4- three hour blocks. These scenarios were

used to familiarize the responders with the capabilities of the

various robots

2

. Scenarios took place on two rubble piles, in

1 This research was conducted while Dr. Scholtz was at NIST.

2 Vendors supplied robots for the technology exercise. The mention

of these robots in this paper does not constitute an endorsement by

the National Institute of Standards and Technology. The robots

are described only to help readers understand the capabilities of

the different robots.

127
the strip mall, on the passenger and hazmat trains, in the

collapsed house, and in the single family dwelling.

The final three hour block of time was used as a mock

incident response. First Responder teams were assigned to

one of four scenarios: single family dwelling, collapsed

house, passenger train, and rubble pile. In the Data Analysis

Section, we explain how these were selected.

Figures 1 – 4 show each of the venues. In addition, a brief

description of each type of disaster is given.

Figure 1. Single family dwelling.

The single family dwelling is partially collapsed due to an

earthquake. The main entrances are compromised.

Responders must enter through either a leaning collapse or

through a 24” triangle breach. There is also a basement that

is accessible from the outside down some steep stairs. The

maze of rooms needs to be mapped and searched for victims.

Figure 2. Rubble Pile.

The rubble pile is a fully collapsed structure with

subterranean voids. There are some entrances supported

loosely by concrete barriers. There are confined dimensions

and problematic rubble that will hamper searching.

Figure 3. The Passenger Train.

The passenger train was hit by the industrial hazmat tanker

cars. The sleeper car is evaluated and has curtained alcoves

on each side of a narrow aisle that should be searched. The

crew car is lying on its side and also needs to be search. The

mailroom in this car needs to be searched but is too small for

a responder in a level A suit to enter.

Figure 4. The House of Pancakes viewed from inside.

The house of pancakes is a partially collapsed building with

the roof almost in contact with the ground on the only

accessible side. Robots must enter through the confined space

under the metal roof or through a breach. There is a maze of

obstacles and debris which will hamper search.

III. ROBOTS

We used both air and ground robots in the initial scenarios.

However, because of safety concerns, the grounds had to be

cleared when using the aerial vehicles so they were not

incorporated into the final mock incident responses. A

number of diverse ground robots were used. These included

robots with manipulators, extreme mobility robots, and robots

128
that could be thrown or otherwise launched into an area the

responders needed to investigate. Some robots had wheels

while others had treads. Some robots had the ability to

change shape (See figures 5 a and b). Figures 6a-6e show the

diversity of ground robots. The robots used in the scenarios

were all teleoperated. One constraint in selecting robots for

various scenarios was that the bandwidth they operated on

had to be compatible. Of course, this was in addition to the

physical constraints imposed by the scenario.

Figure 5a. Shape-shifting robot in lower configuration.

Figure 5b. Shape-shifting robot in raised configuration.

Figure 6a. A robot which navigates using tracks.

Figure 6b. A wedge-shaped track robot with manipulator.

Figure 6c. A small "throwable" robot.

129
Figure 6d. A robot with articulators.

Figure 6e. A wheeled robot with articulators.

IV. DATA COLLECTION AND ANALYSIS

For each scenario NIST personnel took video data and made

observations. In addition, we collected questionnaires from

the responders concerning the representativeness of the

scenario and the team performance. Figure 7 shows the

questionnaire used. Responders were asked to rate each

question on a scale of 1 to 7, where 1 was the low end of the

scale and 7 was the high end. In general responders gave

different ratings to different robots (if there were multiple

ones involved in the scenario) for questions 4 and 5.

Figure 7. Questionnaire used to assess the different venues during the first three

days

These questionnaires were collected from each member of a

responder team during the first three blocks of the exercise.

Table 1 shows the results from these questionnaires

Pa

s

s

e

n

ger Train

R

u

b

ble

Pile

Strip

Mall

Train

D

wellin

g

R

u

b

ble

Wo

o

d

Pile

Pa

n

c

a

ke

s

Representative

Scenario 5.38 5.69 5 6 5.5 5.5 5.86

Representative

Operations 5.29 6 4.58 5.38 5 5 6

Team

Performance 4.67 4.4 5.14 5.25 4.75 4.67 5.17

Bot Capabilities 4.13 3.19 5 5.5 4 5 4.5

Scenario Utility 3.29 3.5 4.86 5.75 4 5 5

Time Required 3.86 4.14 4.43 5.75 4.75 5.5

Robot/Responder

Performance 4 3.93 5.29 4.1 3.75 5 4.92

Operator Interface 3.67 4.67 4.57 5.75 5.67 5 5

The scenarios and the operations performed were rated as 5

or over with the exception of the strip mall. That venue was

not used in our final portion of the exercise. The Hazmat

train was not used as well as that required the use of an aerial

vehicle. The four venues selected for use in the final portion

of the exercise were the passenger train, the rubble pile, the

dwelling and the house of pancakes. All of these were highly

rated as representative of situations responders would

encounter.

The robot/ responder performance and the operator interfaces

for the robots were not as highly rated. There are several

reasons for this. First of all, in many cases, the operator

interface needs to be improved. One goal of this analysis is to

examine the operator interface, not just for usability, but in

the concept of operations. The performance of the robots and

responders is also due to differences in expectations of

responders and the actual capabilities of the robots. Again,

using the robots and developing concepts of operations based

on a better understanding of capabilities is essential to

improving the robot/responder team performance.

V. CONCEPTS OF OPERATIONS

The most interesting data came from observations of

emerging concepts of operations from the various venues.

We describe these four scenarios in the following paragraphs.

A. Single Family Dwelling

The responder team used three robots primarily in this

situation. They used a large robot with a manipulator arm,

which we designate as robot A for this document, a smaller

1. How representative was the scenario of a possible US&R event?

2. Concept of operations used in scenario?

3. Assessment of responder team performance

4. Capabilities of robot

5. Utility of robot in scenario

6. Length of time needed to accomplish the scenario

7. Overall performance of scenario (responders and robot)

130
shape changing robot, which we designate as robot B, and for

a portion of the time they employed a small throwable robot

which we designate as robot C. The responders were setup in

a tented area with power supplied by generators in front of

the single family dwelling as there were no Hazmat concerns.

In addition to the three robots, a search dog was also used.

The robots were operated by the robot developers under the

direction of the responders.

The team leader had the operator of the large robot drive the

robot around the building. He watched the video and

constructed a map of the exterior of the dwelling based on

this information (Figure 8). This also allowed him to

determine the entrances to the dwelling. After the exterior

had been traversed, the team leader sent the larger robot into

the dwelling through the partially collapsed entrance. The

smaller, shape changing robot was sent into the basement of

the dwelling using the stairs. The two operators were sitting

close to each other under the tented area with the team leader

watching the video from both. He used this to map out the

inside area and to determine that the area was safe enough to

send in a dog. A possible victim was identified by the smaller

robot in the basement. A dog was sent in to verify this.

There was an issue when robot A was unable to get into a

suspected space in the upper floor of the building. According

to the map the responder constructed there was an additional

space that had not yet been searched. However, there were

obstacles (collapsed walls and debris) that prevented the robot

from entering this space. Both robot A and B were moved

out of the dwelling and the larger robot, robot A, used the

manipulator arm to grip the smaller robot, robot B, and move

it into the building, assisted by members of the response

team. Once it had moved back into the area, the operator was

able to place robot B on top of the collapsed wall which

allowed the robot B to penetrate farther into the building. In

this operation, the two operators moved close together and

used cameras from both robots to do the placement.

Several other cooperative efforts were seen. In one instance,

robot A dropped robot C through a hole in the main floor.

The operator of robot C used both his camera and the camera

of the robot A to maneuver through the basement area.

B. Rubble Pile

During the rubble pile scenario a responder operating a larger

robot, which we designate as robot D, worked in conjunction

with the rescue dog handlers. Using robot D, the responder

circumnavigated the rubble pile, accessing possible entry

points. The responder identified the existence of a victim

using the microphone on the robot. The robot was also

equipped with a speaker so the responder and the victim

could communicate. This communication enabled the

responder to narrow the search area by asking the victim if

they could “see the robot”. When the victim responded that

the robot was in view, the dog handler then sent in the rescue

dog to pinpoint the victim’s location. Figure 9 shows what the

rubble pile looked like.

Figure 8. The map created by the responder.

Figure 9. Responders searching the rubble pile

C. Passenger Train Wreck

Two robots were used in this scenario. Each robot was run by

an operator under the direction of a First Responder. The

responder asked the robot operators to clear the train and look

for any signs of life on the slanted wrecked train. The first

robot, which we designate as robot E, started at the entrance

on the ground and began to clear the train looking for

survivors / victims. The other robot, which we designate as

robot F, started at the back of an upended train car and

worked its way toward the front. A responder dropped robot

F in a side window and stayed there to do tether management.

The operators were located outside of separate sections of the

trains and communicated over the hand-held radios to each

other. Figures 10 and 11 show two setups at the passenger

train.

131
Robot F eventually got a piece of cloth wrapped around a

tread and was stuck. The operators decided to use robot E’s

arm/claw to grab and try to remove the cloth from the robot

F's tread. Robot E’s operator managed to grab the cloth with

its manipulator but was unable to free the cloth and instead,

dragged robot F a short distance. A second strategy was

developed in which robot E stayed stationary and operator for

robot F attempted to drive away from robot E to free the cloth.

This strategy was successful.

Figure 10. Responders find a place to setup the OCU to search the passenger

train

Figure 11. Another group of responders setting up to search the passenger train

In the second part of this scenario, the team was searching a

train car that was lying on its side. The same two robots were

used, again being driven by their operators under supervision

of the First Responder. The responder asked to have them

clear the train from opposite ends. This time the operators

were set up next to one another. The robots eventually met

up with each other in the center of the dark train and used

each other's lighting to help see a larger area than they would

have been able to see by themselves.

There was another interesting operator event at the trains.

The operator for robot E was quite tired after concentrating so

heavily and another operator offered to replace him. While

turning over control, the original operator gave a verbal

description of where he thought the robot was currently

positioned in the train and drew an imaginary path on the

operator control unit (OCU) using his finger to describe the

center hall layout.

D. House of Pancakes

The House of Pancakes scenario focused heavily on three

robots, which we designate as robots G, H, I, in conjunction

with a rescue dog handler. In the scenario, the House of

Pancakes was meant to represent a recently collapsed

building. The scenario started with the responders teleoperating robot H around the outside of the house to look for

the presence of survivors and to determine the best opening to

enter the house. An open doorway was found and robot H was

navigated through that doorway. Robot H was assumed to

have biohazard sensors on it that could detect hazardous

gases in the environment. Once robot H traversed all

accessible areas of the house, robot H (conceptually)

responded that the environment was safe, the rescue dogs

entered the site to smell for survivors. In the scenario, there

was one survivor near the back of the house which the dog

quickly detected.

In parallel with this, robot G, with robot J in its grippers, was

tele-operated to drive up on the collapsed roof of the house.

Figure 12 shows robot G carrying robot J. The purpose of

this part of the scenario was to have robot G drop robot J into

a breach near the uppermost portion of the roof to allow it to

look around the remaining upper stories of the building to see

if any survivors could be detected.

A small piece of plywood (about 1 m by 1 m) was placed near

the bottom of the collapsed roof to allow robot G to drive up

onto the roof. Once robot G drove up on the plywood and

reached the uppermost portion of the roof, the operator

aligned robot G with the breach and extended its manipulator

to be directly over the breach. The pincher in the manipulator

was then released and robot J was dropped into the breach.

For this scenario, robot J was not functional (it broke earlier

in the week), so the scenario ended here. If robot J was

functional, it would have been used to navigate around the

upper stories of the building to find survivors.

Figure 12. Robot G with robot J in its gripper.

132
VI. IMPLICATIONS FOR DESIGN

Based on the emerging concept of operations we can

determine some priorities for design – and likewise we can

also determine some items that are not as likely to affect

design.

In the single family dwelling we did not find a need for

operators to be on the move. Moreover, since there was no

Hazmat danger, there was not a need for operators to wear

protective gear. However, in the train scenario we found that

the operators worked outside, sitting on the ground.

Therefore, things such as lighting conditions played a big

part in being able to see the OCU. Moreover, being able to

comfortably set up operations in less than ideal conditions has

to be considered when designing the OCU hardware.

The team lead was busy trying to update a map sketched on

his field notebook with information given him by the two

operators of the robots. A shared electronic notebook might

be a good addition when working with teams of robots.

Assume that the team lead could sketch in the initial external

map as the perimeter is being mapped out. If this were done

on a tablet PC, for example, and then used as a shared file

both robot operators could add information to it as they

searched the building. The team lead could have access to

this on the tablet PC and could add information and

annotations as well. The notion of maps surfaces again in the

train scenario when operators change shifts. Having explicit

information for the incoming operator to understand where

the robot is and what has already been searched is valuable.

The use of videos from two robots when doing a cooperative

task was accomplished by having the operators sit close and

leaning over to see the other’s OCU. While there is a need

to have hardened cases for the OCU, it might be feasible to

have hardened display units that could be attached to several

OCUs if it is feasible that robots might cooperate. Then the

video from one robot could be broadcast to several additional

display units. For example, when one operator is picking up

or setting down a smaller robot, both operators need to have a

good view of what is happening so that the smaller robot can

be correctly placed and can move as necessary to enter a void

or start up a steep slope. In these scenarios responders

positioned the smaller robot in the grippers of the larger robot

outside of the buildings. This might not always be the case so

it is essential to provide good video to the operators to

position both robots to ensure that the smaller robot is not

damaged during this operation. Releasing the smaller robot

was a delicate operation in many cases. In the case of the

robot J, this was not an issue. But in the single family

dwelling, for example, it was necessary to place the smaller

robot on a rather steep incline. Therefore the smaller robot

had to be position so that it could immediately start moving

up the incline rather than sliding backwards. This

necessitated ensuring the camera view was on the smaller

robot while releasing the grippers. The operators had to be

closely coordinated to carryout their actions (releasing and

starting to move the smaller robot) at the same time.

The team also made use of sharing resources of the robots. In

the train scenario using two lights (one on each robot), rather

than just a single light helped to speed the search of the train.

Communications need to be provided. In the scenarios we

saw communications between robot operators, between robot

operators and victims, and between responders and the robot

operators. Teams communicated to share robots. Granted

that this was due to limitations of the number of robots

available but we assume that this will most likely be the case

in the future. This would allow teams to know what robots

are available should they find a need for a particular

capability. In the House of Pancakes and in the Single

Family dwelling we saw responders use two robots in parallel.

There is a need for communications between the responders

in these two efforts. As the goal is to quickly locate victims

and to determine how much of the site has been covered, a

way to fuse information coming back from both efforts should

be provided.

We did not simulate a command and control center in this

exercise. This would add another level of communications.

Some questions would be whether the raw data such as video

footage or sensor data would be available directly to the

command and control center on demand. Would it be

sufficient to have a dynamically updated map showing where

teams are working and where the robotic resources are?

Assuming that multiple robots are being used in a scenario,

what type of fusion of information should be done and

transmitted to command and control? Would it be sufficient

to know which robots were currently in use?

A number of awareness issues should also be considered [4].

Knowing which teams are using which robots at any point in

time is essential both for command and control and for the

responder teams. Responder teams might want to know if the

robots being used are “on task”, that is actually searching or

if there is some sort of robot help situation in progress.

VII. DESIGN IMPLICATIONS FROM THE LITERATURE

Murphy and her team have done much work on field studies

that can be added to this analysis. For example, Burke et al.

found that a good percentage of operators’ time in US&R

missions was consumed with gathering information about the

state of the robot and that state of the environment [2]. This

time was significantly greater than the time they spent

navigating. They also found that operators had difficulty

incorporating their small view (through the robot camera)

into the overall picture. Displaying dynamically constructed

maps of the overall area and the actual search areas of the

various teams might help with overall situation awareness.

133
Burke and Murphy found similar issues in another field study

when over 50% of the robot operator communications dealt

with situation awareness concerns [1].

Murphy also found that two humans working together are

nine times more likely to find a victim than one operator

alone. This was not directly incorporated into our scenarios

especially when there was more than one robot involved. The

First Responder moved between robots and did look at the

video but there as not a concern attempt to dedicate another

responder to watching the robot video. If there are multiple

robots involved, must a dedicated responder watch the video

sent back from each robot? Or would it be feasible for a

responder to watch video from several robots, assuming it

could be viewed on a single display [5].

Drury et al. formulated a framework for awareness in humanrobot interactions [4]. As noted in this framework there is a

need for human-human awareness, robot-human awareness,

robot-robot awareness, and humans’ overall mission

awareness. As the robots in our field study were tele-operated

we did not see instances of robot-human awareness. The

robot-robot awareness was also mediated by the human

operators due to tele-operation control.

Yanco et al. studied awareness issues in USAR contests [6].

In this environment they were able to identify issues with the

operator control unit, such as having to fuse information from

multiple windows and lacking information about the area

directly around the robot. [3] contains guidelines for

presentation of information to the operator. While the

contests are good tests of individual robot capabilities, there is

no notion of a concept of operations.

VIII. CONCLUSIONS

We have described a multi-day field exercise culminating in

an opportunity for responders to respond to a mock incident.

In doing this, they selected robots appropriate for the venue

and a concept of operations evolved. We observed the mock

incident responses and noted how the robots, robot operators,

and responders interacted. From this we were able to identify

a number of issues that should be considered for human-robot

interaction design. Some of the issues identified apply to

individual robot OCUs. Other issues are concerned with the

fusion of information to provide an overall assessment to the

commanders.

These designs will need to be tested in the laboratory for

effectiveness and usability but testing them in field exercises

is essential to identify design requirements at a higher level.

It is also interesting to compare these evolving concepts of

operation to task analyses to determine if and how the

strategies used by responders change as new technology is

placed in use [7].

As a final note observations here led to discussion with some

of the responders concerning metrics for evaluating the

effectiveness of human-robot teams. Responders are

concerned with how much of the disaster area is covered in

how much time. Robots can contribute to this by coverage a

good portion of this without putting the responders at risk. A

proposed metric to use for judging the effectiveness of teams

of humans and robots would be the amount of coverage/ time

accomplished with only a robot. This addresses both the

effectiveness and efficiency of the team along with the

objective of minimizing the time responders are at risk.

IX. REFERENCES

[1] Burke, J.L, and Murphy, R. R. (2004). Situation

Awareness and Task Performance in Robot-Assisted

Technical Search: Bujold Goes to Bridgeport (No. CRASARTR2004-23). Tampa, FL:Center for Robot-Assisted Search

and Rescue.

[2] Burke, J., L., Murphy, R.R., Coovert, M.D., and Riddle,

D. L. (2004) Moonlight in Miami: A field study of humanrobot interaction in the context of an urban search and rescue

disaster response training exercise. Human-Computer

Interaction, 19 (1-2), 85-116.

[3] Drury,J., L., Hestand, D., Yanco, H.A., and Scholtz, J.

(2004). Design Guidelines for Improved Human Robot

Interaction, CHI 2004 Poster

[4] Drury, J.L, Scholtz, J.C and Yanco, H. A.(2003).

Awareness in Human-Robot Interactions. In Proceedings of

the IEEE Conference on Systems, Man and Cybernetics,

Washington, DC, October 2003.

[5] Murphy, R.R. & Burke, J.L. Up from the rubble: Lessons

learned about human-robot interaction from search and

rescue. Proceedings of the 49th Annual Meeting of the

Human Factors and Ergonomics Society, Orlando, FL,

September 2005

[6] Yanco, H.A. , Drury, J.L., and Scholtz, J.C. (2004)

Beyond Usability Evaluation: Analysis of Human-Robot

Interaction at a Major Robotics Competition. HumanComputer Interaction, 2004

[7] Burke, J.L. RSVP: An investigation of the effects of

remote shared visual presence on team process and

performance in urban search & rescue teams Dept. of

Psychology, University of South Florida, Tampa, FL, 2006,

140.

134
Multimodal Displays to Enhance Human 

Robot Interaction On-the-Move

Ellen Haas, Ph.D. 

U.S. Army Research 

Laboratory 

APG, Maryland,USA 

ehaas@arl.army.mil

 

 

 

 

Chris Stachowiak 

U.S. Army Research 

Laboratory 

APG, Maryland, USA 

cstachow@arl.army.mil

Abstract— The U.S. Army is exploring the use of advanced 

technologies such as tactile and spatial (3-D) audio displays to 

enhance Soldier performance in human-robot interaction (HRI) tasks. 

A field study was conducted at the U.S. Army Research Laboratory 

(ARL) in 2006 to determine the extent to which the integration of 

spatial auditory and tactile displays affects soldier situation 

awareness in a simulated UV HRI target search task performed in a 

moving HMMWV. Participants were 12 civilian males ranging in 

age from 18 to 46 years, with a mean age of 32 years. Participants 

performed a target search task, in which they searched for one target 

symbol among 50 non-target symbols displayed on an 18-inch 

diagonal computer monitor (a 30° field of view (FOV) visual 

display). Participants received audio and tactile cues to indicate 

on which third of a computer screen the target symbol was located. 

The independent variables were display modality, signal azimuth, 

participant age, and HMMWV movement condition. Display 

modalities were visual displays with supplemental cues in three 

display modalities; spatial audio, tactile, and combined spatial audio 

+ tactile. The dependent variables were participant response time and 

accuracy, as well as the participant’s subjective workload rating of 

display modality effectiveness. Accuracy data indicated that 

participants located over 99% of the targets correctly. Display 

modality was significant in terms of participant workload ratings, but 

was not significant for response time. Response time data indicated 

that no one display modality provided the shortest response 

time to all age groups, for all terrains. Workload with auditory + 

tactile displays was rated lowest of the three display modalities, 

which may have been because the combination audio + tactile 

display incorporated cues from both the audio and tactile modalities, 

an advantage in an environment with strong auditory and tactile 

distractors. The discrepancy between the workload and the 

performance data indicate that a greater understanding is needed of 

the role of each modality in on-the-move operations. Future 

research will deal with multimodal directional cues that can inform 

Soldiers of important HRI events 360° around of their field of view. 

Keywords: audio,tactile,multimodal,displays 

I. INTRODUCTION

 The U.S. Army is exploring the use of advanced 

technologies such as tactile and spatial (3-D) audio displays to 

enhance Soldier performance in human-robot interaction 

(HRI) tasks, including monitoring and/or supervisory control 

of one or more autonomous or semi-autonomous unmanned 

vehicles (UVs). Particularly important Army HRI tasks 

include using a UV operator control unit visual display to 

search for important objects such as targets, and maintaining 

spatial situation awareness in their environments and around 

the UV(s). The Soldier has traditionally relied on the visual 

modality for UV monitoring and supervisory control, but the 

battlefield provides many conditions that challenge the visual 

modality, increase operator workload, and hinder situation 

awareness. Visually challenging conditions include weather, 

darkness, dust, and noise. Operator workload can be 

amplified or challenged by cognitively demanding tasks such 

as individual control of one or more robots by direct control 

or teleoperation, robot sensor control and interpretation, and 

air or ground space management. Soldiers responsible for 

managing UVs may also encounter difficulty when they must 

maintain their situation awareness of the battlefield 

environment, of friendly and enemy battlefield entities, and 

manage robot situation awareness at the same time. 

 Battlefield challenges may also arise from new demands 

for Soldier mobility; some Army system concepts propose that 

robot control operations take place in highly mobile vehicles 

such as High-Mobility Multipurpose Wheeled Vehicles 

(HMMWV) in order to enhance robotic command and control 

function and survivability [1]. In mobile environments, 

vehicle vibration and jolt may tax Soldier visual performance 

[2] and visual search [3], making cues in other modalities 

valuable. 

 Researchers have shown that spatial audio and tactile cues 

can be useful by themselves or in combination (as multimodal 

displays) to supplement visual displays, reduce HRI task 

difficulty [4] and create a greater sense of operator immersion 

in robotic tasks [5] over conditions with visual displays alone. 

Supplementary audio, tactile, or combined audio and tactile 

cueing have been shown to provide shorter response time than 

visual cueing alone [6,7]. The purpose of this paper is to 

describe advanced display technologies that might be useful 

supplementing visual displays, in highly mobile environments, 

and to describe a field-study which explored the extent to 

which vehicle (HMMWV) operations affect user performance 

with audio and tactile displays. Spatial audio displays, 

tactile displays, and combination spatial audio + tactile 

displays are described below. 

135
A. Spatial Audio Displays 

 With spatial audio displays, also known as 3D audio, a 

listener using earphones perceives spatialized sounds that 

appear to originate at different azimuths, elevations, and 

distances from locations outside the head. Because each 

sound is presented in different spatial locations that are 

meaningful to the listener, the sounds can provide tracking 

information regarding object position, velocity, and trajectory 

beyond the field of view [8,9,10]. 

 Spatial audio displays can communicate events, using 

sound coming from a number of directional sound sources. 

For example, radio communications from a commander can 

sound like they originate from the Soldier's front, a hazardous 

agent warning signal may come from the Soldier's right, and a 

signal indicating the position of a remote robot may be heard 

from the general direction and elevation of that robot. Or, if 

microphone arrays are installed on the robot, the Soldier can 

hear the ambient sound transmitted near the robot, which 

permits the Soldier to hear the environment local to the robot. 

For a robotic unmanned ground vehicle (UGV), the 

transmitted sound can convey information such as the motor 

speed, and the type of surface the wheels or tracks are in 

contact with. Research has shown that spatial audio cues 

are useful in human-robot interface target search tasks. 

Spatial audio displays have been shown to increase user 

situational awareness in target search of unmanned aerial 

vehicle (UAV) displays [11] and with target search tasks 

using narrow field-of-view visual displays the size of a 

computer monitor [6]. Because they provide positional cues, 

spatial auditory display cues can also enhance 360-degree 

situation awareness in applications without a visual display 

[8]. 

B. Tactile Displays 

 Tactile displays use pressure or vibration stimulators, also 

known as tactors, which interact with the skin [12]. One 

common example is the vibration function on cell phones. 

Tactors can be worn individually, or in groups on the user’s 

skin, usually on the hand (in gloves), on the arm, leg, or 

abdomen (in belts), or on the torso (in a vest). One or more 

tactors can be used to convey information such as warnings or 

alerts, by vibrating in patterns consisting of different rhythms 

or frequencies. A group of tactors positioned in an array can 

be used to signal directional flow or movement (i.e., by 

simulating movement to the left or right), which can be useful 

for applications such as navigational displays. Research has 

shown that tactile displays have been used to successfully 

provide safety warning information, and communicate 

information regarding orientation and direction as well as user 

position and velocity. Calhoun, Fontejon, Draper, Ruff, and 

Guilfoos [13] found that tactile displays can significantly 

improve detection of errors in UAV teleoperation control tasks 

and can serve as an effective cueing mechanism. 

C. Auditory and Tactile Displays 

 Researchers have explored the use of audio and tactile 

cues separately and in simultaneous combination as HRI 

displays for teleoperation as well as for other applications. 

Gunn, Nelson, Bolia, Warm, Schumsky, and Corcoran [14], 

and Gunn, Warm, Nelson, Bolia, Schumsky, and Corcoran 

[15] used multimodal displays to communicate threats in a 

UAV target acquisition visual search task. They found that 

spatial (3D) audio and tactile cues used separately enhanced 

target acquisition performance over no cueing. Chou, 

Wusheng, Wang and Tianmiao [16] designed a multimodal 

interface for internet-based teleoperation in which live video 

images, audio, and tactile force feedback information were 

combined and presented simultaneously. They found that 

presenting simultaneous multimodal information reduced 

operator mental workload relative to no feedback at all. 

D. Research Focus 

 A field study was conducted at the U.S. Army Research 

Laboratory (ARL) in 2006 to determine the extent to which 

the integration of spatial auditory and tactile displays affects 

soldier situation awareness in a simulated UV HRI target 

search task performed in a moving HMMWV. The objective 

of the study was to determine whether tactile and 3D audio 

technologies could effectively convey information in moving 

vehicle environments that contain relatively high levels of 

vibration and jolt. A second objective was to examine the 

extent to which vehicle (HMMWV) operations affect user 

performance with multimodal cues. Although vehicle 

vibration has been shown to degrade visual performance, it 

may also degrade user perception of tactile cues due to the 

user’s need to tense the torso muscles to steady themselves on 

rough (i.e., cross-country) terrain. It is possible that vehicle 

noise peaks during travel might mask even well-designed 

spatial auditory signals. Research was needed to explore to 

what extent HMMWV vibration and noise affects the 

integration of audio and tactile cues used to provide 

information in localization and visual search in HRI target 

search tasks. This is an important U.S. Army issue because 

Army system concepts propose that Tactical Operation 

Centers be emplaced in highly mobile vehicles in order to 

enhance robotic command and control function and 

survivability [1]. 

II. METHOD 

A. Participants 

 Participants were 12 civilian males. All had a hearing 

level (HL, the decibel level over threshold at which they can 

hear a test stimulus) as determined by an 

audiometer-administered hearing test, corresponding to the 

Army hearing profile H2 or better; an average of no more than 

30 dB HL, no individual level greater than 35 HL at 50, 1000, 

136
and 2000 Hz, and no level greater than 55 HL at 4000 Hz [17]. 

Participants also had at least 20:40 vision as determined by a 

Snellen vision test. Participants ranged in age from 18 to 46 

years, with a mean age of 32 years. Participants performed a 

target search task, in which they searched for one target 

symbol among 50 non-target symbols displayed on an 18-inch 

diagonal computer monitor (a 30° field of view (FOV) visual 

display). 

B. Apparatus 

 Participants received audio and tactile cues as 

supplements to visual cues, to indicate on which third of the 

screen the target symbol was located. Audio cues were 

pre-recorded spatial audio sound files, consisting of the words, 

“target, target” spoken by a female voice. Tactors were eight 

tactile sensors developed by Dr. Lynette Jones at the 

Massachusetts Institute of Technology (MIT) under the ARL 

Advanced Decision Architecture Collaborative Technology 

Alliance (CTA) [18]. The tactors were incorporated into a 

canvas belt that the participant wore on his torso. 

C. Variables 

 The independent variables used were display modality, 

signal azimuth, and HMMWV movement condition. 

Participant age was also used as an independent variable, 

because age had been found to affect response time to display 

cues in previous experiments. Signal azimuth was defined as 

the azimuth location of the target symbol on the display, 

which ranged from -15° to +15°, with 0° being the center of 

the screen, encompassing the entire 30° field of view of the 

computer monitor. Display modalities were visual displays 

with three different modalities of supplementary cues: spatial 

audio, tactile, and spatial audio + tactile. All display cues were 

presented 0°, -90°, and +90° (straight ahead, at the subject’s 

left, and at the subject’s right, respectively). The three 

HMMWV movement conditions were vehicle at a stop with 

engine idling, vehicle traveling over gravel road at 

approximately 12 mph, and vehicle traveling over 

cross-country terrain at approximately 12 mph. 

 The dependent variables were participant response time 

and response accuracy. The participant’s subjective 

workload rating of display modality was also obtained; 

participants were asked to rate the workload for each display 

modality on a one-to-ten scale, with one representing very low 

workload, and ten representing very high workload. All 

measures were obtained after the participant traveled over 

each terrain. 

D. Procedure 

 For the target search task, the computer monitor initially 

showed a visual display consisting of a topographic map with 

a red box in the center. After 1.5 seconds the red box 

disappeared and the computer screen showed the topographic 

map along with 50 U.S. Army map symbols and one target 

symbol, with a vertical green cursor now located at the center 

of the screen. At the same instant, the participant 

experienced the target cue condition that described the 

location of the visual target. The participant was instructed 

to use the alert as a guide to visually locate the target symbol. 

When the participant thought that he knew where the target 

symbol was located, he used the knob controller to move the 

green vertical cursor line on the computer screen as quickly as 

possible to the location of the symbol he thought was the 

target. When the participant moved the line on top of the 

map symbol that he had chosen, he pushed down the knob to 

indicate that he had located the target. 

 When the knob had been depressed, the symbology 

disappeared and the screen with the red box reappeared to 

refocus the participant’s gaze upon the center of the screen. 

Then, 1.5 seconds later, the red box disappeared and the next 

trial began. The green line was relocated at the center of the 

screen at the beginning of each trial. 

 For the experimental trials in each movement condition, 

the participant performed 18 experimental trials (6 trials with 

each of the 3 display modalities). The 18 targets in the display 

search task appeared at the different azimuth angle locations 

between + 15° azimuth, including 0, without repetition, for 

each display modality. Six targets appeared on the left side 

of the screen, between -15° and -6° azimuth. Six targets 

appeared in the center of the screen, between -5° and +5° 

azimuth. Six targets appeared on the right side of the screen, 

between +6° and +15° azimuth. The location of the target 

symbol and the order of target symbol appearance were 

random, without replacement. The 18 targets also appeared 

at random vertical locations within 26°elevation so that half 

the targets appeared between 0° and 13° elevation, and half 

appeared between -13° and 0° elevation. At the end of the 

final display search trial, the movement condition ended. 

Each movement condition lasted from 20 to 30 minutes. 

 After each movement condition, the participant provided 

an on-the-spot single workload rating estimate [19,20] in 

which they verbally assigned a number between 1 and 10 to 

describe the workload associated with each display, with 1 

being very low workload and 10 being very high workload. 

III. RESULTS 

A. Response Time and Accuracy 

 Accuracy data indicated that participants located over 99% 

of the targets correctly (there were no differences as a 

function of any condition or signal modality), indicating that 

response accuracy was not a function of display modality or 

condition. A multivariate analysis of variance (MANOVA) 

for the response time data, which used Wilk’s criterion U as 

the test statistic for within-subjects data and F for 

between-subjects data. Effects which were shown to be 

significant were explored through the use of selected LSD 

post-hoc tests. The results indicated that main effects of 

movement condition (U = .192, p = 0.001) and age (F = 

3186.661, p = 0.001) were significant, as were the two-way 

movement condition x display modality interaction (U = 

0.257, p = 0.05), and the three-way movement condition x age 

137
x display modality interaction (U = 0.084, p = 0.001). There 

were no other significant main effects or interactions. 

Figures 1 through 3 shows the three-way interaction, by 

showing the movement condition x display modality 

interaction for each of the three age groups. 

 Post-hoc tests indicated that for all display modalities, 

participants in their 20s generally had shorter response times 

than participants in their 40s. As can be seen in Figures 1 

through 3, no one display modality provided the shortest 

response time to all age groups, for all terrains. During 

engine idle, the only display modality that provided shorter 

response times within one age group was the tactile display, 

which generated a significantly shorter mean response time 

for participants in their 30s. On gravel terrain, the only 

display modality that provided significantly shorter response 

times within one age group was the audio + tactile display, for 

participants in their 40s. 

 Travel over cross-country terrain provided several 

differences between display modalities, within and across 

each age group, with no one display modality standing out as 

providing the greatest benefit. Participants in their 20s 

showed the shortest response times with audio, and audio + 

tactile displays. Participants in their 30s had significantly 

shorter response times with audio + tactile displays. 

However, participants in their 40s showed significantly 

shorter response times with audio and tactile displays. As 

can be seen, cross-country terrain provided a great deal of 

variability between age groups and display modalities. This 

may be due to the small number of participants in their 30s 

and 40s; there were twice as many participants in their 20s (6 

participants) as there were in their 30s and 40s (3 participants 

in each age group). The relatively small number of older 

subjects may have been one source of variability on the 

demanding cross-country movement condition. Future 

studies should include larger numbers of participants in their 

30s and 40s to duplicate U.S. Army demographics, especially 

since the maximum recruit enlistment age rose in 2007, from 

34 years to 39 years for the U.S. Army National Guard and 

the Reserve [21]. The significant three-way display 

modality x age x movement condition interaction precludes 

the interpretation of the display modality x movement 

interaction as well as the main effects of age and movement 

condition. 

Participants in Their 20s

0

1

2

3

4

5

6

7

Idle Gravel Xctry

Movement Condition

Mean Response Time 

(sec)

Audio

Tactile

Both

. 

Figure 1. Mean Response Time for Movement Condition x 

Display Modality for Participants in their 20s 

Participants in Their 30s

0

1

2

3

4

5

6

7

Idle Gravel Xctry

Movement Condition

Mean Response Time 

(sec)

Audio

Tactile

Both

Figure 2 Mean Response Movement Condition x Display 

Modality for Participants in their 30s 

Participants in Their 40s

0

1

2

3

4

5

6

7

Idle Gravel Xctry

Movement Condition

Mean Response Time 

(sec)

Audio

Tactile

Both

Figure 3. Mean response time for Movement Condition x 

Display Modality for Participants in their 40s 

138
B. Workload 

 An analysis of variance (ANOVA) indicated that for 

workload data, main effects of display modality and 

movement condition were significant (p < 0.05). There were 

no other significant main effects or interactions. A least 

significant differences post-hoc test was performed for 

significant effects. 

 Figure 4 contains mean workload ratings for the different 

display modalities. Post-hoc testing indicated that 

participants rated combination tactile + audio displays as 

having a significantly lower workload than audio and the 

tactile displays used separately. There were no other 

significant differences. One reason for the significantly 

lower auditory + tactile workload rating may have been that 

the combination audio + tactile display incorporated cues 

from both audio and tactile modalities, allowing one display 

modality to provide cues because the combination is more 

powerful in an environment with strong auditory and tactile 

distractors. 

 Figure 5 contains mean workload ratings for the different 

movement conditions. Least significant difference post-hoc 

testing indicated that participants’ workload ratings were 

significantly greater after the cross-country condition, than for 

the engine idle and gravel conditions. There were no other 

significant differences. The workload ratings mirror the 

response time data; cross-country terrain had higher levels of 

noise and vibration, which had an impact on participant 

workload ratings. 

3.2

3.4

3.6

3.8

4

4.2

4.4

Audio Tactile Both

Display Type

Mean Workload Rating

Figure 4. Mean Workload Ratings for Display Modalities 

0

1

2

3

4

5

6

Engine Idle Gravel Cross

Country

Movement Condition

Mean Workload Rating

Figure 5. Mean Workload Ratings for Movement 

Conditions. 

IV.CONCLUSION

 Alone or together, advanced technologies such as tactile 

and spatial (3-D) audio displays can enhance user 

performance in HRI target search tasks. Previous research 

had shown that supplementary audio, tactile, or combined 

audio and tactile display modalities have been shown to 

provide shorter response time than visual cueing alone [7]. 

The results of the current study indicated that for target 

tracking response time and accuracy on a narrow 

field-of-view visual display, the audio, tactile, and audio + 

tactile displays performed equally well as supplements to a 

visual display on most terrains. 

 Data also indicated that performance time and accuracy 

with the tactile display were not limited by movement or 

vibration on the gravel or cross-country terrain. Results 

show that for the target search task, tactor output was not 

masked by participant contact with the seat back during 

vehicle bumps on the gravel or cross-country terrain. 

 The results indicated that display modality was significant 

in terms of participant workload ratings (perceived workload), 

but was not significant for response time. One reason for the 

significantly lower auditory + tactile workload rating may 

have been that the combination audio + tactile display 

incorporated cues from both the audio and tactile modalities, 

an advantage in an environment with strong auditory and 

tactile distractors. However, user perception was not 

reflected in actual user performance. 

 The discrepancy between the workload and the 

performance data indicate that a greater understanding of the 

role of each modality in on-the-move operations is needed. 

The audio display modality may have been limited by the lack 

of headtracker and individualized spatial audio algorithms, 

which may have made them a bit more difficult to localize 

(determine from which direction the cue originated), and thus 

deliver a perception of greater workload. Future localization 

display research is important and relevant not just to target 

139
tracking, but because directional cues can inform Soldiers of 

important HRI events 360° around of their field of view. 

ARL research in the next fiscal year will explore the 

integration of audio with tactile cues in HRI displays, to 

communicate multiple levels of information. 

ACKNOWLEDGEMENT

The authors would like to thank Dave Ostrowski, James 

Gombash, Krishna Pillalamarri, and Theodoric Feng for their 

assistance in the conduct of this study. 

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140
Autonomy (What’s it Good for?)

J. P. Gunderson 

Gamma Two, Inc. 

1733 York Street 

Denver, CO, USA 

jgunders@gamma-two.com

L. F. Gunderson 

Gamma Two, Inc. 

1733 York Street 

Denver, CO, USA 

lgunders@gamma-two.com 

Abstract— This paper focuses on the impacts of mission 

requirements and environmental conditions on the decision to 

incorporate autonomous components into a cognitive system. The 

costs of autonomy are discussed, along with the benefits, and the 

interrelationships between the environmental context and task 

requirements are explored with respect to the application of 

autonomous systems. We address two fundamental questions: 

“Given the environment and the design goals of the intelligent 

system, can autonomy be enabled? Second, if it can be, should it 

be?” 

Keywords: artificial intelligence, autonomy, machine cognition, 

reification 

I. INTRODUCTION

The decision to include autonomous capability in any 

deployed system is complex. It extends beyond the purely 

technical issues into economic, social, and safety concerns. 

While there can be clear benefits to deploying an autonomous 

cognitive system, there are also costs in many potential areas. 

There are costs associated with the design, development, and 

testing of an autonomous system; and there are additional 

costs associated with the loss of control (either actual or 

perceived) that occur with the granting of autonomous 

behavior. This paper presents some of these costs, and 

discusses the cost benefit analysis required by the decision to 

incorporate autonomy into any system. In specific, we look at 

the environmental drivers that push towards autonomy, the 

capabilities needed to support autonomous behavior, and the 

effects of the interaction of intelligence and autonomy on the 

ability of a deployed intelligent system to achieve mission 

goals. 

Before discussing the relative merits of adding autonomy to 

an intelligent system, it would be good to define what these 

characteristics are. Recent papers have focused on the 

differences between intelligence, ability, and autonomy (see 

[3] for an analysis). 

A. Intelligence 

Defining intelligence, even in the restricted domain of 

intelligent systems, can be problematic. We use a simple 

working definition, derived, in part, from work by Albus and 

Meystel[5]. Albus and Meystel define intelligence (after 

expanding a few subordinate terms) in the following manner: 

Intelligence is the ability of a system to act in a manner that 

increases the probability of successfully achieving the 

system’s goals in an uncertain environment. 

We differ from their definition in only one respect, and that is 

to separate the development of the actions from their 

execution. Our working definition is: 

Intelligence is the ability to formulate 

one or more action sequences which can 

increase the probability of successfully 

achieving the system’s goals in an 

uncertain environment. 

The purpose of the change is to separate the cognitive 

capabilities of the intelligent system from the execution 

capabilities. 

B. Autonomy 

Given that one has an intelligent system, what then is an 

Autonomous Intelligent System? There is a tendency to 

equate ‘autonomous’ with ‘hands-off operation.’ As long as 

there is no human with a joy-stick controlling the system, it 

must be autonomous. However, this would suggest that any 

system that follows a hard-coded routine is autonomous. As 

Luc Steels[13] suggests, such systems may be automatic, but 

they are not autonomous. We believe that autonomy is more 

than simply playing back a script. 

The dictionary definition of autonomy is the ability to 

self-govern. This can be defined as the ability to choose one’s 

own course. This ability entails two conditions, first that there 

exist options, and second that the choice between these 

options is determined by the system itself. This results in the 

following definition: 

Autonomy is the capability of system to 

select between multiple possible action 

sequences to achieve the system’s goals, 

based on the current situation and 

internally defined criteria. 

In a dynamic world, with complex goals, it is normally the 

case that many possible solutions to any problem exist. 

However, it may be that in certain highly constrained domains 

only one course of action will achieve a goal. In this case, 

autonomy may hold vacuously. 

The key feature of this definition is the system’s ability to 

select between multiple courses of action. This is in a 

dynamic tension with intelligence. Intelligence is frequently 

141
related to the concept of finding not only a solution to a 

problem, but finding the best solution. However, autonomy 

suggests that the choice between solutions is not imposed 

from the outside. 

Consider a simple system that plays music. If this system is 

given a hard-coded play-list, where each song is played in a 

specific order, there is clearly no autonomy. Now imagine the 

same system, which is programmed to play the least recently 

played song. Again, there is no autonomy since there is only 

one song that is the least recently played. 

Next imagine that the software plays the least frequently

played song. Now it is likely, that several songs will have 

been played the same number of times, and so the system 

would have to select between a number of possible courses of 

action that would each achieve the system’s goal. Now the 

system begins to exhibit autonomous behavior. 

C. Factors affecting Autonomy 

Using these definitions of intelligence and autonomy, the 

question becomes “Can autonomy add value to an intelligent 

system?” and “Should autonomy be applied in a specific 

case?” The rest of this paper looks at what capabilities must 

be present in the intelligent system to enable autonomy, and 

what characteristics of the domain, the environment, and the 

tasks affect the decision to implement an autonomous 

solution. 

II. BENEFITS OF AUTONOMY

Increasingly, intelligent and cognitive systems are being 

deployed into real world environments to achieve task specific 

goals. The domains into which these systems are deployed 

range from complex financial applications to military robotic 

platforms to deep space probes. In each of these cases, there 

are demands that the system achieve the mission goals 

reliably in complex and uncertain environments. Before we 

can answer the question of what benefits autonomy can 

provide, we must look at the current state of deployed 

systems. 

A. Person in the loop 

Most of these systems are tightly controlled ‘person in the 

loop’ systems. In the case of unmanned vehicles, this 

coupling may require multiple humans to operate a single 

deployed system. In many cases the primary function of the 

human in the loop is to process the operational data into 

information (is that a truck parked under the trees?) and 

deciding what to do about it (ignore that truck for now). 

However, there are limited humans available for these jobs 

and they can quickly become fatigued. As a result, there is 

increasing demand to reduce the tight coupling between the 

human controller and the deployed system. This reduction is 

typically accomplished by off-loading the more routine 

operations onto the deployed system. 

In a teleoperated system, much of the value that the human 

brings to the system is the ability to recognize new and 

unexpected situations and to respond appropriately to these 

situations. In these situations, simply enabling autonomy has 

little value, unless the intelligent system can recognize the 

salient aspects of the environment, and formulate appropriate 

responses. 

When an intelligent system is deployed into in domain 

which is neither completely known, nor completely 

predictable it approaches certainty that the system will 

encounter situations that its designers did not envision. The 

system has no prior knowledge of the situation, or how to 

achieve it goals from this state, so it must depend on its 

human component to first make sense of the situation, and 

then determine the correct actions to apply. 

The second significant capability that the human brings to a 

teleoperated system is the ability to choose between several 

different options. A ground vehicle, tasked with the goal of 

reaching a point on the other side of river, might have several 

options. It could drive south several kilometers to a bridge, it 

could drive north a shorter distance to a ford, or it could 

attempt to cross the river at its current location. This is a 

complex, multi-criteria decision problem which is affected by 

many dynamic conditions. While it could be possible to 

formulate a decision theoretic description of the problem and 

(subject to the underlying assumptions) produce an optimal 

solution, this is the kind of problem for which the human can 

produce a satisficing solution and implement that solution on 

the fly. 

B. Autonomy Can Free These Limited Resources 

Humans bring a powerful arsenal of skills and capabilities to 

a teleoperated system. However, skilled people are in short 

supply, are expensive assets to risk, and become fatigued 

quickly. The demands to replace the human in the loop with 

an intelligent, autonomous system are increasing. However, 

there are significant limitations to our current ability to 

provide viable autonomous solutions to these problems. 

The value of an autonomous, intelligent system lies in its 

ability to achieve its goals without relying on a human agent 

to tell it what to do. To achieve this in a given domain it must 

be capable of providing for itself those functions that the 

human provides in a teleoperated system. As discussed above 

those two major functions appear to be a) the ability to 

interpret the features of the dynamic domain, and b) the 

ability to choose between multiple options and select one that 

will increase the probability of achieving the goals. 

III.NECESSARY CAPABILITIES TO SUPPORT AUTONOMY

A. Decision Making 

As described above, the most obvious capability required by 

an autonomous system is the ability to choose between 

multiple options. This topic area has been part of artificial 

intelligence research since its inception, indeed it might be 

argued that the study of artificial intelligence has been the 

study of how to generate problem solutions and choose 

between them[10][11]. 

142
Autonomous decision making can be extremely simple, 

although it is challenging for a system to make good decisions. 

A common approach is to rank candidate decisions into 

groups of ‘roughly equivalent’ classes, where the ranking 

criteria are derived from the need to increase the probability 

of achieving the system goals. The system now looks at the 

class with the highest probability, and selects at random from 

within that class. This provides the benefits of a quick 

decision heuristic, which an procedure that provides a good 

probability of meeting the system’s goals. However, this 

decision mechanism is dependent on the autonomous system 

having a good representation of the current state of the 

domain, and an ability to interpret those objects and events for 

which it has limited prior knowledge. 

As an example, consider the recent series of DARPA Grand 

Challenges. In these events, autonomous ground vehicles 

were given a goal of completing a course defined by a series 

of waypoints. The vehicles had to handle all of the second by 

second driving decisions, based on models of the world, 

sensor data providing the current situation, and a set of ‘road 

rules’ (for example, the vehicle shall not deviate from the 

course by more than x meters). 

Imagine one such vehicle traveling across the desert, the 

laser range finders providing a view of the obstacles ahead, 

the stereo cameras providing data about the edges of the 

roadway, and either GPS or inertial systems providing 

information about the vehicle’s current location, and the 

locations of the waypoints. Suddenly, the sensors indicate a 

roughly one meter obstacle in the center of the roadway ahead. 

The intelligent system can quickly generate a set of responses: 

1. Pull off the roadway and pass the obstacle on the 

side; 

2. Drive over the obstacle; or 

3. Sit and wait, perhaps the obstacle will go away. 

Pulling off the road increases the risk of failing to achieve 

system goals (either by getting stuck in the sand, or by 

violating the road rules), hitting a rock is equally risky, but 

running over a tumbleweed has little risk. In this situation, the 

ability to make a ‘good’ autonomous decision is clearly 

dependent on having a good representation of the current state 

of the domain. 

From this example, it is clear that the ability to produce a 

representation of the domain is a key capability for autonomy. 

Since any system that requires autonomy is going to run into 

situations for which it has no prior programming, it must be 

capable of generating a representation of this new situation. If 

it cannot form some representation of the situation, it cannot 

predict the future states of the domain. Without this 

representation, it cannot generate the possible actions that 

might be used to achieve the system goals. 

In addition, if it cannot predict the possible results of the 

choices, it would be reduced to selecting between them 

blindly, and thus would have no effective ability to increase 

the probability of achieving its goals. Therefore any system 

deployed into a risky, unpredictable world must have the 

ability to perform a bidirectional mapping between a 

representation of the domain, and the signatures and 

affordances of the real world objects and those representations. 

This capability has been defined as reification. 

B. Reification 

In previous work we have defined reification as the 

bi-directional mapping of representation (symbolic data) to 

sensor data[4]. It is the process that allows humans to look out 

the window and see, not a roughly rectangular blob of red 

surrounded by black (which is transected by yellow lines), but 

a sports car in a parking lot. In addition reification allows us 

to utilize that sports car to stop and pickup milk on the drive 

home – thus achieving one of the system’s goals. 

Historically there have been two approaches to enabling 

intelligent systems to situate themselves in the world. These 

are represented by artificial intelligence researchers who work 

top-down from deliberative symbol manipulation and those 

who bottom-up from control systems in robots. The general 

consensus has been that as the two ends work towards the 

middle, the gulf will narrow and narrow until it disappears. 

However, recent research has suggested that the gulf may not 

be bridgeable by work from either side; rather it may require a 

specific research approach that is different from either the 

sensor-based or the symbolic domains. 

From the point of view of the deliberative approach, a 

symbol manipulation system is developed, and it is outside the 

scope of the symbol system to recognize the physical and 

perceptual characteristics that define the thing referred to by 

the symbol. From the viewpoint of the embedded systems 

approach, the crucial task is the recognition of physical and 

perceptual cues, while mapping those cues onto a symbol 

system is outside the scope of the research. 

Underlying both these beliefs is the assumption that once the 

core research was addressed, it would just be a matter of 

pushing the research frontier towards the opposing viewpoint 

until they met. If one continues the bottom-up (or top-down) 

approach long enough, eventually one gets to the top (or 

bottom) and the complete problem is solved (See Figure 1.A). 

Both the top-down and the bottom-up approaches have 

made great strides towards the complete solution. However, 

there seems to be a gap that neither has been able to cross. It 

is clear that both the sensor/effector-to-symbol pathway and 

the symbol-to-sensor/effector pathway are necessary to 

support deployed intelligent systems. It is this bi-directional 

pathway that constitutes reification. 

143
Figure 1 - Possible relationships of Pattern Recognition, Symbol 

Grounding, and Reification. In A, the problem of anchoring symbols to 

sensor/action patterns should be approachable by either top-down or 

bottom-up improvements. However, in B the problem cannot be solved 

by either top-down or bottom-up approaches, since there is no area of 

overlap. Rather, a third approach is required; one that solves the 

reification problem first, which then provides the bridge between symbol 

and sensors. 

We have argued that this reification capability is necessary 

for any autonomous system that is deployed into the real 

world, where it will encounter situations which prevent it 

from achieving its goals, and for which it has to intelligently 

produce a collection of possible solutions, and autonomously 

select from these possible courses of action the one that it will 

undertake. 

IV.COSTS OF DEVELOPING AUTONOMY

Adding autonomy to an intelligent system increases the 

complexity of the software. Adding any new capability will 

increase the costs associated with the design, development 

and testing of software, but when there is significant coupling 

between the existing software and the new capabilities, the 

“ripple effect” can radically increase the resulting complexity 

of the software[9]. The question arises whether adding 

autonomy causes such ripple effects. 

A. Coupling between Autonomy and Intelligence 

From the working definition presented in section I, it is clear 

that there are a number of commonalities between intelligent 

performance and autonomous performance. Intelligence is 

defined as the ability to generate actions that will improve the 

probability of the system achieving its goals. Autonomy is 

defined as the ability to select from these actions. At a basic 

level, a system can be autonomous, if it simply selects at 

random from the available options, and, in this case, there 

would be little coupling between the intelligence and the 

autonomy. However, implicit in the concept of increasing the 

probability of achieving the system’s goals is the notion of 

making a ‘good’ selection from the available options. This 

suggests that there are several system wide aspects (in the 

Aspect-Oriented Programming sense[1]) which increase the 

amount of coupling between the base functionality (an 

intelligent system) and the added capabilities of an 

autonomous system. 

B. Coupling between Autonomy and Reification 

In addition to the coupling between intelligence and 

autonomy, there is also a coupling with the reification process. 

The autonomous selection of an appropriate response depends 

not only on the system’s goals, and capabilities, but on the 

situation in which the system is making this decision. As a 

result the selection of a ‘good’ response is made in the context 

of how the current situation is interpreted, which means that 

the reification system must also provide information to the 

software supporting autonomy. This is another cross-cutting 

aspect of autonomy, which will increase the costs of 

developing the autonomy capability. 

C. Testing an autonomous system 

While the increased coupling between the system modules 

will result in synergistic effects in the cost of developing the 

software, this can pale in comparison to the added costs in 

testing that are required by an autonomous system. Once a 

system is autonomous, it becomes, in many ways, 

non-deterministic. After all, it was the very facts that the 

environment is uncertain and that we could not pre-program 

the correct responses to all situations that motivated the 

decision to add the capability of autonomous behavior to the 

system. 

Under these conditions we can do preliminary testing of the 

system by putting it into pre-analyzed situations, for which we 

believe that we know the correct responses. However, this will 

only tell us that in the situations for which there is no need of 

autonomy, the autonomy hasn’t broken anything. This testing 

does not tell us if the system will perform correctly in the very 

situations where we need to it perform. 

Frequently, the only effective way to test the autonomy is to 

use simulation techniques (Monte Carlo, Bootstrapping, etc.) 

to generate a wide range of ‘typical situations’ and then to 

perform a statistical analysis of the performance. This, in turn, 

requires the development of high resolution, high fidelity 

simulators of the domain and the environment. The increased 

costs of designing, developing, and testing the simulations, as 

well as the supporting code for the gathering and statistical 

analysis of the simulation testing will add significantly the 

overall costs of adding reliable autonomy to an otherwise 

intelligent system. 

V. FACTORS AFFECTING THE AUTONOMY DECISION

 Given that autonomy is a viable solution, many of the 

operations we would like to off-load require constant 

low-level decisions to be implemented in response to 

changing environmental states. There appear to be two 

independent criteria that influence the decision to enable 

autonomous operations on a deployed system: the 

predictability of the environment, and the risk associated with 

autonomous operations 

A. Predictability 

The predictability of the environment is a critical and 

144
complex aspect of the decision to enable autonomy. We are 

using predictability in the modeling sense; specifically: to 

what degree can the future state of the domain be accurately 

predicted from the current state. Fundamentally, if the 

environment is completely predictable, then every possible 

state that the deployed intelligent system can encounter can be 

evaluated in advance, and (in theory, see Schoppers[12]and 

Ginsburg[2]) ‘correct’ responses for each of these can be 

provided to the system. 

Predictability can be compromised in several different ways. 

In a dynamic environment with significant exogenous events 

(for example a battlefield scenario), the future state of the 

environment can only be predicted statistically, if at all. 

However, even in environments where there is ‘complete 

knowledge’ (e.g., chess) the enormous complexity of the state 

space makes it impossible to produce a universal plan. 

Typically, complete predictability can only be reached in 

small and simple problem domain, or for problem domains 

that are totally engineered – such as factory floor and 

laboratory automation systems. 

If the environment is not predictable for either reason, it 

becomes difficult to avoid some level of both intelligence and 

autonomy. Since it is almost certain that some situation will 

be encountered for which the deployed intelligent system has 

no pre-defined, correct response, the system will have to 

generate one or more possible responses if it is going to 

achieve its goals. This will require intelligence. If there are 

multiple possible responses, the system will have to choose 

between them. This will require autonomy. 

This leads to an indirect relationship between the 

predictability of the domain and the need for autonomy in the 

system. The greater the predictability, the less autonomy is 

needed. 

B. Risk 

Risk is another key area that can affect the decision to 

enable autonomous behavior. Since autonomy entails the 

system making its own ‘choices’, there is always the 

possibility that it will make a choice with which the humans 

would disagree. This leads to the question what happens if the 

‘wrong’ decision is made? 

In previous work we have explored autonomy in the domain 

of music selection[5]. We developed a music playback system 

that autonomously selects music to minimize the rejection rate. 

The Personal DJ has the responsibility of selecting music to 

play, and if it picks music we do not like, there is little risk. 

We hit the reject button, and life goes on. 

In other contexts the risk of a wrong decision increases. 

Such a risk can be financial, for example if the Personal DJ 

system is enabled to purchase new music the risk is increased 

significantly. Other risks include damage or loss of property, 

for example, during the DARPA Grand Challenges several 

autonomous vehicles left the roadway, struck obstacles, or had 

other accidents. These risks include property damage, and in 

the upcoming urban challenge, the risks could include 

harming humans. In the future, an autonomous convoy that 

‘decides’ to take a different route, thus delaying a delivery of 

critically needed medical supplies, has taken a risk which 

could result in deaths. In a non-deterministic environment any 

choice can turn out badly, resulting in after-the-fact analyses 

into ‘what went wrong’. 

This gives us another indirect relationship between the 

potential risks associated with deploying an autonomous 

system and the characteristics of the domain and the tasks. 

The greater the risk that a bad decision involves, the less 

likely that autonomy should be enabled. 

C. Trust and loss of control 

In addition to the domain and environment, there are social 

issues associated with the decision to enable autonomy. 

Whenever an autonomous system is deployed it is in a social 

space where there are people who are affected by the system. 

Even an autonomous system deployed as a planetary rover on 

a moon of Jupiter (such as the DEPTHX project[8]), while it 

may not encounter humans while it is exploring, many 

humans will be affected by it performance, and jobs and 

careers may be impacted by its autonomous behavior. 

Ultimately, the decision to deploy an autonomous system 

comes down to the question “How comfortable am I that this 

system will achieve its goals without causing unacceptable 

harm?” This question is the same whether the autonomous 

system is a robotic device or a human sent out to do a mission. 

We are, in general, more used to making this decision when it 

is a human. But the same question is addressed every time we 

delegate a responsibility to any type of machine. Even 

something as simple as the introduction of antilock brakes (a 

primitive autonomous system) required several decades to 

gain acceptance[6]. 

The problem is increased with autonomous, intelligent 

systems, since in many cases there may be honest 

disagreement, even among the human experts, as to what the 

correct course of action is in a given situation. The deployed 

autonomous system will choose some course of action, which, 

due to the non-deterministic nature of the world, might 

succeed or fail. Regardless of the outcome, there is likely to 

be some expert who will second guess the machine. 

The issue of trust is, in part, correlated to the risk in the 

environment. Clearly, if there is significant risk the need to 

trust the system must increase. However, it extends beyond 

the actual risk into perceived risk. Individuals, businesses, and 

societies vary in their need for control, and the essence of an 

autonomous system is that one gives up some level of control. 

Even if the risk is effectively zero, there is still a loss of 

control when a goal is delegated to an autonomous system. In 

some cases this loss of control may be the driving force in the 

decision to implement autonomy in an intelligent system – 

regardless of the ability of the system to achieve its goals. 

VI.AUTONOMY: WHAT IS IT GOOD FOR? 

The domain, the intelligent system, and the mission all 

interact to determine whether autonomy is viable in a given 

145
system. If it is not viable, it becomes ineffective to attempt 

to ‘off-load’ autonomous operations from the human to the 

intelligent system. We have categorized the domain into a 

broad spectrum based on the predictability of the environment. 

With low predictability there is more value that an 

autonomous system can provide, with high predictability, 

there is less need for autonomy. In the same manner, the risks 

associated with a ‘bad’ autonomous decision can be broken 

down into two broad categories, low and high risk: with low 

risk, there are ample opportunities for deploying autonomous 

systems, with high risk domains and missions, the decision 

should be approached cautiously. 

These results are shown in Table 1, below. 

Table 1 

High level partitioning of task and environment space with 

respect to autonomy. 

 Low Predictability High Predictability 

Low Risk Autonomy encouraged Autonomy optional 

High Risk Task dependent autonomy Autonomy discouraged 

A. Low Risk, Low Predictability 

This category is a perfect ground for autonomous systems. 

Since the environment is not easily predictable, it is very 

challenging to develop a non-autonomous system that will 

perform well. Currently, this category is populated by 

teleoperated systems which tie up humans to act as 

information processors, and decision makers. These are 

humans who are frequently highly skilled, and are easily 

fatigued. While the presumption is that they are in control of 

the machine components of the system, in reality they are 

often nothing more than biological sensor suites. In these 

cases the use of autonomy would be of great value. 

B. High Risk, Low Predictability 

In this category the decision to implement autonomous 

systems is more problematic. There are tasks such as 

planetary exploration beyond Mars, where the option of 

teleoperated control is simply infeasible, and the risks are 

primarily financial. In this situation, deploying autonomous 

exploration systems is reasonable. However this category also 

includes battlefield deployment of unmanned combat air 

vehicles, where the risk to human life resulting from 

autonomous activity probably far exceeds any gain. So we 

assign this ‘task dependent autonomy,’ where the decision to 

implement autonomy is controlled by the specific level and 

type of risk, and the possible gains. 

C. Low Risk, High Predictability 

In domains where there is high predictability the added 

value of an autonomous system is low. Since the environment 

is well defined, it is frequently more cost effective to forego 

autonomous solutions. However, there are likely to be niches 

where an autonomous system may be more effective than 

building an automated system. In addition, since the risks are 

lower, this is an excellent class of domains for research and 

development of autonomous systems technology. 

D. High Risk, High Predictability 

This type of system includes areas such as controlling 

refineries, power plants, and other domains where an error can 

have grave consequences, but the domain is well studied and 

fairly predictable. In this category autonomy is not really 

advised. The risks are high, and the marginal benefits of an 

autonomous system are low. 

VII. CONCLUSION

In this paper we have presented working definitions of 

intelligence and autonomy that reduce the confounding effects 

of these terms. Based on these definitions, we have explored 

the requirements needed to support autonomy, the conditions 

in which autonomy provides benefits to the system, and have 

looked at the inherent costs of enabling autonomy in an 

intelligent system. From this we have proposed a simple 

classification schema that can be used to evaluate the question 

“In this domain, with these tasks, is it effective to develop an 

autonomous solutions to this problem?” 

What is an intelligent autonomous system? 

It is a system that has the ability to develop new responses 

to situations it encounters in a dynamic and uncertain world, 

and to choose between those responses to increase the 

probability of achieving its goals. It is intelligent if it can 

encounter new situations and develop responses to achieve its 

goals, it is autonomous if it can select between those 

responses without requiring instruction from other systems. 

By this definition a human, teleoperating a remote control 

vehicle is an intelligent, autonomous system. However, the 

focus of this work is to look at the decision to replace the 

human controlling the vehicle with a cybernetic system of 

some configuration. 

What capabilities are necessary to enable autonomy? 

Before autonomy can be considered, there are several 

capabilities that must exist in the intelligent system. The 

system must be capable of producing multiple possible 

responses to the situations it encounters. If the system can 

only produce one possible response, there is no need for 

autonomy. Second, the system must have a mechanism that 

allows it to select between these responses in a manner that 

will increase the probability of achieving the system’s goals. 

Finally, if autonomy is required the system will encounter 

situations which involve novel states. In order to produce 

appropriate responses, and select from these effectively, the 

system must be capable of reifying these novel situations. 

When is autonomy beneficial? 

It is beneficial in situations where the ability to pre-program 

known, ‘good’ responses is limited due to the environment’s 

constraints. These constraints can be due to lack of knowledge 

146
about the environment, or due to significant uncertainty about 

the state that might be encountered. Whenever it is not 

feasible to pre-analyze the domain, autonomy can bring 

significant benefits to the system. 

What are the contra-indications for enabling autonomy? 

Clearly, if the environment is well defined, well behaved, 

and can be modeled, there is limited value to adding autonomy. 

Having an autonomous telephone dialer is probably of little 

value. If the risks associated with failure are high, the decision 

to enable autonomy must be carefully thought out. 

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147
Definitions and Measures of Intelligence in Deep 

Blue and The Army XUV 

John M. Evans, Ph.D. 

John M Evans LLC 

1 Reservoir Road 

Newtown, CT 06470 

john_evans@snet.net

Abstract—Three different definitions of intelligence are reviewed, 

using Deep Blue as the basis for comparison, and a discussion of 

chess rating points as a metric of performance is presented. It is 

argued that Deep Blue showed intelligent behavior and passed a 

form of the Turing Test. Further applications of similar search 

techniques in the Army XUV are shown to generate behaviors that 

show elements of intelligence in autonomous systems. 

Keywords: definitions of intelligence, measures of intelligence, 

computer chess, Deep Blue, autonomous vehicles, path planning, 

Army XUV 

I. DEEP BLUE

 A remarkable milestone in computer science was achieved 

in 1997 when IBM’s computer, Deep Blue, beat the World 

Champion, Garry Kasparov, at chess [1,2]. This was the 

culmination of fifty years of work on what was considered a 

problem that “penetrated to the core of human intellectual 

endeavor.”[3] This event ranks with the Wright Brothers first 

flight and with the achievement of sustained fission in the 

Manhattan Project: early success with what became (or in this 

case will become) world-changing technologies. 

 It is interesting to note that Hsu, the designer of the VLSI 

chips used in Deep Blue, characterizes the matches with 

Kasparov not as man versus machine but rather as man as 

performer (Kasparov) versus man as toolmaker (Hsu and the 

IBM team). [1] Deep Blue was a remarkable tool, quite 

successful at the specialized task for which it was designed. 

II. DEFINITIONS OF INTELLIGENCE

 Was Deep Blue “intelligent”? Artificial Intelligence 

researchers generally say no, that Deep Blue’s success 

depended on special purpose chips that were designed only 

for evaluating chess moves. Philosophers and psychologists 

generally say no, that there was no self-awareness, no 

consciousness, no real understanding in Deep Blue. 

However, the English mathematician Alan Turing in 1950 

proposed an operational definition of “intelligence” that 

basically said that if a person interacting with another 

unknown entity could not distinguish between a computer and 

a person, then that entity would have to be considered 

intelligent. [4,5] 

 The Turing Test defines “intelligence” in terms of black box 

functionality of a machine in comparison with a human in 

human/machine interaction. Searle, with his famous Chinese 

Room argument, redefines intelligence in terms of 

understanding. [6, 7] Hawkins, in his book On Intelligence, 

defines intelligence in terms of predictive ability. [8] These 

three definitions are not necessarily incompatible, although 

Searle was specifically attacking the Turing Test definition. 

 Deep Blue had all of these facets of intelligence. Kasparov 

felt he was not playing with a machine but with an 

independent intelligence, an entity with an independent mind: 

“Now for the first time we are playing not with a computer, 

but with something that has its own intelligence.”[9] He 

made this statement after winning game 2 of the first match in 

Philadelphia in 1996. In a less charitable mood during the 

losing rematch in New York in 1997, he accused IBM of 

cheating, of having a person directing the game. [10] From 

Kasparov’s standpoint, Deep Blue passed a version of the 

Turing Test. 

III. THE STRUCTURE OF DEEP BLUE

 Deep Blue also had predictive capability (Hawkin’s 

definition of intelligence) and embodied understanding of the 

game of chess (Searle’s definition). To understand this we 

must delve into how Deep Blue operated. 

 The basic principles of playing chess with a computer were 

laid out by Turing in England and Shannon in the U.S. by 

1950 [11, 12]. Since then there has been refinement and the 

addition of bells and whistles, but the main point is that 

increasing computer power makes it possible to look farther 

ahead in a game and that in turn leads to greater skill and 

therefore greater perceived “intelligence”. 

 The chess game for a computer is divided into three parts. 

[1,2] The first part is the opening book, a sequence of 

scripted moves that have been played out many times in the 

past. This is essentially table lookup. 

148
 The second part of the game uses search techniques to 

evaluate different possible moves. At each level of search a 

quantitative value is calculated based on material and board 

positions and the search is selectively deepened along the 

most promising lines. The weights given to different pieces 

and different board positions were developed with the help of 

grandmasters and it is in these evaluation functions that chess 

knowledge is embedded in Deep Blue. Deep Blue calculated 

an average of fourteen plies (half-moves) but in some cases 

went to twenty-ply or even thirty-ply deep evaluations in 

examining possible lines of play. [1,2] 

 The final part of the game is the endgame. Deep Blue had 

all four piece endgames stored in main memory and all five 

piece and some six piece endgames stored on disk. At this 

point it is again a table lookup strategy. 

 In the first and last part of the game, understanding of the 

game of chess is embedded in the “book”, the tables, which 

were created by human grandmasters. In the middle part of 

the game, understanding of chess is embedded in the 

evaluation functions, which again were set by grandmasters 

working with the chip designers and the programmers. In 

terms of Searle’s argument, Deep Blue did not understand 

what was going on when it executed the evaluation functions, 

but it did embody understanding of the game of chess, and 

hence, from Kasparov’s viewpoint, it seemed to possess 

intelligence. 

 From the standpoint of Hawkins definition, Deep Blue was 

exhibiting intelligent behavior by being able to predict the 

future results of its actions. This is the essence of cost-based 

search. 

 Cost based search is not how humans play chess at the 

highest levels. [13] Instead, we exploit the massive parallel 

processing capability of the human mind together with the 

basic ability of the neocortex to recognize and store patterns 

and sequences of patterns (8) and play mostly on the basis of 

pattern recognition. Functional MRI tests show that chess 

experts activate primarily the parietal cortex, where spatial 

patterns are stored, while novices activate primarily the 

temporal cortex, where information on individual pieces and 

their capabilities are stored. Either approach, pattern 

recognition with massively parallel processing or search with 

a Von Neumann architecture computer, obviously works. 

IV. PERFORMANCE METRIC FOR CHESS

 In the case of chess there is an established performance 

metric, Elo’s chess rating points system [14], adopted by 

FIDE (Fédération Internationale des Échecs) as the official 

international rating system for chess players. This is a 

statistical rating based on head-to-head competitions: a player 

taking 3 out of 4 points in a four game match is considered 

200 rating points above the losing player. Newborn has 

developed experimental data based on running real chess 

matches through computer chess programs. His data indicate 

that increasing the depth of search by one level gives an 

increase of at least 100 rating points in skill level. [15] It is 

then just a matter of applying sufficient computing power to 

reach a level of skill beyond that of any human. Deep Blue 

was able to exercise the equivalent of two to three trillion 

instructions per second (using hundreds of special purpose 

VLSI chips on a cluster computer with 36 processors) which 

allowed it to examine two hundred million board positions per 

second and it played in New York at a rating level of over 

2800, on a par with Kasparov [1,2]. 

 The following data on ratings for specific chess playing 

computers and computer programs is from Newborn [2]. 

Figure 1: Rating vs Speed of Search 

V. THE ARMY XUV 

 Another interesting example of using cost-based search to 

generate complex behaviors is the vehicle control for the 

Army’s Experimental Unmanned Ground Vehicle Program, 

commonly referred to as Demo III, which ended in 2003. [16, 

24] (Demo I was teleoperation, Demo II was supervised 

autonomy, and Demo III was targeted at full autonomy for 

scout missions of reconnaissance, surveillance, and target 

acquisition). Many of the ideas from Demo III were 

embodied in the Stanford and Carnegie Mellon winners of the 

DARPA Grand Challenge Road Race in 2005 and this 

technology is now being developed into the Autonomous 

Navigations System for the Army’s Future Combat Systems 

vehicles. 

 This special vehicle, shown in Figure 2, has four wheel 

hydraulic drive with four wheel steering. The navigation 

sensors include scanning ladar, stereo cameras and stereo 

FLIRs, microwave radar, bumpers, tilt sensors, GPS and 

inertial navigation. The mission sensors are in the dome 

package on a shock mount on top of the vehicle. 

 The path planning for Demo III used cost based search. [17, 

18, 19, 21] The cost function is 

Cost = Σ ci * vi 

100 1K 10K 100K 1M 10M 100M 1B 

 Board Positions Searched per Second 

Rating 

2800 

2500 

2200 

1900 

1600 

1300 

149
Figure 2: Army XUV (Experimental Unmanned Ground 

Vehicle) 

where the ci are relative costs or weights (relative importance) 

of different relevant state variables and vi are the current 

values of those variables. There were 16 variables used in the 

cost function, including side slope, forward to back slope, 

ground roughness, ground center height, soil properties, onroad, off-road, vegetation, obstacles, and mission completion 

time. 

 Cost maps were created using a priori knowledge (maps) 

and real time terrain knowledge from vision and ladar 

scanners. A grid of points was cast onto the maps and points 

were connected to provide possible path segments. A search 

was then conducted, calculating the cost for each path 

segment encountered and deepening the search along 

favorable directions to find the lowest cost path from a 

starting point to a finishing point. Search was carried out at 

several levels of resolution: 5 m, 50 m and 500m range maps. 

The 500 m maps gave optimal start and end points for the 50 

m maps, which in turn gave start and end points to the 5 m 

map. At the 5 m map level, pre-calculated trajectories that 

embodied vehicle dynamics (speed, inertia, possible steering 

rates, etc.) were used for computational efficiency instead of 

an arbitrary point grid. [19] 

 Neural nets, which model how our brains work, carry out 

exactly this type of computation, summing the product of 

neural signal strengths times synapse weights. As 

Churchland and Sejnoski note this neatly wraps vector 

representation with compatible matrix processing and allows 

for many types of mental computations. [20] We use 

chemistry to create emotions to modify weights for low level 

behaviors (e.g. fight or flee) and logical computation for more 

abstract behavior generation. 

 The following figures, developed by Balakirsky [21] show 

examples of behavior generation for the Demo III vehicle 

under different weight assumptions. The figures are from a 

simulated operator control unit and use data from topological 

maps of the grounds of the National Institute of Standards and 

Technology (NIST). The NIST maps were only course 

resolution, so the images seem blurred. Red areas are 

obstacles (trees, bushes and fence lines), blue are buildings, 

and green are roads and parking lots. 

 Figure 3 shows a hypothetical path from one point to 

another with the weight for obstacles being high and all other 

weights being low. The faint white line is a direct line from 

the start (lower right) to the finish (just beyond the road in the 

upper left) and the heavier yellow line shows the computed 

path. The vehicle finds an opening through the trees and then 

returns to the direct path to the goal. 

Figure 3: Simulated path planning with obstacles having high 

weights 

 

 In Figure 4 the weights for being off-road are set very high, 

so the vehicle does not go directly toward the goal but instead 

heads toward the nearest road and then stays on roadways 

until it is as close as it can get to the goal, at which point it 

departs from the road and dashes to the finish. 

 Finally, in Figure 5, the weight for being out in the open and 

hence potentially detectible by an enemy force is set very 

high and the cost for mission completion time is set low. The 

result is stealth behavior, running carefully along the tree line 

to stay under overhanging branches as much as possible. This 

is a tactically significant behavior for an Army scout, and it 

was generated by the wonderfully general approach of cost 

based search that matches how our own minds determine 

appropriate behavior. 

 Future work will include learning and recalling appropriate 

weights for different situations. The ability to recognize 

contexts and select appropriate weights depends in turn on 

improved perception to generate image understanding and 

situational awareness. The Demo III program only scratched 

the surface of these issues. 

150
Figure 4: Path Planning with low cost for On-Road and high 

cost for Off-Road 

Figure 5: High Cost for being in the open (detectable by an 

enemy scout) and Low Cost for mission completion time. 

VI. PERFORMANCE METRICS

 There is not single metric for performance of unmanned 

vehicle planning systems to match the chess rating system. 

Instead the focus has been on measuring the performance of 

unmanned systems in navigating known courses of various 

difficulties. Measures of performance include completion of 

segments of the course, time to completion, number of targets 

found in a course and number of interventions. [22, 23, 24] 

For smaller robots artificial test courses have been used for 

evaluation in both simulation and in contests. [23, 25]. 

VII. CONCLUSION

This paper has examined some interesting idiot savant 

capabilities: Deep Blue beat Kasparov and must be 

considered intelligent in its domain, but all it does is play 

chess. The Army XUV showed some intriguing elements of 

intelligent behavior, but it was badly nearsighted, had very 

limited perceptual understanding and was computationally 

bound, so it also showed some truly dumb behaviors at times 

and got lost on a regular basis. Still, the techniques of 

planning and problem solving, one aspect of intelligent robot 

systems, are now fairly well understood, objective test 

methods and metrics are being developed to benchmark 

capabilities and rapid progress is being made. 

REFERENCES

[1] Hsu, Feng-Hsiung, Behind Deep Blue. Princeton: Princeton University 

Press, 2002. 

[2] Newborn, M., Deep Blue. New York: Springer-Verlag, 2003. 

[3] Newell, A., Shaw, C. and Simon, H., from an early paper on chess, as 

quoted in Hsu, op.cit.

[4] Turing, Alan, “Computing Machinery and Intelligence”. Mind, LIX, 

pp433-460, 1950. 

[5] Hodges, A., Turing. New York: Routledge, 1999. 

[6] Searle, J., Minds, Brains and Science. Cambridge: Harvard University 

Press, 1984. 

[7] Searle, J., “Is the Brain’s Mind a Computer Program?” Scientific 

American, 262 (1), 1990. 

[8] Hawkins, J., On Intelligence. New York: Henry Holt & Co., 2004 

[9] Kasparov, Garry, as quoted in Newborn, op. cit. 

[10] Kasparov, Garry, as quoted in Hsu, op. cit.

[11] Turing, Alan, “Digital Computers Applied to Games”, in Faster Than 

Thought, B.V. Bowden (ed.), London: Pitman, 1953. 

[12] Shannon, Claude,”Programming a Computer for Playing Chess”, 

Philosophical Magazine, 41(7), 1950. 

[13] Ross, Phillip E., “The Expert Mind”. Scientific American, August, 2006. 

[14] Elo, A., conceived of the statistical performance rating system used in 

chess. http://en.wikipedia.org/wiki/ELO_rating_system. 

[15] Newborn, op. cit., Appendix H. 

[16] Shoemaker, C., et al., “Demo III: Department of Defense Testbed for 

unmanned Ground Mobility”, Proc. SPIE Conference on Unmanned Ground 

Vehicle Technology, SPIE Vol. 3693, Orlando, FL, April, 1999. 

[17] Albus, J., et al., 4D/RCS Version 2.0: A Reference Model Architecture 

for Unmanned Vehicle Systems, NISTIR 6910, Gaithersburg, MD, 2002. 

[18] Schlenoff, Madhavan, R., and Balakirsky, S., “Representing Dynamic 

Environments for Autonomous Navigation”, Proc. IEEE Int. Conf. on 

Intelligent Robots and Systems, 2003. 

[19] Balakirsky, S. and Lacaze, A., “World Modeling and Behavior 

Generation for Autonomous Ground Vehicle”, Proc. IEEE International 

Conference on Robots and Automation, 2000. 

[20] Churchland, P. and Sejnoski, T., The Computational Brain. Cambridge: 

MIT Press, 1999. 

[21] Balakirsky, S., A Framework for Planning with Incrementally Created 

Graphs in Attributed Problem Spaces, Berlin, Germany: IOS Press, 2003. 

[22] Messina, E. and Meystel, A., Proc. PerMIS 2000-2004. Gaithersburg, 

NIST SP 982, 990, 1014, 1036, 2000-2004. 

[23] Jacoff, A., Messina, E., and Evans, J., “Performance Evaluation of 

Autonomous Mobile Robots”, Industrial Robot, 29:3, May 2002. 

[24] Madhaven, R., Messina, E., and Albus, J., Intelligent Vehicle Systems. 

New York: Nova Science Publishers, 2006. Chapter 3 covers behavior 

generation; chapter 9 covers performance evaluation; chapter 10 is a review 

of the Army Demo I-Demo III programs. 

[25] Jacoff, A., Weiss, B., and Messina, E., “Evoluation of Metrics and 

Performance of USAR Competitions”, Proc. 2003 PerMIS, Gaithersburg,

MD: NIST SP 1014, 2003.

151
Automotive Turing Test

 Steven F. Kalik and Danil V. Prokhorov 

 Toyota Technical Center-TEMA 

 Boston, MA, Ann Arbor, MI, USA 

 {steve.kalik, danil.prokhorov}@tema.toyota.com 

Abstract— The Turing Test is often cited when the intelligence level 

of a presumably intelligent computer program is to be assessed. We 

propose to extend the scope of the test to the domain of intelligent 

automotive vehicles. We discuss possible formats for such a test, 

and consider different measures produced by these tests. 

Keywords: Turing test, imitation game, human-like driving 

behavior, human driving intelligence, intelligent vehicle, robot driver, 

vehicle intelligence, road test, driving simulator, vehicle interaction, 

safe driver.

I. INTRODUCTION

Alan Turing’s original paper [1] proposed two central ideas 

that revolutionized thinking about modern computing. Of the 

two, perhaps the more revolutionary idea was the conception 

of the Universal Computing machine. But, the one which in 

many ways did more to capture the imagination of society, is 

the Turing Test (TT), based on the “Imitation Game” as 

Turing originally coined it. 

As originally proposed, the imitation game pits an 

interrogator (C) against a man (A) and a woman (B). The 

interrogator is separated from the man and woman in such a 

way as to limit his observations to only the interactions they 

have with him and with each other. As Turing originally 

conceived it, those interactions were all verbal, relayed to the 

interrogator via an intermediary or, more preferably, through a 

set of teletype terminals. Thus, the responses of each player 

to the questions of the interrogator, and to each other’s 

responses to those questions, define the only input the 

interrogator receives to decide which is the man and which the 

woman. What makes the game far from simple is that only 

one of the players is trying to be correctly identified. In this 

case, let’s describe it as Turing did, as player B. The 

remaining player, A in this case, seeks to deceive the 

interrogator by giving them answers and statements that they 

hope will convince the interrogator to apply the opposite 

labels to the two players. The interrogator may ask 

questions and observe the responses until such time as he feels 

he knows which labels to apply, but the ability of A to give 

false answers or to respond in ways that would deliberately 

mislead the interrogator ultimately leaves the interrogator 

with doubt about which player to trust, and therefore with 

only an estimate of which player is which. This makes the 

game challenging with the placement of labels decided in a 

sense statistically, based on a set of responses observed by the 

interrogator. 

As is well know, Turing’s ultimate variant of this game 

replaced A with a computer, posing the critical question: 

“Will the interrogator decide wrongly as often when the game 

is played like this, as he would when the game is played 

between a man and a woman?” [1]. This form of the 

question creates a statistical measure of distinguishability out 

of the set of decisions made by the interrogator as they play 

the imitation game multiple times. A comparison of the 

distinguishability measures between the cases when humans 

exclusively play the roles of A and B in the game, and when 

machines enter the game and take the role of the deceiver (A), 

utilizes these measurements to quantify the original question 

Turing sought to improve upon, which was simply “Can 

machines think?”[1]. Thinking in this case carries the normal 

but poorly-defined meaning humans usually ascribe to it when 

they speak of human thought. 

We propose a similar approach in this paper: use human 

decisions about their notions of human and machine behaviors 

to determine statistically over a set of decisions whether 

behavior observed appears intelligent enough to be called by 

the same name. Thus we propose the Automotive Turing 

Test (ATT), and explore some basic ideas and properties 

suggested by such a test. 

We begin by identifying the central features of the original 

Turing test in Section II, and consider the implementation of 

those features in the automotive domain in Section III. 

Section IV presents several different ways the automotive 

Turing test may be constructed. We discuss different 

features of those implementations. Section V considers the 

goals and measures of the ATT with regard to human goals 

for intelligent vehicles, pointing to required future work. 

Finally, Section VI concludes the paper. 

II. KEY COMPONENTS OF THE TURING TEST

Turing’s original conceptualization of the TT provided the 

following benefits: 

1. A way to explicitly quantify a vague idea (“thinking” in 

Turing’s case). 

2. Use of a human as an active sensor to identify 

human-like behavior 

3. Simplification of the test structure to probe the essence 

of the assessed behavior while maintaining impartiality.

It is this third point that attracts our attention here. 

To legitimately retain the TT label in the work we propose 

for this paper, we must retain the key elements of the original 

TT : 

1. Limited observations between the players and the 

interrogator 

152
2. The ability of the players and the interrogator to 

interact directly in the domain of the test and not 

beyond 

3. The opportunity to try to deceive the interrogator, and 

4. The ability of both players and interrogator to 

recognize and influence the context of the observations 

and the responses returned during the game. 

While we feel that it is critically important to maintain the 

quantitative nature of the TT, with its central measure that 

compares ensembles of label applications between cases when 

(1) two humans act as players and (2) a machine and a human 

act as the players, (in both cases the interrogator being 

human), we recognize that this is not the only version of the 

game people think of today (see, e.g., [3]). But, to the extent 

possible we adhere to these key features of the test as 

originally conceived. Where we diverge from this, we note 

it explicitly. 

III. TURING TEST IN THE AUTOMOTIVE DOMAIN

Just as Alan Turing posed the question “Can machines 

think?”, we now pose the question “Can machines drive?” 

Just as Turing’s use of “think” carried with it human aspects 

of thinking, so too do we carry components of human driving 

into this test, seeking to capture aspects that we associate with 

a human driver into our use of the word “drive”. As this 

paper and perhaps future work will illustrate, the exact form 

of what we will call the Automotive Turing Test (ATT) is 

still evolving. The Turing test goal of being 

indistinguishable from a human may or may not be the target 

we as a society choose to approach, given the number of 

traffic accidents observed each year across the globe. On the 

other hand, as we point out, there may be some benefits to the 

limitations humans show, too. 

To convert the Turing test into the automotive domain, we 

must transform the key elements from section II into a form 

appropriate for testing driving behavior, and into interactions 

appropriate to a driver controlling an automobile. To do so, 

we remove from consideration the verbal interactions central 

to the original TT, and replace them with behavioral and 

signaling interactions available to vehicles. 

So far we conceive the ATT to be administered in two 

different environments, and in each environment, the test can 

be administered in two different ways. Each of these 

provides opportunities for the test to address different aspects 

of human driving behavior. 

Environments 

The environments in which we initially imagine the test 

being administered are: 

• On real vehicles 

• In a life-like simulation environment 

In both cases, to stay with the spirit of the TT, the 

interrogator must be prevented from seeing anything inside 

any vehicles to which they will be expected to apply labels, 

and by extension, from seeing any information other than the 

vehicles behavior. 

In either testing environment we require that the space over 

which the vehicles will drive be large enough, in terms of road 

length, space covered, and potential driving situations that can 

be encountered, so that drivers will need to learn a map of the 

environment and patterns of dynamics in that environment to 

get around conveniently and effectively. This assures the 

exercise of driving skills at both tactical (on the order of one 

second) and strategic (seconds – minutes – hours) scales 

during the administration of the test. 

As some situations in the real world occur on the road much 

less frequently than others, we recognize the importance of 

explicitly building the opportunity for these events to occur 

into the testing environment. For example, near-accident 

situations are much less common than normal driving 

situations [2]. Yet, such rare situations, if recreated either by 

surprising behavior from another vehicle, or through the 

inclusion of unexpected behavior from other agents in the 

testing environment, would present great opportunities to 

assess the limits of human and intelligent machine capabilities. 

Hence the ATT environment, whether in a real or a virtual car, 

should include the ability to implement driving surprises. 

Administration methods 

The administration methods we initially imagine are: 

• Vehicle to Vehicle format: Each participant – the 

interrogator and both players A and B - drive 

separate vehicles in the environment. 

• Road Test format: A single player is to be labeled 

as a human or machine driver by an interrogator 

who sits in the vehicle to be observed, similar to 

the way a human driving instructor would pass 

judgment on a driver’s license applicant during a 

student driving test. 

In both of the administration methods, human drivers have 

access to maps or navigation systems to support their route 

choice decisions. We offer driver-readable maps or 

driver-understandable navigation systems to support 

preparation for and driving in the testing environment, so that 

a standard access to knowledge of the environment can be 

assured. 

Since today’s vehicles communicate with each other in only 

limited ways (obviously excluding driver sticking his head out 

the window and yelling or gesturing) if the test were to be run 

today, only standard signaling devices such as turn signals and 

brake lights would be available for communication from the 

driver. Observations of the environment including all 

vehicles, agents, objects, etc., are also visible to all players 

and the interrogator to inform everyone involved of the 

actions happening around them. 

However, if at a future time, additional technologies were 

added to standard vehicles such as message passing 

equipment or warning systems for vehicle to vehicle 

communication, or aircraft-like “black box recorders” to 

record all vehicle and environmental data, one could easily 

imagine incorporating such tools into the ATT. We 

153
elaborate on the use of data recorders for the purpose of the 

ATT in Section IV. 

How the tests are run 

We distinguish the test vehicles of players from all others in 

the environment by clearly marking or labeling such vehicles. 

To run an instance of the imitation game allowing only 

vehicle to vehicle interaction for the case of two test vehicles, 

we place vehicles labeled X and Y respectively at their 

starting locations. The interrogator gives them goal 

locations to reach which may differ from each other’s, but 

which will require them to traverse a common subset of 

streets and intersections, and which will lead them to 

encounter common driving situations, or to create driving 

situations for each other. The interrogator knows to which 

destinations these players will need to drive. 

When the players reach their destinations, they may be 

given new destinations that will again take them over another 

common set of roads and through common intersections and 

traffic situations according to the requirements set in the 

previous paragraph. The interrogator always knows these 

destinations so that he can choose his own actions to allow 

observation of and/or desired behavioral interaction with the 

players over the course of their trip. We allow this process 

to iterate until the interrogator decides he knows which labels 

A and B to ascribe to the vehicles originally designated X and 

Y. 

Once the labels have been applied, a round of the game is 

considered over, and the correctness of the decision recorded. 

New assignments, new starting positions and destinations are 

selected for the test vehicles. 

Although Turing originally constructed the test to provide 

an elaborately quantitative measure of whether a human 

interrogator interacting with an intelligent machine could be 

convinced to mislabel the players in the game as frequently 

when the players were both human as when they were human 

and machine, a more common usage today asks a simpler 

variant of that question: can a human observer distinguish the 

behavior of the intelligent vehicle from the behavior of a 

vehicle driven exclusively by a human driver. 

When this alternate version of the TT is used, as in the Road 

Test administrations, we modify the previous route granting 

procedure to allow the interrogator to explicitly modify the 

route as necessary at any time during the test to exercise the 

drivers’ capacities and to explore their decisions and 

behaviors in the face of route selections or modifications, as 

will be discussed in more detail later in the paper. 

In the next section, we explore the implications of the 

different testing environments and the opportunities raised by 

testing in each combination of administration and 

environment. 

 IV. VARIANTS OF THE AUTOMOTIVE TURING TEST 

Given the two different administration methods described 

above, and the two different environments in which the test 

could be given, there are already four different variations of 

the test under consideration. A fifth, passively interrogated 

form of the test will also be briefly considered later, wherein 

only databases are reviewed and compared to decide whether 

the driver is human or machine. We begin here by 

describing four variants with active interrogators, as shown in 

Table 1. 

TABLE I 

AUTOMOTIVE TURING TEST VARIANTS

ENVIRONMENT AUTOMOTIVE 

TURING 

TEST 

 VARIANTS Real World Simulated 

Environment 

Vehicle-toVehicle 

Format 

Variant 1: 

Real World 

Environment with 

only Vehicle–to- Vehicle 

Interaction 

Variant 2: 

Simulated Environment 

with only Vehicle-toVehicle Interaction 

ADMINISTRATION 

Road Test 

Format 

Variant 3: 

Real World Environment 

with Road Test Format 

Variant 4: 

Simulated Environment 

with Road Test Format 

Variant 1: Real World environment with Vehicle-to- Vehicle 

interactions.

In this first variant described, we imagine an interrogator 

and two separate players in their respectively marked vehicles, 

receiving destinations and route suggestions and driving to 

them as described earlier. 

Remember that the interrogator in this vehicle-to-vehicle 

interaction variant can supply new destinations and route 

guidance only before a player sets out on their route to the 

destination. Along the way, the interrogator, when seeking 

to change a vehicle’s route, must interact with the players’ 

vehicles only in the same ways that other vehicles in the 

environment can interact with the players. That is, 

interrogators influence the players’ vehicles by driving close 

enough to them to make the actions of the interrogators’ 

vehicle relevant to the driving decisions of the players. 

The type of interaction described here is imagined to 

primarily influence a player’s tactical behavior in the time 

surrounding the interaction. 

The interrogator may also want to test strategic decisions by 

a driver, such as choosing when to take a new route to a 

destination. To do this, the interrogator might try something 

like trying to tie up traffic so the player’s vehicle must 

re-evaluate, and perhaps revise, the route they planned to take. 

However, performing actions such as these influence a 

potentially very large number of people in addition to the 

154
players in the game, and may be considered inappropriate for 

the Real World Vehicle-to-Vehicle variant of the ATT. The 

route change can be more safely addressed in the domain of 

simulated environment, and more explicitly addressed through 

the Road Test variants. 

Variant 2: Simulated environment with Vehicle-to-Vehicle 

interactions.

Just as the Vehicle-to-Vehicle interaction variants can be 

administered in the real world, so too can they be 

administered in a high-fidelity simulation environment, or in a 

virtual reality world. In this variant we remove the risk 

inherent in real world experiments to provide a safer test 

environment that still permits direct but controlled 

interactions between the players and the interrogator. This 

permits the interrogator to consider a broader range of actions 

with which to challenge the players than they had available to 

them before, when lives, limbs, property, and in fact even 

simply the convenience of non-involved parties were at stake. 

Now the penalties for behaviors that would influence these 

concerns can be modulated and explored, and if desired, even 

ignored in the construction of the testing environment. 

For example, imagine an interrogator wishing to test the 

sensitivity of players to loss of time resulting from 

perturbations of the traffic flow through the environment in 

which the test is taking place. The interrogator could 

influence and test what it takes to get a response from the 

players by moving to a position he knows both vehicles will 

need to pass through or inescapably close to. (In many older 

cities, such central transit points are easy to imagine.) By 

uncivilly blocking a high traffic intersection, or perhaps more 

simply, by just adjusting the gaps at any intersection that the 

interrogator will accept to enter or pass across a traffic flow, 

the interrogator can modify some gross behaviors of the 

traffic system that will ripple out from that part of the 

environment. This puts players in the position of having to 

decide whether to wait out the delay, or to select an alternate 

route. Patterns in this behavior (and the resulting 

interactions with vehicles and other agents in the 

environment) can then be used to help the interrogator 

distinguish between different players before they make their 

decisions which player is human and which a robot driver. 

Possibly, in the simulated environment, players themselves 

might also select some of these behaviors in an effort to lead 

the interrogator to the labeling conclusion that the player 

seeks to achieve. 

However, this experimental freedom for the interrogator 

comes at a price. Now, many of the things that came for free 

in the Real World environment such as sensory stimulation, 

and the inherent costs humans associate with risk to their 

person or property must be artificially created. For example, 

to provide the same sights, sounds, smells, and other sensory 

phenomena to the driver, we require novel simulation 

platforms that can re-create those effects. While a few high 

fidelity simulators exist in the world today, in most cases 

many variables are ignored if they are not critical to the 

experiment of interest. (Olfactory stimulation, for example, 

need not be included as part of following a truck in a 

simulator, but in the real world it might influence the decision 

a human driver would make about whether to pass a truck or 

remain behind it on the road.) 

The key advantage to both Vehicle-to-Vehicle interaction 

variants of the ATT are that they adhere most closely to 

Turing’s original test. 

Whether the simulated reality in the ATT is a perfect 

recreation of any known location in our world is not important 

for this vehicle-to-vehicle variant of the test. But, since one 

typical purpose in measuring the “intelligence” of a vehicle is 

to help decide when such a vehicle would be safe to introduce 

to our roads in the real world, we would benefit by requiring 

all vehicles to treat the simulated environment the same way 

they would treat the environment in the real world. To 

achieve this, we need them to act in the following ways and 

have the following characteristics: 

• They must obey traffic laws and traffic control 

devices and customs; 

• They must avoid curbs, trees, vehicles and 

pedestrian figures; 

• They must have similar fields of view and 

limitations on their knowledge of the environment 

as they would in the real world (limited but wide 

field of view dependant on modeled head 

orientation; olfactory, auditory and tactile feedback 

separated from the visual field of view; reasonable 

directionality to all sensory input to provide the 

normal input available for a human driving in a 

vehicle); and 

• They must have limited but reasonable control 

capabilities over the virtual vehicles they are 

driving, similar to the capabilities they would have 

over vehicles they would drive in the real world. 

Having made these demands of our test to help fit it to a 

human goal for the domain of intelligent vehicles, we observe 

that this strong requirement can actually be relaxed without 

upsetting the pure intelligence testing measure developed by 

the imitation game. This is based upon the notion that the 

imitation game versions of the TT and ATT are not really 

about safety, or obeying traffic laws per se, but about 

producing behaviors that are indistinguishable from human 

behaviors in the environment in which they are observed. 

We claim here that in imitation game based ATT’s, obeying 

traffic laws will only matter to the players of the game to the 

extent that humans also choose to obey those laws in the 

given environment. However, if we ultimately hope to 

convince ourselves and society that an intelligent vehicle is as 

safe as, or safer than, human driven vehicles on the road (and 

therefore worthy to share the road with humans), we will want 

to consider ways to create the test environment so the value of 

obeying traffic laws and civil behavior (and the cost of 

disobeying them) is clearly understood and similar for all 

players in the testing environment, and is similarly weighted 

to what it is now in real world. The often raised issue about 

155
how similar is human behavior in a simulated environment is 

then revisited here, but this time with an eye to parameters 

that can be built into the simulation environment to help 

enforce the kind of behavior we might desire. One 

considered approach to enforce this is to offer something of 

value (perhaps points that can be inherently valuable to a 

machine, or exchanged for prizes or money by a human player 

after they finish taking part in the test), and to place “law 

enforcement” drones in the simulated environment 

(location-locked camera-like systems or mobile police-like 

vehicles in the environment) that either deterministically or 

probabilistically penalize the player for violations of the laws 

in that environment. Such additions to the environment 

might be a noted increase in the effort necessary to construct 

it, but might still be beneficial for future consideration. 

However, a test that tests for differences from a standard 

obviously carries with it all of the strengths and weaknesses of 

that standard, should one achieve the goal of being 

indistinguishable from the standard. With this in mind, we 

now turn our attention to other variants of the ATT whose 

measures perhaps allow more freedom in what is being 

measured. The Road Test format, with its implication of 

Pass or Fail grading for driving at a level suitable to be 

considered worthy of being called human, is perhaps more 

easily adapted to address the societal goal of intelligent 

vehicles that are not just human-like, but as clever as a human 

and at least as safe. 

Variant 3: Real World environment with Road Test format.

This variant is similar to the variant 1 except that it prevents 

the interrogator from driving his or her own vehicle. 

However, in contrast to the variant 1, the interrogator is 

always present in the vehicle, and he can observe its behavior 

constantly, rather than episodically, as in the first two 

variants. 

The interrogator can provide instructions to the driver in at 

least two ways, verbally or electronically. The instructions 

should be relevant to driving, e.g., “make lane change”, “turn 

right over there”, etc. This variant of the ATT should not 

turn into a domain-nonspecific testing the driver for natural 

language understanding. 

The instructions provided to the driver must be given with a 

reasonable lead time for the driver to execute the appropriate 

maneuver safely. Alternatively, the instructions could be 

delivered at any time so long as the driver is empowered to 

just ignore illegal or unsafe instructions from the interrogator. 

Variant 4: Simulated environment with Road Test format.

This variant is similar to variant 3 except that the real world 

environment is replaced by the simulated environment. In 

contrast to variant 2, the interrogator cannot drive his own 

virtual vehicle, but may enjoy the benefit of constantly 

observing the player’s behavior. 

Variant 5: Passive interrogation via recorded behavior 

comparison to database of human behavior. 

A fifth variant, alluded to earlier, is available if recorded 

data becomes available through black-box type recorders. 

We proceed here to describe the ATT in that form, and to 

bring out the interesting points it reveals. 

It bears stating that this departs from the original TT in two 

ways. First, passive interrogation removes any aspect of 

en-route interaction with the driver to be assessed, reducing 

the interrogator’s role to that of mere observer. Second, this 

test could be implemented as a computerized classifier system, 

which offers interesting opportunities, but places the human 

even further out of the testing loop, so that now they only 

prescribe the statistical tests employed, with the rest of the test 

executable without human intervention. 

To explore this version in some detail, in this variant of the 

ATT, the behavioral estimate of the player is made not on 

second-to-second unfolding observations, but upon the 

ensemble recording of their behaviors over a set of routes 

driven during the test run. This passively interrogated, 

after-the-fact analysis of the data recorded from a new driver 

is then compared to a larger set of recorded data from known 

human drivers driving under similar conditions. Such a 

comparison would help reveal the relationship of the newly 

recorded dataset to the pool of human data encapsulated by 

the larger multi-driver ensemble. 

We note here that even if a newly recorded player’s 

behavior differed significantly from that of other vehicles in 

the database of vehicles driven by humans, it would not 

necessarily mean that that new player was a machine. But, 

such a finding of significant difference form the behavior of 

the comparison set would certainly be worthy of further 

review, as it identifies a driver whose behavior doesn’t fit the 

behaviors of the others in the ensemble to which it was 

compared. Obviously all proper statistical concerns must be 

observed to make sure that both the sample of new driver 

behavior and the ensemble of behavior to which it is 

compared are large enough and rich enough to provide 

statistical reliability in the estimates they provide. 

Interestingly, we see no reason why data recordings from the 

real world couldn’t be just as valid as those collected in 

simulation. 

Other variants 

During considerations of the idea of the ATT, a number of 

other variants have come up. 

One as yet poorly explored variant considers the inclusion of 

real vehicles driven by a player or interrogator via remote 

control. In both the vehicle-to-vehicle, and Road Test 

administrations of the ATT, this option could be included. 

This is interesting in particular because it raises a basic 

question. “Would the driving behavior be distinguishable 

between a human driving in a car as is normal today, and a 

human driving a real car via remote control?” Such a test 

adheres closely to the imitation game form of the ATT. But, 

more importantly, it gets directly at the heart of what it means 

to have your “skin in the game”. We hypothesize that there 

will be discernable differences in behavior and interactions 

156
with other vehicles when the remote driver is safely 

ensconced away from the vehicle that is at risk. 

If it were of interest, perhaps in a future where automated 

machines were the safer way to travel, another variant could 

be imagined, in which the passive interrogator version of the 

ATT is reversed to compare a human driver’s recorded 

behavior data to a database of super-safe machine drivers. In 

that environment, this method might decide when the human 

driver’s behavior differed little from super-safe machine 

drivers to be indistinguishable from them, making it safe to 

allow them to share the roads. Such a situation could also 

arise as part of the integration of human driven vehicles into 

environments that were established as isolated roadways 

initially built for the exclusive use of intelligent autonomous 

vehicles, as has been suggested by the earlier automated 

vehicle projects like those demonstrated by the DOT in the 

late 1990’s. 

V. WHAT DOES THIS AUTOMOTIVE TURING TEST (ATT)

ACTUALLY MEASURE, AND DO WE WANT THAT? 

The measure of intelligence in the original TT [1] is actually 

quite interesting, as are the related measures coming out of the 

ATT variants described in this paper. In the original TT, the 

judgment made by the human interrogator is based upon their 

observations of the behaviors of the players. But, the 

behaviors of the players are actually based upon B’s (likely) 

honest attempt to represent herself faithfully, and on the 

deceiver’s (A’s) ability to model, duplicate, and display B’s 

behavior appropriately to deceive the interrogator into 

thinking that player A is in fact player B. The model that A 

builds could also be augmented to include a model of the 

expectations of the interrogator C, capturing what A expects C 

to look for in his attempt to recognize honest player B from 

deceiver A. This would allow deceiver A to exhibit that 

behavior first, or to prepare a counter behavior that either 

influences C’s expectations of B or nullifies the effect of B’s 

behavior (as in Turing’s suggested example statement by B 

“I’m the woman”, followed by A’s comment “Don’t listen to 

him, I’m the woman”). 

While human guile, and to some extent its duplication by a 

machine, are inherently part of the definition of human 

thinking in the original TT and imitation game, the other 

interpretations of the TT idea ease this requirement. Instead, 

they replace it with a demand for sufficient skill in a particular 

task or a set of tasks. This implies that outside of 

conversational games, guile is not necessarily the ultimate 

definition of intelligence. It is at this point that the Road 

Test version of the ATT comes into play, because for most 

people the goal of vehicle intelligence is not to take the risks 

that humans would, but rather to be as safe and effective on 

our roadways as humans are today, or more so. Thus, for the 

Road Test version of the ATT, we seek a vehicle that is smart 

enough to be safe in situations where a human might not be. 

This differs substantially from the Vehicle-to-vehicle ATT 

variants, which would give us a measure of whether or not a 

human observing the behavior of our intelligent vehicle could 

reasonably have the same expectations of this vehicle’s 

behavior that they would have of a human driver’s. Given 

the number of accidents on our roadways every year, it is 

reasonable to ask even then whether this would be an 

acceptable standard. To add to the Vehicle-to-Vehicle variants 

the element of safety, we would need to require that our set of 

human players be limited to humans known to be notably 

good or safe drivers. This is a standard which, while 

obvious when grossly violated, may still be somewhat loosely 

defined currently and harder to assure than we might like.

So, if not absolute safety from an intelligent vehicle, what 

do we gain by using a purely human standard of intelligence? 

We may gain two important things: (1) a behavioral estimate 

of the driver we observe as matching a model of behavioral 

expectations which we build in our own mind, and (2) an 

estimate of how far we should trust that driver to actually use 

that model. 

The creation of a model of the observed driver behaviors 

allows us to pre-plan actions to take if the observed driver 

either maintains their currently estimated course of action, or 

changes to one or the other of the predicted actions. This 

pre-computing and caching of solutions to more probable 

future situations is a valuable skill that allows faster 

identification of situation changes and faster reactions to those 

changing situations. This pre-computation is lost or 

significantly curtailed when encountering vehicles that drive 

in ways very different from the way we expect, as can be 

observed when driving for the first time in an unfamiliar 

location. This can be most striking when traveling in a land 

where cars drive on the opposite side of the road from a 

driver’s previous experience. But, over time, through 

observation and mental modeling we reconstruct a model of 

the behavior of others around us. We can then incorporate 

their actions into our own repertoire and expectations. The 

inherent flexibility associated with this skill of learning and 

adapting is central to the aspects of intelligence that come to 

mind when we describe humans as smarter than a brittle 

artificial system that is otherwise highly trained but limited to 

a single fixed set of already-solved problems. But, 

underlying this aspect of intelligence, is the action of 

observing others, modeling their behavior, and adapting our 

own to be indistinguishable from theirs. This is the key 

measure tested by the TT and the Vehicle-to-Vehicle ATT, 

and this is what makes us safer in new situations, which is 

something we would like to see for intelligent vehicles. 

 If predictability similar to a human’s is valuable because it 

allows us to prepare for upcoming situations, knowing the 

limit of that predictability is also valuable, but in a different 

way. One value is that, when we remember this limitation 

and explicitly include it in our thinking, it keeps us watching 

for the unexpected. This reduces our chances of becoming 

overconfident, and limits the risk to which we are willing to 

expose ourselves. 

Another, and perhaps more important value of the limitation 

of the predictability of other vehicles, is that it creates a social 

contract between vehicles on the road to ensure their mutual 

157
safety. The expectation of some unpredictability in the 

behavior of others around us (the element of surprise 

mentioned in Section III), and a respect for the sovereign right 

of others to behave in ways that we might not have predicted, 

leads us to create buffer zones around vehicles that are larger 

than would be required if the behavior of other vehicles was 

perfectly predictable. 

A good example illustrating the points above in everyday 

driving can be seen in severely foggy or snowy weather, when 

vehicles often close the gaps between themselves and the 

vehicle ahead and rely to a larger extent than usual on 

following the taillights of the vehicle in front of them. When 

a driver of the lead vehicle overestimates the predictability of 

the rest of the environment, that mistake can ripple back 

through the entire chain of vehicles, with each in turn 

overestimating the reliability of the actions of the vehicles and 

environment in front of them. These chains of unmet 

expectations in the reliability of other drivers may lead to 

much larger accidents than might occur if each driver adjusted 

their estimates of how far to trust the other drivers more 

appropriately to match the actual environmental conditions. 

Concluding this section, we wish to touch upon the amount 

of intelligence required to pass Road Test variants of the 

ATT. 

In general, it does not require a lot of conscious efforts for 

an experienced human driver to drive a vehicle safely, 

especially in a familiar environment, e.g., repetitive drives 

from home to work and back. Potentially significant mental 

efforts seem to be used only for complex navigational tasks in 

a busy traffic environment, or when a driver chooses to do 

several tasks simultaneously (e.g., talking on a cell phone 

while checking directions on the map and driving), likely at 

the expense of an elevated risk of an accident. 

Sometimes a driver must exercise a quick and correct 

judgment and decision making to avoid an accident or 

minimize its severity. For example, if a child suddenly 

jumps out on the road in front of the vehicle, a driver must 

quickly execute a suitable avoidance maneuver (humans 

would do so instinctively, but unfortunately not always 

successfully). A driver might opt for driving in a ditch next 

to the road to avoid hitting the child. 

When an animal suddenly appears on the road, drivers 

sometimes choose the “stay the course” behavior for their 

vehicles, as evident in many US states by the sight of dead 

animals lying on the road. 

What if a ball suddenly appears in front of the moving 

vehicle? Does this mean that a child might follow the ball in 

the next second? This hints at the need for an intelligent 

vehicle to possess intelligence broader than what is 

immediately applicable to driving or simply distinguishing 

humans from animals. It is highly desirable that the 

intelligent vehicle have enough intelligence not only to react 

quickly but also to exceed any human driver in ability to 

avoid the collision with a human or an animal. 

VI. CONCLUSION

In this paper we proposed several variants of the 

Automotive Turing Test (ATT) based on both the original 

imitation game version of the TT, and on subsequent 

extensions of the TT into domains of expert performance. 

We sketched out several implementations of the ATT for 

consideration and discussion on the topic. The first 

implementation is administered with only vehicle-to-vehicle 

interactions, while the second is administered “Road Test” 

style in a way very similar to student driver testing. These 

two administration styles and methods were described for 

implementation on both cars in the real world, and in a high 

fidelity simulation environment where risk to life, limb, or 

property can be removed. We discussed the existing value 

of testing for human levels of intelligence, including its 

inherent limitations and perhaps diverging goals. We 

distinguish it from a more typical automotive definition of 

vehicle intelligence, which uses the safety of vehicle 

occupants as a proxy for vehicle intelligence. We also 

pointed out how modeling human weaknesses within a system 

can actually strengthen the robustness of the system to protect 

against system perturbations or failures. 

We acknowledge readily that there is a great deal more work 

to be done to instantiate the ATT, and to deliver intelligent 

vehicle driver systems and intelligent driver support systems 

that can compete with human drivers in the ATT. While 

focusing initially on human-like driving qualities, we point 

out how the ATT discussion might prove useful to target 

development of intermediate steps in the process of realizing 

intelligent driving systems in which accidents no longer occur. 

We also foresee opportunities through this work to explore the 

system of interacting human behaviors that take place on our 

roads, and to explore the true nature of human intelligence as 

it is reflected in vehicle control decisions. 

ACKNOWLEDGEMENT

SFK also thanks his family (DK, RK, & VK) for their support 

and understanding during the preparation of this paper. We 

also want to thank Michael Samples for useful comments 

about a draft of this paper. 

REFERENCES

[1] A.M. Turing, “Computational Intelligence and Machinery”, Mind, Vol 

LIX, No. 236, pp 433-460, 1950 

[2] Dingus,T.A., Klauer,S.G., Neale,V.L., Petersen,A., Lee, S.E., Sudweeks,J., 

Perez, M. A., Hankey, J., Ramsey,D., Gupta,S., Bucher,C., Doerzaph,Z.R., 

Jermeland,J., and Knipling,R.R., “The 100-Car Naturalistic Driving Study, 

Phase II – Results of the 100-Car Field Experiment”, DOT HS 810 593, April 

2006 

[3] S.J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, p. 

2, Prentice Hall, 2nd edition, Pearson Education Inc, Upper Saddle River NJ, 

2003. 

158
Autonomous Robots with Both Body and

Behavior Self-Knowledge

B Brent Gordon

In the spirit of the conference theme, the interplay between autonomy and intelligence, the general direction of

this paper is to describe how autonomous robots could begin

to autonomously develop intelligence. (For us a robot is

always a constructed, embodied, situated system.) Most of

the discussion will take place at the abstract level of system

architecture and design principles. For the sake of discussion

we assume that the robots under consideration have a reactive

or behavior-based system as part of their architecture. This is

reasonable since it is true of a high proportion of autonomous

robots built today.

1) Developmental robotics framework: The framework in

which we will work is that of biologically motivated developmental, or epigenetic, robotics. Recall that developmental

robotics is based on the idea of a (cognitive) robotic system

developing knowledge of itself, its environment, and the

dynamic activities of its world in (possibly overlapping and

irregular) phases, analogously to the development of a human

child [1] [2] [3]. In this approach learning is experiential—

there is no alternative for animals and humans—and therefore

the act of learning and creating of intelligence is, unavoidably,

autonomous.

2) The biological analogy, sensing: For more insight we

set up a biological analogy that takes into account some major

components of a robot’s, and human’s, sensorimotor systems

and software architecture. Clearly the external sensing on

the robot should correspond to the human external senses of

seeing, hearing, smell, and taste, even if seeing does contribute

to proprioception. Human vestibular senses are calibrated

against ambient gravity, so whether to consider them selfsensing or external depends on the circumstances. Touch is

composed of several different kinds of nerve endings, among

which we consider sensitivity to light, heavy, or sharp pressure,

or to heat and cold, as external. Next, there is a good analogy

between the kinesthetic sensors on the robot that measure

motion and forces for moving parts and the human somatic

nervous system, comprised of the nerves that run between the

spinal cord and muscles and bones. Similarly, robotic sensors

measuring power levels, fluid levels, temperature levels at

different locations, etc., are roughly analogous to the nerves

of the autonomic nervous system that run between the spinal

cord and various internal organs.

3) The biological analogy, architecture: As for the software components, if the robot has a deliberative or cognitive

component in its architecture, we will take that to be in analogy with human reasoning and planning, without particular

concern for exactly where in the brain that is located, if

indeed it is localized anywhere. From a behavioral perspective

we will consider that, under the analogy, the behavior-based

component should correspond to the movements we humans

make all the time without thinking about them or directing

any conscious attention to them—in other words, our learned

habits. (The set of human behavioral reflex responses, which

includes the vestibular-ocular reflex and the posture reflex

among others, don’t make as interesting or useful an analogy.)

It turns out that most of our common and habitual movement

is governed by the cerebellum. A useful and experimentally

sound model is that the cerebellum contains multiple pairs of

behavior models, where each pair consists of a forward, or

predictive, and an inverse, or controller, model; and moreover,

these behaviors can be combined together with weighting

factors chosen according to how well their forward models

match the new or desired behavior [4] [5] [6].

4) The Body and Behavior Self-Knowledge Design Principle (BBSK): Given a cognitive autonomous robot, increase, or

make sure it is provided with a lot of, physical self-sensing.

Then collect not only all the environmental and self-sensor

readings but also the states and behaviors of each control

element in the robot’s behavior-based system, and make all

this data available to robot’s cognitive system in addition to

logging it.

The idea to collect and log this data was presented by Doug

Gage already last year [7], and it was around that same time

that Aaron Sloman [8] gave me the idea of making it available

to the robot’s cognitive subsystem. To see in part why we have

elevated these ideas to the status of a design principle, push

BBSK across the biological analogy to the human side. Then

the data being collected and provided to the brain is more than

sufficient for proprioception, the knowledge available to us at

all times of where all the parts of our bodies are and how they

are moving, whether we consciously access that information

or not. It also more than suffices for dynamic internal selfmodels. And insofar as it has been checked, proprioception and

internal dynamic self-models are virtually universal across the

animal kingdom; the latter, for example, have been confirmed

in some insects [9]. In addition we can anticipate how BBSK

should lead to a number of useful architectural features, some

of which we describe below.

5) The value of logging: Originally [7] suggested that even

just logging the data would provide users and developers

“with hard data to support system adaptation and on which to

base discrete product improvements,” as well as substantially

simplify developers’ debugging problems. On these grounds

and the declining cost of sensors, processing, and memory

storage, he argued that the costs of implementing the BBSK

principle even just to this extent (in our terminology) would

be more than outweighed by the benefits.

159
6) Implementation substrate: To simplify the exposition

from this point forward we will assume that the robot has

been entirely behavior-based and that we are now adding to

its architecture a suitably powerful cognitive component with

plenty of learning capability. Even had the robotic system been

constructed with a hybrid architecture from the start, there

would still be a Robotic Body-Mind Integration Problem [10]

since the cognitive component works in the symbolic language

of logical reasoning while the behavior-based component

works in the numerical language of control theory, and thus

the two don’t understand each other.

Next, as a thought experiment, for definiteness and to avoid

complications in the exposition, imagine our robot is a multilink arm with a dexterous manipulator mounted on a mobility

platform, with whatever visual and other external sensors you

like. Thus its physical design allows it to do much more than

can be built into its behavior-based architecture, so it is a good

candidate for taking the next step. So we add to it all manner of

self-sensing, and alongside the old behavior-based system we

install a powerful cognitive learning and reasoning package.

Now the point is, in hardware and software we still have to

set up the I/O from the sensors to the cognitive system, from

the reactive system to the cognitive system, from the cognitive

system to the reactive system, and from the cognitive system to

low-level control, where its signals may override or be blended

with those from the reactive system, perhaps. We henceforth

assume all that has been handled.

Currently we believe that the input from the kinesthetic

self-sensors should come into the cognitive system without

any preset structure or hierarchy, in order to give the cognitive system itself the maximum flexibility. In particular, the

cognitive system could then determine its own hierarchical

indexing scheme, or have several, and change entries around,

according to its needs and as those needs change. Of course

real experiments or experience could ultimately suggest a

different approach.

7) Multi-resolution, multi-perspective, dynamic, body selfmodel: One of the first and most significant outcomes is

the feasibility of a more comprehensive and detailed explicit

internal body self-model, i.e., of the robot’s physical structure

and how its parts move individually and together. Humans,

for example, learn to know where the parts of their bodies

are and how they are moving, and at any given moment one

may consciously refer to this model focusing at any point on

the body with any level of resolution, or may let it run in the

background until some anomaly calls conscious attention to

it. The basic idea is to implement something analogous in the

robot.

8) Multi-level motor control: In a similar spirit as the

previous paragraph, with a systematic increase in self-sensing

throughout its body, the robot may be able to coordinate

movements it could not previously. For example, new feedback loops, and hence finer motor control, may be available

for some joints. Or, because sensory data from everywhere

is centralized, it may become possible to coordinate the

movements of parts whose motion could not previously be

coordinated. The idea is that these new coordinated behaviors

would supplement, not replace, the coordinations that were

already present in the behavior-based system.

9) Behavior model: In the meanwhile, the cognitive system potentially has a complete model of the behavior-based

system, and could be able to follow or even anticipate the

behavior-based system’s responses. This should provide yet

another layer of supervisory control, for added overall system

robustness.

10) Integrating new habits with old: One of the things

humans can do is learn a new behavioral skill, such as walking

or driving a car, and practice it until its proper execution

requires a minimum of direct conscious cognitive attention.

At that point, and in the case of basic physical coordination

and competencies, only then, it becomes possible to learn

something else or develop cognitively while performing that

behavior. According to our analogy something very similar

should be true for our robot. While the robot’s behavior is

being controlled by its behavior-based system, the cognitive

system is freed up to learn or develop or do whatever. While

the cognitive system is controlling the behavior, such as when

the robot is learning a new skill, then the cognitive system

is not free to develop. Thus, as new behavioral patterns are

learned and mastered, they need to be saved as habits. In

practice this will probably not be as delicate a question as that

of how to integrate them with the existing behavior patterns

of the reactive system, as this issue, while not identical with

the Robotic Body-Mind Integration Problem mentioned earlier

(paragraph -.6), skirts very close to it.

11) Interplay between development and learning: To come

full circle, and return back to the ideas in paragraph -.1, we

suggest that the intricate relationship between development

and learning, apparently a topic of great debate in human

development circles [1], provides a good mirror for questions

about the interaction between autonomy and intelligence.

Rephrasing some of the major views in the developmentlearning debate, maybe intelligence takes place in a context

set up by autonomy (Piagetian), cf. [2]. Or maybe the two are

mutually coupled, so that intelligence advances autonomy, and

autonomy enables, limits, or triggers intelligence (dynamical

systems), cf. [11]. Or maybe there is no real boundary between

autonomy and intelligence at all when we properly consider

the dynamics and subsystems at all scales (the complex

systems view), cf. [12] for a biological study. Perhaps all of

these views are useful in different contexts.

160
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[1] Lungarella, M., et al., Developmental robotics: a survey. Connection

Science 15 (2003), 151–190.

[2] Metta, G., et al., Development and robotics. In Proceedings of the

IEEE-RAS International Conference on Humanoid Robots (Tokyo,

Japan), 2001, pp. 33–42.

[3] Asada, M., et al., Cognitive developmental robotics as a new paradigm

for the design of humanoid robots. Robotics and Autonomous Systems

37 (2001), 185–193.

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models for motor control. Neural Networks 11 (1998), 1317–1329.

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and taskable systems. In Performance Standards for Intelligent Systems

Workshop (Gaithersburg, Maryland, USA), 2006, pp. 52–53.

[8] Sloman, A., personal communication.

[9] Schomaker, L., Anticipation in cybernetic systems: a case against

mindless anti-representationalism. In 2004 International Conference

on Systems, Man and Cybernetics vol. 2, pp. 2037–2045.

[10] Kawamura, K., Dodd, W., Ratanaswasd, P., Robotic body-mind integration: next grand challenge in robotics. In Proceedings of the

2004 IEEE International Workshop on Robot and Human Interactive

Communication (Kurashiki, Okayama, Japan), 2004, pp. 23–28.

[11] Kuhl, P., Language, mind, and brain: experience alters perception. In

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115.

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Development of Cognition and Action. Cambridge, MA, MIT Press

(A Bradford Book), 1994.

161
A Cognitive-based Agent Architecture for Autonomous Situation Analysis 

 Gary Berg-Cross Wai-Tat Fu Augustine Kwon

 EM&I University of Illinois ICF International 

 Gary.berg-cross@em-i.com wfu@uiuc.edu akwon@icfi.com

 

Abstract 

We discuss the start of a project investigating a cognitive 

basis for intelligent agents that can approach the problem of 

situation understanding. We suggest that a practical system 

of intelligent agents can be build adapting existing agent 

modeling frameworks, ontologies from semantic web 

technology as well as a reasonable situation domain 

models. These can be brought together with a suitable 

cognitive architecture ACT-R which could be used to 

provide key roles in more human like situational awareness 

capability in emergency and disaster operations, especially 

where sensor information is harvested from semantically 

heterogeneous data sources. Existing situational ontologies 

and vocabularies can be supplemented by using DOLCE’s 

formal ontology. This serves as a metalevel ontology that 

can relate different ontology modules and can generate new 

categories to extend an ontology (by agent learning) as 

needed. semantically-rich, conceptual level 

representations of real-world events. A Descriptions & 

Situations ontology provides a theory of ontological 

contexts capable of describing various types of context 

including non- physical situations, plans, beliefs, as 

entities so they can be communicated and understood 

between agents. We believe our system architecture 

provides a relatively good built-in infrastructure to meet 

fairly rigorous performance measurement requirements and 

has general applicability in a wide variety of situations. 

Keywords: cognitive models, disaster situations, 

information fusion, intelligent agents, ontologies, situation 

understanding 

1. Introduction 

As witnessed by the diversity of papers in past PerMIS 

conferences no single technique or tool available to 

build/develop intelligent systems (IS)/ agents has proven 

adequate to address all the functionality desired. This is 

true even for even relatively simple software information 

agents using the meaning of the data for information 

sharing such as envisioned in the original Semantic Web 

(SW) concept . Currently, there are multiple views on a 

suitable architecture and the nature of the knowledge 

needed to develop successful SW agents. Moreover, agentbased information integration, as typically discussed for the 

Semantic Web is not the only type of information fusion 

being actively researched. Sensors provide systems access 

to real-time (or near real-time) streams of actual, low-level 

events. These serve as input to other agents which structure 

this data and integrate it with higher level concepts. Such 

sensor-based processing is used in many domains, including 

disaster response, crisis management, modern battlefield 

operations and health monitoring. These situations are 

characterized by multiple, distributed heterogeneous 

information sources, and rapidly changing situations that 

may include mobile agents/objects. Special agent 

capabilities are needed because situations involve a large 

number of inter-dependent, dynamic objects that change 

their states in time and space, and engage each other in 

fairly complex relations. As a result, required intelligent 

systems capabilities include effective methods for situation 

recognition, prediction, and reasoning activities. 

Collectively these capabilities have been called situation 

management (Jakobson et al 2006). Research to build such 

situation understanding agents requires processing dynamic 

situations using complex cognitive modeling, design and 

population of formal situation ontologies, collection and 

fusion of sensor. Current agent systems still have difficulty 

accommodating things like diverse spatiotemporal 

information, within a single analytic context in a suitable 

period of time. Yet as part of analytic process for 

understanding situation humans easily integrate both 

quantitative and qualitative information assessments to 

quickly arrive at analytic conclusions. The discrepancy may 

in part be in part due to the duality behind human cognitive 

architecture. The dual processing theory (Evans, 2008) 

distinguishes between cognitive processes that are: 

• unconscious, rapid, automatic and high capacity, 

and 

• those that are conscious, slow and deliberative. 

This characterizes human reasoning as a robust interplay 

between an easily believed perception-based system and a 

more cognitively demanding logic-based reasoning system. 

This views intelligence as a developed phenomena that 

balances multiple reasoning mechanisms, together with 

scruffy modules of knowledge which learn to deal with 

situations that are only partial predictability, due to 

dynamics, and the absence of precisely defined states. BergCross (2004, 2006) has suggested that a multi-level hybrid 

architecture, based on a cognitively realistic foundation, 

could approximate human performance for this class of 

problems. To be practical such an architecture would build 

on the existing agent models, semantic web technology and 

standards, as well as a reasonably adequate knowledge and 

162
domain models. The direction proposed herein is an 

architecture that leverages recent advances in machine 

learning, distributed agent technology and semantic 

representation brought together within a suitable cognitive 

architecture - ACT-R. ACT-R is particularly suitable for 

performance measurements of intelligent capability because 

it has been widely shown to be capable of equally human 

performance on a range of task and match human learning 

improvement profiles over time. This paper discusses an 

ACT-R architecture designed to learn and perform aspects 

of situation assessment. Our discussion of work is divided 

into three parts. First we describe what is understood about 

situation understanding cognitively and the ingredients of a 

situational ontology is described. Simple domain ontologies 

can enhanced by leveraging some foundational ontology 

and their modules to formalize concepts like 

“Participation”. Framing moels like Description of 

Situations can be used to model how knowledge of 

information can shared by agents. Second, the ACT-R 

architecture is described showing how it might be populated 

and training on situational understanding tasks. A third and 

final section summarizes the feasibility of the approach and 

describes future research and development plans using 

specific types of situations. 

2. Situational Knowledge and & Ontologies 

As intelligent agents ourselves we generally understand the 

idea of “situations” and an agent with such a comprehension 

can be said to have “situational awareness” (SAW). A 

rational empirical approach to SAW & understanding is 

general defined with three sequential components: (1) 

perception/awareness of elements/objects in the 

environment within a volume of time and space, (2) along 

with a comprehension of their functional nature and 

organizational relationships (their “meaning”) (3) as well as 

an ability to go beyond SAW to project the status & 

relations of situated objects in the near term as an empirical 

test of “expectations”. A top-down, rational model of SAW 

incorporates an agent’s goals & objectives into its reasoning 

about events, relations and situations. This helps upper-level 

agents reduce the number of possible relations definable 

within an agent’s knowledge to constrain situational 

possibilities. By knowing something about what is expected, 

attention on relevant events and relations can improve agent 

operation (Matheus et al. 2005). 

Situation/context-aware systems have been proposed as an 

important class of applications and an important step 

towards ubiquitous computing. Examples of such agent 

systems described at the 1st International Workshop on 

Agent Technology for Disaster Management (Nicholas et 

al., 2006) include discussion of agent architectures to handle 

coordination via intents, multi-agent learning to support 

urban planning, and training based on agent-based situation 

simulation. Often such work involves sensor-based data 

being fused into situational information. Typical 

architectures are multi-agent with sensor agents responsible 

for an initial degree of processing and higher agents 

responsible for associating these inputs with object concepts 

with still “higher” agents responsible for assembly into a 

“situations”. Such systems assemble operating pictures 

using precision geospatial environment information layers 

(modifiable digital overlays) that can support decision 

making based on the detailed “knowledge” shared by the 

agents sensing a physical environment. An example of a 

such a system called SAPPHIRE (Situation Awareness and 

Preparedness for Public Health Incidents using Reasoning 

Engines) shown below dealing with Public Health Incidents 

(Mirhaji & Coyne 2007). Pollution sensing includes 

CO,SO2,H2S,NO etc. with a half dozen meteorological 

factors and chromatography data such as ethane, ethylene 

etc. Systems like SAPPHIRE are not limited to direct sensor 

feeds, and can include entire reports of other agent’s 

processing (hence NLP capability as shown in the Figure). 

In such architectures agents at higher levels in the 

“network” may have more responsibility in that they may 

use more elaborate communication protocols, taking into 

account and monitoring the information provided by lower 

level sensor agents. Several things are needed to make such 

systems effective. For sensor data fusion, common sensor 

standards are needed to create a common sensor data model. 

But to be able to handle conflicting information from 

sensors, higher agents might need a capability for 

incremental, flexible perceptual/ conceptual learning, which 

is feasible through retrieval of relevant memories. A 

common model is that situations are a high-order 

knowledge type of concept that are formed using existing 

concepts. We assume that situation knowledge is formed by 

an agent’s interaction history with the environment and that 

agents can form situation “concepts” by “observing” that 

certain patterns of sequence of inputs from the environment 

. Thus for agent learning evaluative feedback should follow 

a certain action/class of actions, or the next input event from 

the environment given the current action. But beyond this 

proper knowledge is needed to support agent reasoning. 

Ontologies are used as part of the SW thrust into intelligent 

agents to define vocabularies so queries and assertions can 

be exchanged among 

agents (Heflin, 2003). More recently ontologies 

understanding (Kokar et al, 2004 ). We illustrate the use for 

two levels of ontologies for understanding and representing 

situational knowledge – a mid-model of situations and a 

more foundational model that captures more of the event 

aspects of participation in situations as well as the 

relationship between descriptions and situations as metaknowledge which can be used by agents. The SAW 

“model” (Matheus et al 2003) is a “light” ontology 

capturing the core elements and relations of a rational 

agents view of situations, as shown in Figure 2. In the SAW 

ontology model there are primary classes: 

SituationObject, PhysicalObject, and Events. The 

organizing point in the ontology and resulting models is

163
is defined as a relationship to 3 things: Goals, SituationObjects 

and Relations. Situation Objects are entities in a situation that 

participate in Relations and can have characteristics (i.e., 

Attributes). These Attributes define values of specific object 

charc characteristics, such as expected/unexpected, weight

 or c or color. SituationObjects may be PhysicalObject (a 

sub-type) with Volume, Position and Velocity. 

Figure 2. Core Concepts in the SAW Ontology (Matheus et al, 2003) 

Relations are used in a structuring geometrical (not well 

represented in SAW) and positional aspects of these 

concepts. For example, to represent bus transportation 

systems for flood situations being reported on by 

environmental sensors, we need both realistic street 

information that can be overlayed by bus stops and also 

more abstract timetables along with timetables and 

circumstances that apply in emergencies as sensors report 

water height, closed streets etc. This would allow us to 

represent situations about stop on a bus route that is “near” 

the intersection of two streets. However, in event of a flood 

this information will be supplemented by elevation 

information and perhaps closeness to streams. Such 

features are not typically captured in geospatial data bases 

and processable by GIS functions. In our architecture this 

information would be integrated by a situational agent using 

components of information from sensor agents and also 

making use of geospatial repositories of information. To 

handle these requirements the SAW ontology can be 

improved by grounding it in a more foundational ontology 

like DOLCE developed within the WonderWeb Project (EU 

FP5). DOLCE is a cognitively based, “reference” ontology, 

consisting of about 30 classes, 80 properties and many more 

axioms. It is designed to provide a sufficiently neutral base 

to map, integrate, and build domain ontologies, such as an 

improved SAW ontology. DOLCE includes the idea a highlevel participation pattern of objects taking part in the 

Events on the SAW model. DOLCE conceptualizes 

endurants (Objects or Substances) and perdurants (Events, 

States, or Processes) as distinct types linked by the relation 

of “participation”. Participation patterns help us understand 

the structure of repeating events that occur for types of 

situation. As shown time in Figure 3 indexing is provided 

by the temporal location of the event at a time interval 

duration, while the respective spatial location at a space 

region is provided by the participating object. The general 

pattern in Figure 3 uses an extended version of UML which 

can be converted to the DOLCE-time-plus (Gangemi et al, 

Triage

ER 

NLP,

Prosser,

Classifier

Syndrome 

Detection

Baseline 

Histories 

Outbreak, Cluster

Detection

Outbreak

Characterization

Communication, Alert, , Notification 

Information

Integration

Countermeasur

Response 

Manaement

Outbreak 

Managemen

Investigation

Domain Ontologies

SAPPHIRE Ontologies

164
2004) light ontology and is intuitively applicable to 

situations of interest to us including disasters, and health 

monitoring. Particularly nice for representing the knowledge 

that agents need to fuse information into situations is 

DOLCE’s use of an Information-object design 

schema/patter as shown in Figure 3. This model formalizes 

how descriptions, which one agent may receive from 

another, serve as Descriptions for Situations. This 

extension to the DOLCE foundational ontology, is called 

the Descriptions and Situations Ontology (D&S) and can be 

used to define agent workflow - how the information in 

messages can be used by an actor who play a specific role. 

The D&S ontology (Gangemi & Mika, 2003) is based on a 

conceptualization that supports a first-order manipulation of 

descriptive objects (such as clinical plans, evacuation 

routes, emergency plans, institutions, etc.) in effect, theories 

and situations (such as cases, facts, settings)1

. D&S’s 

explicitly committed conceptualization is a distinction 

between an unstructured world or context, and an 

intentionality (description) that recognizes (some say 

constructs) a structure (situation) in that world or context. 

One nice thing about the commitment using D&S is that it 

supports organizing domain theories for areas like disasters 

& healthcare into different ontologies as well as into 

different descriptions or situations. For example, “a flood 

situation” is a disaster entity whose conceptualization is 

realized in several modules - disaster, transportation, 

hydrology, geography etc. Finally the DOLCE models can 

serve as modular, meta-level ontologies that can relate 

different ontology modules and can generate new categories 

to extend an ontology (by agent learning ) as needed. 

3.Rationale for ACT-R 

ACT-R was picked based on many well known technical 

merits, including its activation-based rational action 

selection processes that closely resemble a human cognition 

process, along with various domain models. Under the 

proposed multi-agent based framework, the primary 

functionality of individual agents can be determined by one 

or more plugged-in ACT-R models. This can use ontology 

translators similar to those proposed by Wray et al. (2004) 

to allow various ACT-R models and their host agents to 

share domain as along with inference knowledge captured 

in various ontologlies, A system of different intelligent 

agents involved in various steps of the SAW process can 

work collectively to achieve a common system goal. Each 

ACT-R model has its own set of knowledge representation, 

input/output modules that allow it to interact with the 

external world as well as with other agents/models, and has 

its own learning mechanisms that allow it to adapt to the 

new situations and environmental conditions. Since ACT-R 

 

1 When D&S plugged into DOLCE it results in “DOLCE+” 

with description being a non-physical endurant. A situation 

is added as a top level. 

is developed based on the principle that knowledge are 

always rationally deployed to decide on the next set of 

actions, each model can behave rationally based on the 

existing knowledge it has. For example, ACT-R has a built 

in Bayesian learning mechanism that allows it to retrieve the 

relevant knowledge structure based on its need probabilities 

at a particular context of situations (Anderson & 

Lebiere,1998), as well as a reinforcement-like learning 

mechanism that learns to adapt to the statistical structures of 

the environment so that actions that have led to successful 

outcomes before will more likely be selected in the future 

(Fu & Anderson, 2006). These sophisticated mechanisms 

allow each ACT-R model to gradually adapt and learn the 

skills and knowledge required for different situations. 

Figure 4 Individual ACT-R Architecture 

Another important advantage of the ACT-R architecture is 

its ability to span components of cognition that have 

traditionally been treated as separate in cognitive 

psychology. For instance, a model of ISR analysis will 

likely involve reading and language processing, spatial 

processing, memory, problem solving, reasoning, and skill 

execution and acquisition. ACT-R not only has a generic 

knowledge representation across these different components 

of cognition, but it also specifies how these components are 

integrated to produce behavior. At its lowest level, ACT-R 

has both a spreading-activation mechanism to predict 

accessibility of declarative knowledge and a reinforcementlearning mechanism to predict the future success of certain 

actions. For example, knowledge that is needed frequently 

or repetitive actions with certain outcomes will eventually 

lead to skilled behavior that can be deployed with little 

cognitive resources. At the middle level, deliberate acts 

such as attending to relevant parts of the environment or 

pressing the right key on a device requires intelligent 

integration of multiple sources of information and 

background knowledge to generate intelligent behavior. To 

165
this end, ACT-R has a set of “subsymbolic” 

mechanisms that arbitrate how various sources of 

information should be integrated, and how and what 

actions should be selected and executed at different 

situations. Mechanisms at this level are found to be 

critical to various training goals. For example, work on 

computer-generated forces (Pew & Mavor, 1998) 

shows that training for people interacting with and 

against synthetic partners is effective only when these 

agents perform elementary actions like real people. 

Similar results were obtained by Jones et al. (1999), 

who show that training is effective only when synthetic 

pilots make turns with the timing of real pilots. A 

cognitive-based agent is therefore essential to ensure 

that the simulated environments appear “real” during 

training of operators. At the highest level, long-term 

knowledge are stored as a large set of declarative 

memory elements and procedural rules. This set of 

knowledge can obtained through a diverse set of 

training scenarios in various situation analysis 

environments. Taken together the direction proposed 

here is a multi-level approach that leverages our 

understanding of cognitive agent architecture in 

integrating three levels of information processing 

behavior as shown in Figure 5. In the high level a 

distributed agent architecture such as Cougaar (2007) 

and foundational ontology such as DOLCE will likely 

provide a means to process high level situational 

knowledge that may requires immediate attentions. At 

the rational level ACT-R will act as a bridge to connect 

high level and low level situational information 

processing behaviors through its proven strength in 

pattern matching and conflict resolution. At the low 

level an unconscious behavior such as machine learning 

will help to transform sensor fed raw data to ongoing 

situational knowledge through data filtering and fusion. 

Under the proposed hybrid approach, the anticipated 

major contribution of ACT-R will likely come from a 

Figure 5 Three levels of Behavior 

 

P rocedural m odule

(P roduction ru les)

G o al an d de clara tive

m o du les

P attern m atching &

C onflict resolution

info filte ring and fusio n

H igh level kn ow led ge representation and sharing (O ntolo gies)

R ational level

AC T-R

Low level

M achine learning

H igh level

O ntologies &

A gent architecture

166
rational level where most rule based behaviors through a 

human-like cognitive capability take place. 

One of our research goals is to capitalize on the success of 

ACT-R in simulating the rational/adaptive nature of human 

information processing to coordinate activities in low level 

information fusion/selection and high level semantic 

ontological reasoning to support distributed decision 

making process in autonomous situation analysis. 

4. Summary and Future Research 

In this paper we presented a multi-level approach to 

cognitive agent situational understanding and awareness. 

The first level of performance analysis helps to understand 

cognitive criteria underlying success with SAW and pointed 

out potentially problematic areas and real-time issues with 

agent knowledge which can be addressed by improved 

ontology. 

Performance measurement of a cognitive based distributed 

multi-agent systems (MAS) offers unique challenges that 

must be addressed explicitly in its agent infrastructure. A 

study done by Helsinger et al. (2003) shows that Cougaar’s 

system architecture already provides a relatively good builtin infrastructure to meet fairly rigorous performance 

measurement requirements and a unique ability to use such 

data to adapt to environmental changes. The challenge and 

future research in our proposed solution is how to expand 

the Cougaar’s built-in performance measurement capability 

to tightly integrate various ACT-R based cognitive plug-ins 

models to provide a more powerful and flexible autonomous 

situation analysis platform. 

References 

Anderson, J. R. & Lebiere, C. (1998). The atomic 

components of thought. Mahwah, NJ: Erlbaum. 

Berg-Cross, Gary. A Pragmatic Approach to Discussing 

Intelligence in Systems, Performance Metrics for Intelligent 

Systems (PerMIS) conference 2004. 

Berg-Cross, Gary Developing Knowledge for Intelligent 

Agents: Exploring Parallels in Ontological Analysis and 

Epigenetic Robotics, (invited paper) Performance Metrics 

for Intelligent Systems (PerMIS) conference 2006 

Cougaar (2207) http:// cougaar.org/ 

EU FP5 WonderWeb project 

(http://wonderweb.semanticweb.org) by the Laboratory for 

Applied Ontology (http://www.loa-cnr.it.) 

Evans, Jonathan “DUAL-PROCESSING ACCOUNTS OF 

REASONING, JUDGMENT AND SOCIAL COGNITION” 

in Annual Review of Psychology (2008, in press) 

Fu, W-T. & Anderson, J. R. (2006). From recurrent choice 

to skill learning: A reinforcement-learning model. Journal 

of Experimental Psychology: General, 135(2), 184-206. 

Helsinger, A., R. Lazarus, W. Wright and J. Zinky (2003). 

Tools and techniques for performance measurement of large 

distributed multiagent systems. the second international 

joint conference on Autonomous agents and multiagent 

systems Melbourne, Australia ACM Press 

Jakobson, Gabriel, Buford, John and Lewis, Lundy A 

Framework of Cognitive Situation Modeling and 

Recognition Military Communications Conference, 

2006. MILCOM 2006, Washington, DC, Altusys Corp., 

Princeton, NJ, Oct. 2006 

Nicholas R,. Jennings, Milind Tambe, Toru Ishida, 

Sarvapali D. Ramchurn, First International Workshop on 

Agent Technology for Disaster Management, Hakodate, 

Japan 8th May 2006 

M. Kokar, C. Matheus, K. Baclawski, J. Letkowski, M. 

Hinman, J. Salerno, Use Cases for Ontologies in 

Information Fusion. In Proceedings of FUSION’04, 

Stockholm, Sweden, pages 415-422, June 2004. 

C. Matheus, M. Kokar, K. Baclawski, J. Letkowski, C. Call, 

M. Hinman, J. Salerno and D. Boulware, “SAWA: An 

Assistant for Higher-Level Fusion and Situation 

Awareness”, In Proc. SPIE Conference on Multisensor, 

Multisource Information Fusion, pages 75-85. (2005) 

Parsa Mirhaji, MD & Robert Coyne, The Semantic Web 

And Health Information Systems, SICoP Conference 2 

(April 25 2007) 

Pew, R. W., & Mavor, A. S. (1998). Modeling human and 

organizational behavior: application to military 

simulations. Washington, DC: National Academy Press.

Wray, R. E., Lisse, S., & Beard J., Ontology infrastructure 

for execution-oriented autonomous agents. Knowledge 

Engineering and Ontologies for Autonomous Systems 2004 

AAAI Spring Symposium Volume 49, Issues 1-2, 30 

November 2004, Pages 113-122 

167
Can the Development of Intelligent Robots 

Concepts & Issues from Developmental/Ep

Lisa Meeden (Swarthmore College); Douglas Blank (Br

James Marshall (Sarah Lawrence College); Odest Chadwicke 

University); Charles C. Kemp (Georgia Tech); Gary Berg-C

Ch

Different ConceCognition and R

From Max Lungarella2004

Biological vs. EngineeringApproaches

Intelligence M

etric 

And cannot really bRather it ought to boverall performance variety of situationsIntelligence as a S

isolated system.. is intelligence should 

group/society that iand Lefteri

H. Tsou

Open vs. Closed problems

More generally then, intelligence can be defined as "the ability

for a 

behavior to meet its goals in a range of environments." .....Taking treasonable to assess the intelligence capability of a machine that e

problems in a manner similar to that of evolving phyletic lines in the 

Fogel (2002)

168
On communicating with semantic 

•The problem of designing .... semantic machines has because brains and machines work on very different p•A solution to the problem is to describe how brains crethen express it in information by making a symbol as a another brain in pairwis

e

communic

ation. 

•Understanding of the neurodynamics by which brains enable engineers to build devices with which they can pairwise, as they do now with colleagues, though not wshared actions.Why do brains work this way? Animals and humans survive infinitely complex world despite having finite brains. Their mocoping is to construct hypotheses in the form of neural activithem by movements into the environment. All that they can khypotheses they have constructed, tested, and either acceptsame limitation is currently encountered in the failure of macfunction in environments that are not circumscribed and drascomplexity from the real world. Truly flexible and adaptive inoperating in realistic environments cannot flourish without m

(Walter J Freeman) PerMIS

169

2
Can the Development of Intelligent Robots 

Concepts & Issues from Developmental/Ep

Lisa Meeden (Swarthmore College); Douglas Blank (Br

James Marshall (Sarah Lawrence College); Odest Chadwicke 

University); Charles C. Kemp (Georgia Tech); Gary Berg-C

• Developmental robotics is a newly emerging 

interdisciplinary field that studies how autonomous robots can learn to acquire behavior & knowledge on their own, strictly through their interactions with the surrounding environment. 

• What is our understanding of how innate 

mechanisms for abstraction, prediction, and selfmotivation can be realized in such autonomous systems? 

• What aspects of autonomous robot behavior 

should be preprogrammed as fixed policies and what aspects should be considered latent variable to be learned and adapted over time?

• How can the field reach consensus on the 

methods by which developmental robotic research should be evaluated, and progress benchmarked, and what is the relationship of developmental robotics to autonomous robot manipulation?

170
Overview of Session

Introduction: Gary Berg-Cross (EM&I) Session Organizer Thematic Presentations (about 25 minutes each including • Overview of the developmental robotics field and its issue

Swarthmore College. 

• Self-motivation & How Innate Mechanisms for Abstractio

motivation can be Realized in Autonomous Systems: DouCollege) & James Marshall (Sarah Lawrence College)

• Innate and Adaptive Behavior in Lifelong Robot Learning

(Chad) Jenkins Brown University

• Can Developmental Robots Meet Real Human Needs?: C

Tech

Panel Discussion

45 minutes (Moderator Gary Berg-Cross)

Topics for discussion may include:• Can developmental robotics meet real needs?• How can the reliability of such applications be assured?• Can a developmental approach out-perform existing approaches• What are reasonable milestones for the developmental robotics • H

o

w will we know when the field has made progress towards its 

• Can standardized platfor

ms be created?

• Are social situations necessary to enable the developmental pro

– If so, what types of human/robot interactions will be needed?

171
Performance Metrics for Int

Overview of

Developmental Robotic

Lisa Meeden

Swarthmore College 

172
Performance Metrics for Int

What is developmental robo

 Interdisciplinary approach at the intersection 

developmental biology, developmental psychneuroscience, AI and robotics 

 Inspired by the fact that most complex biolog

organisms undergo an extended period of devbefore reaching their adult form and capabilit

 Rather than building robots to perform specif

defined tasks, developmental robotics seeks topen-ended, autonomous learning systems thcontinually adapt to their environment

173
Performance Metrics for Int

You can only learn what you 

already know

 Machine learning systems work by taking sm

and building on what is already known

 However, it has proven difficult to move very 

the starting point

 Under a developmental process, a system can 

advance what it knows by placing itself into where it almost knows something, and then l

 Applied repeatedly, such a developmental pro

potentially lead to much more complex, genebehavior than has been achieved to date

174
Performance Metrics for Int

Goals of the field

 Seeks to instantiate and investigate biol

psychological models by building robot(primarily the focus of Epigenetic Rob

 Seeks to design better robots by applyin

from developmental biology and psych

175
Performance Metrics for Int

Some origins of the fiel

 Drescher (1991), Made up minds: A con

approach to Artificial Intelligence

 Elman, Bates, Johnson, Karmiloff-Smit

and Plunkett (1996), Rethinking innatenconnectionist perspective on developme

 Ferrell & Kemp (1996), An Ontogenetic 

Perspective to Scaling Sensorimotor In

176
Performance Metrics for Int

Origins (continued)

 April 2000, Workshop on Development and L

funded by NSF and DARPA

 October 2000, White paper suggesting a new 

on Autonomous Mental Development submitNIH, and DARPA

 September 2001, First Epigenetic Robotics (E

Conference held (2007 conference at Rutgers month)

 June 2002, First regularly scheduled Internat

Conference on Development and Learning (I

177
Performance Metrics for Int

Combines aspects of many pr

approaches

 Embodied intelligence (Braitenberg, Br Evolutionary robotics (Nolfi, Floreano) Lifelong learning (Thrun, Jenkins) Machine learning, especially reinforcem

learning (Barto)

178
Performance Metrics for Int

Distinctive aspects of develop

robotics

 Reduces reliance on innate knowledge Provides innate learning mechanisms, allowi

to construct its own representations of its bodenvironment

 Reduces reliance on external goals and tasks Provides intrinsic motivation, allowing the ro

choose actions based on internally generated 

 Increase reliance on human-robot interaction 

providing the necessary scaffolding to learn

179
Performance Metrics for Int

Without pre-defined tasks or 

how can we judge succes

 This question was addressed in the Apr

issue of the AMD Newsletter, response

• Evaluate whether the complexity of behav

increased over time

• Create a grand challenge, where the possib

not specified in advance and encompass a of simple yet varied behaviors

• Use human psychometerics• Having very different morphologies from 

robots may develop skills so different from that we may not be able to analyze them

180
Performance Metrics for Int

Further reading

 Weng, McClelland, Pentland, Sporns, S

Sur & Thelen (2001), Autonomous MenDevelopment by Robots and Animals

 Lungarella, Metta, Pfeifer & Sandini (2

Developmental Robotics: A Survey 

 Meeden & Blank (2006), Introduction t

Developmental Robotics

181
Evaluation of an Integrated Multi-Task Machine

Learning System with Humans in the Loop

Aaron Steinfeld, S. Rachael Bennett, Kyle Cunningham, Matt Lahut,

Pablo-Alejandro Quinones, Django Wexler, Dan Siewiorek*

Jordan Hayes†, Paul Cohen‡, Julie Fitzgerald**, Othar Hansson†, Mike Pool††,

Mark Drummond‡‡

* School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

† Bitway, Inc; ‡U. of Southern California; **JSF Consulting; ††IET; ‡‡SRI International

Correspondence: steinfeld@cmu.edu & radar@bitway.com

Abstract— Performance of a cognitive personal assistant,

RADAR, consisting of multiple machine learning components,

natural language processing, and optimization was examined with a

test explicitly developed to measure the impact of integrated machine

learning when used by a human user in a real world setting. Three

conditions (conventional tools, Radar without learning, and Radar

with learning) were evaluated in a large-scale, between-subjects

study. The study revealed that integrated machine learning does

produce a positive impact on overall performance. This paper also

discusses how specific machine learning components contributed to

human-system performance.

Keywords: machine learning, intelligent systems, mixed-initiative

assistants, evaluation

I. INTRODUCTION

The RADAR (Reflective Agents with Distributed Adaptive

Reasoning) project within the DARPA PAL (Personalized

Assistant that Learns) program is centered on research and

development towards a personal cognitive assistant. The

underlying scientific advances within the project are

predominantly within the realm of integrated machine

learning (ML). These ML approaches are varied and the

resulting technologies are diverse. As such, the integration

result of this research effort, a system called Radar, is a

multi-task machine learning system.

Annual evaluation on the integrated system is a major theme

for the RADAR project, and the PAL program as a whole.

Furthermore, there is an explicit directive to keep the test

consistent throughout the program. As such, considerable

effort was devoted towards designing, implementing, and

executing the evaluation. This paper summarizes efforts to

validate the hypothesized beneficial impact of the integrated

machine learning present in Radar.

It is also important to note that the RADAR project differs

from the bulk of its predecessors in that humans are in the

loop for both the learning and evaluation steps. Radar was

trained by junior members of the team who were largely

unfamiliar with the underlying ML methods. Generic human

subjects were then recruited to use Radar while handling a

simulated crisis in a conference planning domain. This

allowed concrete measurement of human-ML system

performance. It is important to consider personal assistance

systems in the context of human use due to their inherent

purpose.

There have been past attempts at creating digital assistants

to aid users in the performance of complex activities. Possibly

the most memorable and infamous example of these is the

animated paperclip accompanying Microsoft Word. Agents

such as these are usually most valuable to a novice, as

opposed to an experienced user.

On the opposite end of the spectrum of assistants, we can

find those that are human. While human assistants are

malleable, intuitive, accommodating, and are able to expand

their knowledge, they lack certain characteristics present in an

ideal digital assistant. Humans assistants lack perfect recall,

incur latencies on time critical tasks, cannot rapidly compute

optimizations and execute other taxing algorithms, are more

susceptible to periodic performance losses due to turnover and

constrained availability, and cannot operate continuously.

Furthermore, human assistants do not scale well – providing

an assistant to every human in an organization is cost

prohibitive on several metrics.

Radar is an attempt to achieve the best of both worlds by

focusing on a cognitive digital assistant. The presence of

learning is the main distinction when using the prefix

“cognitive.” The knowledge it obtains can be used to

automate and prep tasks, thus providing the assistance of a

human without the limitations of a human and making digital

assistance more adaptable and suitable for the user.

A. The Radar System

Radar is specifically designed to assist with a suite of

white-collar tasks. In most cases, the specific technologies are

designed to be domain agnostic (e.g., email categorizing,

resource scrounging, etc). However, for the purposes of the

evaluation, the base data present in Radar and used for

learning is centric to the domain of conference planning. As

such, certain components appear to be domain-specific but

their underlying technologies are more extensible (e.g.,

182
conference-related email categories, room finding, etc).

While evaluation testing was performed on several Radar

1.x versions, they generally contained the same machine

learning components (Table 1). The major variations were due

to engineering and user interaction improvements in a number

of components and the removal of the Briefing Assistant for

engineering reasons. Again, the individual ML technologies

will not be described in detail here – the focus here is to show

that such integrated systems can provide real benefit and

evaluation can be accomplished in a manner robust to

unforeseen synergies and use.

An important distinction is whether a ML component

“learns in the wild” or requires special interaction to gain

knowledge. Learning in the wild (LITW) is a primary mission

of the RADAR project and is specific to learning that occurs

through the course of daily use. Brute force spoon-feeding and

code-driven knowledge representation is not LITW. To count

as LITW, learning must occur through regular user interaction

and user interfaces present in Radar.

An example of brute force encoding would be asking

someone to copy the campus building specifications into

Radar all at once. However, learning is LITW if Radar

decides knowing the capacity of a certain room is really

important, Radar asks the user for the capacity, and the user

looks it up and enters the specific value.

Table 1 details which components in Radar 1.1 were LITW

and what their specific assistance entails. Note that this list is

continuously growing and more components are expected in

the next major release of Radar. Likewise, the next release is

expected to include tighter integration between ML

components. Additional detail on Radar components and

capabilities is deferred to other papers.

B. Test Conditions and Hypotheses

In order to show the specific influence of learning on overall

performance, there were two Radar conditions – one with

learning (+L) and one without (-L). In the context of the

evaluation test, learning was only LITW. Learning acquired

through knowledge engineering by a programmer or through

brute force encoding would be available in both the +L and -L

Radar conditions.

To the user, Radar was essentially a system layered into

Outlook. The components in Table 1 are either behind the

scenes (e.g., Scone, AnnoDB) or visible as modified Outlook

views (e.g., Email Classifier, VIO) or separate windows (e.g.,

STP). In many ways, the user interaction development aspect

of Radar lagged behind the learning components. This was

largely due to limitations in Outlook and user interaction will

be improved in the next version of Radar.

A third condition where subjects utilize conventional off the

shelf tools (COTS) allowed estimates to be made on the

overall benefit of integration, optimization, engineered

knowledge, and improvements in user interaction as compared

to the current state of the art. For this application, this toolset

consisted of an unaltered version of Outlook, the schedule in

an Excel spreadsheet instead of the STP, a web portal to the

room reservation system, and the conference website which

could be manually updated.

The primary mission of the evaluation test was to examine

two top-level hypotheses. These were:

1. Radar with learning (+L) will do better than Radar

without learning (-L)

2. Radar will do better than conventional tools (COTS)

The comparison in Hypothesis 1 is commonly called the

Learning Delta. Additional hypotheses, detail on methods, and

findings can be found in [1].

C. Related Work

As previously mentioned, this was a multi-task ML system

and therefore required a complex scenario for rigorous

evaluation. Unfortunately, research utilizing human subjects

to evaluate multi-task cognitive digital assistants with

demanding tasks of this nature is limited, and so few

comparison cases are available.

Furthermore, evaluations of ML systems are largely based

on simulation (e.g., [2, 3]), comparison to traditional methods

(e.g., [4]), subject judgments on system performance (e.g.,

[5]), or have sparse details on human subject evaluation (e.g.,

[6]). It is quite possible that this is generally the result of the

kind of system that is built – something that is not meant to be

an assistant but, rather, is designed to perform a task that has

specific rules. An assistance system, when designed and

evaluated, should be tested with humans in the loop (e.g., [7]).

As far as the rest of literature is concerned, there is

relatively little literature on evaluation results of cognitive

digital assistants and their focus tends to be specific to a

Table 1. Radar components

LITW Component Capability

X CMRadar-Rooms (Room Finder) Resource scrounging by learning room reservation owner behaviors

X Email Classifier Task-oriented label assignment to email messages based on prior activity

X Space-Time Planner (STP) Elicitation of facts about the world in order to do better optimizations

X Virtual Information Officer (VIO) Classification and extraction to assist information updates on websites

X Workflow by Example (WbE) Batch website updates from training on input files

Annotations Database (AnnoDB) Email parsing and related natural language processing

Scone Knowledge representation support for the AnnoDB

X Briefing Assistant (BA) Summarization of activity based on prior activity (Note: not deployed)

183
narrow range of learning (e.g., [8, 9]). This may be because

most of assistants of this nature are design exercises, lack

resources for comprehensive evaluation, not evaluated with

humans in the loop, and/or proprietary and unpublished.

II. METHOD AND MATERIALS

A key requirement for the annual evaluation test was

repeatability and a consistent level of difficulty so that

performance improvements can be measured across years. At

a fundamental level, this is nearly impossible to achieve in a

complex test of this nature. As such, the goal was to start with

a test scenario that was challenging enough to accommodate

synergistic learning effects, component advances, and new

research directions for the out-years. A common condition,

working the problem with conventional off the shelf tools

(COTS) is run for each test, thus permitting benchmarking of

small changes to the protocol and each test’s stimulus package

(e.g., specific crisis, additional tasks, etc). Furthermore, the

stimulus package for the test is bound by parameters that are

broad enough to prevent training to the test, but narrow

enough to ensure that the stimulus package will measure the

ML technologies present in the version of Radar being tested.

As mentioned, this is a system consisting of Radar and a

human. At a high level this means that human subjects may

need, or be required, to perform specific tasks manually. The

utilization of a COTS condition where there are no Radar

tools makes the ability for full manual execution a

requirement. This nuance also allows for tasks and stimuli that

are currently difficult for strictly software tools to complete

autonomously – mixed effort towards task completion is

perfectly acceptable and expected. Removal of manual control

can occur if Radar technology replaces the manual inputs. For

example, a user interface that allows subjects to manually

scrounge for resources can be removed if a Radar component

can be used to perform this task.

A. Storyline and Simulated World

The general scenario for the evaluation was that the subject

was filling in for a conference planner who was indisposed, to

resolve a crisis in the current conference plan. This crisis was

major enough to require a major shuffling of the conference

schedule and room assignments that, in turn, triggered

secondary tasks. These included supporting plans (e.g.,

shifting catering, AV equipment delivery, adjusting room

configuration, etc), reporting (e.g., make changes to the

website, issue a daily briefing, etc), and customer handling

(e.g., “here is the campus map”). Noise stimuli were also

present in the form of unrelated email, unusable rooms,

unrelated web pages, and other clutter content.

The materials included an email corpus and simulated world

content. The need for repeatability over time led to the

requirement for a simulated world. This consisted of facts

about the world (e.g., characteristics of a particular room) and

conference (e.g., characteristics of each event).

The simulated world and the initial conference were

designed to provide clear boundaries on the types of tasks

subjects would need to complete, yet also permit large-scale

information gathering, precise measurement of learned facts,

and the opportunity to induce a substantial crisis workload.

The conference itself was a 4-day, multi-track technical

conference complete with social events, an exhibit hall, poster

sessions, tutorials, workshops, plenary talks, and a keynote

address. The conference was populated with over 130

talks/posters, each with a designated speaker and title. All

characters were provided with email addresses and phone

numbers. Many were also given fax numbers, website

addresses, and organizations.

The physical space was a modification and extension of the

local university campus. In addition to modifying the student

union, two academic buildings and a hotel were created and

populated. These latter three buildings were instantiated to

protect against campus entry knowledge in the subject pool.

This information was presented to the subject in the form of

revised university web pages easily accessible from the

subject’s home page.

Other static web content included a conference planning

manual (complete with documentation of standard task

constraints), a read-only file with the original schedule, and

manuals for the tools used by the subjects.

Subjects were also given access to a working, realistic

“university approved” vendor portal where goods and services

could be ordered for the conference. These included

audio-visual equipment, catering, security, floral

arrangements, and general equipment rentals. Email receipts,

complete with computed prices and hyperlinks to

modification/cancellation pages, were delivered to the

subject’s mail client in real time. All vendor interactions were

via web forms since automatic or Wizard of Oz handling of

subject e-mails can lead to problems with stimulus

consistency and realism. This had face validity since many

real-life counterparts are web-based, including the subject

signup website used during recruitment.

The corpus initialization for each experiment included:

• The predecessor’s conference plan in the file format of

the condition toolset

• Other world state information – e.g., room reservation

schedule, web pages detailing room characteristics, etc.

(Figure 1, top and middle)

• The vendor portal, loaded with the initial orders (Figure 1,

bottom)

• Stored e-mail from the original conference planner,

including noise messages and initial vendor orders

• Injected e-mail, including details of the crisis, new tasks,

and noise (e.g., Table 2)

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Cost is a major barrier for experimental research and a large

portion is attributable to stimuli and artifact development. We

have made the commitment to provide much of the stimuli

and supporting content described here to external parties for

re-use. This occurs through the Airspace website [10].

B. Email Corpus

The email corpus was constructed but occasionally utilized

anonymized real content where appropriate (e.g., noise

messages). There were initial attempts to acquire an existing

email corpus centric to a conference planning activity but this

posed significant challenges in the realm of Institutional

Review Board (IRB) approval due to the need to anonymize

all content – including subtle cues that would reveal identities.

Prior attempts within the project to perform such a step

produced haphazard results where entity anonymization was

not sufficient.

Even a real conference planning email corpus free of IRB

constraints would not be entirely adequate. A real corpus

would still require considerable alignment with a simulated

world (e.g., websites, rooms, etc.) and would not necessarily

match the ML technologies present in the system. For

example, the corpus for the real conference may completely

lack website update tasks and focus heavily on what local

tours to include in the registration packet.

This early investigation led to the determination that the

corpus should be fabricated with an eye towards realism and

the ML being tested. A team of undergraduate English majors

was employed to create a detailed backstory corpus,

independent messages detailing one or more tasks, and noise

messages. The students were given a series of story arcs,

guidelines, and a handful of characters with some specific

assigned personalities (e.g., formal, annoying, etc). This effort

included a directive to the email authors to let natural errors

occur in their writing (e.g., signal message in Table 2). Some

characters were assigned personality types that would also

lead to different writing styles and email body structure (e.g.,

terse, bad spelling, etc). Other directives included the

utilization of event, paper, and room descriptor variations (e.g.,

“Dowd in Stever”). Resulting content was screened for fit to

the specifications, alignment with world facts, and template

syntax adherence.

All email corpus content was in a structure which supported

date shifting and variable substitution (e.g., Table 2, sender of

the noise message). Date anchors and variables were stored in

a separate file. These allowed for easy modification of key

Table 2. Sample messages

Signal Message Noise Message

From: jpsontag@ardra.org

To: bor@cs.cmu.edu

Subject: Lucia di Lamermoor

I hate to be a pest, but I finally got

tickets to the opera, Lucia di

Lamermoor for my wife on our

aniversary. It is wednesday night. I

want the whole day to ourselves, so I

can avoid crashing out plans, that

would be great! Let me know. The

other days are fine. Thank! J.P.

From: var="kimMail"

To: bor@cs.cmu.edu

Subject: Hey Uncle Blake!

I have a favor to ask you--Mom and Dad's anniversary is coming up, and I wanted to

do something special for them, especially since they've been so supportive of the

whole wedding concept. I was thinking about getting them tickets to go see "The

Phantom of the Opera" when the Broadway Series came to Pittsburgh. I know that

sometimes you can get cheaper tickets through work, so I was wondering if that was

possible for this show. Please let me know asap so that I can make arrangements!

Thanks, you're the best!

Kim

Figure 1. Static web and vendor portal examples

185
values by the external program evaluators and time shifting of

the corpus for experiment execution.

C. Objective Performance Measurement

As experiment-friendly conference planning performance

measures are not readily available, a new method was utilized.

It was extremely important that this measurement be tied to

objective conference planning performance rather than a

technology-specific algorithm (e.g., F1 for classification).

This technology agnostic approach also permits accurate

measurement of component synergies and human use

strategies.

Creation of this measurement was largely achieved through

an evaluation score designed and developed by the external

program evaluators (authors JF, MP, and PC). This complex

score function summarized overall performance into a single

objective score (“Final_Score” range from 0.000 to 1.000).

Performance was in terms of points collected by satisfying

certain conditions coupled with penalties for specific costs.

These included quality of conference schedule (e.g.,

constraints met, special requests handled, etc), adequate

briefing to conference chair, accurate adjustment of the

website (e.g., contact information changes, updating the

schedule on the website, etc), and costs incurred while

developing schedule. Such costs included both the budget and

how often subjects asked fictional characters to give up their

room reservations. Additional detail on scoring is deferred to

other documents. At the top level, the score coefficients were

2/3rd for the schedule (including penalties for costs incurred),

1/6th for website updating, and 1/6th for briefing quality.

In addition to this measure, subjects also completed a

post-test survey designed to measure perception of system

benefit, assistance, and other related metrics. Details on the

survey design and results are reported elsewhere [11].

D. Procedure

Each subject was run through approximately 3 hours of

testing (1 for subject training and 2 for time on task). Each

cohort of subjects for a particular session was run on a single

condition (COTS, Radar -L, or Radar +L). When possible,

cohorts were balanced over the week and time of day to

prevent session start time bias. Follow-up analyses on this

issue revealed no apparent bias. The nominal cohort size was

15 but was often lower due to dropouts, no-shows, and other

subject losses (e.g., catastrophic software crash). Cohorts

were run as needed to achieve approximately 30 subjects per

condition.

Motivation was handled through supplemental payments for

milestone completion (e.g., the conference plan at the end of

the session satisfies the constraints provided). Subjects were

given general milestone descriptions but not explicit targets.

These milestones roughly corresponded to the top-level

coefficients in the score function.

III. RESULTS

A. Data Source for this Example

There were several test windows during the run-up to the

data shown here. This corresponds to COTS and Radar 1.1

tested with a stimulus package of 107 messages, 42 of which

were noise.

The crisis for this package was a loss of the bulk of the

conference rooms for 1.5 days (out of 4 total). A variety of

other small perturbations rounded out the task set. These

Figure 2. Radar 1.1 results on Crisis 1 (Score 2.0)

Table 3. RADAR 1.1 means and t-test comparisons

Condition Mean Comparison p-value

COTS 0.452 Overall Delta (With Learning > COTS) <0.0001

No Learning (-L) 0.492 Learning Delta (With Learning > No Learning) <0.0001

With Learning (+L) 0.605 Nonlearning Delta (No Learning > COTS) <0.041

186
included changes to room details, speaker availability, session

preferences, and website details. This stimulus package (aka

Crisis 1) was designed by the external evaluators. As of this

paper, the external evaluators have designed three different

crisis packages.

The subject pool used for analysis, after exclusions and

dropouts, was 29, 34, and 32 (COTS, Radar -L, and Radar +L).

As such, this test accumulated 64 cumulative hours worth of

time on task by subjects with a multi-ML system.

Scheduling and scoring for the conditions shown here was

not in parallel. COTS data was collected in the fall of 2005

and the Radar data was collected in the spring of 2006. The

data described here were scored with version 2.0 of the

external evaluator’s scoring algorithms (aka Score 2.0).

B. Final_Score Results

Figure 2 shows between subject performance across the

three conditions. The Learning Delta (the difference due to the

inclusion of machine learning) is 0.113, which is

approximately 74% of the Overall Delta (improvement over

COTS). This suggests that machine learning was the prime

contributor to the performance gains. In this graph, all

condition differences are significant and in the expected

direction for the initial hypotheses (Table 3).

The need for an integrated evaluation with humans in the

loop becomes especially apparent when examining the

makeup of the Deltas (Figure 3). Subjects noticeably altered

their strategies and use of assistance technology based on the

presence/absence of specific features. For example, COTS

subjects clearly focused on updating individual website

corrections (e.g., “my name is spelled wrong”) over other

activities – probably due to familiarity with website form

manipulations. Likewise, subjects in the Radar conditions

took full advantage of autonomous components to relieve time

pressure (i.e., schedule optimizer in both -L and +L, batch

website updating in +L, etc).

Gains due to publishing the schedule to the website can be

tied explicitly back to WbE, but is not the only place where

WbE can contribute/detract from overall performance (Table

4). Note that while the Email Classifier contributes to many

factors of the score function, its role is to surface the task and

not to assist with the completion of the task itself. As such, the

negative Learning Delta for the briefing component (Figure 3)

is not solely due to a deficiency of the Email Classifier. In fact,

this difference is due to human decision making related to task

allocation – almost twice as many subjects in the nonlearning

condition as in the learning condition compiled a briefing

(56% vs. 28%). Task identification is not the same as task

prioritization, hence the importance of an overall task

performance measurement.

IV. DISCUSSION

The results clearly show that Hypothesis 1 (ML helps) holds

true. Likewise, Hypothesis 2 (Radar is better than COTS) is

also true. Furthermore, it is clear that component value was

highly dependent on how subjects allocated effort – some

technologies were underutilized based on strategic decisions.

The initial concern at the start of this endeavor was that the

methods and materials would not be adequately sensitive to

measure mixtures of ML technologies that were still being

formulated. This concern is still valid in that there are new

ML components being developed for the next version of

Figure 3 Score component impacts on the overall score (Score 2.0)

Table 4. Learning contributors to score component

Score Component Learning Contributors

Scheduling STP, CMRadar-Rooms, Email

Classifier

Publishing Schedule

to Web

WbE

Other Web Changes VIO, WbE, Email Classifier

Briefing Email Classifier

187
Radar. The decision to measure at the top human-Radar

system level was an attempt to be robust to unknown ML

technologies. While this limits the ability to directly account

for specific component benefit, this approach clearly captures

high-level benefits and use patterns for human in the loop

multi-task ML.

While not shown here, there have been other human subjects

tests with other versions of the system and the protocol. These

have shown changes in performance due to variations in ML,

HCI, engineering, crisis difficulty, and human training. As

such, the test method and materials have also been shown to

be suitable for measuring shifts in performance due to a

variety of system and scenario effects.

ACKNOWLEDGMENTS

Othar Hansson and Mike Pool joined Google and Convera

(respectively) after contributing to this work.

This material is based upon work supported by the Defense

Advanced Research Projects Agency (DARPA) under

Contract No. NBCHD030010. Any opinions, findings and

conclusions or recommendations expressed in this material

are those of the authors and do not necessarily reflect the

views of the DARPA or the Department of Interior-National

Business Center (DOI-NBC).

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188
Survey Measures for Evaluation of

Cognitive Assistants

Aaron Steinfeld, Pablo-Alejandro Quinones, John Zimmerman,

S. Rachael Bennett, Dan Siewiorek 

School of Computer Science, Carnegie Mellon University

Pittsburgh, PA, USA

{steinfeld@, paq@andrew, johnz@cs, srbennet@andrew, dps@cs}.cmu.edu

Abstract— A survey designed to measure subject perception of

benefit, ease of use, usefulness, collaboration, disorientation, flow,

and assistance was used to evaluate two releases of an integrated

machine learning cognitive assistance system. The design and

validity of this evaluation survey is discussed in the context of an

information overload experiment.

Keywords: subjective performance, intelligent systems, evaluation

I. INTRODUCTION

As part of the RADAR project, a cognitive assistant

equipped with integrated machine learning capability is

regularly evaluated in human subject experiments. This effort

is driven by the belief that machine learning, especially when

implemented in complex integrated systems, needs to be

evaluated on realistic tasks with a human in the loop.

Furthermore, the evaluation is designed to examine the impact

of machine learning under information overload conditions.

Unfortunately, research utilizing human subjects to evaluate

machine learning centric digital assistants with demanding

tasks of this nature is limited. As such, few comparison cases

are available. Worse, survey tools to measure user perception

of such systems are even harder to find in the literature.

Validated surveys are especially valuable in that cross-domain

and cross-application comparisons are often more appropriate

than purely objective metrics.

Evaluations of many machine learning systems are largely

based on simulation (e.g., [1, 2]), comparison to traditional

methods (e.g., [3]), and subject judgments on system

performance (e.g., [4]). It is quite possible that this is

generally the result of the kind of system that is built –

something that is not meant to be an assistant but, rather, is

designed to perform a task that has specific rules. An

assistance system, when designed and evaluated, should be

tested with humans in the loop (e.g., [5]).

There is relatively little literature on evaluation results of

cognitive digital assistants and their focus tends to be specific

to a narrow range of machine learning (e.g., [6, 7]). This may

be because most of assistants of this nature are design

exercises, lack resources for comprehensive evaluation, not

evaluated with humans in the loop, and/or proprietary and

unpublished.

Likewise, explorations of suitable exit surveys (e.g., [8-11])

provided promising survey questions but uncovered few

measures validated for cognitive personal assistants.

NASA-TLX was considered but deemed too narrow for

examination of certain system assistance nuances.

This paper addresses the subsequent efforts by the RADAR

testing team to develop and validate a survey for evaluating

complex technologies under information overload.

A. System and Conditions

Radar, the project’s implemented system, is specifically

designed to assist with a suite of office tasks. In most cases,

the specific technologies are designed to be domain agnostic

(e.g., email categorizing, resource scrounging, etc). However,

for the purposes of the evaluation, the base data present in

Radar and used for learning is centric to the domain of

conference planning. As such, certain components appear to

be domain-specific but their underlying technologies are more

extensible (e.g., conference-related email categories, room

finding, etc).

In order to show the specific influence of learning on overall

performance, there were two Radar conditions – one with

learning (+L) and one without (-L). In the context of the

evaluation test, learning was only “learning in the wild”

(LITW). Such machine learning is specific to learning that

occurs through the course of daily use. Brute force

spoon-feeding and code-driven knowledge representation is

not LITW. To count as LITW, learning must occur through

regular user interaction and user interfaces present in Radar.

The other experimental condition described here is which

version of Radar (1.0 or 1.1) was tested. There were

significant improvements in both usability and engineering

from Radar 1.0 to 1.1.

II. METHOD

A. Materials and Storyline

Extensive detail on the protocol, materials, and findings on

other metrics, especially those specific to overall task

performance, can be found in [12, 13]. As mentioned, this

paper is focused on the survey design and results.

The general scenario for the evaluation was that the subject

189
was filling in for a conference planner, who was indisposed,

to resolve a crisis in the current conference plan. This crisis

was major enough to require a major shuffling of the

conference schedule and room assignments that, in turn,

triggered secondary tasks. These included supporting plans

(e.g., shifting catering, AV equipment delivery, adjusting

room configuration, etc), reporting (e.g., make changes to the

website, issue a daily briefing, etc), and customer handling

(e.g., “here is the campus map”). Noise stimuli were also

present in the form of unrelated email, unusable rooms,

unrelated web pages, and other clutter content.

The materials included an email corpus and simulated world

content. The need for repeatability over time led to the

requirement for a simulated world. This consisted of facts

about the world (e.g., characteristics of a particular room) and

conference (e.g., characteristics of each event).

The simulated world and the initial conference were

designed to provide clear boundaries on the types of tasks

subjects would need to complete, yet also permit large-scale

information gathering, high resolution on learned fact

variation, and the opportunity to induce a substantial crisis

workload.

The conference itself was a 4-day, multi-track technical

conference complete with social events, an exhibit hall, poster

sessions, tutorials, workshops, plenary talks, and a keynote

address. The conference was populated with over 130

talks/posters, each with a designated speaker and title. All

characters were provided with email addresses and phone

numbers. Many were also given fax numbers, website

addresses, and organizations.

The physical space was a modification and extension of the

local university campus. In addition to modifying the student

union, two academic buildings and a hotel were created and

populated. These latter three buildings were instantiated to

protect against campus entry knowledge in the subject pool.

This information was presented to the subject in the form of

revised university web pages easily accessible from the

subject’s home page.

Other static web content included a conference planning

manual (complete with documentation of standard task

constraints), a PDF of the original schedule, and manuals for

the tools used by the subjects.

Subjects were also given access to a working, realistic

“university approved” vendor portal where goods and services

could be ordered for the conference. These included

audio-visual equipment, catering, security, floral

arrangements, and general equipment rentals. Email receipts,

complete with hyperlinks to modification/cancellation pages

and computed prices, were delivered to the subject’s mail

client in real time. All vendor interactions were via web forms

since automatic or Wizard of Oz handling of subject e-mails

can lead to problems with stimulus consistency and realism.

This had face validity since many real-life counterparts are

web-based, including the subject signup website used during

recruitment.

The corpus initialization for each experiment included:

• The predecessor’s conference plan in the file format of

the condition toolset,

• Other world state information – e.g., room reservation

schedule, web pages detailing room characteristics, etc.,

• Stored e-mail from the original conference planner,

including noise messages and initial vendor orders,

• The vendor portal, loaded with the initial orders, and

• Injected e-mail, including details of the crisis, new tasks,

and noise.

B. Survey Metrics

The survey questions, and their respective categories, are

shown in Table 1. All ratings were a 7-point scale with

anchors at 1, 4, and 7 (Strongly agree, Neutral, Strongly

disagree). Categories – e.g., metrics – were not revealed to the

subjects.

Questions in the Ease of Use, Usefulness, Disorientation,

and Flow categories were drawn from surveys validated in

other fields [10, 11]. Questions 10, 11, and 13 in the

Collaboration section were adapted from surveys validated in

computer supported cooperative work research [8, 9]. Given

the dramatic differences from the fields in which these survey

questions were validated, there was some concern that

adaptation for complex intelligent systems would not result in

valid measures.

For the purposes of analysis, responses to each question

within each category were flipped to have the same

positive/negative direction and averaged as a group. This

category level rating is referred to as an index (e.g., Ease of

Use index). The exception is the General category – these are

not designed to measure a common metric, so they are left

independent.

Questions 16 and 17 were specifically designed to examine

how the specific mixture of user interaction, machine learning,

and automation affected perceived relationships within

collaboration. Ideally, a good mixture will lead to a low score

for Question 16 and a higher score for Question 17. This

would mean the system was perceived as behaving as an

assistant, rather than a taskmaster. The fear with machine

learning, and in fact all assistance software, is that the needs

of the software (e.g., confirmation, corrections, reminders,

etc) will lead to user perception that the locus of control is

with the software, rather than the user. It is possible to

envision cases where a system has good usability and

excellent machine learning, but the nature of the interaction

leads the user to feel that they are serving the software.

D. Procedure

Each subject was run through approximately 3 hours of

testing (1 for subject training and 2 for time on task). The

survey was given at the end of the session. Each cohort of

subjects for a particular session was run on a single condition

(COTS1, Radar -L, or Radar +L). When possible, cohorts

 1 Conventional Off The Shelf, see [13] for more details.

190
were balanced over the week and time of day to prevent

session start time bias. Follow-up analyses on this issue

revealed no apparent bias. The nominal cohort size was 15 but

was often lower due to dropouts, no-shows, and other subject

losses (e.g., catastrophic software crash). Cohorts were run as

needed to achieve approximately 30 subjects per condition.

Motivation was handled through supplemental payments for

milestone completion (e.g., the conference plan at the end of

the session satisfies the constraints provided). Subjects were

given general milestone descriptions but not explicit targets.

All subjects were recruited from local universities and the

general public using a local human subject recruitment

website. Subjects were required to meet the following criteria:

• Between the ages of 18 and 65,

• Do not require computer modifications,

• Fluent in English, and

• Not affiliated with or working on the RADAR project.

III. RESULTS

There were several test windows during the period reported

here. The survey results data in this document correspond to

Radar 1.0 and 1.1 tested on the stimulus package referred to

as Crisis 1. The survey reliability data is for the Radar 1.1 test

only. Details on Radar 1.1 and Crisis 1 can be found

elsewhere [12, 13].

The Radar 1.0 subject pool used for results analysis, after

exclusions and dropouts, was 31 and 47 (-L, and +L). Radar

1.1 pool size was 34 and 32. As such, these two tests

accumulated 158 cumulative hours worth of time on task by

subjects with a multi-task machine learning system.

A two-way ANOVA model on Version (1.0, 1.1) and

Learning (-L, +L) was run. Differences between the latter on

the survey measures were largely not significant. The

exception to this was Usefulness which was viewed as better

for Radar +L (F-Ratio, 5.05; p-value 0.026). However, almost

every survey measure reported that Radar 1.1 was an

improvement over Radar 1.0 (Table 2). Only Question 1

(Confident did task well) was marginally significant.

Figure 2 shows the corresponding means for Version and

Table 1. Survey Questions

General

1. I am confident I completed the task well. (r)

2. The task was difficult to complete. (r)

3. I could have done as good of a job without the software

tools. (r)

Ease of Use Cronbach’s alpha: 0.87

4. Learning to use the software was easy. (r)

5. Becoming skillful at using the software was easy. (r)

6. The software was easy to navigate. (r)

Usefulness 0.94

7. Using similar software would improve my performance in

my work. (r)

8. Using similar software in my work would increase my

productivity. (r)

9. I would find similar software useful in my work. (r)

Collaboration 0.69

10. I disagreed with the way tasks were divided between me

and the computer.

11. Tasks were clearly assigned. I knew what I was supposed

to do. (r)

12. The software did exactly what I wanted it to do. (r)

13. I found myself duplicating work done by the software.

14. I could trust the software. (r)

15. The software kept track of details for me. (r)

16. The software was assisting me. (r)

17. I was assisting the software.

Disorientation 0.81

18. I felt like I was going around in circles.

19. It was difficult to find material that I had previously

viewed.

20. Navigating between items was a problem.

21. I felt disoriented.

22. After working for a while I had no idea where to go next.

Flow 0.57

23. I thought about other things.

24. I was aware of other problems.

25. Time seemed to pass more quickly. (r)

26. I knew the right things to do. (r)

27. I felt like I received a lot of direct feedback. (r)

28. I felt in control of myself. (r)

All responses on 7-point scales:

1 2 3 4 5 6 7

Strongly Neutral Strongly

agree disagree

(r) = scale reversed for index averages and analysis

Table 2. Improvement for new system version

General Survey Questions F-Ratio p-value

1. Confident did task well 3.89 0.051

2. Task difficult to complete 5.31 0.023

3. As good without software 17.3 <0.0001

Survey index F-Ratio p-value

Ease of Use 10.9 0.0012

Usefulness 4.88 0.029

Collaboration 6.03 0.015

Disorientation 4.13 0.044

Flow 4.31 0.040

Relationship Metric F-Ratio p-value

Assistant vs. Taskmaster

(Q17 – Q16, higher is better)

10.2 0.0018

191
Learning. While the interaction comparisons were not

significant, it is worth noting is that there is an apparent

overall pattern where improvements across versions are less

pronounced when machine learning is present in Radar. This

matches ground truth in that the majority of the user

detectable improvements between versions were in the

usability area.

Also, Radar 1.0 -L has a negative Assistant value; subjects

felt this instance of Radar was more of a taskmaster than an

assistant. The latter finding is not surprising in that the Radar

1.0 user interaction was extremely onerous and only marginal

assistance was provided by the software due to the lack of

machine learning. This suggests that the machine learning in

Radar 1.0 was enough to offset these known deficiencies.

In general, the index collections performed reasonably well

when tested for measurement reliability using the Radar 1.1

data (Table 1). Only the Flow index was markedly below the

0.7 reliability acceptance threshold used in the literature.

Collaboration was right on the edge.

An initial estimate of the validity of the Assistant vs.

Taskmaster relationship metric is to examine how well it

correlates to Question 3 (As good without software).

Theoretically, ratings on this metric should decrease as

Question 3 increases – i.e., software that is considered a

taskmaster will not be regarded as valuable by the end user.

This was indeed the result for this data set and these measures

were correlated (-0.42; p-value <0.0001; Figure 2). As such,

early indications are good with respect to metric validity.

However, additional research is needed with more precise

measures of assistant/taskmaster ground truth.

IV. DISCUSSION

At the time of the Radar 1.1 test there were still unaddressed

issues in usability and engineering. The limited perceived

differences in the Learning effect beyond Usefulness, contrary

to findings from performance metrics [12, 13], may be due to

these remaining issues. Possible explanations include: (a) the

poor user experience depressed positive machine learning

influences and (b) the improvements in machine learning were

not perceptible in a between subjects study design.

At the time of this writing, the next round of annual Radar

experiments is underway and additional data on issues like the

impact of machine learning and index reliability will become

available. Early indications are especially promising on the

ability of these metrics to capture the precieved value of

machine learning. A larger Learning effect is expected since

both the user experience and machine learning aspects of

Radar have improved substantially. Unfortunately, final data

and analyses are not available yet.

There was a clear feeling within the team that the user

interfaces for Radar 1.0 and 1.1 were masking the value

provided by the machine learning. To some degree, the results

presented here confirm this suspicion and reinforce the

importance of good user interaction design.

Having said this, the improvement in survey scores from

Radar 1.0 to 1.1 mirrors the ground truth improvements made

to the system itself. This, combined with the good reliability

results, suggests that these survey measures have merit for

other experiments on human use of intelligent assistance

systems.

ACKNOWLEDGMENTS

At Carnegie Mellon, Robert Kraut provided valuable help

with previously validated surveys. Kyle Cunningham, Django

Wexler, and Matt Lahut assisted with the development of the

study materials.

Jordan Hayes (Bitway, Inc) and Othar Hansson integrated

Radar and assisted with experiment execution. Julie Fitzgerald

Figure 1. Mean survey responses (Assistant & indices, higher is better; statements, 1=strongly disagree, 7 strongly agree)

192
(JSF Consulting), Mike Pool, and Paul Cohen (University of

Southern California) served as external evaluators and

generated the crisis stimulus. They, with Mark Drummond

(SRI International), provided significant input on the protocol.

This material is based upon work supported by the Defense

Advanced Research Projects Agency (DARPA) under

Contract No. NBCHD030010. Any opinions, findings and

conclusions or recommendations expressed in this material

are those of the authors and do not necessarily reflect the

views of the DARPA or the Department of Interior-National

Business Center (DOI-NBC).

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Proc. IEEE Digital Avionics Systems Conference.

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expert-oriented knowledge base authoring tools. In

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Systems Workshop. 2002.

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[6] Shen, J., Li, L., Dietterich, T. G., and Herlocker, J. L.

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International Conference on Intelligent User

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International Conference on Intelligent User

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L., McNally, M., and Cadiz, J. J. Coordination,

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Conference on Computer Supported Cooperative

Work (CSCW). 1998.

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A., Coordination in teams: Evidence from a

simulated management game. Journal of Applied

Psychology, under review.

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M., Quinones, P.-A., Wexler, D., Siewiorek, D.,

Hayes, J., Cohen, P., Fitzgerald, J., Hansson, O.,

Pool, M., and Drummond, M. Evaluation of an

Integrated Multi-Task Machine Learning System

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Figure 2. Assistant vs. Taskmaster metric as compared to “I could have done

as good of a job without the software tools” (negative slope is better)

193
Development of Tools for Measuring the 

Performance of Computer Assisted Orthopaedic Hip 

Surgery Systems 

Nicholas G. Dagalakis, Yongsik Kim, Daniel Sawyer, Craig Shakarji 

Manufacturing Engineering Laboratory 

National Institute of Standards and Technology 

100 Bureau Dr. Stop 8230 

Gaithersburg, MD 20899-8230 

Abstract— In the late seventies a sensor was invented, which 

could track the movement of athlete body parts. In the early 

eighties an improved version of this sensor was introduced, by 

a group of NIST researchers, for the calibration and the 

performance testing of industrial robots. In the late eighties 

people experimented with the use of these sensors for human 

brain operations and in the early nineties these sensors were 

introduced to orthopaedic operations and the field of 

Computer Assisted Orthopaedic Surgery (CAOS) was born. 

Although significant progress has been made in the design and 

use of these sensors for medical applications, there are still 

sources of accuracy errors that must be addressed. This paper 

describes our work on the development of tools for the 

calibration and performance testing of CAOS systems, which 

can be used inside operating rooms. 

Keywords: computer assisted surgery, computer assisted

orthopaedic surgery, hip arthroplasty, phantom, artifact

I. INTRODUCTION 

In the early eighties a group of National Institute of Standards 

and Technology (NIST) researchers, working for the 

NIST/Center for Manufacturing Engineering, the predecessor 

of the NIST/Manufacturing Engineering Laboratory, modified 

an athlete body tracking sensor [11

], so that it can be used for 

robot calibration and performance measurements [2]. An 

extensive study of the sources of measurement errors of this 

sensor and its controller was performed. Soon this sensor 

became a commercial product and it has been used by 

manufacturers and users of industrial robots, for their robot 

calibration and performance measurements, for the last 20 

years. In the early nineties Nolte L.P. [3] used this type of 

 

1

 Certain commercial products and processes are identified in 

this paper to foster understanding. Such identification does 

not imply recommendation or endorsement by the National 

Institute of Standards and Technology, nor does it imply that 

the products and processes identified are necessarily the best 

available for the purpose. 

tracking sensor for precision enhancement in spine surgery. 

Spine surgery tools were equipped with probes holding three 

or more target Light Emitting Diodes (LEDs), that were 

tracked to determine the surgical tool position and orientation. 

A Dynamic Reference Base (DRB) coordinate frame, 

equipped with three or more target LEDs, was attached to the 

vertebra undergoing surgery. Appropriate mathematical 

transformations converted the surgical tool’s position and 

orientation to DRB frame coordinates, thus facilitating the 

insertion of screws at the right position and orientation in the 

overwhelming majority of cases. At about the same time 

Lavallee S. [4] performed spine surgeries using a similar 

tracking sensor system. Lavallee experimented with surface 

registration for the identification of characteristic bone 

landmarks, instead of simple point registration. He also 

experimented with a robot carrying a laser beam for surgical 

drill tool alignment. Soon these techniques were extended to 

total hip and knee arthroplasties and the field of Computer 

Assisted Orthopaedic Surgery (CAOS) was born. 

The market for the use of CAOS systems inside an operating 

room in order to guide orthopaedic operations has evolved 

significantly from the original Selspot athlete body tracking 

sensor system. The Selspot system used two lateral effect 

photodiode camera tracking sensors, while most of the modern 

CAOS systems use two or three Charge Coupled Device 

(CCD) cameras with active LED targets or passive sphere 

targets illuminated by infrared light. People have also 

experimented with electromagnetic tracking sensors, with 

electrical coil targets and other technologies. Although these 

types of targets do not require line of sight with the sensor and 

thus can operate inside the human body, they are susceptible 

to interference from electromagnetic waves reflected by metal 

surfaces inside the operating room. Computer Assisted 

Orthopaedic Surgery systems consist of tracking camera 

sensors, tracking markers (targets), a computer and other 

relevant electronics [5]. During an operation the markers are 

attached to bones, surgical tools and implants. The three 

dimensional space position of the markers is determined with 

194
respect to a reference frame and based on that information the 

position and orientation of tools, bones and implants is 

calculated and used to generate useful surgery information. 

Comparison of conventional versus CAOS assisted 

arthroplasty operations have demonstrated that CAOS systems 

show significant improvement in the desired surgical result. 

In particular CAOS systems help reduce the variability of the 

positioning of prosthetic components from the desired 

optimum position and orientation, thus permitting a more 

consistent placement of the prosthetic components [6, 7]. 

It did not take very long though for the users of CAOS 

systems to recognize that the tracking sensors have accuracy 

problems, which may jeopardize the outcome of the surgical 

operation. The original NIST study identified several sources 

of errors. Some of them could still be relevant and can 

introduce positioning errors for the modern CAOS systems. 

Here is a list of these possible sources of errors: 

1. Camera optics. 

2. Detector irregularities. 

3. Target operating conditions, like temperature, non 

uniform radiation field, distance from the camera 

sensors, etc. 

4. Camera position and orientation determination with 

respect to the tracking sensor system reference 

coordinate frame. 

5. Sampling rate frequency of multiple targets. 

The image generated by each target on the camera tracking 

sensor is usually an irregular blob with non-uniform intensity 

distribution. It is up to the controller of each tracking system 

to decide how to assign XY coordinates to this type of image. 

A simple rotation of the target, with no position change, could 

alter the value of the measured XY coordinates. In the case of 

slow sampling rate tracking systems the target might move 

while its position is still being sampled. The general 

conclusion of the NIST study was that these tracking systems 

have a sweet region of low error for target positions located 

within the 80 % of the camera detector field of view. This 

error increases as the target moves away from this central 

region. 

The focus of the work reported in this paper is to address the 

accuracy problems associated with the use of Computer 

Assisted Orthopaedic Surgery (CAOS) systems, by 

implementing well calibrated artifacts, called phantoms by 

most medical professionals. 

II. BRIEF REVIEW OF TOTAL HIP ARTHROPLASTY 

OPERATION 

Various human diseases and activities can damage the hip 

joint and lead to severe pain and loss of mobility. Surgery to 

replace the damaged joint with an artificial one, prosthesis, is 

usually the last resort in order to alleviate pain and restore 

mobility [8]. This operation was invented by Dr. Charnley, a 

British surgeon, in the sixties who was honored with 

knighthood for his contribution. During the operation the 

head of the femur (thigh bone) is removed with a saw and the 

pelvis socket is reshaped in to a hemisphere with a scraping 

tool called a reamer. There are two major categories of joint 

prostheses, the cemented and the uncemented ones. The 

cemented are attached to the bone with an epoxy cement, 

while the uncemented have a porous external surface where 

bone can grow in order to attach the prosthesis to the skeletal 

bone. The hip prosthesis consists of two major parts; the 

femoral component and the acetabular component (see images 

in Figure 1 and 2). The femoral component is made of a metal 

stem and a metal or ceramic ball head and is intended to 

replace the upper part of the femur bone. The acetabular 

component is usually made of a concave metal shell cup, and a 

plastic inner liner. During the operation the pelvis socket, is 

reshaped before the acetabular prosthesis head can be inserted. 

The initial step before the operation is to determine the 

coordinates of the center of rotation of the hip and ankle joints 

in order to calculate the length of the leg. This test must be 

repeated before the conclusion of the operation and 

adjustments must be made in order for the patient to exit the 

operating room with the proper length leg, since a portion of 

his femur bone and pelvis have been removed. Another 

critical step of this operation is the attachment of the 

acetabular component of the prosthesis. It has been found that 

the metal shell cup must be placed with precise angular 

orientation otherwise the prosthesis could fail due to 

dislocation, impingement and premature wear. The angles 

that define the correct angular orientation are defined with 

respect to the patient pelvis frontal (coronal) and transverse 

coordinate planes, which are difficult to locate while the 

patient is lying on the operating table. 

After the acetabular component has been inserted the femur 

bone cavity is reshaped in order to accept the stem of the 

femoral prosthesis. The size and shape of the stem can vary 

from one patient to another. The femur bone cavity is usually 

shaped with manual tools although orthopaedic surgeons are 

also experimenting with robotic milling tools [9]. The robotic 

tool creates a smooth surface cavity, which should be less 

prone to stress concentrations that can lead to bone fractures. 

After a brief stay at the hospital and sometimes a rehabilitation 

facility, the patient will walk briefly with the help of a walker, 

crutches or a cane and finally the great majority will walk 

freely without assistance. This operation together with the 

total knee arthroplasty operation, are considered by some to be 

the greatest surgical developments of the twentieth century, 

because of the number of patients who have benefited and the 

severity of the pain that has been alleviated. 

195
III. PRECISION ENGINEERING TOOLS FOR TESTING 

COMPUTER ASSISTED ORTHOPAEDIC HIP SURGERY 

CAOHS SYSTEMS 

Precision and robotic engineers have a need for high 

performance ball and socket joints, which have no backlash 

and low friction, so they invented the magnetic ball and socket 

joint shown in Figure 3. The basic component of this device 

is the magnetic socket shown in Figure 4 [101

]. This device is 

usually made of magnetic stainless steel and has a cylindrical 

hollow cavity at its center. A cylindrical magnet is fitted in 

that cavity and secured at the desired position with plastic 

shims and epoxy glue. The image at the top of Figure 4 shows 

a socket fitted with a magnet, while the image below shows a 

socket before the attachment of the magnet. This design 

allows for the control of the magnetic force by selecting the 

proper magnet and shim thickness for the application. The 

shims control the size of the gap between the top of the 

magnet and the surface of the ball. The ball touches the rim of 

the socket at three small arcs located 1200

 from each other 

(see images on Figure 4). These arcs are created by pressing 

hard another ball on the rim of the socket. The socket joint 

ball is usually made of magnetic stainless steel and it is 

attracted to the socket by the force of the magnet. This force 

should be strong enough to keep the ball always in contact 

with the socket, but not very strong which might generate 

excessive wear on the ball surface. 

Figure 3. Precision magnetic ball and socket 

joint 

Figure 1. Femoral part of hip prosthesis

Figure 2. Acetabular part of hip prosthesis 

196
The magnetic ball and socket device offers a convenient 

precision joint tool, but many precision and robotic 

applications require fixed or adjustable length links. To meet 

that need people have invented the ballbar shown in Figure 5. 

A ballbar can have fixed or adjustable length and has magnetic 

balls at both ends, mounted on its tips. In the case of 

Coordinate Measuring Machines (CMMs), these artifacts can 

be employed to perform a subset of performance tests that are 

described in an American National Standard. Although not 

required these artifacts are frequently calibrated for center-tocenter distance. That is, the distance between the centers of 

the two magnetic balls is independently calibrated. These 

artifacts are then measured, by the CMM, in several locations 

and orientations, which were selected because of their 

sensitivity to error sources associated with the geometrical 

construction of the particular class of CMM. 

For the calibration of the phantom described in this paper, a 

Direct Computer Controlled (DCC) CMM was employed. 

This class of machine is error corrected using a high accuracy 

laser interferometer, electronic levels and precision 

straightedges. After error correction of the CMM, ballbars are 

then employed, as described in the American National 

Standard, to highlight possible problems in the CMM 

performance before measurement of critical parts are 

performed. 

IV. THE COMPUTER ASSISTED ORTHOPAEDIC HIP 

SURGERY (CAOHS) ARTIFACT 

For best clinical results our artifacts (phantoms) are designed 

to resemble the skeletal joint or organ, which is the subject of 

the operation and the suggested performance tests resemble 

important tasks of the actual surgical operation. In order to 

reduce the fabrication and maintenance cost of these devices, 

we use commercially available precision parts wherever 

possible in the phantom structure design. 

The most important component of the hip joint is the ball and 

socket joint, which we decided to add to our artifact 

(phantom). Most ordinary mechanical ball and socket joints 

have backlash and are difficult to clean and inspect for wear, 

because they are sealed. However precision engineers use 

magnetic ball and socket joints (see Figure 3) and bars (see 

Figure 5), which have none of the above mentioned drawbacks 

and are commercially available for reasonable prices and are 

used for the calibration and testing of precision measurement 

machines, like CMMs and Industrial Robots (IRs). 

Furthermore these joints can be fitted with various strength 

small size magnets, which can be selected for the proper size 

bar and joint orientation, so that the contact force will be 

sufficient to ensure that the bar will not separate from the joint 

socket during the test and not so large that results in excessive 

surface wear. 

Figure 4. Precision magnetic sockets 

Figure 5. Ballbar calibration test 

197
Our first phantom resembles a pelvis coordinate frame, as 

shown in Figure 6 and a femur bone connected with a 

precision magnetic ball and socket joint, as shown in Figure 7. 

Because the magnetic socket of this device is horizontal it is 

called Horizontal Joint-Operating Room-CAOHS (HJ-ORCAOHS). 

The CAOHS phantoms are designed to perform at least three 

performance tests relevant to hip arthroplasty operations. 

Such as are the following: 1) measure the CAOS system 

accuracy of the determination of the location of the 

coordinates of the center of rotation of the hip joint, 

represented here by the precision magnetic ball and socket 

joint, 2) measure the CAOS system accuracy of moving along 

straight lines at distances comparable to the size of human 

adult large bones, along two orthogonal directions, 3) measure 

the CAOS system accuracy of angular moves relevant to 

orthopaedic hip surgery. If the CAOHS phantoms prove 

useful for orthopaedic operations, similar devices will be 

developed for the human knee joint, shoulder joint, etc. 

The first HJ-OR-CAOHS phantom was fabricated a few 

months ago (see Figure 8). It is made of an L shape horizontal 

XY orthogonal coordinate frame, a joint horizontal mount, the 

magnetic ball and socket joint and a femur bar. The XY 

coordinate frame has small target holes (see Figures 13 and 

14) at regular intervals of 15 mm, designed to fit the pointed 

probe tip of the CAOS systems target assemblies. These are 

plates with four or more active or passive markers, which can 

be mounted on surgical tools. It also has two larger holes for 

the mounting of DRB target assemblies. The femur bar also 

has two larger holes for the mounting of DRB target 

assemblies, which can be used for the determination of the 

coordinates of the ball center of rotation. The tips of all the 

HJ-OR-CAOHS phantom bars are machined to form various 

angles, which are useful for hip arthroplasty operations (see 

Figures 11 and 12). An arc at the base of the coordinate frame 

has been fitted with target holes spaced at regular angular 

increments, which adds an additional angular calibration and 

testing capability (see Figure 9). The magnetic ball and socket 

joint are commercially available and are made of stainless 

steel material, while the rest of the parts are made of Invar, for 

better thermal stability inside an operating room. 

Figure 6. The CAOHS phantom coordinate 

frame superimposed on a pelvis model 

Figure 7. The CAOHS phantom coordinate 

frame and femur bar connected with a 

magnetic ball and socket joint 

Figure 8. The first prototype of the HJ-ORCAOHS phantom 

198
Figure 9 shows the L shape XY coordinate frame with its 

target holes marked X1 to X20 on the X axis (horizontal in the 

figure) and Y1 to Y15 on the Y axis (vertical in the figure). 

The nominal incremental distance between these target points 

is 15 mm, which gives a nominal X axis length of 300 mm and 

a nominal Y axis length of 225 mm (see Figure 11). The X 

axis is longer because it is intended to approximate the length 

of an adult femur bone. The distance between any two target 

holes is measured between the tips of the two holes. Although 

the nominal distance can be calculated assuming a nominal 

increment of 15 mm, between neighboring holes, the actual 

distance is determined through careful calibration, which will 

be described in a future paper. 

Figure 13 shows the nominal dimensions of the target holes. 

Special attention was given to the drilling of these holes in 

order to achieve smooth clean hole walls and tip and a hole 

axis, which is as close as possible orthogonal to the 

corresponding coordinate frame XY axis. Several drill bits 

were used and each one was not used for more than four holes. 

Every single one of the target holes was examined and 

photographed under a microscope. One concern was the 

presence of burrs, which could prevent the tip of the CAOS 

system target probe from reaching the tip of the target hole. 

Figure 14 shows a typical hole image, which reveals that the 

hole tip is really a hemispherical surface and not a sharp tip as 

Figure 13 implies. It is thus important that during CAOS 

testing the pointed probe tip of the CAOS systems target 

assemblies can reach that hemispherical surface and not be 

X20 (X,0,0)

Y15 (X',Y,0)

Origin (0,0,0)

X1

Y1

X20 (X,0,0)

Y15 (X',Y,0)

Origin (0,0,0)

X1

Y1

Figure 9. The L shape XY coordinate frame with the 

target holes 

R30 

R15 

R45 

R60 

R75 

≈ ∠15

≈ ∠30

≈ ∠45

≈ ∠60

R30 ≈ ∠75

R15 

R45 

R60 

R75 

≈ ∠15

≈ ∠30

≈ ∠45

≈ ∠60 ≈ ∠75

Figure 10. The origin arc with the target holes 

defining certain angles with respect to the X 

coordinate axis 

225 mm

300 mm

450

17.50

225 mm

300 mm

450

17.50

Figure 11. The angles between the adjacent planes 

labeled in the figure can be used for the evaluation 

of surgical cutting tools 

Two holes for mounting

DRB target assemblies

600

370 mm

Figure 12. The femur bar showing the two angled 

planes and DRB mount holes 

199
able to move laterally by any significant amount because that 

motion will introduce measurement errors. 

 

The HJ-OR-CAOHS phantom offers two different options for 

testing the ability of a CAOS system to measure angles. One 

may involve the use of the saw blade, spatula or other similar 

tool and the other the pointed probe tip of the CAOS systems 

target assemblies. The end planes of all the phantom bars are 

shaped to form angles that are commonly used during hip 

orthopaedic operations. From Figure 11 it can be seen that the 

X axis bar of the phantom coordinate frame terminates at a 

nominal 450

 angle, which is considered by many orthopaedic 

surgeons as the best choice for the hip acetabulum prosthesis 

inclination angle. The Y axis bar of the phantom coordinate 

frame terminates at a nominal 17.50

 angle, which is considered 

by many orthopaedic surgeons as the best choice for the hip 

acetabulum prosthesis anteversion angle. The femur bar 

terminates at a nominal 600

 angle, which is preferred by many 

orthopaedic surgeons for the decapitation of the damaged head 

of the femur bone. The arc around the origin of the coordinate 

XY frame axes has five target holes at nominal angles of 150

, 

300

, 450, 600, 750, with respect to the X axis. These are three 

point angle measurements and allows pointed probe tip 

measurement tests. 

NIST staff have calibrated all the critical features on the HJOR-CAOHS using an industrial grade Coordinate Measuring 

Machine (CMM). These features include the target hole 

locations and the center of rotation. In all cases the expanded 

uncertainty U with k = 2 in the determination of the three 

dimensional coordinate is less than 0.08 mm. A future 

publication will report on the calibration procedures and an 

additional publication will describe the results of industrial 

testing. The coordinates of the ball center of rotation are 

measured with respect to the CMM reference coordinate frame. 

Using coordinate transformation algorithms similar to those 

used for the calibration of IR work cells, it is possible to refer 

these coordinates to the HJ-OR-CAOHS phantom coordinate 

frame, thus making the use of the phantom independent of the 

metrology instrument used for its calibration. 

A new version of the OR-CAOHS, which has an angled 

magnetic ball and socket joint similar to that of a human 

pelvis, is also being designed. 

V. CONCLUSIONS 

We have described the use of common and inexpensive 

precision engineering and industrial robot calibration tools for 

the design of an artifact (phantom), which may be used for 

measuring the performance of CAOS systems inside operating 

rooms. This phantom can also be used for the calibration of 

CAOS systems. Calibration is of course primarily the 

responsibility of the manufacturer of CAOS systems and it can 

be performed after fabrication and during servicing operations. 

We have designed and fabricated a horizontal joint computer 

assisted orthopaedic hip surgery phantom (artifact). This 

device appears to be working very well and it was recently 

calibrated and sent to a medical research group for testing. 

Calibration and testing results will be reported in future 

publications. 

ACKNOWLEDGEMENTS 

We would like to thank Dr. J. B. Stiehl, Dr. D. A. Heck and 

Dr. W. L. Bargar, for their valuable orthopaedic surgery 

advice and encouragement. We would also like to thank Mr.

Damien Bertot for the laser scanning of several human bone 

models, Dr. Junhwan Kim for generating solid CAD models 

of individual bones and assemblies, and Mr. Marvin 

Kingsbury for the fabrication of the HJ-OR-CAOHS phantom. 

1.64 mm

1350

1300

1.00 mm

0.83 mm

0.23 mm

1.38 mm

1.64 mm

1350

1300

1.00 mm

0.83 mm

0.23 mm

1.38 mm

Figure 13. Cross section view of the target hole 

Figure 14. Microscope images of a target hole 

200
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[6] Haaker R.G.A., Tiedjen K., Ottersbach A., Rubenthaler F., 

Stockheim M., Stiehl J.B., “Comparison of Conventional 

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Imageless Navigation System,” Clin Orthop , Vol. 426, pp 

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[8] Thomas B.J., Stiehl J.B., “Basics of Total Hip 

Replacement Surgery,” in the book “Navigation and Robotics 

in Total Joint and Spine Surgery,” by Stiehl JB, Konermann 

WH, Haaker RG, Publisher Springer-Verlag, 2004. 

[9] Bargar W.L., Bauer A., Borner M., “Primary and Revision 

Total Hip Replacement Using the Robodoc System,” Clinical 

Orthopaedics, Vol. 354, pp. 82-91, 1998. 

[10] ATT Metrology Services, Redmond, WA, 

http://www.attinc.com/target-pg5.htm

201
Abstract— Minimally invasive surgical procedures using long 

instruments have profoundly influenced modern surgery by 

decreasing invasiveness, therefore minimizing patient recovery 

time and cost. However, surgical procedures using long tools 

inserted through small ports on the body deprive surgeons of the 

sense of touch (haptics), depth perception, dexterity, and 

straightforward hand eye coordination that they are accustomed 

to in open procedures. While there have been significant 

advances in almost all of the above areas, haptic feedback 

systems for robot-assisted surgery are lacking in development. In 

this paper we present: 1) the development of accurate robot-arm 

dynamic model (using model-based control) with the goal of 

minimizing unwanted tool-tissue interaction forces in robotassisted surgery, 2) the development of an ergonomic 7-DOF 

haptic feedback system, and 3) the recently developed 

laparoscopic grasper with force feedback capability attached to 

the end of the robot arm and controlled by the haptic device. 

Index Terms— Mitsubishi PA-10, Haptic Device, Laparoscopic 

Grasper, Robot-Assisted Minimally Invasive Surgery. 

I. INTRODUCTION

OBOT-assisted surgery has led to significant 

improvement within the medical field. These systems 

incorporate advantages from minimally invasive surgery 

(MIS), such as reduced patient trauma, recovery time, and 

lower health care costs, to name a few. In a surgical 

#

This work was supported in part by National Science Foundation grants EIA0711040, and CAREER award IIS-0711038 and the American Heart 

Association grant 0160368U. 

 a

 Corresponding author. J. .P. Desai is with the Department of Mechanical 

Engineering, University of Maryland, College Park, MD 20742 USA (email: 

jaydev@umd.edu) b

G. Tholey is with Conferno Solutions, King of Prussia, PA, USA (email: 

gtholey@conferosolutions.com) c

C. W. Kennedy is with the John Hopkins University, Baltimore, MD USA 

(email: ckenne10@jhmi.edu) 

1. Portions reprinted, with permission, from “Gregory Tholey and Jaydev P. 

Desai, A Modular, Automated Laparoscopic Grasper with Three-Dimensional 

Force Measurement Capability, page 250-255, 2007 IEEE International 

Conference on Robotics and Automation, Roma, Italy, 2007”. © [2007] 

IEEE. 

2. Portions reprinted, with permission, from “Gregory Tholey and Jaydev P. 

Desai, A General Purpose 7 DOF Haptic Device: Applications towards 

Robot-Assisted Surgery, Accepted for publication in IEEE/ASME 

Transactions on Mechatronics, To appear in 2007”. © [To appear in 2007] 

IEEE. 

3. Portions reprinted, with permission, from “Christopher Kennedy and Jaydev 

P. Desai, Model-Based Control of the Mitsubishi PA-10 Robot Arm: 

Application to Robot-Assisted Surgery, 2004 IEEE International Conference 

on Robotics and Automation, vol. 3, pp. 2523-2528, New Orleans, LA, 2004”. 

© [2004] IEEE. 

environment it is essential to have low interaction forces with the 

tissue and/or organ to prevent unwanted harm to the patient and 

the surgical staff. As a result, we developed a dynamic model of 

the Mitsubishi PA-10 robot arm for low velocity applications such 

as surgical tool placement or teleoperated soft tissue manipulation. 

The PA-10 is ideal for precise manipulation tasks due to the 

backdrivability, accurate positioning capability and zero backlash 

afforded by its harmonic drive transmission. However, the 

compliance and oscillations inherent in harmonic drive systems 

make the development of an accurate dynamic model of the robot 

extremely challenging. The Mitsubishi PA-10 robot is significantly 

used in research laboratories worldwide [1, 2] and in our prior 

work, we have addressed [3] the transmission modeling and low 

velocity, low impedance implementation for the PA-10 robot arm 

in a research environment. 

 Current robotic surgical systems, such as Da Vinci Surgical 

System (Intitutive Surgical Inc.) do not provide haptic feedback to 

the surgeon. This lack of haptic feedback has led several 

researchers to develop haptic devices for surgical and various 

applications. Massie and Salisbury [4] developed the Personal 

Haptic Interface Mechanism (PHANToMTM), which is 

commercially available and used for many different applications. 

Additional mechanisms that use serial or parallel configurations 

have also been developed [5-8]. Serial mechanisms, such as the 

PHANToM, lack a sufficient force feedback capability without 

adding significant weight and inertia to the mechanism and 

typically do not have a grasping interface capable of providing 

force feedback. Parallel mechanisms can provide sufficient force; 

however, they have a smaller workspace and also lack a grasping 

interface. Therefore, a need exists for the development of a 

surgical haptic interface that can reflect forces for some of the 

robotically-assisted surgical procedures. Based on this motivation, 

we have developed a haptic device with seven degrees of 

positional feedback capability and four degrees of force feedback 

capability. 

 Several researchers have developed novel surgical tools to 

accurately measure the tool-tissue interaction forces during 

surgical procedures. One area of research involves solutions that 

incorporate sensors into current laparoscopic tools using strain 

gages, force/torque sensors, or custom designed sensors on the 

shaft or jaws of the tool to measure tool-tissue interaction forces. 

Morimoto et al [9] and Bicchi et al [10] implemented strain gage 

sensors on the tool shaft that allow for measurement of indirect 

grasping forces and surgical manipulation forces, respectively. 

Dargahi et al [11] utilized a MEMS-based approach to measure 

normal forces at the jaws, however, cost and sterilizability issues 

were not discussed. Prasad et al [12] developed a 2-DOF force 

sensing sleeve to measure bending forces in 5 mm laparoscopic 

instruments. While these various designs can accurately measure 

the surgical forces, they have disadvantages towards incorporating 

Haptic Feedback System for Robot-Assisted Surgery#

Jaydev P. Desai*a, Gregory Tholeyb, and Christopher W. Kennedyc 

*Robotics, Automation, Manipulation, and Sensing (RAMS) Laboratory 

University of Maryland, College Park 

R

202
them into an actual surgical setting. Previously developed 

surgical instruments with force measurement capabilities [12-

15], have the disadvantage of costly, non-disposable sensors, 

large jaw designs, and low degrees-of-freedom for force 

measurement. In addition, most of the previous research lacks 

modularity for easy conversion between tool types (e.g. 

grasper, cutter, and dissector) without removing the entire 

surgical tool. Based on this motivation, we have developed a 

modular and automated laparoscopic grasper with tridirectional force measurement capability and a modular, 

disposable tool shaft for quick conversion between surgical 

modalities, such as grasping, cutting, and dissection. The 

current prototype has incorporated the advantages of previous 

graspers in a compact design for use in a clinical setting 

 The paper is organized as follows: In section 2, a dynamic 

model of the Mitsubishi PA-10 robot arm is presented. In 

section 3, the development of a haptic device with seven 

degrees of freedom is presented and in section 4, the 

automated laparoscopic grasper with tri-directional force 

measurement capability is presented. Finally in section 5 

concluding remarks are presented. Our overall research goal is 

the development of a haptic feedback surgical system that uses 

a robotic arm with an attached laparoscopic tool to perform 

surgical procedures and have the capabilities of measuring the 

tool-tissue interaction forces through a haptic feedback 

interface (see Fig. 1). 

Fig.1. Telemanipulation setup for characterizing tissue using a haptic 

device to control the robot arm with attached laparoscopic grasper. © 

[2007] IEEE. 

II. THE MITSUBISHI PA-10 ROBOT ARM

 The Mitsubishi PA-10 robot arm is a 7 degree-of-freedom 

robot arm with open control architecture and is manufactured 

by Mitsubishi Heavy Industries (see Fig. 2a). The four layer 

control architecture is made up of the robot arm, servo 

controller, motion control card, and the upper control 

computer. 

a) b) 

Fig. 2. a) Mitsubishi PA-10 robot arm and servo driver, and b) 

Flowchart of Mitsubishi PA-10 four layer control architecture. © 

[2004] IEEE. 

 A flow chart for the control system is shown in Fig. 2b. The 

host computer runs the QNX real-time operating system and 

we have been able to achieve communication rates of up to 

700 Hz with the robot servo driver through the ARCNET

(ARCNET is a token passing LAN protocol developed by 

Datapoint Corporation) motion control card and custom-made 

software. The robot joints are actuated by three-phase AC servo 

motors and harmonic gear transmissions. Joint positions are 

measured through resolvers at the joint output axis, with a 

resolution of 0.000439o

 over +/- 3 output revolutions. Control of 

the robot can be achieved in either 'Velocity mode' or 'Torque 

mode'. HDT provides advantages such as zero backlash due to 

natural pre-loading. However, there are also several disadvantages 

such as non-linearity due to friction, alignment error of the 

components, and transmission losses due to the compliance in the 

system. All of these were found to be critical in the modeling of 

the Mitsubishi PA-10 robot arm. In the following subsections we 

will describe in detail our methodology to estimate and model the: 

a) velocity dependent and position dependent friction, b) torsional 

stiffness, and c) gravity effects. The above items (a) through (c) 

comprise the model of the HDT. 

A. Modeling 

 Parameter identification of the Mitsubishi PA-10 robot arm was 

carried out using the system and control architecture described 

above. Although the system did allow us to control the motor 

torque of each joint, we preferred to conduct our experiments in 

'Velocity mode' when possible because this allowed for better 

high-gain trajectory tracking due to the increased feedback loop 

rate. 

1) Harmonic Drive Model 

 We consider the model of the harmonic drive to be composed 

of friction, gravity, and stiffness. The non-linear expression for 

torque transmission in harmonic drives is thus given by: 

T N T ( ) T ( ) T ( ) T (T ,T ,T ) in cf vf g c cf vf g = θ + θ + θ + & (1) 

where Tin is the input torque, N is the transmission ratio (N is 50 

for all joints), Tcf is the coulomb friction, Tvf is the velocity 

dependent friction torque, Tg is the gravity torque, and Tc is the 

torque used to deform the wave generator. A schematic for the 

proposed control system is shown in Fig. 3. 

2) Friction 

 (a) Velocity-dependent friction: 

To determine the friction-velocity relationship for the joints of the 

PA-10, each joint of the robot was commanded to move at a 

constant velocity and the mean torque required to maintain the 

velocity was taken to be the friction for that value of velocity. To 

characterize the friction behavior at low velocity, data for 

velocities between 0.02 rad/s and 0.1 rad/s were collected in 0.02 

rad/sec increments and between 0.1 rad/sec and 0.4 rad/sec in 0.1 

rad/s increments. Five trials were performed for each velocity 

value in both the positive and negative directions, for a total of 80 

measurements per joint. For joints 2, 4, and 6 (which are 

influenced by gravity), the robot was mounted on the wall to 

negate the effect of gravity. Data for joints 1, 3, 5, and 7 were 

collected with all the joints in the vertical position. After 

collecting data for all 7 joints, we fit three different friction models 

using least-squares techniques. The three models tested were: 1) 

kinetic plus viscous friction model, 2) cubic polynomial model, 

and 3) Stribeck curve model. The results of this analysis are shown 

in Table 1. We compared different friction models based on their 

203
ability to fit experimental data. The results presented in Table 

1 are the weighted residual variance and the mean-squared 

error (MSE) per degree-of-freedom (DOF) for each model 

after being fit to the collected experimental data. The Stribeck 

model provided a reasonably good approximation for the 

friction torque in all 7 joints of the Mitsubishi PA-10 robot 

arm that we have modeled. 

Fig. 3. The proposed control system includes models of friction, gravity, 

and nonlinear stiffness of the wave generator. © [2004] IEEE. 

The expression for this model is given by: 

 (1 - e ) s θ F v sgn( θ) F o (θ) F vf T

)

c V

θ (- &

& & & = + + (2) 

where Tvf is the viscous friction torque, Fo, Fv, Fs and Vc are 

the Stribeck coefficients, and θ

& is the rotational velocity. The 

best fit approximation of experimental data by a Stribeck 

curve is shown in Fig. 4 for both positive and negative 

velocities for joint 1. This approach was used for all the 

velocity and position calculations in this paper. 

(b) Position-dependent friction: 

 Friction in the HDT is strongly position dependent due to 

kinematic error in the transmission. The error signature can 

display frequency components at two cycles per wavegenerator revolution and several subsequent harmonics. Based 

on the above, the error function including two harmonics of 

wave generator rotation can be expressed as: 

θ A sin(θ φ ) A sin(2θ φ ) erfn 1 wg 1 2 wg 2 = + + + (3) 

where Ai are the amplitudes of the sinusoids, φi is the phase 

shift, and θwg is the wave generator position. This expression 

is of limited use in our case, because it is not possible to 

measure the output axis rotation. The amplitudes in equation 

(3) are therefore impossible to accurately determine for our 

robot. Although kinematic error has a significant effect on the 

torque transmission characteristics of HDTs, we found that 

compensating for coulomb friction using the torque required 

to maintain slow velocity eliminated almost all the effects of 

kinematic error. Therefore, we neglected the effect of 

kinematic error in the feedforward implementation of our 

model. The parameters for periodic torque function for 7 joints are 

shown in Table 2. 

3) Gravity Compensation 

 The parameters used for gravity compensation in our model 

were taken from the catalog values for the masses and the center of 

mass locations for the robot links. The effect of gravity was 

significant only for joints 2 through 6 when the robot was mounted 

on a pedestal. The gravity torques for joints 2 through 6 were 

calculated and the catalog values for the link masses and lengths. 

4) Estimation and modeling of nonlinear stiffness 

 Harmonic drives exhibit significant compliance when externally 

loaded. This is apparently due to deformation of the wave 

generator [16]. Our experimental tests on the Mitsubishi PA-10 

robot arm revealed that wave generator compliance has a 

significant effect on the robot arm dynamics. Since wave generator 

deformation must be a function of the load on the system, we 

chose to model this torque as a function of the gravity torque and 

friction torque. Our methodology for determining the stiffness 

parameters for joint 4 is described in detail in [17]. Fig. 5 shows 

the steps in the model identification process for joint 1. The 

parameters for joints 1 through 4 are given in Table 3, including 

the slopes of each of the three linear regions, the transition points 

for the linear regions, and the value for stiffness when the external 

torque is zero. The effects of stiffness are not significant for joints 

5 through 7; therefore we have neglected them in our model. 

B. Experimental Verification of the HDT Model 

To verify our model-based controller for the Mitsubishi PA-10 

robot arm, we fed-forward the torques computed by our model to 

track an end-effector trajectory. The chosen trajectory was a 

lemniscate in the y-z plane of the base coordinate system given by: 

 x = 0.6 ,

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

+

= 2

2

t

1 sin

2

t cos

y 0.2 ,

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛

+

= + 2

2

t 1 sin

2

t cos

2

t sin

z 0.1 0.4 (4) 

The end-effector position followed the lemniscate position while 

the end-effector orientation remained constant relative to the base 

coordinate system. We computed the necessary joint angles for 

this trajectory using the kinematic parameters and inverse 

kinematics solution for 6 joints. The results of the end-effector 

trajectory tracking experiments are shown in Fig. 6. The mean 

end-effector error was 7 mm, with a maximum error of 42 mm 

occurring at the beginning of the experiment. After the initial large 

error, the maximum tracking error was 19 mm. 

Viscous Model Cubic Model Stribeck Model 

Joint Model DOF Residual 

variance, 

weighted 

MSE per 

DOF 

Model DOF Residual 

variance, 

weighted 

MSE per 

DOF 

Model DOF Residual 

variance, 

weighted 

MSE per 

DOF 

1 2 12.1404 4.0351 4 1.8519 1.9260 4 2.2302 2.1151 

2 2 22.2687 6.5672 4 2.8653 2.4327 4 5.2306 3.6153 

3 2 114.6001 29.6500 4 16.9548 9.4774 4 2.7248 2.3624 

4 2 415.8925 104.9730 4 0.1180 1.0590 4 1.4081 1.7041 

5 2 147.7424 37.9256 4 33.4068 17.7034 4 0.6805 1.3403 

6 2 1.7577 1.4394 4 0.4013 1.2006 4 0.5518 1.2759 

7 2 741.2334 186.3080 4 1.9773 1.9887 4 0.7370 1.3685 

Table 1. Data for three different models of velocity dependent friction including the viscous friction model, the cubic model, and the Stribeck model. © [2004] 

IEEE.

204
Joint f1 A1 φ1 f2 A2 φ2

1 15.87 7.50 2.30 47.60 5.05 12.80 

2 47.60 6.00 -1.50 310.50 2.50 -6.90 

3 39.65 0.70 0.10 239.20 0.55 5.50 

4 39.65 5.00 -3.15 262.50 0.10 3.98 

5 23.80 0.50 4.95 95.80 0.40 7.50 

6 23.80 0.10 -1.50 95.80 0.50 5.50 

7 23.80 0.10 -1.00 95.80 0.50 2.50 

Table 2. Parameters for periodic friction torque for 7 joints including two harmonics of wave generator rotation. © [2004] IEEE. 

. 

-0.5 0 0.5 -50

0

50

Joint 1 Rotational Velocity (rad/s)

Friction Torque (Nm)

Experimental friction data 1 standard deviation 

Stribeck curve approximation 

Stribeck curve approximation 

Positive velocity: 

Negative velocity: 

F(v) = 34.96 + 29.72v - 2.81(1-e(-v/0.34) ) 

F(v) = -36.71 + 26.48v + 2.29(1-e (v/0.62)) 

Fig. 4. Represenative plot of stribeck curve fit to velocity-dependent 

friction data for joint 1. © [2004] IEEE. 

0.5 1 1.5 2 2.5

-100

-50

0

50

100

Joint 1 Position (rad)

Output Torque (Nm)

TT 

CF

TT - CF - GT TT - CF - GT - TN 

TT - CF 

TT = Total Feedback Torque

CF = Coulomb Friction Torque

GT = Gravity Torque

TN = Torsion 

Fig. 5. Representative plot for joint 1 for steps in model identification 

process. © [2004] IEEE. 

Joint Slope 1 

(Nm/Nm) T1 (Nm) Slope 2 

(Nm/Nm) T2 (Nm) Slope 3 (Nm/Nm) 

Zero Gravity 

Stiffness (Nm) 

Joint 1 +ve -0.0044 -25.36 2.33 -12.70 0.26 2.32 

Joint 1 -ve 0.04 12.16 -1.93 19.53 -0.34 0.19 

Joint 2 +ve 0.31 -23.48 2.63 -11.75 0.20 -1.44 

Joint 2 -ve -0.06 11.16 -2.80 21.75 -0.27 0.45 

Joint 3 +ve 0.21 -5.78 4.26 -3.49 0.09 0.81 

Joint 3 -ve 0.02 5.30 -3.60 2.64 -0.07 -0.30 

Joint 4 +ve 0.22 -19.58 5.08 -16.27 0.92 2.57 

Joint 4 -ve -0.68 15.14 -4.11 19.23 -0.18 -1.45 

Table 3. Parameters for nonlinear stiffness as a function of gravity torque for joints 1 through 4. © [2004] IEEE.

-0.2 -0.1 0 0.1 0.2 0.3 -0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

Base Coordiante System y-axis

Base Coordinate System z-axis

End-effector Trajectory Tracking Experiment

Commanded Trajectory

Actual Trajectory 

Fig. 6. End-effector trajectory tracking experiment. © [2004] IEEE.

III. 7 DOF HAPTIC DEVICE

 We have developed a haptic device with seven degrees of 

positional feedback capability and four degrees of force 

feedback capability. It is a closed-kinematic chain that 

consists of a user interface and spatial mechanism 

connected via a universal joint. The haptic device provides 

force feedback along three orthogonal axes and also the 

grasping /parting force. This device is part of an overall 

haptic feedback system (see Fig. 1). Through the haptic 

device, we will be able to control the robot arm with 

attached surgical tool that is capable of measuring the 

forces at the end-effector in 3D [15]. The user interface 

(see Fig. 7) of the haptic device will control the slave robot, 

such as the Mitsubishi PA-10. The surgical tool attached to 

the robot arm can measure and feedback forces to the haptic 

device to reflect them to the user through the spatial force 

feedback mechanism (see Fig. 7), which would include 

forces in X, Y, and Z direction, in addition to the grasping 

force through the grasping mechanism (θ) (see Fig. 8). This 

device can also be used for a variety of other applications 

such as the automotive industry, gaming industry, or as a 

rehabilitation aid for people with finger, hand, and/or 

forearm injuries. 

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Fig. 7. Prototype of the haptic device. © [To appear in 2007] IEEE. 

Fig. 8. Details of the grasping mechanism. © [To appear in 2007] 

IEEE. 

The three degree-of-freedom spatial force feedback 

mechanism mounts to the base of the haptic device and 

attaches to the user interface at the grasping mechanism via 

a universal joint (see Fig. 9). It consists of 3 direct drive DC 

motors with encoders (manufactured by Maxon, 

Burlingame, CA) for the X, Y, and Z directions and three 

linear slide guides (manufactured by Misumi, Schaumburg, 

IL) that act as prismatic joints. The three slide guides are 

mounted in series and orthogonal to each other and thus 

create a three degree-of-freedom mechanism for position 

and force feedback. 

 The maximum force output of each joint of the spatial 

mechanism is governed by the motor characteristics, pulley 

dimensions, and frictional losses. The direct drive DC 

motors are capable of providing up to 181 mNm of 

continuous torque. In addition, all three motor pulleys on 

the spatial mechanism consist of a 6.35 mm diameter 

grooved pulley. Therefore, the motor/pulley system can 

theoretically produce approximately 56N of force. 

However, frictional losses reduce this number to 

approximately 40N (as measured experimentally on each 

axis). 

A. Kinematics and Workspace 

The haptic device is designed as a closed kinematic chain 

with a universal joint connecting the spatial force feedback 

mechanism to the user interface. Therefore, the kinematics 

of the haptic device can be decoupled into two separate 

halves that both end at the universal joint. These kinematic 

equations can then be used to the find the position of the 

prismatic joint on the user interface. The position of the 

prismatic joint can be mapped to the corresponding 

translation of the end-effector of the slave robot in the 

global coordinate frame (mapping not described in this 

paper). The movement of the grasping mechanism 

correlates to the opening/closing of the jaws of the 

laparoscope. Starting with the forward kinematics of the 

user interface, we placed coordinate frames on both halves 

of the haptic device. Next, we obtained the D-H parameters 

of each half that are shown in Table 4 and Table 5. The 

reachable workspace of the haptic device is the intersection 

of the workspace of the user interface half and the 

workspace of the spatial force feedback mechanism. 

Therefore, we developed an algorithm to determine this 

volume to verify the reachable workspace of the haptic 

device was sufficient for the range of motion in MIS 

procedures. This reachable workspace represents an 

estimated volume of 0.0041 cubic meters with dimensions 

of 0.1905m wide by 0.1905m deep by 0.1143m high as 

shown in Fig 10. 

IV. AUTOMATED LAPAROSCOPIC GRASPER WITH THREEDIMENSIONAL FORCE MEASURING CAPABILITY 

 We have developed a modular and automated 

laparoscopic grasper with tri-directional force measurement 

capability and a modular, disposable tool shaft for quick 

conversion between surgical modalities, such as grasping, 

cutting, and dissection. The current prototype has 

incorporated the advantages of previous graspers in a 

compact design for use in a clinical setting. The design of 

the laparoscopic grasper was guided by our previously 

designed automated laparoscopic graspers and the 

advantages they incorporated, such as low backlash, 

compact design, and tri-directional force measurement 

capability [15]. In addition to these characteristics, the 

current prototype was significantly improved by using 

smaller sensors, a significantly smaller shaft diameter (~8 

mm), a linear actuation mechanism requiring no tensioning, 

and a disposable, modular instrument for easy conversion 

between surgical modalities. 

 Our design consists of two components; namely, the 

actuation mechanism and the modular tool (see Fig. 11). 

The actuation mechanism uses a DC motor with gearbox 

and encoder that drives a leadscrew and linear positioning 

assembly (see Fig. 12). This linear positioning assembly 

connects to a push-rod that is part of the modular tool. The 

push-rod is contained within and translates along the shaft 

of the tool and actuates the two jaws of the tool using a 

linkage. The design uses 4 strain gages mounted on the 

shaft of the tool near the end of the shaft closest to the jaws 

to measure the horizontal and vertical forces exerted on the 

tool end-effector (see Fig. 13). Additionally, a small 

resistive force sensor is mounted in one of the jaws to 

measure the normal force during grasping and palpation 

tasks. The resistive sensor (SF-4 model, manufactured by 

206
CUI, Inc) has an overall size of 5mm long by 5 mm wide 

with a height of 1mm. 

Fig. 9. Details of the spatial force feedback mechanism. © [To appear 

in 2007] IEEE. 

Table 4. D-H parameters for the user interface. © [To appear in 2007] 

IEEE. 

Table 5. D-H parameters for the spatial force feedback mechanism. © [To 

appear in 2007] IEEE. 

Fig. 10. Reachable workspace of the haptic device. © [To appear in 

2007] IEEE.

Fig. 11. Prototype of the modular laparoscopic grasper. © [2007] 

IEEE. 

Fig. 12. Actuation mechanism of the modular laparoscopic grasper. © 

[2007] IEEE. 

Fig. 13. Sensor locations on the modular tool. © [2007] IEEE. 

Some of the characteristics of this sensor are a response 

time of less than 6 μsec, a maximum load of approximately 

29 N, and a resistance range from 10,000 MΩ at zero load 

to approximately 15 Ω at full load. The four strain gages 

(model 125UN, manufactured by Vishay Intertechnology, 

Inc.) have overall dimensions of 3.05 mm wide by 6.99 mm 

long with a strain range of ±3%. The control of the 

laparoscopic grasper is achieved using the QNX real-time 

operating system, data acquisition card, and motor 

amplifier. This program operates at 500 Hz and implements 

a PD controller to control the position of the jaws, which is 

given by: 

T = K p (qd − q)( ) + Kd q& d − q& (5) 

where T is the motor torque, K p and Kd are the 

proportional and derivative gains, qd and q are the 

desired and actual positions of the jaws, and qd& and q& are 

the desired and actual velocities of the jaws. 

A. Calibration 

 The calibration of the sensors on the prototype 

laparoscopic tool is required for accurate measurement of 

the tool-tissue interaction forces. Specifically, the preload 

in the normal force sensor and the manufacturing tolerances 

of the prototype make it necessary to calibrate each sensor 

once it has been placed on the tool. To perform this 

calibration, an electro-mechanical device that is capable of 

generating a linear force and recording the values was used 

(see Fig. 14). Calibration of the resistive sensor for normal 

force was performed by removing the jaw with the resistive 

207
Fig. 14. Electro-mechanical device for calibration of force sensors. © 

[2007] IEEE. 

sensor from the modular tool and attaching it to the side of 

the aluminum fixture using a cyanoacrylate adhesive (see 

Fig. 14). Fig. 15 shows the results of this calibration 

procedure. The resistive sensor measurements have been 

filtered using a 5th order Butterworth filter to eliminate the 

high frequency noise. Additionally, a least squares linear 

regression was used to derive a best-fit mathematical model 

for the loading and unloading of the calibration curve, 

given by: 

F 0.60 2.4 4.9 0.13 3 2

loading = x − x + x − (6) 

F 0.43 3.4 10 13 7.6 1.2 5 4 3 2

unloading = x − x + x − x + x − (7) 

where Floading and Funloading is the normal force in 

Newtons for loading and unloading curves respectively. 

 Calibration of the strain gages was performed by 

mounting the entire prototype to the aluminum fixture on 

the mechanical calibration device. The prototype was 

clamped at the actuation mechanism base with sufficient 

force to prevent any movement; thus mimicking the 

constraint in surgery where the tool would be attached to 

the end-effector of a robot arm. The loading was performed 

at a rate of 0.1 N/sec at 90° intervals (top, bottom, left, and 

right) on the jaw (see Fig. 16). The measurements from the 

load cell and all four strain gages were recorded and plotted 

to obtain the calibration curve. Fig. 17 shows an example 

calibration curve for the top loading point with similar plots 

for each loading point that are shown in Fig. 16. As shown 

by Fig. 17, a linear relationship exists between the strain 

gage output and the actual force measured by the load cell. 

A least squares linear regression was used to derive the 

best-fit mathematical model of the calibration curve for 

each strain gage. The models for each of the calibration 

curves for the top, bottom, left, and right loading points are 

given by: 

Ftop = 59xtop + 0.092 (8) 

Fbottom = 61xbottom − 0.11 (9) 

Fleft = 59xleft − 0.23 (10) 

Fright = 62xright − 0.27 (11) 

where F is the magnitude of force in Newtons exerted at 

the specified loading point and x is the strain gage output in 

volts at the specified loading point. 

Fig. 15. Calibration curve for the resistive sensor. © [2007] IEEE. 

Fig. 16. Location of the strain gages on the flex shaft and loading 

positions for the strain gage calibration. © [2007] IEEE. 

B. Tissue Characterization Experiment 

 As a demonstration of the capabilities of our modular 

laparoscopic tool, we have conducted a tissue 

characterization experiment to evaluate the force measured 

by the tool when grasping simulated tissue samples of 

varying stiffness. The simulated tissue samples were made 

up of Hydrogel material [18]. For this experiment, we 

selected three Hydrogel samples (corresponding to soft, 

medium, and hard tissue) that had a significant variation in 

stiffness and would be easily differentiated with one’s 

fingers. The samples were identical in size and thickness, 

therefore, the only variable would be the required force to 

deform the samples by the same magnitude. The 

experimental setup consisted of using our 7 DOF haptic 

device (in section III) to control the laparoscopic tool’s 

jaws and grasp each of the simulated tissue samples. As 

each sample was grasped, the normal force measurement at 

the jaws and the angle of the jaws were recorded. As shown 

in Fig. 18, the results show that the laparoscopic grasper 

can differentiate between samples of different stiffness. All 

three samples shown were grasped to a jaw angle of 

approximately 1°, therefore, all incurring the same 

deformation but a significantly different normal force for 

each sample. The soft Hydrogel sample showed a 

maximum force of 0.4 N while the medium Hydrogel 

sample showed a maximum force of 1 N and the hard 

Hydrogel sample showed a maximum force of 2.2 N for the 

same angular displacement of the jaw. Additional tissue 

grasping trials were performed with similar results for 

validation. Therefore, the grasper’s capability of 

distinguishing between tissues of different stiffness has 

208
been demonstrated. 

Fig. 17. Strain gage calibration curve for application of a force to the 

top of the jaws. © [2007] IEEE. 

Fig. 18. Characterization of the Hydrogel samples using the modular 

laparoscopic grasper. © [2007] IEEE. 

V. CONCLUSION

In this paper we presented: (a) a dynamic model of the 

Mitsubishi PA-10 robot arm for low velocity applications 

such as surgical tasks, (b) a seven degree of freedom haptic 

device that can be used for applications in robot-assisted 

minimally invasive surgery and, (c) a modular, automated 

laparoscopic grasper with tri-directional force measurement 

capability. The integration of the robot arm, the haptic 

device and the laparoscopic grasper form a complete haptic 

surgical system. Such a system incorporates advantages 

from minimally invasive surgery, improves dexterity, 

eliminates surgeon tremor, reduces surgeon fatigue and 

above all provides haptic feedback to the surgeon. Future 

work includes telemanipulation experiments that use the 7 

DOF haptic device to control PA-10 robot arm with the 

prototype laparoscopic tool mounted on its end-effector for 

performing tissue suturing experiments and evaluating the 

overall system capability. 

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209
Prototype Rover Field Testing and 

Planetary Surface Operations 

Edward Tunstel 

Jet Propulsion Laboratory 

California Institute of Technology 

Pasadena, CA USA 

tunstel@robotics.jpl.nasa.gov 

Abstract—The success of robotic surface missions on remote 

planetary surfaces can be facilitated by test campaigns on Earth using 

a combination of prototype, proto-flight, and space flight hardware. 

A series of field operations and testing activities were performed 

using prototype and proto-flight rovers in the years leading up to the 

NASA Mars Exploration Rovers mission (MER). A brief overview 

of those activities is presented. Robotic activities and tasks 

exercised during the Earth-based field trials provided insights for 

later flight rover operations during the Mars missions. The 

distribution of mission intelligence and autonomy across a remote 

human-robot collaborative team is highlighted. Aspects of the 

mobility and robotic arm performances achieved by the MER flight 

rovers, spanning several years of surface operations, are summarized. 

Keywords: Mars exploration, field testing, surface operations

I. INTRODUCTION

In January 2004, NASA began a Mars surface mission by 

landing two spacecraft there, each delivering a rover to 

explore distinct surface regions on opposite sides of the 

planet. The first rover, Spirit, was landed in the Gusev Crater 

on Mars and second, Opportunity, was landed in an area on 

Mars called Meridiani Planum. Scientific and technological 

objectives for the mission are accomplished using the two 

rovers and their science instrument payloads. 

Preparation for and execution of the MER mission involved 

the use of various rover prototypes, engineering models and, 

of course, space flight qualified rovers to refine and conduct 

an effective field operations approach. Ensuring the 

functionality of rover systems and their operation for remote 

planetary missions is complicated by a number of challenges. 

Among them is a need to produce highly robust and/or fault 

tolerant hardware and software since operational failures 

cannot easily be addressed at the remote site by humans. It is 

also a challenge to ensure that the system will work as 

designed in an environment that is unknown or not well 

understood prior to the mission. The lack of information 

about the target environment presents additional challenges 

associated with anticipating most possible scenarios that could 

be encountered by the system and therefore dealt with in a 

safe and acceptable manner. It is often difficult if not 

impossible to duplicate conditions and scenarios expected in 

the target planetary environment when testing the system on 

Earth. Nonetheless, Earth-based tests serve to build 

confidence in how the designed system will behave and help 

to reduce or mitigate risks associated with untested modes of 

the system due to lack of information about the target 

environment. Depending on the budgets and schedules for 

development of robotic flight mission hardware, a project may 

not have sufficient resources or time to perform all tests on 

the actual flight rover hardware that will be used on the planet 

surface. In such cases, prototype and proto-flight 

(engineering model) hardware are used before flight hardware 

becomes available [1]. However flight-like these systems 

may be, they can usually be used to exercise or develop 

operations approaches for some portions of the 

hardware/software or the mission operations system typically 

being developed in parallel. Prototype and proto-flight 

systems enable some of the testing needed to develop and 

verify operational functionality in outdoor settings while 

specific testing done to qualify flight hardware for launch, 

space, and planetary physical environments are done using the 

actual flight vehicles in the cleanest facilities. 

During the years leading up to the launch of the MER 

vehicles and their journey to Mars, a series of Earth-based 

field operations campaigns were conducted in desert locations 

of southwestern USA. The intent was to use MER-like 

prototype rovers to explore ways of performing remote field 

science including planning and assessment of rover traverses 

and robotic arm operations in Earth terrain similar to that 

expected at the Mars landing sites. A prototype rover called 

FIDO (Field Integrated Design & Operations) and a 

high-fidelity MER engineering model were used to rehearse 

mission operability and validate onboard mobility 

functionality in complex geological settings, respectively. 

These were end-to-end field trials involving networked 

operations and command workstations, satellite 

communications, remote field networking and support 

equipment, and integrated science instrumentation onboard 

the rovers. The tests were conducted via satellite from the Jet 

210
Propulsion Laboratory (JPL), hundreds of miles from the test 

sites. For each field trial, multiple Martian days (sols) of 

mission-like rover activity sequences were physically 

simulated with a focus on developing and rehearsing the MER 

surface mission operations approach. The end-to-end 

implementation, however, was not an exercise of the many 

parallel activities involved in the overall MER operations 

process [2] since the test environment was limited as a 

mission emulation and certain aspects of the MER process 

were still under development at the time. Nonetheless, these 

activities provided insights into rover and operations team 

performance that could be expected during the mission on 

Mars, ultimately facilitating successful and sustained mission 

operations on Mars for over 3.5 years thus far. 

This paper provides a brief overview of field operations 

activities conducted using prototype rovers prior to the MER 

landings. It also summarizes aspects of the performances 

achieved by Spirit and Opportunity during their surface 

missions on Mars with a focus on mobility/navigation and 

robotic arm functionality achieved by these flight rovers. 

II. PROTOTYPE ROVER OPERATIONS TESTING ON EARTH

It is often difficult to find, or impractical test in, Earth 

environments with the same conditions of target planetary 

environments. However, analogue Earth terrains are 

accessible that allow meaningful functional evaluation of 

rover systems for planetary environments. Field operations 

using Mars rover prototypes were conducted in such analogue 

terrains in the years of 2001-2003. 

A. FIDO Field Trials 

In 2001 and 2002 FIDO (Fig. 1) was used to rehearse 

realistic simulations of rover operations planned for the MER 

mission [3, 4]. Each mission rehearsal involved over 60 

science team participants from multiple institutions including 

NASA, U.S. Geological Survey, and a host of universities. 

This team constituted the Science Operations Working Group 

(SOWG) responsible for conducting the test from JPL. Each 

time, the FIDO system was used to physically simulate a 

20-sol mission scenario generally representative of what the 

MER Spirit rover would be commanded to execute over a 

period of 10 Earth days. At the field sites, a small team of 

field geologists and rover engineers handled logistics and 

related activities. 

The primary objective was for the SOWG to use a remote 

rover system and rover-mounted instruments to acquire data 

for formulating and testing hypotheses about the geologic 

evolution of the field site. Mission operations for those field 

trials were “blind” and fully remote. That is, the rover was 

commanded via satellite communications from JPL, and prior 

knowledge of the desert test sites was limited to large (tens of 

square kilometers) aerial images and spectral data typical of 

real Mars orbital observations. 

Fig. 1. FIDO rover during MER-FIDO field trial, Arizona USA, 2002. 

For all intents and purposes, all operations were conducted 

as if the rover were on Mars, in compliance with MER flight 

rules (operational constraints on rover and ground data system 

use) and using many of the same types of rover planning and 

command functions to be employed during the actual MER 

flight mission. The Planetary Robotics Laboratory at JPL 

was used to emulate the MER mission operations area, and 

collaborative software tools for robotic science operations 

planning [5] were used as the Ground Data System (GDS). 

The GDS processed and distributed downlink imagery and 

data to those participating in the mission operations facilities 

at JPL. Satellite link capability was available via satellite 

modem connection between networked computers and a 

satellite dish antenna allowing remote commanding of the 

rover via the Internet. Fig. 2 roughly illustrates the 

communications arrangement for each FIDO field trial. 

Fig. 2. Illustrated operations configuration for MER-FIDO field trials. 

B. MER Engineering Model Field Trial 

An additional field test was conducted to further support 

preparation for conducting semi-autonomous rover activities 

on Mars. It was conducted over a period of five days in the 

summer of 2003, between the launches of Spirit and 

Opportunity. A high-fidelity MER engineering model called 

211
the Surface System Test Bed (SSTB) (Fig. 3) was used. This 

rover is essentially identical in form, function, and capability 

to Spirit and Opportunity, with the exception of actual solar 

arrays and some electronics that operate off-board via a 

physical power, electronics, and communications tether. 

Fig. 3. MER Surface System Test Bed engineering model. 

At the field site, a small team of rover engineers conducted 

surface navigation tests and handled logistics and related 

activities while several field geologists conducted soil 

properties experiments and measurements on soil excavated 

using one of the rover’s wheels. A small subset of the MER 

mission operations team (command sequence developers, 

rover mobility engineering analysts, and ground data system 

personnel) participated remotely from JPL. 

The main objectives for the MER SSTB field tests were to 

validate onboard navigation software functionality in realistic 

outdoor terrain and acquire outdoor imagery for related 

off-line use. An additional focus was verification and 

validation of the end-to-end command sequencing and uplink 

as well as the telemetry downlink and health assessment cycle 

of mission operations. Objectives included testing surface 

operations capabilities for navigation in undulated terrain 

under natural (Earth) lighting conditions, and in scenarios 

involving science target approaches and fine positioning at 

science targets for robotic arm use. 

A portion of the actual MER mission support facility and 

GDS at JPL was used, including collaborative software tools 

for science operations planning as well as robotic motion 

planning and sequencing [5, 6]. The same satellite link 

capability used for prior FIDO trials was employed with an 

additional field trailer needed to support SSTB operations via 

tether between the rover and the in-trailer support equipment. 

Command and telemetry were transmitted wirelessly between 

the field trailers in this case as indicated in Fig. 4, which 

roughly illustrates the communications arrangement for the 

SSTB field trial. 

Mission operations for the MER field trial were fully 

remote but not “blind.” The MER GDS processed and 

distributed data received via satellite to those participating in 

the MER mission operations facilities at JPL. With the use 

of a MER engineering model as the test rover, this field test 

was most relevant for the actual flight rovers and the GDS 

software tools employed, all of which allowed rehearsal of 

actual tactical mission operations processes and procedures. 

Fig. 4. Illustrated operations configuration for MER-SSTB field trial. 

III. SUMMARY OF EARTH-BASED FIELD RESULTS

The field venue for the 2001 trial was a small arroyo on the 

southern edge of the Soda Mountains in the Mojave Desert of 

California USA (almost 180 miles from JPL). Activities 

executed in the field included autonomous traversal to 

specified targets, approaching rock targets and deploying 

multiple instruments among other things [3]. FIDO

traversed a total distance of 135 m over the desert terrain 

throughout the field trial. A number of short and long 

traverses were interspersed among many stationary science 

investigation activities. The longest continuous autonomous 

traverse was 40 m, and the average rover speed during 

traverses was 60 m/hr while negotiating and avoiding 

obstacles and terrain hazards [3]. 

The venue for the 2002 trial was an ancient flood plain in 

Gray Mountain, Arizona located approximately 40 miles north 

of Flagstaff, Arizona USA (almost 500 miles from JPL). 

Field operations consisted of similar science and robotic 

activity sequences exercised in 2001 with FIDO traversing a 

total distance of 202 m including short and long traverses 

interspersed among stationary science activities along the 

way. The longest continuous autonomous traverse was ~70 

m, and the average rover speed during traverses was 60 m/hr 

[4]. A narrative of the 2002 field trial, daily rover operations, 

and scientific findings can be found on the Internet at 

http://marsrovers.jpl.nasa.gov/fido. 

Field trials with FIDO helped develop the operations 

approach for MER by enabling simulation of MER processes 

for rover activity planning and sequencing relatively early in 

the project when flight hardware and certain GDS software 

212
tools were not yet available. The processes would eventually 

be exercised via Operations Readiness Tests, which could 

fully exercise the MER operations processes using 

high-fidelity proto-flight rovers and MER GDS software tools 

under realistic conditions. 

The field venue for the MER SSTB field trial was a dry 

lakebed located in Edwards, California USA (almost 100 

miles from JPL) where tests were performed over a period of 

five days. Two test sites were used; one was relatively flat 

with a sparse rock distribution and the other was a gulley with 

undulated terrain. Building upon results of prior indoor and 

outdoor mobility validation tests at JPL, navigation traverses 

were completed over shallow hills including short (~10 m) 

approaches toward vertical walls, sloped walls, and 

discontinuous terrain drop-offs. The first remotely planned, 

commanded, and analyzed traverses using part of the MER 

GDS and onboard navigation software were performed during 

this field trial. The testing served to validate stereo image 

processing software (used for hazard detection and avoidance 

in support of autonomous navigation) in nominal outdoor 

terrain. It was also valuable for acquiring imagery of 

different types of terrain than were available in earlier test 

environments and was useful for off-line testing of image 

processing and hazard detection software at a later date. 

SSTB is still used today to occasionally validate first-time or 

otherwise risky sequences before trying them on Mars. 

IV. INTELLIGENCE AND AUTONOMY IN MER OPERATIONS

The experiences and results gained from Earth-based field 

tests generated lessons-learned for remote semi-autonomous 

rover operations. Many were directly followed in the 

conduct of actual MER mission operations and/or led to 

feature enhancements for GDS or rover onboard flight 

software. Comparable robotic activities and tasks were 

exercised during the Earth-based field trials and provided 

insights for expected performance during the mission on 

Mars. Some scenarios were encountered on Mars that were 

not thoroughly tested for during field trials but the training 

and expertise of the mission operations team along with the 

flexible rover system design enabled compensation and 

adaptability in all cases encountered. 

As benefactors of experience gained during field testing on 

Earth, the Spirit and Opportunity rovers performed well 

throughout their respective 90-sol prime missions on Mars [7, 

8] and several extended mission periods since then [9]. Both 

rovers have far out-lived their prime mission durations and 

continue to explore for over 3.5 years beyond their landing 

dates thus far. Fig. 5 shows photo-realistic MER models as 

insets in actual images during their extended missions. What 

has turned out to be a long duration mission may not have 

been possible without the Earth-based mission operations 

team (and longer than projected hardware operational 

lifetimes). In this case, humans supplied and gained science 

intelligence through remote use of meager robotic autonomy. 

For the MER mission, intelligence is largely human while 

autonomy is necessarily robotic (until human missions to 

Mars become possible). 

Fig 5. Spirit (left) in Columbia Hills and Opportunity (right) in Endurance 

Crater on Mars (Special-effects images created using photo-realistic rover 

models & image mosaics acquired during their missions. Rover model size 

approximated based on size of rover tracks in actual mosaic). 

Within the MER surface mission operations system (Fig. 6) 

the SOWG, a rover engineering analysis and sequencing team, 

and the rovers form a closed-loop human-robot control system 

(notwithstanding the NASA’s Deep Space Network and 

supporting teams and systems beyond the scope of this paper). 

Humans collaborate with the rovers to achieve best 

performance of onboard mobility and robotic arm software to 

maximize the acquisition and return of science data. 

Engineering analysts effectively function in the feedback loop 

of the human-robot system (Fig. 7) as human observers of 

rover state as well as maintainers of the best state knowledge 

for delivery to the uplink planning team. SOWG and 

engineering sequencing functions are manifested in the feed 

forward loop and can be thought of as providing reference 

inputs and serving as compensators for the rover system based 

on engineering state and recommendations from engineering 

analysts. Fig. 7 shows a simplified view of the operations 

process as a closed-loop control system; see [2] for a more 

definitive reference detailing the process. 

Fig. 6. Illustrated operations configuration for the MER mission. 

213
 

Fig. 7. Simplified human-robot system for MER remote surface operations. 

Within this closed-loop human-robot system the science 

instrument, image, and engineering data telemetered to Earth 

drive the exploration plans for the next sol. The Earth-based 

planning process proceeds with generation of rover motion 

and science instrument command sequences that will carry out 

the intended activities. The engineering sequencing team 

refines motion commands using their perception of the rover 

surroundings and knowledge of rover behavior [10, 11]. This 

is facilitated by analyses that result in engineering 

recommendations for making the best use of the rover 

functionality including, for example, offline use of ground 

tools and human perception to localize the rovers and update 

their poses after accumulation of onboard odometry errors. 

This collaborative loop of human intelligence and rover 

autonomy serves to facilitate proper execution of the sol’s 

command load on Mars. Nominally, each rover is sent a 

command load daily and autonomously executes uplinked 

sequences throughout a period of 3-6 hours around local noon 

(with occasional nighttime communications or immobile 

science activity). In this manner, human-guided robotic 

execution leads to exploration progress, which generates new 

data and images that feedback into the cyclic process, 

ultimately leading to scientific discovery. 

V. SUMMARY OF PERFORMANCE ON MARS

Selected aspects of Spirit and Opportunity mobility, 

navigation, and robotic arm performances are summarized 

below. Additional engineering details about their missions 

and relevant operational performance metrics can be found in 

[10-12] and [9], respectively. The performance summarized 

here is based on operations through mid-September 2006, 

after which a new software upgrade was uplinked to the 

rovers including substantial functional enhancements 

expected to yield performance improvements. To date, the 

new functional enhancements have not been used frequently 

enough to generate new performance data of sufficient 

quantity to report here. 

A. Total Traverse Distance and Navigation Speed 

September 15, 2006 was sol 960 for Spirit and sol 940 for 

Opportunity. By that date, the total traverse distance for 

Spirit was 6876 m, of which 3126 m (45%) was traversed 

autonomously. Also by then, Opportunity traversed 9130 m 

total, of which 2465 m (27%) was traversed autonomously. 

Rover average autonomous traverse rates depend on terrain 

traversability. A given rover may traverse flat and hazard-free 

terrain at a faster average rate than it would a sloped and 

rocky terrain due to the increased deliberation required to 

assure safe traversal in the latter case. While directed/blind 

traverses can achieve speeds over 100 m/hr, autonomous 

traverses employ onboard image processing to detect and 

avoid geometric hazards and achieve, as a result, driving 

speeds from 10 m/hr in obstacle-laden terrain up to 36 m/hr in 

safe terrain [13]. Traverses that employ visual odometry 

execute more slowly due to the more frequent image 

processing required to provide the best position estimates, and 

due to short mobility motions needed to ensure close spacing 

between consecutive images used for visual odometry. These 

image acquisition and motion constraints limit the visual 

odometry traverse rate to about 10 m/hr. Other factors 

contributing to the relatively slow execution times of MER 

autonomous mobility are the low computation speed of the 20 

MHz RAD6000 and the fact that dozens of tasks in the 

real-time system share a single address space and cache [13]. 

Spirit’s average and maximum autonomous traverse rates at 

its Mars landing site (Gusev crater, ~ 7% rock abundance 

[14]) thus far are 15.06 m/hr and 34.35 m/hr, respectively. 

The same traverse rates for Opportunity at its landing site 

(Meridiani Planum) thus far are 22.09 m/hr and 36.0 m/hr, 

respectively. The Meridiani Planum site is largely devoid of 

rocks, and observations suggest a rock abundance of only a 

few percent [14]; we use 3% here. In each case, the average 

traverse rates are taken over all traverse sols (up to Spirit’s sol 

960 and Opportunity’s sol 940) that included autonomous 

navigation. Figs. 8 and 9 show histogram distributions of 

autonomous navigation traverse rates for both rovers over the 

course of their missions through mid-September 2006. 

Fig. 8. Navigation speeds during 100 autonomous traverses for Spirit. 

Sequencing Team 

Rover Analysts 

Spirit/Opportunity 

Uplink 

Command 

Sequences 

Downlink 

Telemetry 

execution

SOWG 

science 

activities 

 State knowledge 

 Recommendations  rover kinematic state 

 engineering & image data 

214
The histograms show that most of Spirit’s autonomous 

traverses were executed at relatively slower rates than most of

Opportunity’s. Both rovers executed relatively few 

autonomous traverses at rates greater than about 30 m/hr. 

Spirit’s autonomous traverses were generally slower across 

the more challenging terrain at Gusev crater, which presented 

more geometric hazards for the rover to avoid. Thus far, 

Opportunity has executed two-thirds fewer traverses in 

autonomous mode than Spirit. This too is indicative of the 

lesser challenging terrain at the Meridiani Planum site, which 

was relatively flat and open, and thus obviated the need for 

autonomous avoidance of non-traversable hazards during 

many traverses. Among the most challenging terrains 

encountered, relative to those involved in Earth testing, were 

surfaces of substantial slope (over 50% of the rovers’ 45o tilt 

stability limit), surfaces of softness sufficient to induce high 

wheel slip, and surfaces soft enough to yield to the rover’s 

ground pressure, embed the wheels and resist motion. 

Fig. 9. Navigation speeds during 33 autonomous traverses for Opportunity. 

B. Science Target Approach 

In addition to traversing from place to place, science rovers 

must deploy instruments in contact with or in proximity to 

reachable rocks and soil specimens. An instrument positioning 

system capable of precision placement of in situ instruments 

from mobile platforms is essential [15]. Spirit and 

Opportunity perform this function using a 5 degree-offreedom (DOF) robotic arm known as the Instrument 

Deployment Device (IDD). It is mounted in a frontal area 

beneath the rovers’ solar panel. Its end-effector is a rotary 

turret to which science instruments are mounted, and the 

remaining 4 DOFs are used to place the instruments onto 

science targets within the arm’s kinematic work volume ~ 

0.14 m3

). The instruments mounted on the IDD end-effector 

turret include a microscopic imager (MI) to capture close-up 

images, a Moessbauer spectrometer (MB) to detect 

iron-bearing minerals, an Alpha-Particle-X-Ray Spectrometer 

(APXS) to determine the elemental chemistry of surface 

materials, and a Rock Abrasion Tool (RAT) for exposing 

fresh material beneath weathered rock surface layers via 

controlled-force loading and physical abrasive action. The 

arm is also used to position the spectrometers for placement 

onto an instrument calibration target and science-related 

magnets mounted at different locations on the rover body. 

Rover mobility is used to approach a position offset from a 

science target such that the target is within the work volume 

of the IDD. A successful target approach is typically followed 

by placement of instruments onto the target using the IDD. 

Controlled placement of instruments in contact with science 

targets is facilitated by contact sensor feedback. Redundant 

sets of contact sensors on each instrument provide tactile 

feedback used by software to halt arm motion upon expected 

or unexpected contact. IDD joint and contact sensor 

telemetry facilitates determination of errors between 

commanded and actual placement positions. 

An approach-traverse is on the order of 10 m and intended 

to terminate with a specific science target within the IDD 

work volume. The science target is selected and designated 

by mission operators in stereo imagery acquired prior to the 

approach. Each rover was required to be capable of 

approaching a science target in a single command cycle 

whenever within 2 m of the target at the start of a sol. 

Depending on the initial approach distance from a target, 

complexity of terrain between rover and target, and other 

considerations, target-approaches do not always succeed on 

first attempts. On occasion, more than 1 sol is needed to 

reach certain targets, particularly when approach distances are 

longer than 2 m, and since images of the IDD work volume at 

the rover's final location are required (until recent software 

upgrades) before IDD deployment is permitted. 

Fig. 10. Frequencies of science target approach distances executed by Spirit

among a sample set of 16 approach-traverses. 

Figs. 10 and 11 show histograms of the science target 

approach distances for both rovers. Most approach distances 

have been less than 6 m and several approach-traverses over 

10 m have been executed by each rover thus far. Out of 16 

target approaches considered for Spirit, 14 (88%) were 

successfully executed in a single sol. Out of 18 considered 

for Opportunity, 13 (72%) were successfully executed in a 

215
single sol. All other target approaches were completed within 

2 sols. The longest single-sol target approaches among those 

considered here were 15 m and 11.8 m, respectively, for Spirit

(sol 685) and Opportunity (sol 803). 

Fig. 11. Frequencies of science target approach distances executed by 

Opportunity among a sample set of 18 approach-traverses. 

C. Robotic Arm Instrument Placement 

Instrument placement is achieved autonomously (including 

switching from one instrument to another) by realizing a 

combination of kinematic configurations that are pre-taught 

and/or newly commanded. Cartesian and joint-space motions 

are determined via onboard calculation of inverse kinematics 

and position error compensation. Original MER operational 

guidelines required human confirmation of the rover position 

prior to each IDD use, making each approach and instrument 

placement take at least two sols, but software upgrades 

mentioned earlier will make approach and placement possible 

in the same sol. The requirements on placement performance 

hold in either case. 

While human operator assisted techniques have been 

employed to achieve position accuracies as good as 0.8 mm as 

well as improvements in repeatability performance [16], we 

consider average performance over a large sampling of 

instrument placements executed without operator assistance 

(beyond designation of placement targets). Figs. 12 and 13 

shows sample histories of instrument placement errors for 

each rover's IDD and individual arm-mounted instrument. 

These data are associated with instrument placement activities 

during Mars surface operations. Across all instruments, the 

data represent over 1200 placements through sol 944 for Spirit

and nearly 1200 placements through sol 893 for Opportunity

on rock, soil, and rover-mounted targets. Placement errors are 

derived from IDD and instrument contact switch telemetry as 

well as front stereo Hazard Camera range data evaluations. 

Instrument placement performance, considering errors in 

placement of all instruments, reveals average absolute 

positioning accuracies of 6.81 mm for Spirit and 5.84 mm for

Opportunity. 

Fig. 12. Robotic arm positioning errors for Spirit when placing the APXS, 

MB, MI, and RAT instruments onto science targets. Data covers instrument 

placements through sol 944. 

Fig. 13. Robotic arm positioning errors for Opportunity when placing the 

APXS, MB, MI, and RAT instruments onto science targets. Data covers 

instrument placements through sol 893.

VI. CONCLUSION

As the space community embarks on future robotic 

missions to surfaces of other planets or the moon, Earth-based 

robotic prototypes will become more important for proving 

and rehearsing surface operations approaches. This paper 

gives a brief overview of field trials leading up to the MER 

surface mission that employed two prototypes, FIDO and the 

MER SSTB. These field operations tests provided venues for 

rehearsing, validating, and refining aspects of the mission 

operations process, tools, and approaches used on MER. 

They also provide opportunities to train mission personnel on 

how to use autonomous rovers to conduct remote field-based 

science and identify technologies that require additional 

development and/or evaluation. 

Mobility and robotic arm performance achieved by Spirit

and Opportunity on Mars cannot be directly compared to the 

216
Earth-based field trial performances since, at various stages of 

the latter, different hardware, software, and environments 

were used. The Earth-based tests allowed operators to refine 

approaches to operating the systems during the actual mission. 

On Mars the mobility and navigation software kept both 

rovers safe from terrain hazards while making progress 

toward commanded goals. Both rover robotic arms enabled 

acquisition of science data leading to conclusive evidence that 

water once existed on the surface at their respective landing 

sites [7, 8]. The performance data summarized herein is based 

on operations through mid-September 2006. Since then, new 

autonomy software enhancements expected to yield 

performance improvements were uplinked but not sufficiently 

exercised to yield reportable results yet. They include a global 

path planner, visual target tracking to further automate 

approach-traverses, and autonomous deployment of the IDD 

after an approach-traverse, without the additional command 

cycle that was previously required. 

The creativity and expertise of human mission operators 

has kept the rovers operating despite slowly degrading or 

aging hardware components. Robotic execution errors 

reported by mobility or navigation and robotic arm software 

were often due, to some extent, to some form of human error; 

that is, command sequence/sequencing errors, unaccounted 

for system operational behaviors not experienced during Earth 

testing, or isolated shortfalls of planning tools or processes. 

All of these were later rectified as lessons-learned for the 

MER mission, still ongoing at the time of this writing. 

While all mission scenarios cannot be anticipated and tested 

for, robust and fault tolerant system design coupled with safe 

mission operations can ensure successful surface missions. 

Thus far, all novel situations encountered during the MER 

surface mission, that were not thoroughly tested for during 

field trials, have been successfully overcome thanks to the 

training and expertise of the mission operations team along 

with the flexibility of rover system design. 

ACKNOWLEDGMENT

The research described in this paper was performed at the 

Jet Propulsion Laboratory, California Institute of Technology, 

under contract with the National Aeronautics and Space 

Administration. Research and field activities were supported 

by the Mars Exploration Program and Mars Technology 

Program, Science Mission Directorate. 

Outstanding contributions of the FIDO rover engineering 

team, MER Mobility/Navigation Working Group and MER 

field scientists Robert Anderson and Albert Haldemann are 

acknowledged. Collaboration and support provided by the 

MER Project and personnel is gratefully acknowledged, with 

special thanks to John Callas, Jennifer Trosper, and Jacob 

Matijevic of JPL and MER Principal Investigators, Steve 

Squyres of Cornell University and Ray Arvidson of 

Washington University, St. Louis. Expert Internet and 

satellite networking assistance from Claudia De Luna, 

Kathleen Walsh, William Adler of JPL and Thomas Nielson 

and Andre Finlinson of Lyman Bros., Inc. is acknowledged. 

Major credit goes to the MER Mission Operations team with 

special thanks to the Mobility/ IDD Analysts for performance 

assessments reported herein. 

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217
Planning to Fail - Reliability as a 

Design Parameter for Planetary Rover Missions

S. Stancliff, J. Dolan 

The Robotics Institute 

Carnegie Mellon University 

5000 Forbes Ave 

Pittsburgh, PA, USA 

{sbs,jmd}@cs.cmu.edu

A. Trebi-Ollennu 

Jet Propulsion Laboratory 

California Institute of Technology 

4800 Oak Grove Drive 

Pasadena, CA, USA 

Ashitey.Trebi-Ollennu@jpl.nasa.gov 

Abstract—The Mars Exploration Rovers (MER) have been 

operating on Mars for more than three years. The extremely high 

reliability demonstrated by these rovers is a great success story in 

robotic design. This reliability comes at a high cost, however, both 

in the initial cost of developing the rovers and in the ongoing 

operational costs for their mission extensions. If it were possible to 

design rovers with reliability more in line with their mission 

requirements (in the case of MER, 90 days), considerable cost 

reductions could be achieved. This will be even more important for 

future planetary robotic missions due to greatly increased mission 

durations. 

In this paper we present an overview of our ongoing research in 

the area of predicting robot mission reliability, and we show how a 

mission designer can trade off reliability against costs in order to find 

an optimal reliability target for a given robotic mission. Our results 

show that for a given mission there is an optimal reliability range 

with respect to cost and that having rovers with reliability that is too 

low or too high is suboptimal from an economic standpoint. This 

suggests that a better cost-reliability tradeoff can be obtained by 

"planning to fail" by designing rovers which have lower reliability 

than current legacy designs. 

Keywords: planetary rovers, mission design, mission cost, 

reliability, failure, risk.

I. INTRODUCTION

In the near future, NASA intends to send rovers to Mars for 

missions lasting an order of magnitude longer than the 

intended duration of the Mars Exploration Rovers (MER) 

mission. If these future rovers follow legacy designs, then 

increasing the mission duration by an order of magnitude will 

require that the rovers be built using components with failure 

rates an order of magnitude lower. Since NASA rovers 

already make use of some of the most reliable components 

available, it is doubtful whether components with order of 

magnitude lower failure rates are available, let alone 

affordable. 

In order to increase rover mission durations without 

incurring exponential increases in rover costs, it is necessary 

to consider 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. 

In the mobile robotics literature there is little formal 

discussion of reliability and failure. When reliability is 

mentioned, it is usually qualitatively, and in passing. 

Reference [1], for example, mentions intermittent hardware 

failures as an explanation for gaps in experimental data but 

makes no attempt at characterizing the failures. 

A handful of prior papers make use of reliability 

engineering for analysis of mobile robot failure rates. 

Reference [2] provides an overview of robot failure rates at 

the system level (i.e., robot model X failed Y times in Z hours 

of operation) and also breaks down failures according to the 

subsystem that failed (actuators, control system, power, and 

communications). Reference [3] extends the work in [2] both 

by the inclusion of additional failure data of the same type and 

also by addition of new categories of failure—those due to 

human error. Reference [4] provides a detailed analysis of 

failures experienced by some of the robots used in searching 

the World Trade Center wreckage in 2001. Reference [5] 

provides failure data for robots used in long-term experiments 

as museum guides. 

What these papers have in common is that they use 

reliability engineering tools in the assessment of existing 

robots. Our work differs in that it addresses how to use 

reliability engineering tools for designing robots and robotic 

missions. 

In earlier work we have developed methods for using 

reliability engineering tools to predict the probability of a 

robot failing during a mission [6], and we have used these 

tools to compare the performance of different robot and robot 

team configurations [7]. The only known work preceding 

ours in the area of predicting mobile robot team reliability is 

[8]. That paper's methods are similar to ours in that they are 

based in the reliability literature, but that work has a narrow 

focus on teams of robots with cannibalistic repair capability. 

In contrast, we are developing a general methodology that can 

be applied to a wide variety of robot teams and missions. 

The work presented in this paper differs from our earlier 

work by addressing the relationship between robot reliability 

and overall mission cost, and demonstrating how this 

relationship can be used to identify an optimal reliability level 

218
TABLE 2 

COMPONENTS COMPRISING POWER SUBSYSTEM

Component Quantity Failure Rate (1/h) 

Battery 2 2.10x10-7

Battery control board 2 4.00x10-7

Mission clock 1 1.00x10-7

Power distribution unit 1 1.70x10-6

Power control unit 1 1.90x10-7

Shunt limiter 1 1.14x10-5

Electrical heater 2 3.00x10-6

Radioisotope heater 2 1.36x10-5

Thermal switch 2 9.50x10-5

TABLE 3 

SUBSYSTEM USAGE BY TASK IN HOURS

Subsystem Transit Assemble Return 

Power 6 8 6 

Computation&Sensing 6 4 6 

Mobility 6 8 6 

Communications 2 4 2 

Manipulator 0 8 0 

TABLE 4 

SUBSYSTEM PROBABILITY OF SURVIVAL BY TASK

Subsystem Transit Assemble Return 

Power 99.86% 99.81% 99.86% 

Computation&Sensing 99.87% 99.92% 99.87% 

Mobility 99.97% 99.96% 99.97% 

Communications 99.98% 99.97% 99.98% 

Manipulator 100% 99.94% 100% 

which minimizes mission cost. 

II. EXAMPLE MISSION SCENARIO

A. Missions and Tasks 

Consider a planetary exploration mission where a team of 

rovers is tasked to install a solar panel array for a 

measurement and observation outpost. The mission consists 

of carrying 50 solar panels from the landing site to the outpost 

and then assembling them. The size of the solar panels is 

such that two rovers are needed to carry and assemble one 

panel. 

For the purposes of the reliability analysis, the task of 

assembling a solar panel is broken down into three subtasks: 

 - Transit to the outpost, 

 - Assemble the panel, and 

 - Return to the landing site. 

We assume that failure occurs only at the end of a subtask. 

This allows us to avoid dealing with partially completed 

subtasks. This simplification does not limit the resolution of 

the representation because tasks can be restated into smaller 

subtasks if needed. 

B. Rovers and Components 

For this analysis we assume that the rovers on the team are 

identical. The rovers are considered to be made up of several 

subsystems that are independent from the standpoint of 

reliability. The specific partitioning is not important to the 

methodology, but for the analyses in this paper the rovers are 

divided into the subsystems listed in Table 1. 

The subsystem reliabilities listed in Table 1 were 

calculated from the failure rates of the major components in 

each subsystem. An example component breakdown for the 

power module is shown in Table 2. Due to the limited 

amount of failure data available for planetary rovers, the 

failure rates in Table 2 were derived from the RAC databooks 

([9]) which are commonly used for reliability prediction in 

aerospace and military applications. Additional details on the 

calculation of subsystem failure and the combining of 

component failure rates can be found in [10]. 

We assume that the failure of any single subsystem leads 

to failure of the entire rover. For the current example 

mission this is a reasonable assumption, since all of the 

subsystems must be functioning in order to complete the 

mission subtasks. 

The probability of a subsystem failing during a task is 

found using standard reliability engineering methods 

assuming a constant failure rate. Two inputs determine the 

module failure probability: the module failure rate and the 

length of time for which the module is operated during the 

task. The durations shown in Table 3 were assigned using 

reasonable assumptions about the relative durations of 

different tasks and the relative usage of different modules. 

During the transit task, the panels are assumed to be locked in 

a fixed position not requiring manipulator actuation. 

The probability of survival for a subsystem for a given 

task is given by the equation 

tλ P e− = , (1) 

where t is the amount of time that the subsystem is used 

during the task and λ is the failure rate for the subsystem. 

Using (1) and the data from Tables 1 and 3, we calculated 

the probability that each subsystem will survive each task. 

These probabilities are shown in Table 4. 

III. APPROACH

The experiments in this paper make use of the method 

described in [5] for predicting probability of mission 

completion. In this method, the mission is represented using 

a state machine that is simulated stochastically. 

The simulation is repeated many times, with the average 

score of all trials giving the overall probability of mission 

completion. The results of the simulations were verified by 

hand calculation for a few simple cases. 

TABLE 1 

ROVER SUBSYSTEMS AND RELIABILITIES 

Subsystem MTTF (h) 

Power 4202 

Computation&Sensing 4769 

Mobility 19724 

Communications 11876 

Manipulator 13793 

219
A. Relationship Between Team Size and Mission Success 

Using this method, we first examine the relationship 

between the number of rovers on the team and the probability 

of completing the mission. Figure 1 compares teams of two 

to six rovers, each composed of the baseline components with 

the subsystem reliabilities shown in Table 1. This analysis 

can be used to determine a minimal team size for a required 

probability of mission success. For instance, if we set the 

required probability of mission success at 99.5% then Figure 1 

shows that the team must consist of at least six rovers. 

B. Relationship Between Component Reliability and Mission 

Success 

Figure 1 shows that a six-rover team exceeds the mission 

reliability requirement. In such a case, a mission designer 

may wish to choose lower-reliability components in order to 

decrease mission costs. The same simulations used to create 

Figure 1 can be used to determine the minimum component 

reliabilities required to meet a particular mission reliability 

requirement. Figure 2 compares six-rover teams using 

components with reliabilities which vary from 60% to 100% 

of the values in Table 1. From this we find that we can 

achieve the 99.5% goal by using components with 95% of the 

reliabilities shown in Table 1. 

We have now determined that the smallest team with the 

lowest-reliability components which can achieve the design 

goal of 99.5% probability of mission success is a six rover 

team with the component reliabilities shown in Table 5. We 

use this team as the baseline for the comparisons that follow. 

C. Relationship Between Component Reliability and Cost 

The reliability of the rovers is related to the overall 

mission cost in two ways. First, there is the increased cost 

associated with higher-reliability rovers. Second, there is the 

increased expected value of the mission when using 

higher-reliability rovers due to a higher probability of mission 

success. 

1) Cost of Reliability: In choosing components from 

which to build rovers, a designer would usually make choices 

among a small number of alternative components, each 

providing a certain reliability for a certain cost. However, in 

the early stages of design the mission designer may not have 

complete information about available components. In this 

case, it is useful to have a parametric model of the 

cost–reliability relationship. Reference [11] provides a 

general model for this relationship, which is given as 

( ) ( )

( )











−

− = − ⋅

i

i

R R

R R

c f

max

min exp 1 (2) 

where Ri is a reliability of interest between Rmin and Rmax ; c is 

the relative cost of Ri compared to Rmin ; f is the feasibility of 

reliability improvement (a number between 0 and 1); and c is 

the resultant relative cost of Ri with respect to Rmin. 

This equation can be used to calculate the relative cost of 

the components used by the six-rover teams with differing 

component reliabilities. These costs are plotted in Figure 3 

as a percentage of the baseline team cost, using Rmin=0, 

Rmax=1 and f=0.95. We examine the effect of changing the 

feasibility constant later in this paper. 

Launch costs are also affected by rover reliability. 

More-reliable rovers will weigh more, due to increased size of 

more-reliable components and due to increased component 

redundancy. We have not found a model for the 

reliability–weight relationship in the literature. As an initial 

approximation for launch costs we assume that the 

relationship between weight and reliability is directly linear 

and that the relationship between launch costs and weight is 

also directly linear. 

 0

 10

 20

 30

 40

 50

 60

 70

 80

 90

 100

 60 65 70 75 80 85 90 95 100

Probability of mission completion (%)

Component reliability (% of Table 1 values)

Fig. 2. Mission Reliability as a Function of Component 

Reliability

 30

 40

 50

 60

 70

 80

 90

 100

 2 3 4 5 6 7

Probability of mission completion (%)

Number of robots

Fig. 1. Mission Reliability as a Function of Team Size

TABLE 5 

COMPONENT RELIABILITIES GIVING 99.5% PROBABILITY OF 

SUCCESS FOR SIX-ROBOT TEAM 

Subsystem MTTF (h) 

Power 3992 

Computation&Sensing 4531 

Mobility 18738 

Communications 11282 

Manipulator 13103 

220
2) Expected Value of Mission: Any mission must have some 

inherent value to it. For some missions there will be an 

obvious economic or strategic value to which a dollar amount 

can be assigned. For a mission that lacks such an obvious 

dollar value, the cost of the baseline mission itself can be used 

as a lower bound for this inherent mission value, since the 

sponsor presumably expects some return on the investment. 

Multiplying the probability of mission success by the 

inherent value of the mission gives an expected value for a 

given team configuration. For example, the relationship 

between component reliability and expected mission value is 

given by Figure 2, with the vertical axis relabeled as 

“expected value as percent of inherent value”. 

D. Overall Mission Cost–Reliability Relationship 

Taking the expected mission value calculated above and 

subtracting the rover development and launch costs gives us 

an estimate of the net expected gain for the mission. We 

ignore operating costs here since we expect them to be 

roughly constant with respect to rover reliability (probably 

slightly higher for lower-reliability rovers due to the increased 

need for intervention). 

In order to combine these costs meaningfully, we assign 

real dollar values to the various costs for the baseline team. 

These values are estimated from the costs of the MER mission, 

along with the assumption that the rovers for this mission 

would be somewhat cheaper and smaller than the MER rovers 

due to advances in technology and also because they are 

single-purpose machines. The values we assigned for the 

baseline team are shown in Table 6. Figure 4 then plots these 

component costs and values as well as the net expected gain 

as a function of rover component reliability. 

IV. CONCLUSIONS

The most significant thing revealed by Figure 4 is that there is 

clearly an optimal reliability range with respect to the 

expected gain of the mission, and that this optimal reliability 

is significantly lower than the reliability of the baseline 

(legacy) design. 

The shape of the expected gain curve shows that for 

low-reliability rovers the cost of failure drives the expected 

gain value down, while for very high-reliability rovers the 

high cost of the rovers themselves drives the expected gain 

down. The optimal reliability range therefore lies in a 

medium-reliability region where neither of these costs is as 

high. 

In order to evaluate the effects of some of our 

assumptions on these conclusions, we have repeated the above 

analysis for different values of the feasibility constant (since 

this value was arbitrary) and of the mission inherent value 

(since we used a lower-bound estimate for this value). These 

results are shown in Figures 5 and 6. These figures show that 

while the shape of the expected gain curve changes somewhat 

with these parameters, the overall trends remain the same, and 

both figures support the argument that the optimal range for 

mission reliability is at a lower level than we would intuitively 

consider to be the case. 

While we expect that these curves will vary for different 

missions, we expect that the general trends will hold, 

indicating that it can be economically wiser to "plan to fail" 

by building rovers which have lower reliability than current 

legacy designs. 

ACKNOWLEDGEMENT

This research was carried out at the Jet Propulsion Laboratory, 

California Institute of Technology, under a contract with the 

National Aeronautics and Space Administration and funded 

through the Director's Research and Development Fund 

Program. 

 30

 40

 50

 60

 70

 80

 90

 100

 40 50 60 70 80 90 100

Rover cost (% of baseline team)

Component reliability (% of Table 1 values)

Fig. 3. Relative Cost of Rovers as a Function of Component 

Reliability

-200

-100

 0

 100

 200

 300

 400

 500

 55 60 65 70 75 80 85 90 95 100

$ (Millions)

Component reliability (% of Table 1)

Expected value

Rover cost

Launch cost

Expected gain

Fig. 4. Net Expected Gain with f=0.95, value = $450M

TABLE 6 

BASELINE TEAM COSTS AND REWARDS

Item Cost ($ Millions) 

Robot cost (entire team) 150 

Launch cost (entire team) 300 

Inherent value of mission 450 

221
REFERENCES

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Rosenthal, B. Sellner, R. Simmons, K. Snipes, A. Schultz and J. Wang, 

"Designing robots for long-term social interaction," Proc. 2005 IEEE/RSJ Int. 

Conf. Intelligent Robots and Systems (IROS 2005), pp. 1338–1343. 

[2] J. Carlson and R. Murphy, "Reliability analysis of mobile robots," Proc. 

2003 IEEE Int. Conf. Robotics and Automation (ICRA 2003), pp. 274–281. 

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failures," Proc. 2004 IEEE Int. Conf. Robotics and Automation (ICRA 2004), 

pp. 4987–4994. 

[4] M. Micire, "Analysis of the robotic-assisted search and rescue response to 

the World Trade Center disaster," M.S. thesis, University of South Florida, 

2002. 

[5] I. Nourbakhsh, C. Kunz and T. Willeke, "The mobot museum robot 

installations: a five year experiment," Proc. 2003 IEEE/RSJ Int. Conf. 

Intelligent Robots and Systems (IROS 2003), pp. 3636–3641. 

[6] S. Stancliff, J. Dolan, and A. Trebi-Ollennu, "Mission reliability 

estimation for repairable robot teams," Int. J. Advanced Robotic Systems, Vol. 

3, No. 2, Jun., 2006, pp. 155–164. 

[7] S. Stancliff, J. Dolan, and A. Trebi-Ollennu, "Mission reliability 

estimation for multirobot team design," Proc. 2006 IEEE/RSJ Int. Conf. on 

Intelligent Robots and Systems (IROS 2006), pp. 2206–2211. 

[8] C. Bererton and P. Khosla, "An analysis of cooperative repair Capabilities 

in a team of robots," Proc. 2002 IEEE Int. Conf. Robotics and Automation 

(ICRA 2002), pp. 476–486. 

[9] RAC Automated Databook, Version 2.20, IIT Research Institute, 1999. 

[10] S. Stancliff, J. Dolan, and A. Trebi-Ollennu, "Towards a predictive 

model of mobile robot reliability," Carnegie Mellon University technical 

report CMU-RI-TR-05-38, 2005. 

[11] A. Mettas, "Reliability allocation and optimization for complex 

systems," Proc. IEEE Annual Reliability and Maintainability Symp., 2000, pp. 

216–221. 

-100

 0

 100

 200

 300

 400

 500

 55 60 65 70 75 80 85 90 95 100

$ (Millions)

Component reliability (% of Table 1)

Expected value

Rover cost

Launch cost

Expected gain

Fig. 5. Net Expected Gain with f=0.5, value=$450M

-200

 0

 200

 400

 600

 800

 1000

 55 60 65 70 75 80 85 90 95 100

$ (Millions)

Component reliability (% of Table 1)

Expected value

Rover cost

Launch cost

Expected gain

Fig. 6. Net Expected Gain with f=0.95, value= $900M

222
A Decision Space Compression Approach for 

Model Based Parallel Computing Processes 

Robert J. Bonneau 

George Ramseyer 

AFRL /IFTC 

Rome, NY 13441. U.S.A. 

Abstract— Currently there are many DOD applications where 

warfighters are asked to make critical decisions based on 

environmental conditions that are highly complex and where 

there is incomplete knowledge of the local conditions. An 

example of such a situation is that of the theater commander 

who must deploy his C2/ISR assets such as communications 

and sensing platforms without complete knowledge of the 

local electromagnetic environment and its effect on his ability 

to maintain good information exchange and reconnaissance 

data for his forces. This type of situation falls into a broad 

class of problems where decision theory and complex physical 

models must interact for optimal performance such as 

investment analysis, weather prediction, and organizational 

dynamics. Such problems have been cast in the 

mathematical framework of “partial observability” where only 

some components of the environment are known. We thus we 

need to model the uncertainty of the environment and weigh 

our actions accordingly. The approach conventionally used 

for such optimization is a Partially Observable Markov 

Decision Process (POMPD) where we can model both our 

situational knowns and unknowns and come up with the best 

actions to take based on our model of what we know and do 

not know. We propose to develop a distributed computational 

framework that manages the complexity of such a process for 

large system optimization and provide an approach to 

parallelize and maintain operation for the system as more 

information and updates to our underlying environmental 

models change. 

Keywords: Partially observable Markov Decision Process, 

Resource Allocation

I. INTRODUCTION

The problem of parallelization and complexity reduction in 

POMDPs is a relatively current topic given that the 

conventional approach is to assume a static model for our 

physical world and then compute our optimal decision policy 

based. Once the decision policy is computed, the 

computational work is over until the underlying system 

variables are changed and the process starts again. The 

problem with this approach is that there are many different 

policies of interest for a particular environment and the 

timescale are short for our underlying model assumptions. 

Additionally, most polices that involve up to 1000 

environmental observations can take many hours if not days 

to compute on a single CPU. The ability to have perfectly 

Reduced 

Ontology/ 

Uncertainty

In Model 

Decision 

Process 

Principal 

Components

Markov 

Random 

Fields 

POMDP Complex 

Physical 

System 

Parallel 

Computing

Model 

Fig. 1. Process based query of information

about an environment

223
optimal decisions with uncertain information is intractable but 

even approximate solutions to POMDPs have been judged NP 

hard. As a result we develop a parallel computing model to 

manage this scenario by compressing our physical model 

using a subdivision strategy of Markov random field modeling 

in conjunction with principal components analysis. Our 

Markov random field approach allows us to subdivide our 

observation space to parallel subcomponents and our principal 

components analysis enables us to reduce the size of our 

overall physical model such that we can predict our optimal 

action for changing and highly complex scenarios. This 

system is shown in Figure 1. 

The next challenge of our approach to map our 

mathematical method on to a software architecture. We will 

use a multi-user publish and subscribe services architecture 

for our implementation. The approach is designed to enable 

multiple users to enter decision policy requests into our 

computing model and have the model optimize over the 

complex physical system as well as multiple users. Decision 

policy requests will be entered into our POMDP process as 

XML based schema. These schema will be translated by the 

POMDP process into physical process requests as a function 

of optimization criteria and then returned to the user after 

optimization. Because our architecture enables parallel 

execution and dynamic updating of user request policies, we 

are able to factor in multiple users with potential influencing 

dependencies in our environment and build a global decision 

policy over our physical model as user requests are added. 

Similar policy requests that enter the system after one user 

initially enters a policy are not recomputed but accessed in a 

lookup table fashion. Eventually as a global policy is reached, 

the amount of computation drops off dramatically as policies 

are accessed through lookup functions rather than 

recomputing the entire scenario. As underlying model data 

change these changes are then reflected in only those parts of 

the global policy that must be recomputed. Thus users that 

enter the system after some initial period will have relatively 

quick turn around to their policy requests and any new 

updates to the system will be computed as background 

processes in non real time. The computational load is 

governed by the fidelity needed for each user requests, the 

uncertainty about our models, and the rate at which the 

underlying models change and therefore influence our 

statistics. The system analogy for this process is a 

web-based search engine where the rank of relevant pages is 

pre-computed and users access the pages through lookup 

tables that are connected to the most recent updates. The 

software structure is shown below in Figure 2. 

II. POMDP METHOD 

POMDPs are a derivative of Markov decision processes that 

focus on complex scenarios that do not have complete 

knowledge of all states and all relevant a-priori information. 

We first wish to describe requirements and the complexity of 

adding more tasks to a single high bandwidth process and a 

methodology for transitioning to collaborative distributed 

computing processes. In order to evaluate the cost of this 

action our POMDP process consists of defining states s1, and 

s2 which consist of the belief b that an event such as a target 

being present or data from a communication process being 

available, actions a1, and a2 which consist of adding more 

bandwidth to our process vs. adding spatially distributed 

processes, and observations z1 and z2 which consist of 

observing whether or not our belief that an event occurred is 

true. 

Using this approach we can assign our reward 

function: r(ai,si)) for taking a particular action, and event 

probabilities p(zi|si) for predicting the consequence of our 

action. Additionally we must assign our transition 

probabilities between states p(si|si,ai.) of performance. We 

can now show our Markov state diagram for two sets of 

actions and a hierarchical valuation framework as is shown in 

Figure 3. 

Fig. 2 Optimization software approach 

224
Fig. 3ab: Markov States and Valuation Framework 

We now seek to compute the value of each process tree over 

set of beliefs b by taking expected value 

Vp( ) b E Vp ( ) ( ) b p s( )Vp(s)

S

= = ∑ (1)

The expectation is a linear function and can be expressed as a 

vector . We then compute the value function Vp

t-1(s) using a 

Bayesian recursive filter that steps up and down the tree in 

Equation 1 with 

Vp

t

( )s maxR s a ( )γ , p s' a s ( ) , p z s' ( )Vpz

t 1 –

(s' )

Z

∑

s S ∈

= + ∑

(2) 

We denote γ as the weighting factor from our previous state 

estimate s’ to our current state s. This form of equation is 

known as Bayesian filtering or Bayesian recursive filtering 

and other forms of such filters include the Kalman filter. 

Thus Equation 2 shows how the POMDP process takes the 

integrated value of our observation of our previous state 

p( ) z s' Vpz

t 1 – ( ) s' and weights this with p(s' a s, ) the effect 

of our action on having been in the previous state s’ to now 

being in our current state s. The reward function is a related 

function that allows us to guide our valuation process using 

statistical metrics of our process as it proceeds. We will 

discuss this further in section VI. As is shown in Figure 3a/b 

for probability state interval p1 we have an action a1 with 

corresponding vector and probability state interval p2 

with action a2. We now graph the function 

Vt( ) b max

α Γ ∈

= bα

 (3) 

for each action over the states s1,s2. This graph then shows 

us the value distributing our process to more nodes vs. the 

costs of adding more tasks and bandwidth to our existing 

process such as computing time, communication overhead, 

and priority of tasking a particular platform.

III. LIKELIHOOD OBSERVATION SPACE

We now define the method for observing the underlying 

factors shown as a sequence of variables y1....yn that are the 

influencing factors of adding/subtracting additional bandwidth 

or platforms including processing overhead, communications, 

algorithm complexity, modulation scheme, and frequency 

allocation. Thus we can pose these underlying system 

variables in the context of likelihood ratios 

 

α

α

Fig. 4. Iterative process valuation algorithm 

225
( , , , , | )

( , , , , | ) ( , , , , )

1, 2, , 1 2 3 0

1, 2, 1 2 3 1

1 2 3 f y y y y H

f y y y y H

L y y y y

Y Y YN N

Y Y YN N

N K

K K

K

K

=

 (4.1) 

 

Our observations then consist of evaluating multi-variable 

likelihood ratios in order to determine whether our algorithm 

objectives are being met. Thus in a target tracking example, 

if our algorithm process can be evaluated such that we have a 

hypothesis of target present we have: 

( , , , , | ) 1 2 3 H0 L y y y y K N H0 – state not present – 

corresponds to Z0 (4.2) 

and target absent we have 

 ( , , , , | ) 1 2 3 H1 L y y y y K N H1 – state not 

present – corresponds to Z1 (4.3) 

Additionally, as is shown in Figure 4 the addition of a-priori 

information into the likelihood estimation process allows us to 

examine a wider availability of outcomes of our decision 

process and reduce the uncertainty of our process. This also 

decreases the computational complexity of our process. 

IV. MARKOV RANDOM FIELD BASIS ORDERING 

Our Markov model begins by nested basis set 

structure is shown in dyadic form in Figure 5. The orthogonal 

basis functions φ(ω) and ψ(ω) are the high and lowpass 

filters respectively and divide the signal in frequency by one 

half with each stage of the decomposition. Correspondingly 

the signal is downsampled by a factor of 2 at each stepA Kth 

order model defined on the multiresolution structure is 

defined in either 1 dimension with t ∈ { } 1 2, , ..., K(T + 1)

Such a structure for 1D signals takes the form of a binary tree 

structure. To represent this random field we define a given 

node in the binary tree structure as s, its parent node as sγ

where γ shifts the basiscoefficients from parent sγ to child 

s shown in Figure 5. This set of points is denoted as Γs and 

it is the union of 2 mutually exclusive subsets. Now if we 

have the random variable Z representing the current state of 

any 

Γs at any stage of the tree in 1 dimension then we insert 

our local scale iterative relationship, the basic probabilistic 

Markov relationship is defined as 

pZt t Γsαi

ZT T Γs , ∈ , ∈ Zt t Γsα ZT T Γ ∈ s ( ) , ∈ ,

= 

pZt t Γsαi

ZT T Γs, i , ∈ , ∈ Zt t Γsα ZT T Γ ∈ s i , ( ) , ∈ ,

 (5.0) 

 Using the above multiresolution Markov structure 

we must develop an ordering structure for the basis 

coefficients before they are placed in vectors for insertion into 

the covariance matrix for principal components analysis. . 

They key is to order the coefficients so that the natural 

Markov structure of relevant features of the data detected. 

Since the Markov increments are between a scales and we 

wish to have a continuous basis progressing from one scale to 

the next we must both examine coefficients with an upward as 

well as downward sweep of the binary and quadtree 

structures. 

V. PRINCIPAL COMPONENTS ORDER REDUCTION AND REWARD 

PROCESS

There are three sets of constraints that must be 

included in order to adequately characterize the information 

content of our process and resulting reward functions. Our 

Fig. 5. Markov random field structure 

226
rewards are characterized by computing overall computational 

algorithmic complexity. This has been characterized by 

Kolmogorov as the length of the shortest computer program 

that describes an object. More importantly complexity can be 

bounded by the Shannon capacity for random objects or 

sequences as follows 

 

I 

C max

p X( )

= IXY ( ) , (6) 

Since we are using a distributed computing methodology we 

will start our complexity metric based on the variance of the 

underlying density functions that characterize the complexity 

of adding or subtracting additional nodes or bandwidth. For 

each of our quantities yi , these are usually composed of 

vectors of information that comprise our density functions. 

Thus if yi is a vector [s1,...........,sn] of samples projected 

onto our Markov basis sets characterizing the outputs of one 

of our node processes, then we can compute a covariance 

estimate of the vectors to characterizes the variance or 

entropy and stationarity of our distribution with an 

undistorted copy of this vector xi with 

 

Γe xi yi ( ) , xi yi = 〈 〉 , (7) 

Similarly we can compute the mutual information between 

sample vectors sets if this information is described as 

 Γm yi yj ( ) , yi yj = 〈 〉 , (8) 

If we determine the variance of the two random variables 

then we can expression 8 as the Fisher or mutual 

information between the two random variables. The 

inverse of this function is then the Cramer Rao bound 

which has been used extensively to describe 

spatial/spectral localization. 

 Thus from expressions 7 and 8 we can compute 

the eigenvalues (principal components) of these 

covariances over all density functions and samples of each 

random variables. 

 

λˆ e λeij

j 1 =

m

∑

i 1 =

n

= ∑

 ( 9.1) 

 

λˆ m λmij

j 1 =

k

∑

i 1 =

n

= ∑

 (9.2) 

Thus we wish to set our reward functions such that the 

ratio 

Rrs ( ) , sup

S

λˆ m

λe

ˆ = -------

 (10) 

is maximized such that our mutual information is 

maximized and our entropy and overall complexity is 

minimized until our threshold criteria of detection is met 

over all the possible states of our system. Figure 6 shows 

our ability to quantize and compress the raw information in 

our distributed process before evaluating the mutual 

information and entropy thereby reducing the 

computational complexity and reducing the overall 

complexity of our POMDP process. Our system block 

diagram is shown below. 

Thus our reward function rewards the maximum 

mutual information of our process, with the minimum 

entropy and lowest overall computational complexity. We 

can see this process implemented in our process below 

where as we reduce the entropy in our scene we aggregate 

Markov 

Data 

Structure 

Physics 

Model 

Archived 

Data 

A priori 

Information 

Eigen-value/ 

Principle 

Components 

Process 1 Parallel Computing Process 

Process 2 

Process 3 

Fig. 6. Parallelization process 

227
the number of levels and branches of our decision process 

and improve our estimate of our scenario based on what we 

already learned. This information might include building 

model information and the corresponding expected 

scattering profile. With more added information the 

algorithm selection process can occur more efficiently than 

without. Thus by using and updating our a-priori 

information our uncertainty is reduced our partially 

observable process becomes more observable. This 

enables us to go from highly complex estimates of our 

scenario to very compact or compressed estimates about 

the state of our scenario. This compressed state is known 

as our compressed belief space. This compressed belief 

space then can be updated with new information much 

more quickly given what we already know about a scenario. 

Our entire process is shown below. 

V. DISTRIBUTED EMBEDDED EXAMPLE SCENARIO COMPUTING 

MODEL AND TESTING

There has been much work in distributed 

computing, distributed communications and distributed 

sensing. Our architecture will make use of a processing 

engine that will enable users making requests to optimize 

the electromagnetic environment for their application. We 

will describe how to optimize such an environment from a 

knowledge based search perspective given a-priori 

information taken by past sensor and communications 

platforms and updating with live sensor and 

communication information only when this information 

meets the users search requirements and does not duplicate 

existing stored data that has been previously collected. 

Our goal is thus to observe our environment with 

new information to reduce our overall uncertainty and 

improve our a-priori knowledge database without duplicating 

information we have already retrieved. We therefore optimize 

our use of sensing and communications assets for new 

information and draw existing information from databases to 

improve the performance of our reconnaissance search 

procedure. This process of exploiting a-priori information 

dramatically reduces the computational overhead of our ISR 

search algorithms and improves their accuracy. We will also 

distribute the optimization among multiple user’s 

requirements or processes to make optimal use of the 

platforms for all users. In communications and sensing the act 

of distributing the process allows us to trade spatial diversity 

for bandwidth by increasing the spatial sampling of our 

environment and therefore creating more parallel 

communications or sensing channels. 

VI. CONCLUSION

Thus our actions consist of ISR operations, our 

states consist of ISR truth information, and our 

observations consist of detection our communication. 

Our algorithms are incoherent such as video data, coherent, 

such as synthetic aperture radar, and partially coherent such 

as distributed platform observation of the space in question 

with multiple electromagnetic sensors. Our model is a 

finite difference time domain and ray tracing wave 

propagation model and we wish to update our site specific 

clutter models with new information from our system. We 

can use his approach to improve the overall computational 

efficiency of analysis of large scale ISR operations. 

Fig. 7 Overall process block diagram 

228
REFERENCES

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Targets in Multiplicative Clutter IEEE Radar Conference, 

April 2006 Page(s):84 – 93. 

[2] Bonneau, R.J., A Rate Distortion Method for Waveform 

Design in RF Image Formation

Applied Imagery and Pattern Recognition Workshop, 2005. 

Proceedings. 34th

19-21 Oct. 2005 Page(s):63 – 69 

[3] Capraro, G.T.; Capraro, C.T.; Wicks, M.C.; Liuzzi, R.A., 

Artificial intelligence and waveform diversity, Integration of 

Knowledge Intensive Multi-Agent Systems, 2003. International 

Conference on

30 Sept.-4 Oct. 2003 Page(s):270 – 274 

[4] Ying He; Chong, K.P., Sensor scheduling for target 

tracking in sensor networks, Decision and Control, 2004. 

CDC. 43rd IEEE Conference on, Volume 1, 14-17 Dec. 2004 

Page(s):743 - 748 Vol.1 

229
Physically-Proximal Human-Robot 

Collaboration for Air and Space Applications

Ella M. Atkins 

University of Michigan 

Aerospace Engineering Dept., 1320 Beal Ave. 

Ann Arbor, MI 48109 

ematkins@umich.edu

Abstract—In Aerospace applications, human safety is of paramount 

importance given harsh environmental conditions that require 

persistent electromechanical life support. The resulting inherent 

proximity between humans and “robotic support” requires effective 

communication and collaboration in emerging systems where the 

robot is not strictly a “tool” for a human operator/pilot to command. 

This paper investigates the challenges of human-robot collaboration 

in the context of two critical Aerospace applications, airspace 

management and planetary surface exploration. We first present a 

spectrum of alternative air traffic management designs ranging from 

centralized to fully-decentralized. Discussion focuses on roles of 

human versus synthetic decision-makers, associated efficiency 

bounds, and metrics for quantifying performance and safety. Next, 

a space exploration scenario is investigated in which robots and 

human astronauts are both modeled as “agents” with specific skills 

and resources available for tasking by a (computerized) planner. 

Emphasis is placed on real-time reconfiguration when astronauts 

purposely deviate from their default plan or are in need of assistance, 

accounting for astronaut-initiated activities while proactively 

enhancing astronaut safety. 

Keywords: human-robot interaction, airspace management, space 

robotics 

I. INTRODUCTION

A “robot” is a machine that performs complicated tasks, 

typically a mechanism guided by automatic controls [1]. 

Deployed robotic platforms range from heavy industry 

systems on the assembly line to disaster response teams and 

small-scale toys or “battlebots” intended for amusement. In 

the context of Aerospace, a “robotic” system generally 

translates to a highly-automated aircraft or spacecraft. 

Drawing a closer physical analogy to systems we call “robots” 

on Earth, space robots also include planetary surface rovers 

and space-based manipulators. Aerospace robotic systems 

share a common attribute: they operate in environments that 

cannot sustain human life without mechanical assistance. As a 

consequence, vehicles are designed with the pervasive goal of 

maintaining safety for human occupants or collaborators as 

well as maintaining high levels of efficiency and productivity. 

Early air and space vehicles were designed to convert 

human directives into actuator commands. Pilot controls 

were mechanically linked to control surfaces and engines, and 

spacecraft computers expected the uplink of detailed 

instructions such as thruster burn sequences or pointing and 

data acquisition commands. Such systems enabled humans 

to physically and virtually escape the Earth’s surface. 

However, their labor-intensive operation required substantial 

human support just to execute the suite of “reflexive” 

functions analogous to the breathing, walking, and talking 

humans constantly perform with minimal cognitive overhead. 

Once sufficiently-mature computing and sensing 

technologies were available, Aerospace automation efforts 

focused on guidance, navigation, and control (GNC), enabling 

the Aerospace vehicle to stabilize itself and achieve explicit 

spatiotemporal objectives such as “go to waypoint x at time t” 

for an aircraft or “over time interval ti point in direction y” for 

a spacecraft. More recently, advanced trajectory optimization 

and guidance laws have increased autonomy level a step 

further, minimizing operator overhead through translation of 

surveillance/science targets or waypoints to continuous-time 

motions [2][3][4]. Additionally, mission planning tools for 

air and space systems have enabled the translation of goals to 

activity sequences optimized over available resources and 

scheduled on timelines [5]. 

Researchers are beginning to study the interaction of 

multiple vehicles in the context of collaborative 

task/trajectory planning and cooperative control [6][7]. 

However, such systems currently assume a level of 

homogeneity in physical capabilities and response logic 

difficult to achieve with mixed human (human-piloted) and 

robot teams. Conversely, researchers specifically studying 

human-robot interaction (HRI) have made significant 

advances to facilitate human direction of robotic teams for air 

and space, with emphasis on the presentation of sensor data 

and specification of commands in an intuitive manner that 

focuses operator attention and maximizes human situational 

awareness [8]. However, in typical HRI systems the role of 

the “robot” is subordinate to the role of the human. 

Collaboration is seen through the eyes of the human operator 

or companion issuing a command to be directly and 

deterministically translated into action by the robot. As a 

result, the burden of inferring intent and formulating 

high-level goals is placed on the human, both for the human’s 

activities and those of their robotic companion(s). 

We propose an alternate model by which robotic 

companions are assigned the responsibility of maintaining and 

230
adapting their intent and goals. This is not a universally new 

idea, but it is new to air and space systems substantially 

constrained by safety thus high levels of conservatism with 

respect to automation. The goal is to increase system-level 

efficiency through increased levels of autonomy while 

maintaining a level of safety comparable to or exceeding the 

level of safety available in human-directed systems. Given 

high bandwidth and computational capacity possible in future 

networked air and space systems, we anticipate a level of 

performance that substantially exceeds that possible for a 

system constrained by human direction of physically complex, 

operationally diverse robotic vehicles and companions. 

This paper introduces two Aerospace problem domains that 

illustrate the challenges associated with operating 

heterogeneous air and space vehicles efficiently in close 

physical proximity to one another. The first topic, airspace 

separation assurance, is of paramount importance to long-term 

aviation safety and efficiency. We demand substantial 

increases in air traffic capacity (thus density) than possible 

today, but if aircraft collide both are likely to incur sufficient 

damage to crash. The second topic is robot collaboration 

with a suited astronaut in an extraterrestrial exploration 

scenario. This discussion focuses on a specific strategy to 

plan human and robot activities but opportunistically respond 

when human perceptual and reasoning capabilities indicate a 

preference for deviating from default activity sets. 

II. AIRCRAFT SEPARATION ASSURANCE IN COMPLEX AIRSPACE

Airspace management is currently performed by human air 

traffic controllers, augmented by local pairwise deconfliction 

through algorithms such as the Terminal Collision Avoidance 

System (TCAS, TCAS-II). This operational paradigm has 

been deployed long-term, but as density requirements increase, 

we are beginning to break the ability of both civilian and 

military air traffic controllers to effectively allocate airspace 

and manage traffic through geometrically-comprehensible 

corridors, queues, and minimum spacing directives. 

Air travelers experienced record delays in 2007, a trend 

expected to worsen in the future [9]. Additionally, pressure 

is building to introduce highly-automated unmanned air 

vehicle systems (UAS) into civilian airspace. The United 

States Department of Defense (DoD) fields an increasingly 

large set of small-scale micro air vehicles (MAVs) through 

highly-sophisticated high-cost UAS. Efficient management 

of civilian and military airspace is needed. In a worst-case 

scenario, active battlespace environments require manned 

helicopters and fighters to share (low-altitude) airspace with 

rotary wing and fixed wing MAVs and UAS. Such 

heterogeneous traffic mixes are not feasible with current 

human-directed separation protocols, but instead will require 

increased local cooperation not directed by centralized human 

controllers. Military personnel have recognized the challenge 

of increasing traffic density in critical battlespace 

environments, particularly given the need to mix vehicles with 

diverse objectives at low altitudes. As described below, 

concepts such as dynamic (self-organizing) airspace command 

and control have begun to emerge, with the goal of replacing 

the current “airspace exclusion zones” used to organize 

battlespace traffic through human air traffic management. 

The concept of dynamic airspace management and mixed 

vehicle fleets operating in close proximity, however, is just 

that. Realistic implementation plans for achieving 

superdensity operations over homogeneous and heterogeneous 

traffic mixes are at the early conceptual stage. Below, we 

describe a spectrum of possible airspace organization 

structures, from current centralized operations to a futuristic 

fully-decentralized paradigm that will mandate automated 

flight. Perhaps most realistic near-term solutions are 

partially-decentralized models, also described below. We also 

propose metrics for evaluating the efficiency and safety of the 

different airspace management options. 

A. Dynamic Airspace Command and Control (C2) 

The air over active military zones can quickly become 

congested with a variety of air vehicle types with a variety of 

mission objectives. Consider the Baghdad region, over 

which operators have ranged from civilian transport (Baghdad 

airport) to heavy-lift rotorcraft, manned fighters, and a suite of 

MAVs and UAS. All share the same low-altitude airspace, 

and traffic is necessarily dense during peak periods of activity. 

The manned fighter is fast and maneuverable but cannot 

easily identify the MAVs and UAS in time to avoid them. The 

MAVs and UAS are typically quite maneuverable but are very 

slow thus cannot evade a fighter moving rapidly in an 

unpredictable fashion. Flight envelopes differ: rotorcraft 

can hover but fixed-wing aircraft will stall below 

platform-dependent minimum airspeeds. Such traffic cannot 

all be organized into queues or cooperative formations due to 

the diversity of their flight envelopes and their missions. 

The result is that traffic densities are currently kept artificially 

low, and exclusions zones are manually generated to separate 

disparate operator types. A benchmark future military goal 

is to maintain persistent surveillance of critical areas (by 

MAVs and UAS) while simultaneously supporting rapid 

response to opportunistically-identified targets (by fighters). 

Achievement of this goal inherently requires mixing fighters 

and unmanned aircraft in the same airspace, which in turn 

requires resolution of the challenge to deconflict slow and 

fast-moving traffic when the fast-moving (manned aircraft) 

traffic has the highest operational priority (e.g., delivering a 

critical munition). 

B. Airspace Organization and Deconfliction Modes 

To adequately address the formidable challenges of civilian 

and military airspace management, the spectrum of 

management options must first be understood and assessed. 

Figure 1 illustrates this spectrum, ranging from the current 

“centralized” paradigm on the left to the 

maximally-automated “fully-decentralized” paradigm on the 

right. Multiple management models exist within each 

“class” of airspace management. The level of technological 

challenge typically increases moving from left to right on this 

231
spectrum, although different challenges are present for 

capable partially-decentralized versus fully-decentralized 

operational paradigms, as will be discussed below. The 

following discussion of modes and metrics is applicable to 

homogeneous commercial transport queues. It is also more 

general, encompassing mixed fleets of traffic to be managed. 

Traffic may be distinguished and grouped by mission 

(transport/transit, surveillance, special-purpose), pilot class 

(manned vs. unmanned), and aircraft performance envelope. 

Vehicles with similar missions (e.g., transiting in a common 

direction) and overlapping performance characteristics (e.g.., 

common cruise airspeed) may be densely packed, more 

densely with an autopilot than with direct human pilot inputs. 

C. Centralized Separation Management 

Since air travel became an accepted means of transportation, 

a centralized air traffic management system has been in place. 

In its current instantiation, [human] air traffic controllers and 

[human] pilots communicate verbally to share flight plans and 

requests for redirection most commonly due to pilot 

preference, adverse weather, or vectoring around other traffic. 

In open (low-density) airspace, the current system works well 

for transport, military, and recreational pilots. Voice 

communication, however, is not a feasible option for 

unmanned aircraft and is a low-throughput often ambiguous 

form of communication even for manned aircraft. As a 

result, although most directives are still verbally 

communicated, air-ground datalinks are beginning to provide 

pilots with improved data (e.g., weather) and to provide air 

traffic controllers with precise flight trajectory histories and 

entered flight plans. 

The Figure 1 graph shows Air Traffic Controller (ATC) based 

deconfliction as the “entry-level” form of airspace 

management. This is the historical model relying on radar 

and verbal communication, with extensions to better distribute 

workload [10] or better represent data [11]. Although still 

centralized operationally, modern aircraft flight management 

systems are capable of computing and maintaining 

fuel/time-optimal flight plans onboard [12][13], securing an 

initial clearance then ATC approval for flight plan revisions to 

ensure the centralized air traffic database maintains an 

updated model of airborne aircraft position and intent. This 

centralized structure enables aircraft departures to be timed in 

a manner that greatly reduces the number of deconfliction 

maneuvers required, provided aircraft are able to follow their 

scheduled flight plans. Adverse weather, unexpected flight 

plan deviations, and overly-dense peak period traffic 

compromise efficient flows. In civilian aviation, throughput 

constraints given adverse weather and/or peak operational 

periods at major urban airports are the primary drivers to 

move beyond the current human-managed centralized air 

traffic paradigm. Military aviation experiences analogous 

weather and peak operation issues plus additional challenges 

related to an adversarial environment and mixed fleet, as 

discussed previously. 

By its nature, centralized air traffic control requires 

long-range communication. Response latencies are inherent 

particularly due to delays associated with verbal 

communication and pilot/controller translation of presented 

data to appropriate commands or reactions. To minimize this 

delay, traffic is organized in queues, and flight plans are 

structured as a 4-D (position and time) waypoint sequence 

connected by constant trim state (constant climb rate, turn rate, 

airspeed) segments. Although intuitive, this structure is rigid 

and limited in extensibility by human geometric reasoning 

constraints. 

D. Partially-Decentralized Separation Management 

Over the last decade, the air transportation system has made 

its first incremental step into partially-decentralized 

operations through the use of onboard collision avoidance 

with TCAS and more recently an improved TCAS-II. As a 

supplement to the otherwise centralized air traffic control 

system, TCAS both warns the pilot of potential 

near-misses/collisions and also proposes a conflict resolution 

strategy. Research in local conflict resolution has made 

substantial theoretical and practical progress [14], and 

alternative concepts for efficiently routing diverse vehicle 

classes are being studied [15]. With the assumption that 

other aircraft will act unpredictably, however, the number of 

aircraft that can be successfully deconflicted in a common 

local volume is highly constrained. 

As depicted in Figure 1, the “next step” toward 

higher-density, partially decentralized airspace management, 

most applicable near-term to military applications, is to group 

unmanned vehicles with similar missions and common 

Centralized

trajectory 

optimization/

deconfliction

Airspace

allocation 

with local

deconfliction

Homogeneous

UAS cooperation

+ airspace 

allocation

Comprehensive

cooperation

across all

air vehicle assets

ATC-based

deconfliction

Fully-Centralized Partially-Decentralized Fully-Decentralized

Heterogeneous

UAS cooperation 

+ shared piloted

aircraft intent

Fig. 1. Airspace Separation Management Architectural Spectrum.

232
performance envelopes into locally-networked cooperative 

teams. Such a team could be presumed to internally 

self-deconflict, such that the air traffic controller could treat 

the entire multi-vehicle team as one entity for airspace 

management purposes. Then, rather than allocate a static 

airspace volume to this team, it would fly as a dynamic 

“occupied airspace” volume that moved with the team and 

would be deconflicted dynamically with other aircraft. 

Researchers have proposed a variety of formation-based and 

consensus-based protocols for organizing unmanned aircraft 

in a manner that locally deconflicts the networked team [6][7]. 

The remaining challenge is to group these teams with other 

aircraft that either do not or cannot join the team. Note that a 

piloted aircraft is an example of a vehicle that would not 

typically join a cooperative control team, since joining the 

team would require the pilot to fly hands-off to meet the 

stringent inner-loop requirements imposed for stable 

cooperative control systems. Vehicles with dissimilar 

missions and/or dynamic envelopes may also be unable to 

cooperate. Given these substantial cooperation constraints, 

the best we can hope to achieve near-term is partial 

decentralization, with air traffic control (human or 

computerized) deconflicting and allocating airspace for the set 

of disparate teams and individual vehicles. As a result, the 

delays associated with centralized air traffic management are 

inherited by partially-decentralized systems, although density 

may be substantially improved for those vehicle groups able 

to cooperate. 

E. Fully-Decentralized Separation Management 

A fully-decentralized air traffic management would not rely 

on contact with a central authority, computerized or otherwise. 

As an ultimate goal to streamline air transportation and 

military battlespace, the challenge then is to guarantee that all 

air vehicles are able to communicate and reason in a manner 

that guarantees no collisions will occur. Unambiguous 

communication of intent is an incremental step beyond partial 

decentralization that will enable vehicles with different 

performance envelopes and/or goals to cooperate rather than 

be separated by a central authority. The mathematical goal 

of separation assurance is to constrain the four-dimensional 

space-time in which each aircraft operates such that there are 

no potential conflict regions. This space can be contracted to 

its minimal extent through team-based cooperative control as 

discussed above. For vehicles unable or unwilling to join 

these teams, however, occupation space over time can still be 

contracted substantially through shared intent. For an 

unmanned (automated) vehicle or highly-automated manned 

vehicle, this intent could translate to a precisely-followed 

future trajectory (e.g., transport aircraft landing on parallel 

runways). 

For piloted aircraft such as fighters, no knowledge of future 

intent translates rapidly to no knowledge of future 

spatio-temporal position due to the ultra-high maneuverability 

and speed of these aircraft. Communication of intent, while 

not taking the “stick out of the pilot’s hand”, would then be a 

valuable tool to constrain the general direction and speed of 

travel to nearby aircraft. Consider a battlespace environment 

populated with surveillance UAS but with an occasional 

munition delivery by a manned fighter. The pilot is unlikely 

to follow an automated delivery trajectory at least near-term, 

but their intent can be known minutes in advance, enabling 

sufficient time for UAS to clear the delivery area. The 

alternatives are for the slow-moving UAS to be avoided by 

the higher-priority fighter, not the ideal scenario, or for the 

space to be manually cleared by a central controller, requiring 

far more overhead and time than would properly-executed 

UAS avoidance of the anticipated narrow delivery corridor. 

Airspace management through shared intent is the 

highest-level stage of airspace management at which human 

pilots can remain in the loop. The final option, the most 

sophisticated but also the most technologically challenging, is 

for all aircraft, of all types and with all mission objectives, to 

self-organize in a maximally efficient and safe manner. 

Certainly teams would form, and altitude could be used to 

separate aircraft of different classes and traveling different 

directions. But, the hard problem is to truly maximize 

density and heterogeneity, given that vehicles will want to 

transit when they want to transit and how they want to transit. 

The building blocks are beginning to be formed: cooperative 

control, shared intent, dynamic airspace allocation when 

needed. For truly decentralized operation these strategies 

must be merged into a complete and correct architecture and 

augmented for all vehicles certified to occupy civilian and 

military airspace.

F. Airspace Management Evaluation Metrics 

As we seek to increase airspace density while maintaining 

flexibility and safety, clear performance metrics are essential 

to assess tradeoffs between the different architectural as well 

as algorithmic options. Safety is of paramount importance, 

which for separation assurance can be quantitatively 

translated into probability of a near-miss and/or collision. 

This probability, in turn, is a function of airspace density and 

disparity (heterogeneity) between nearby vehicles. Disparity 

is quantified in terms of their comparative performance 

envelopes, onboard “intelligence” and sensing abilities (for 

piloted and autonomous systems), and mission objectives. 

Disparity is minimized for UAS of the same type with 

compatible objectives, the criteria for a cooperative team. 

Predictability (determinism) also plays a major role in 

assessing the likelihood of a near-miss or collision. Any 

aircraft formation or transport aircraft in a busy queue relies 

on predictability today. The notions of cooperative control, 

shared intent, and locally-negotiated airspace use require 

predictability in a more challenging but analogous manner. 

As strategies evolve, both mathematical proof of algorithm 

correctness and reliability guarantees for software and 

networks will be essential. 

Finally, the primary reason to push for new airspace 

management protocols is to improve efficiency, in terms of 

throughput, which translates to maximizing density, and in 

233
terms of efficiency (flight time, fuel use) for each vehicle. In 

terms of metrics, optimizing efficiency tends to require 

tradeoffs with optimizing safety. In an efficient air 

transportation system, density will be maximized, all aircraft 

will be able to fly their most efficient individual routes at the 

best time. Necessarily, individual optimality is relaxed to 

satisfy safety constraints. The quantitative goal, then, is to 

minimize the compromise in efficiency (and goal 

achievement) necessary to satisfy safety constraints over the 

air vehicle network. 

It is important to remember that the reason we fly is to 

ultimately achieve a set of mission objectives. Transport 

aircraft passengers demand comfort, efficiency, and a “sense 

of safety” – not necessarily perceived even if safety is 

mathematically guaranteed during flight through a 

seemingly-chaotic group of UAS. Manned aircraft pilots 

still want control of their aircraft – this raises debates about 

the ultimate roles of pilot vs. automation, particularly during 

high-stress emergency situations such as damage or failure 

scenarios that compromise performance [16]. In the context 

of separation assurance, however, a manually-piloted aircraft 

at best constrains cooperation to “shared intent”. At worst a 

pilot may demand complete operational freedom (e.g., to 

deliver a munition), requiring complete evacuation of the 

nearby airspace. Qualitative metrics will likely be required 

to assess level of pilot, operator, and passenger acceptance of 

alternative airspace management protocols. It is anticipated 

that a more “autonomous”, less-centralized system will need 

to slowly evolve rather than be selected near-term, moving 

continuously along the Figure 1 spectrum rather than with 

discrete “jumps”. This requirement, in turn, mandates 

smooth transitions between operational paradigms, metrics for 

which will evolve as concrete algorithms are readied for 

large-scale deployment. 

III. ROBOT COLLABORATION WITH A SUITED ASTRONAUT

Extraterrestrial exploration currently shares few of the 

“application” attributes of dense airspace management – 

Moon and Mars will be sparsely populated for quite some 

time thus “traffic jams” are improbable. However, common 

questions emerge when modeling and quantifying the 

collaboration between humans and robots, the answers to 

which for the more “mature” airspace management 

applications may have relevance to emerging extraterrestrial 

exploration applications. How can the robot and human 

“agents” communicate and coordinate their activities? How 

are disparate goals and preferences reconciled? How do we 

manage, direct, and accommodate the preferences of human 

and robotic explorers while maximizing the safety and 

efficiency at which objectives are pursued and achieved? 

Manned exploration missions have thusfar been modeled 

through the “eyes” of the mission controller and astronaut 

explorer as explicit mission directors. Robots provide sensor 

data but are explicitly tasked with accompanying and 

supporting their human companions. Thus, although capable 

planner/scheduler systems accurately assign activities to 

timelines, human oversight is pervasive – not through the 

joystick but through the “micromanagement” of goals. Such 

management is warranted in some cases, given practical 

limitations in robotic perceptual and reasoning capabilities 

to-date. However, given that robotic technology will 

continue to mature but astronaut perception will still be 

limited by life support systems (a spacesuit), the assumption 

of “one-way” command issuance may not be optimal. 

We propose an alternate viewpoint for human-robot 

collaboration in which both human and robot are subject to 

explicit task assignment based on mission goals, but also in 

which both are able to redirect their activities independently, 

so long as they share their intent with mission control, likely 

itself a collaborative human/computational system. In this 

manner, human and robotic agents are both able to 

“decentrally” request assistance in the context of task 

completion of safety, and both are able to act opportunistically 

based on their observations. 

An early prototype of a “collaborative” rather than 

“human-directed” planetary surface exploration architecture is 

described in [17]. As a “first step” to collaboration, this 

system presumes a centralized planning/scheduling system 

analogous to centralized air traffic coordination but in a 

fully-computerized (automated) instantiation. Task allocation 

and scheduling among an agent team is not new, although task 

allocation for humans as well as robotic systems is somewhat 

unconventional. Reacting to unexpected events, also 

supported by this architecture, is also not new. However, 

typically the unexpected events take the form of 

low-probability dynamicism in the environment, component 

or system failures, and actions by non-cooperative agents. 

As a partially-decentralized system, our human-robot agents 

are allowed, and in fact encouraged, to seize opportunities and 

ask for assistance. Thus, they freely deviate from their plans, 

not because they are unable to achieve the tasks assigned to 

them, but because they act on their “individual” initiative to 

improve the mission. In known environments, such 

“unexpected deviations” would likely be infrequent and 

perhaps even discouraged given the ability of the centralized 

agent to optimize globally rather than locally. However, 

extraterrestrial exploration is “exploration” because the 

environment is not known, thus local agents, human or robotic, 

will be at the forefront – able to seize data collection, sample 

retrieval, or even energy collection opportunities as transient 

events in weather and wind or in resource usage [spikes] 

prompt deviant action. 

Below, the architecture and early prototype testing from [17] 

are summarized in the context of human-robot collaboration 

during extraterrestrial missions. Although the simulation 

and hardware-based experiments were necessarily limited in 

extent, it both demonstrated the utility of collaboration and 

indicated areas where additional feedback regarding resource 

consumption and task timings are required. 

A. Architecture Overview 

Figure 2 illustrates the components of the “reference” 

234
human-robot collaboration architecture we have devised to 

study the ability of human and robotic systems to “equally” 

direct their individual and cooperative activities. Analogous 

to a mission control or base station directing and monitoring a 

human/robot extravehicular activity (EVA) team, a centralized 

planning and execution system has been implemented. The 

planner directs the activities of each mobile “agent” based on 

agent capabilities, goals, and feedback from the agents (e.g., 

location, available energy). The planner also responds to 

directives issued by astronaut or robotic team members. 

Such directives range from “I need help” to “I’m doing 

another task” to “Please ask a rover (or astronaut) to execute a 

new task” (as defined in the communication). 

 As shown in Figure 2, our architecture is composed of 

three interacting components: a team planner, a coordination 

executive, and the mobile agents deployed in the field (or 

simulated to facilitate experiments with many rovers). The 

centralized planning module takes the current state of the 

world and computes a plan for all mobile agents so that the 

maximum number of high-priority tasks can be accomplished 

by the team in a minimum amount of time. The implemented 

design-to-time algorithm [17] can be configured to react 

quickly and possibly suboptimally or to consume the 

additional time needed for plan optimization. 

The execution module controls the flow of information 

between planner and distributed rover-astronaut team, 

dispatching actions in real-time in accordance with the 

nominal policy and any dynamic updates. This “coordination 

executive” gathers and processes available state information 

from the rovers/astronauts to enable detection of off-nominal 

events. Off-nominal events are managed by either adjusting 

the policy (e.g., task execution timings) or by notifying the 

planner that plan modifications are required. Rovers are 

assumed to understand high-level directives (task 

descriptions) and either to correctly execute them or return an 

annotated error message indicating the task was not 

successfully accomplished. Our implementation is configured 

such that the simple simulated agents can be replaced with 

“real” robotic and astronaut agents. Specifics of the 

time-controlled planner/scheduler algorithm and coordination 

executive are found in [17]. 

B. Summary of Test Results and Observations 

Each rover was simulated or interfaced (in hardware) with a 

single process executed as navigation, task execution, 

message processing, and update generation threads. In 

simulation, navigation included Gaussian perturbations to 

provide realistic deviations from the expected execution 

profile. Each astronaut was simulated in an analogous 

manner. A case with three rovers and one astronaut is shown 

in Figures 3 and 4. Each figure illustrates task schedules for 

each agent “resource” placed on a timeline. The grey 

regions represent traversals, while blue, yellow, green regions 

represent tasks such as taking pictures, acquiring samples, or 

measuring environmental conditions at a particular site 

(waypoint). Figure 3 shows a comparison between planned 

(lower) and actual (upper) task timelines. These deviations 

are not substantial and were the result of the simulated 

deviations from expected task execution times. With a real 

rover in difficult terrain, we observed substantially more 

discrepancy between expected (planned) and actual traversal 

times, illustrating a challenge for coordination when 

multi-agent (cooperative) activities are planned. 

Figure 4 illustrates real-time replanning for a case where the 

astronaut requires assistance (e.g., injury, low on oxygen, 

The action of taking a picture took more time 

that expected in this case.

Execution Time 

(during simulation)

Navigation time is higher than expected due to 

a lower speed of rover 3 at that time.

Expected 

Schedule

Fig. 3. Planned vs. Actual Execution Times (Simulation) [17].

Coordination 

Executive Team Planner

Mission Planner and Executor

World state & 

task execution status 

Actions

→Position updates

→Task Status

→Sensor Data

→Agent Requests

→High level actions

→Rover sensor info 

(to astronaut) 

Fig. 2. Planetary Exploration Planning & Collaboration [17].

Astronaut Calls 

for Help Time response is controlled with the 

modified best first search algorithm.

Rover 3 is sent to 

rescue the astronaut

Rovers 1 and 3 are scheduled to accomplish a new 

set of tasks. Some previously planned tasks will be 

deleted while new tasks are added.

Fig. 4. Contingency Response to Astronaut Request for Help [17].

235
etc.). In response, a rover (r2) is redirected to assist the 

astronaut (e.g., provide a spare oxygen supply). If the 

remaining agents are undisturbed, they could complete their 

originally-scheduled activities, but the activities of the 

astronaut and rover r2 would be ignored. Instead, the 

planner reschedules activities for rovers r1 and r3, directing 

them to complete as many high-priority tasks abandoned by r2 

and the astronaut as well as their own high-priority tasks. 

To identify additional challenges associated with 

human-robot collaboration, we performed tests with a “real” 

6-wheel robot and human “astronaut”. With no manipulator, 

the rover’s “skills” were to store samples acquired by the 

astronaut (requiring cooperation) and take pictures/video of 

“interesting” sites. The astronaut could collect samples and 

convey perceptions of the environment to identify interesting 

sites, but could not carry the samples (and remain productive) 

or acquire picture/video data. 

Overall, once baseline rover navigation and path planning 

algorithms were in place, tests went smoothly. The primary 

lesson learned, however, was that discrepancies between 

actual and predicted rover traversal speed can be substantial, 

especially in situations where the rover diverts around 

[unknown] obstacles. Given our collaborative task in which 

an astronaut collects and stores samples on the rover, either 

the rover or the astronaut had to wait a substantial time for the 

other to arrive, depending on the numerical value we set for 

expected rover traversal speed. This is an important issue to 

be resolved, likely through intermediate status reports to 

better synchronize agents over long-term tasks, particularly 

given unanticipated task execution speed-ups and delays. 

D. Human-Robot Collaboration Evaluation Metrics 

As with airspace management, metrics are an important 

means to assess alternative architectural and algorithmic 

options for extraterrestrial human-robot collaboration. 

Safety and efficiency are again the primary considerations. 

Safety can be quantitatively measured by response time of 

each agent and the planner given critical (dangerous) events. 

Efficiency can be measured in terms of time and resource use 

for individual agents and collaborative groups to accomplish 

tasks. Efficiency is substantially compromised when 

substantial delay is encountered, as with the astronaut waiting 

for the rover to arrive. Conversely, performance can be 

boosted beyond that originally considered possible through 

opportunistic task insertion by astronauts and rovers. 

As with airspace management, any exploration architecture 

requires acceptance by mission controllers and astronauts as 

well as quantitative performance evaluation. Astronauts, 

mission controllers, and mission scientists will all have a 

strong impact on the ultimate system. We can support this 

decision through a series of qualitative metrics. The impact 

on astronauts can be measured through perceived workload 

and situational awareness when being tasked and during the 

accomplishment of individual and collaborative tasks as well 

as communication of opportunistic deviations and requests for 

assistance. As evidenced by the eventual science community 

acceptance of onboard data processing for spacecraft, the 

science community is interested in acquiring the maximum 

amount of high-quality data. Thus, they would also be 

interested in the quantitative performance evaluation 

(objective achievement). The goal of mission control is to 

accomplish mission objectives while maintaining situational 

awareness and safety. The key to acceptance will be to field 

a “collaboration architecture” that is more safe and more 

informative than less-collaborative alternatives. 

IV. CONCLUSION

This paper has studied human-robot collaboration in the 

context of airspace management and human-astronaut 

planetary surface exploration. A spectrum of airspace 

management paradigms were presented, ranging from the 

current “centralized” standard to a fully-decentralized 

futuristic system that would support substantially more dense 

and disparate operations. Due to legacy and ultra-high safety 

requirements, aircraft are still manually routed by human 

controllers with local automated (pairwise) deconfliction. 

Migration to a more efficient “automated” system faces 

formidable technological, operational, and psychological 

challenges. Technologically, we must develop a common 

representational and communication framework enabling 

manned and unmanned aircraft of different sizes and designs 

to share common airspace. Operationally, we must reduce 

uncertainty to retain or even enhance safety given 

ultra-high-density traffic, requiring extension of “traffic 

queue” and “miles in trail” models to safe but minimal wake 

and maneuverability-based constraints. Psychologically, we 

must gain the trust of the human pilot, passenger, and operator 

through incremental implementation and long-term 

performance excellence. The keys to success are capable 

and correct management and coordination algorithms and 

implementations – anything less risks a popularized disaster 

that could compromise acceptance indefinitely. 

Collaborative planetary surface exploration was also 

presented in the context of enhanced efficiency due to the 

introduction of “initiative-driven” rather than 

“human-directed” robotic companions. Our hypothesis is that 

human initiative is superior for opportunistic plan revision but 

that computerized offline scheduling is superior to 

human-directed scheduling (e.g., on a spreadsheet). As such, 

humans and robots are assigned a default, coordinated plan, 

but astronauts and robots, based on mission objectives, could 

dynamically revise their activities without argument and 

safety-oriented activities (e.g., astronaut rescue) would be 

efficiently accommodated. Although much work in 

communication, coordination, and knowledge representation 

remains, we presented a baseline architecture capable of 

planning and dynamically accommodating changes due to the 

environment or to agent-initiated plan deviations. 

For both presented air and space robotic domains, a common 

set of performance metrics emerged that are crucial for 

assessing efficiency, safety, and robustness due to enhancing 

robotic systems with initiative and deliberation capabilities 

236
versus continuing to deploy exclusively human-directed 

systems. We believe such unique Aerospace challenges will 

serve as important drivers for truly collaborative and 

coordinated human-robot operations. NASA’s emphasis on 

manned space exploration with robotic support will ultimately 

mandate the study of multiple “viewpoints” in human-robot 

collaboration, emphasizing novel technological capabilities 

rather than demonstrating the use of existing technology. 

Airport congestion and delays have resulted in a call to triple 

airport capacity (throughput) in the coming decades, and a 

nearly overwhelming insurgence of unmanned air vehicles has 

led to pressure for manned and unmanned aircraft to share 

common airspace. Physically-proximal human-robotic 

operations are inevitable on Earth, in the air, and in space. 

Elevating robotic system deliberation, awareness, and 

response capabilities to “see and act” independent of its 

human companions must be performed carefully but surely 

must be performed to support the demands society will place 

on transportation and exploration systems. 

V. REFERENCES

[1] Merriam-Webster OnLine (http://www.webster.com/). 

[2] D. Conkey, G. Dell, G., S. Good, J. Bristow, “EO-1 

Formation Flying Using AutoconTM”, Proceedings of the 

IEEE Aerospace Conference, Vol. 7, 55-61, 2000. 

[3] G. Inalhan, M. Tillerson, J. How, “Relative Dynamics and 

Control of Spacecraft Formations in Eccentric Orbits”, 

Journal of Guidance, Control, and Dynamics, Vol . 25, No. 1, 

January– February 48-59, 2002. 

[4] D. Chavez-Clemente and E. Atkins, “Optimization of 

Tetrahedral Satellite Formations,” Journal of Spacecraft and 

Rockets, Vol. 42, No. 5, July-August, 699-710, 2005. 

 [5] S. Chien, B. Cichy, A. Davies, D. Tran, G. Rabideau, R. 

Castano, R. Sherwood, D. Mandl, S. Frye, S. Shulman, J. 

Jones, S. Grosvenor, S., “An Autonomous Earth-Observing 

sensorWeb,” IEEE Intelligent Systems and Their Applications, 

20:3, 16-24, 2005. 

[6] W. Ren and E. Atkins, “Distributed Multi-Vehicle 

Coordinated Control via Local Information Exchange,” 

International Journal of Robust and Nonlinear Control, Vol. 

17, Issue 10-11, pp. 1002-1033, July 2007. 

[7] W. Ren, R. W. Beard, and E. M. Atkins, "Information 

Consensus in Multivehicle Cooperative Control: Collective 

Group Behavior through Local Interaction," IEEE Control 

Systems Magazine, Vol. 27, Issue 2, April, pp. 71-82, 2007. 

[8] T. Fong, C. Thorpe, and C. Baur, “Advanced Interfaces 

for Vehicle Teleoperation: Collaborative Control, Sensor 

Fusion Displays, and Remote Driving Tools”, Autonomous 

Robots 11(1), July 2001. 

[9] D. Q. Wilber, “Air Travel Delays: Bad, Getting 

Worse,” Washington Post, Washington, DC, Aug. 1, 2007, 

Page A01. 

[10] A. Yousef, and G. L. Donohue, “Temporal and Spatial 

Distribution of Airspace Complexity for Air Traffic 

Controller Workload-Based Sectorization," 4th Aviation 

Technology, Integration and Operations Forum, AIAA 

2004-6455, Chicago, Illinois, Sept. 20-22, 2004. 

[11] B. Sridhar, S. Grabbe, K. Sheth, and K. D. Bilimoria, 

“Initial Study of Tube Networks for Flexible Airspace 

Utilization," Guidance, Navigation, and Control Conference 

and Exhibit, AIAA 2006-6768, Keystone, Colorado, Aug. 

21-24, 2006. 

[12] Betts, J. T., “Survey of Numerical Methods for 

Trajectory Optimization,” Journal of Guidance, Control, and 

Dynamics, Vol. 21, 1998. 

[13] Schultz, R. L., “Three-Dimensional Trajectory 

Optimization for Aircraft,” Journal of Guidance, Control, and 

Dynamics, Vol. 20, 1997. 

[14] C. Tomlin, I. Mitchell, R. Ghosh, “Safety Verification 

of Conflict Resolution Maneuvers,” IEEE Transactions on 

Intelligent Transportation Systems, Vol. 2, No. 2, Jun. 2001. 

[15] M. Xue and E. M. Atkins, “Noise-Minimum 

Runway-Independent Aircraft Approach Design for 

Baltimore-Washington International Airport,” Journal of 

Aircraft, American Institute of Aeronautics and Astronautics 

(AIAA), 43(1):39-51, Jan-Feb 2006. 

[16] Y. Tang, E. Atkins, R. Sanner, "Emergency Flight 

Planning for a Generalized Transport Aircraft with Left Wing 

Damage," Proc. Guidance, Navigation, and Control 

Conference, AIAA, Hilton Head, SC, Aug. 2007. 

[17] M. Ransan and E. Atkins, "Human-Robot Team Task 

Scheduling for Planetary Surface Missions," Proc. 

Infotech@Aerospace Conference, AIAA, Rohnert Park, CA, 

May 2007. 

237
Analyzing the Performance

of Distributed Algorithms

Robert N. Lass, Evan A. Sultanik and William C. Regli

Drexel University

Department of Computer Science

3141 Chestnut Street

Philadelphia, PA 19104

{urlass,eas28,regli}@cs.drexel.edu

Abstract — A large class of problems in multiagent systems can

be solved by distributed constraint optimization (DCOP). Several

algorithms have been created to solve these problems, however, no

extensive evaluation of current DCOP algorithms on live networks

exists in the literature. This paper uses DCOPolis—a framework

for comparing and deploying DCOP software in heterogeneous

environments—to contribute an analysis of two state-of-the-art DCOP

algorithms solving a number of different problem types. Then, we

use this empirical validation to evaluate the use of both cycle-based

runtime and concurrent constraint checks.

I. INTRODUCTION

With the proliferation of small, inexpensive computers able

to communicate wirelessly, the importance of distributed algorithms will likely grow in the coming years. This makes

investigation of performance metrics and evaluation procedures for these types of systems particularly important. Due

to the number of factors that influence the performance of a

distributed system it is difficult to predict how a system will

perform.

As an example, this paper examines metrics used to compare

Distributed Constraint OPtimization (DCOP) algorithms. We

empirically assesses cycle-based runtime, the primary (theoretical) performance metric in common use, in a number of

different live network settings including MANETs.

In the remainder of this paper we first give background

on DCOPs and several types of problems we investigate.

We then describe a DCOP testbed that we have created to

compare algorithms on live networks. We report on a series

of experiments run on this testbed using the Adopt [8] and

DPOP [11] algorithms. In doing so, we evaluate CBR as a

predictor of actual runtime. Finally, we present an analysis of

our results that suggests the coefficients to the CBR equation

are actually a function of the algorithm and problem domain,

which invalidates CBR (and its special-case ccc) as a general

metric for comparing DCOP algorithms, but suggest that it

may be useful as a metric for predicting asymptotic runtime

or even for comparison if the coefficients are know.

II. DISTRIBUTED CONSTRAINT OPTIMIZATION

A large class of multiagent coordination and distributed

resource allocation problems can be modeled as DCOP problems. DCOP has generated a lot of interest in the constraint

programming community and a number of algorithms have

been developed to solve DCOP problems [8], [6], [11], however, existing metrics for comparing these algorithms do not

adequately capture the many intricacies inherent in solving

DCOPs on live networks.

This is complicated by the fact that DCOP algorithms are

currently implemented in simulation; there is no record in the

literature of any significant evaluation of DCOP algorithms

on live networks. Furthermore, cycle-based runtime (CBR)

metric, for example, has coefficients that are meant to represent

network constants, however, no reasonable values for these

coefficients are yet known, and the correct values of these

coefficients may dictate the ranking of DCOP algorithms. This

paper explores when it is useful and when it is not useful to

use these metrics.

A. Definitions

A “DCOP” is a problem in which a group of agents must

distributedly choose values for a set of variables such that

the cost of a set of constraints over the variables is either

minimized or maximized.

Formally, a DCOP may be represented as a tuple

hA, V, D, f, α, σi, where:

A is a set of agents;

V is a set of variables, {v1, v2, . . . , v|V |};

D is a set of domains, {D1, D2, . . . , D|V |}, where each

D ∈ D is a finite set containing the values to which its

associated variable my be assigned;

f is a function

f :

[

S∈P(V )

Y

vi∈S

({vi} × Di) → N ∪ {∞}

(where “P(V )” denotes the power set of V ) that maps

every possible variable assignment to a cost. This

function can also be thought of as defining constraints

between variables;

α is a function α : V → A mapping variables to their

associated agent. α(vi) 7→ aj implies that it is agent

aj ’s responsibility to assign the value of variable vi.

Note that it is not necessarily true that α is either an

injection or surjection; and

238
σ is an operator that aggregates all of the individual

f costs for all possible variable assignments. This is

usually accomplished through summation:

σ(f) 7→

X

s∈

S

S∈P(V )

Q

vi∈S

({vi}×Di)

f(s).

The objective of a DCOP is to have each agent assign

values to its associated variables in order to either minimize

or maximize σ(f).

A “Context” is a variable assignment for a DCOP. This can

be thought of as a function mapping variables in the DCOP

to their current values:

t : V → (D ∈ D) ∪ {∅}.

Note that a context is essentially a partial solution and need

not contain values for every variable in the problem; therefore,

t(vi) 7→ ∅ implies that the agent α(vi) has not yet assigned a

value to variable vi. Given this representation, the “domain”

(i.e., the set of input values) of the function f can be thought

of as the set of all possible contexts for the DCOP. Therefore,

in the remainder of this paper we may use the notion of a

context (i.e., the t function) as an input to the f function.

B. Examples of DCOP Problems

1) Graph Coloring: Given a graph G = hN, Ei and a set

of colors C, assign each vertex, n ∈ N, a color, c ∈ C,

such that the number of adjacent vertices with the same color

is minimized. Graph coloring is a commonly-cited problem

used for evaluating DCOP algorithms [8], [6].

DCOP Encoding: For each vertex ni ∈ N, create a variable

in the DCOP vi ∈ V with domain Di = C. For each pair

of adjacent vertices hni, nj i ∈ E, create a constraint of cost

1 if both of the associated variables are assigned the same

color: (∀c ∈ C : f(hvi, ci,hvj , ci) 7→ 1). A and α cannot be

generically defined for graph coloring; they will depend on the

application. Most publicly-available benchmark problem sets

create one agent per variable [9].

2) Distributed Multiple Knapsack Problem (DMKP): Given

a set of items of varying volume and a set of knapsacks of

varying capacity, assign each item to a knapsack such that the

amount of overflow is minimized. Let I be the set of items,

K be the set of knapsacks, s : I → N be a function mapping

items to their volume, and c : K → N be a function mapping

knapsacks to their capacities.

DCOP Encoding: for each i ∈ I create one variable vi ∈ V

with associated domain Di = K. Then for all possible context

t:

f(t) 7→

X

k∈K

(

0 r(t, k) ≤ c(k),

r(t, k) − c(k) otherwise,

where r(t, k) is a function such that

r(t, k) = X

vi∈t−1(k)

s(i).

C. Evaluation

Cycle-based runtime (CBR) [3], a popular and simple metric

used by researchers to evaluate DCOP algorithms, is evaluated

in this section. The focus was chosen to be on CBR (over

other metrics such as non-concurrent constraint checks [7])

since CBR and its special-case ccc are the metrics most often

employed in evaluating DCOP algorithms in the literature [8],

[11], [6].

III. EXPERIMENTAL SETUP

A. Software

The reference implementations for the Adopt and DPOP

algorithms (coded by their respective authors) were designed

to be run in simulation; although extending the code to be run

on a live network was not hard, configuring it for automated

batch processing of experiments in such a setting was nontrivial. Therefore, the implementations of these algorithms as

provided in the DCOPolis1 package were used.

DCOPolis was chosen as the testbed for our experiments

because it was originally designed as framework for comparing

and deploying distributed decision processes in heterogeneous

environments. At the time the experiments were performed,

DCOPolis had three DCOP algorithms implemented: Adopt,

DPOP and a na ̈ıve algorithm called Distributed Hill Climbing.

Only Adopt and DPOP were used for our experiments.

DCOPolis differentiates itself from existing frameworks and

simulators (like FRODO [10] and those used in testing Adopt

and OptAPO) in two fundamental ways:

1) DCOPolis was designed to allow for both simulation

of DCOPs on a single computer and full deployment

of DCOP solvers on many types of live networks,

including traditional wired networks and ad-hoc wireless

networks; and

2) DCOPolis is able to instantiate a DCOPs and start the solution process completely distributedly. This means that

there is no need for configuration files, nor is there any

need for a central agent/server that initializes/instantiates

the rest of the group.

All of the code is freely available under the GNU public

license.

B. Pseudotree Generation

A similarity between Adopt and DPOP is that they both

assume the existence of a tree ordering over all of the variables

in the problem. The pseudotree has an invariant that for

each pair of variables hvi, vj i that are neighboring in the

constraint graph it must be the case that viis either an ancestor

or descendent of vj in the pseudotree. The pseudotree also

contains a backedge between all pairs of neighbors in the

constraint graph that do not have a parent/child relationship in

the pseudotree. For each v ∈ V , α(v) must know the relative

tree position (i.e., ancestor, descendent, or parent) of each

constraint graph neighbor of v. The authors of both Adopt

and DPOP assume that the agents would simply elect one

1http://dcopolis.sourceforge.net/

239
agent to create this ordering which is then broadcast to the rest

of the group. Since the runtime of both algorithms is highly

dependent on the structure of the pseudotree, we ensured that

for each problem instance in our experiments the algorithms

were given identical pseudotrees.

C. Computing Devices

Five HP-TC1100 tablet PCs with 1Ghz Intel Pentium M

processors and 512M of RAM were connected via Ethernet

to a Netgear FS108 switch. No machines were connected to

the switch other than the ones taking part in the experiment

and the switch was not connected to the Internet or any other

network. All the machines were running Ubuntu 6.06 Linux

with a 2.6.15-27-686 kernel.

D. Problem Datasets

1) Multiagent Task Scheduling: Experiments on the data

multiagent task scheduling C TÆMS dataset referenced in [21]

was attempted, however, DPOP was unable to solve any of

these problems. This was likely due to the problems’ large

number of variables and domain sizes. This is analyzed in

§V-A.

2) Graph Coloring: The USC Teamcore project has a variety of sample problem data files in their DCOP repository [9]

which were used in our analysis. The graph coloring problems

were from the “Graph coloring dataset” and range from 8 to

30 variables. In these experiments, a subset of the problems

containing 12 and 14 variables was used.

3) Distributed Multiple Knapsack Problem: DCOPolis has

a utility for creating random DMKP data files. Twenty-five

problem sets were created, consisting of five of each of

the following: many small bins (ten), many small objects

(twelve); few small bins (three), many large objects; few large

bins, many small objects; few small bins, wide variety (high

standard deviation) of objects and a wide variety of bins, many

small objects. These data files are available from the authors’

website.

E. Cycle-based Metrics

In the first publication introducing Distributed Constraint

Satisfaction, [23], Yokoo, et al. evaluate algorithms by counting the number of cycles needed to determine a solution.

The cost of communications is not taken into account, which

the authors note and explain by stating that they do not

have a standard way to compare communication costs and

computational costs.

Cycle-based runtime (CBR) was introduced in [3] as a

metric that takes into account the number of constraint checks

performed in each cycle as well as the communications latency

between cycles. CBR is computed as

CBR(m) = L × m + ccc(m) × t,

where t and L are constants respectively relating to compute

time and communications time, m is the number of cycles,

and

ccc(m) = Xm

k=0

max

a∈A

cc(a, k),

where cc(a, k) is the number of constraint checks performed

by agent a in cycle k.

Given the fact that a single host on the network can

support multiple agents (and assuming that each host has a

single processor), CBR must take into account the number of

machines used in the solution of the DCOP. Therefore, we

propose a slight modification to CBR that accounts for the

distribution of agents on the hosts:

ccc(m) = Xm

k=0

max

h∈H

X

a∈Ah

cc(a, k),

where H is the set of all hosts and Ah is the set of agents

on host h. In other words, all agents that are running on the

same host must compete for time from the single CPU, so

these agents are in effect running synchronously during each

cycle. Therefore, for all agents that are sharing a host we

need to sum over the number of constraint checks during each

cycle instead of taking the maximum. Given an experiment

where maxh∈H |Ah| = 1 (which implies |H| ≥ |A|), the two

equations are equivalent. For the remainder of the paper we

shall use this augmented definition of CBR.

IV. RESULTS AND ANALYSIS

The results of the graph coloring and the DMKP experiments can be seen in Figure 1 and 2 respectively. In both

graphs, Adopt and DPOP both show a linear correlation

between runtime and CBR. In Figure 3, the results of running

graph coloring problems with large domains and fifty sparsely

connected vertices is shown. DPOP was unable to solve many

of these problems due to the algorithm running out of memory

while trying to construct the massive hypercubes for this

problem domain. There are DPOP variants[16], [18] that may

scale better, but they are not yet implemented in DCOPolis.

Pearson’s linear correlation coefficient was calculated for

the runtime and the CBR metric. For each of the datasets

except one we were able with 99% certainty to reject the null

hypothesis that the distributions were not linearly correlated in

favor of the alternate hypothesis that CBR and actual runtime

are linearly correlated. Pearson’s coefficient has a student’s t

distribution, which is what we used to test these hypotheses.

Our smallest test statistic value was 4.05. The one test for

which we failed to reject the null hypothesis was for the DPOP

data in Figure 2. It is clear from looking at the graph, however,

that the large cluster of DPOP data supports the claim that

CBR is a valid metric for predicting actual runtime.

L and t were calculated empirically for each of the domains

and algorithms. The average time spent sending and receiving

data during each cycle was calculated and used as L. The

average runtime per cycle—not counting time required for

communication—was used as t. As shown in Table I, these

coefficients were quite different between algorithms and problem domains.

V. CONCLUSIONS AND FUTURE WORK

We have shown that CBR is an excellent predictor of

asymptotic runtime. We have also shown that the L and t

coefficients in the CBR metric are not in fact constant, even

240
 1

 10

 100

 1000

 10000

 10000 100000 1e+06 1e+07 1e+08 1e+09 1e+10

Runtime (seconds)

Cycle-Based Runtime

Adopt

DPOP

Fig. 1. Actual runtime versus cycle-based runtime for a subset of the USC

Teamcore graph coloring problem set. Both Adopt and DPOP exhibit a linear

correlation. Both axes are scaled logarithmically in order to reduce clustering

around the origin.

 1

 10

 100

 1000

 10000

 1000 10000 100000 1e+06 1e+07 1e+08 1e+09

Runtime (seconds)

Cycle-Based Runtime

Adopt

DPOP

Fig. 2. Actual runtime versus cycle-based runtime for a randomly-generated

set of DMK problems. Both the number of knapsacks and number of items

were varied. Both Adopt and DPOP exhibit a linear correlation. Both axes

are scaled logarithmically in order to reduce clustering around the origin.

 1

 10

 100

 1000

 100000 1e+06 1e+07 1e+08 1e+09 1e+10 1e+11

Runtime (seconds)

Cycle-Based Runtime

Adopt

DPOP

Fig. 3. Actual runtime versus cycle-based runtime for a randomly-generated

set of eight-color graph coloring problems with fifty vertices. There are only

three DPOP data points; the other seven failed due to a lack of memory.

Both Adopt and DPOP exhibit a linear correlation. Both axes are scaled

logarithmically in order to reduce clustering around the origin.

PROBLEM DOMAIN ALGORITHM L t

Graph Coloring Adopt 75.91 60.74

DPOP 46.4 1985.69

DMKP Adopt 54.01 68.62

DPOP 215.78 4299.18

TABLE I

EMPIRICALLY-DETERMINED VALUES FOR THE CBR COEFFICIENTS.

when the network environment is constant. These coefficients

are best represented as a function of the algorithm and the

problem domain, and it is currently unclear how these can

be predicted through traditional simulation. CBR therefore

falls short as a metric for comparing algorithms, unless the

coefficients for each algorithm are known a priori. We have

provided a list of these coefficients for a number of different

problems. In the future we hope to expand this list and

also investigate new metrics such as non-concurrent constraint

checks [7].

The runtime of these algorithms is highly dependent on the

variable ordering given by the pseudotree. Our next experiments will be to measuring the impact of alternate techniques

for generating these trees, such as [2].

DCOPolis supports the use of the Sefirs2simulation kernel

and MATES network simulator [22], which essentially creates

a virtual machine that runs in simulated time. We hope to

use our live network data to calibrate these simulations to

allows for a comparison of DCOP algorithms empirically

in simulation, without the need for theoretical comparison

metrics like CBR or access to a cluster of computers or testbed

like the one created for this paper.

A. A note on comparisons

It is not the authors’ intent to directly compare the algorithmic performance of Adopt and DPOP in this paper. The

reference implementations for these algorithms (coded by their

respective authors) were designed to be run in simulation;

although extending the code to be run on a live network

was not hard, configuring it for automated batch processing

of experiments in such a setting was non-trivial. Therefore,

the implementations of these algorithms as provided in the

DCOPolis package were used. These implementations were

created by authors other than the original algorithm designers,

based solely upon the algorithms described in the respective

papers. Furthermore, there are other techniques and variations

of both Adopt [4], [20], [1] and DPOP [19], [18], [5], [17],

[14], [13], [12], [15] that may have performed differently given

our experimental datasets.

Although the data in this paper seem to suggest DCOPolis’

implementation of DPOP outperforms ADOPT in terms of

runtime, they are insufficient to objectively declare DPOP

a better algorithm. The favorable runtimes of DPOP may

be due to our selection of small problems; larger problems

(e.g., coloring problems with large domains and C TÆMS

problems) cannot be run with DCOPolis’ implementation of

DPOP because the hypercubes DPOP generates require far

2http://sefirs.sourceforge.net/

241
too much memory. DPOP’s worst-case memory usage scales

exponentially with respect to the average domain size [11],

while Adopt scales polynomially [8]. For example, Figure 3

shows a graph of experiments that used randomly generated

graph coloring problems of fifty sparsely connected vertices

using eight colors. Of the ten experiments, only three completed for the DPOP algorithm; the other eight failed due to

the inability to allocate enough memory. All of the Adopt

problems finished.

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242
An agent structure for evaluating micro-level MAS

performance

Christos Dimou, Andreas L. Symeonidis and Pericles A. Mitkas

Aristotle University of Thessaloniki

Thessaloniki, Greece

cdimou@issel.ee.auth.gr, asymeon@issel.ee.auth.gr, mitkas@eng.auth.gr

Abstract — Although the need for well-established engineering approaches in Intelligent Systems (IS) performance evaluation is urging,

currently no widely accepted methodology exists, mainly due to lack

of consensus on relevant definitions and scope of applicability, multidisciplinary issues and immaturity of the field of IS. Even existing

well-tested evaluation methodologies applied in other domains, such

as (traditional) software engineering, prove inadequate to address

the unpredictable emerging factors of the behavior of intelligent

components. In this paper, we present a generic methodology and

associated tools for evaluating the performance of IS, by exploiting

the software agent paradigm as a representative modeling concept for

intelligent systems. Based on the assessment of observable behavior

of agents or multi-agent systems, the proposed methodology provides

a concise set of guidelines and representation tools for evaluators to

use. The methodology comprises three main tasks, namely metrics

selection, monitoring agent activities for appropriate measurements,

and aggregation of the conducted measurements. Coupled to this

methodology is the Evaluator Agent Framework, which aims at

the automation of most of the provided steps of the methodology,

by providing Graphical User Interfaces for metrics organization

and results presentation, as well as a code generating module that

produces a skeleton of a monitoring agent. Once this agent is

completed with domain-specific code, it is appended to the runtime

of a multi-agent system and collects information from observable

events and messages. Both the evaluation methodology and the

automation framework are tested and demonstrated in Symbiosis, a

MAS simulation environment for competing groups of autonomous

entities.

Keywords: performance evaluation methodology, autonomous agents, multi-agent systems, automated evaluation

I. INTRODUCTION

Evaluation is an integral part of any complete scientific

or engineering methodology. Evaluation methodologies enable

researchers to test the quality and applicability of their findings, as well as to set the limits and define the appropriate

environmental or intrinsic parameters for optimal performance.

The benefits of a well-defined evaluation methodology lead to

detection of defects, safety and overall quality of a system.

But, mainly, evaluation helps researchers to thoroughly comprehend the internal characteristics and impact of their newly

proposed methods and ideas.

Although the need for generalized evaluation methodologies

in the field on Intelligent Systems (IS) is indisputable, currently no such effort is known to the authors. This remarkable

lack of means for evaluating the performance of intelligent

systems may be attributed to a number of reasons. First, it

is argued that IS technology has not yet reached a certain

degree of maturity. Despite being in the center of attention

for more than six decades, it is only recently that Artificial

Intelligence (AI) and IS are applied to realistic problems. It is,

thus, evident that more experience and time are needed in order

to help this field reach the desired maturity level. Second, the

research area of IS combines background theory and practices

from a number of diverse scientific fields, including artificial

intelligence, computational theory, distributed systems, even

cognitive psychology and sociology. A coordinated course of

action is therefore required, one that will integrate expertise

gathered from all the above mentioned areas. Moreover, existing evaluation methodologies for conventional software do not

suffice in the case of IS, due to the unpredictable performance

properties that are not known at design time and may emerge at

the execution of IS. Finally, there is a remarkable and possibly

unresolvable lack of consensus on definition of relevant terms

and scope of applicability of IS. It is, indeed, very difficult to

define evaluation methods when there is no agreement on even

the fundamental definitions, on what an intelligent system is,

what constitutes an emergent behavior or what the scope of

an IS should be.

Currently, researchers and developers that desire to evaluate

their systems, often devise their own ad-hoc, domain-specific

evaluation methods. It is often the case that these methods

are biased (most of the times with no deliberation) so that

they produce the optimal results for a very strict set of

environmental parameters and assumptions. Moreover, their

ad-hoc nature prevents third parties to repeat the experimental

setting and verify the findings.

In this paper, we present a complete, generic, domain

independent methodology for evaluating IS performance, as

well as a supporting software tool that automates most of the

evaluation process. The proposed methodology exploits the

software agent paradigm as a representative modeling concept

and implementation vehicle for intelligent systems. Indeed,

agents may be regarded as entities that exhibit autonomous

behavior in unknown and dynamic environments [11], being

capable of encapsulating any existing intelligent technology,

spanning from genetic algorithms [9], data mining [14] and

machine learning [12], to reinforcement learning [6] and

complex decision making techniques [5]. Agents rarely operate

in isolation; they most often form groups or societies, either

243
cooperating towards a common goal [13] or competing against

each other on limited resources [17]. Thus, in multi-agent

systems (MAS) [3] evaluators may focus on different levels

of system granularity, ranging from single agent computational

units and agent autonomy to multi-agent interaction, or even

on complex global cooperation/competition aspects of MAS

societies. The problem of performance evaluation is then

reduced to three fundamental tasks, namely a) selection of

appropriate metrics, b) monitoring agent activities for appropriate measurements, and c) aggregation of the conducted

measurements. Within the context of this work, we address the

above tasks, by providing a concise set of methodological steps

and guidelines, as well as the corresponding agent structure

that autonomously performs most of the monitoring workload.

The remainder of this paper is structured as follows: Section

II reviews the current state-of-the-art in IS evaluation; Section

III briefly presents the scope and basic concepts of our evaluation methodology; in Section IV, the automated evaluation

tool, the Evaluator Agent, is presented; Section V applies

the proposed methodology to Symbiosis, a MAS simulation

environment for groups of autonomous entities and illustrates

the results; Section VI concludes the paper and proposes some

thoughts on future directions.

II. RELATED WORK

Evaluation has been tightly coupled with artificial intelligence, since the early days of AI [15]. On their effort to

define the capabilities and limits of machines, AI pioneers defined hypothetical evaluation benchmarks in order to compare

potential computer behavior against the human intellect (e.g.

[10]). The initial enthusiasm soon faded (during the infamous

AI winter), giving room for traditional software evaluation,

which focused on performance and quality assessment of

conventional software products, as well as related productivity

metrics. It is only recently, that IS have drawn once again

the attention of computer scientists and engineers, this time

with more realistic goals in specific engineering problems.

However, as already mentioned, current evaluation efforts do

not go beyond ad-hoc solutions.

There are two general approaches to the IS evaluation

problem: the bottom-up and the top-down. The former, as

elaborately represented by [19], advocates the definition of

formal constructs and languages that will enable the definition

of the appropriate terms and scope of IS. Evaluation will

thereafter be gradually built upon these formal foundations.

The latter approach observes that existing or newly implemented systems urge for evaluation methodologies and it is

therefore preferable to instantly evaluate them at any cost.

According to this approach, experiences from different ad-hoc

evaluation attempts will be generalized into a concise domainindependent methodology, which in turn will be established at

the time that IS reach a sufficient maturity level.

III. THE GENERALIZED EVALUATION METHODOLOGY

Our generic evaluation methodology positions itself in compliance to the above-mentioned top-down approach. Motivated

by the urging need to evaluate agent systems that are deployed

with currently existing techniques, we provide a complete

framework that will be readily available for developers. As

a generic methodology, it could not take into account intrinsic

implementation details, such as specific algorithms; instead

it focuses on observable performance, which derives from

system events and messages exchanged between the system

modules and components. Moreover, this methodology addresses performance issues on different levels of granularity.

It is the developer’s choice to identify and focus on specific

performance aspects at any level of detail, ranging from

independent computational units of agents, to autonomous

agents, groups of agents, or the entire MAS

With respect to each of the three basic tasks of the performance evaluation process (i.e. metrics, measurement and

aggregation), our methodology provides either a theoretical

representation tool with an accompanying set of guidelines or

automated tools that assist evaluators throughout the process

(see [2] for a detailed presentation of the methodology). More

specifically:

1) Selection of metrics. Metrics are standards that define

measurable attributes of entities, their units and their

scope. Before any other decision, the evaluator must

choose which attributes of the system he/she is interested

in. For this purpose, we provide the Metrics Representation Graph (MRG), a hierarchical representation

of metrics for a specific domain. Each node of MRG

corresponds to a single metric. Leaf nodes represent

directly measurable metrics (simple metrics), i.e. metrics that can be assigned with a specific measurement

value. Measurable metrics, in turn, compose higher level

metrics (composite metrics) that cannot be assigned

with a specific value, but rather represent a higher

level concept that is easier understood by the evaluator,

using linguistic terms. For example, the simple metrics

of numberOfMessages and numberOfAgents may be

composed to produce the scalability composite metric.

Traversing the hierarchy upward, one moves to higher

level composite metrics, until one reaches the root,

which is the total systemEfficiency. The evaluator is

required to traverse the MRG and select only the metrics

that are of interest to him/her. The general structure of

MRG is provided in Figure 1.

2) Measurement. Measurement is the process of ascertaining the attributes, dimensions, extend, quantity, degree of

capacity of some object of observation and representing

these in the qualitative or quantitative terms of a data

language [8]. Having selected the appropriate metrics,

measurement is the next fundamental methodological

step that systematically assigns specific values to these

metrics. Typical measurement methods consists of experimental design and data collection. A measurement

method is the answer to the question. Since measurement

methods are difficult to summarize and categorize, we

provide an automated tool for producing an Evaluator

Agent that autonomously monitors the execution of a

244
system

efficiency

composite

metric

composite

metric

composite

metric

composite

metric

composite

metric

simple

metric

simple

metric

simple

metric

simple

metric

simple

metric

simple

metric

simple

metric

weight weight

weight

weight weight weight weight

weight weight weight weight weight

Fig. 1. General structure of the Metrics Representation Graph

system and records data related to the selected metrics.

The Evaluator is presented in detail in Section IV.

3) Fuzzy Aggregation. Once the measurement procedure

has been defined, the resulting metric-measurement pairs

have to be aggregated to a single characterization for

the investigated system. Aggregation, or composition, is

the process of summarizing multiple measurements into

a single measurement is such a manner that the output

measurement will be characteristic of the system performance. Aggregation groups and combines the collected

measurements, possibly by the use of weights of importance, in order to conclude to atomic characterization for

the evaluated system. For example, an evaluated system

may perform exceptionally well in terms of response

time metrics (timeliness), but these responses may be far

from correct (accuracy). Fuzzy sets [18] have been incorporated, since they provide efficient means of dealing

with measurement of different scales and types, as well

as of concluding to performance characterization, which

is closer to the human language. Before aggregating the

results, the evaluator needs to define appropriate fuzzy

variables and membership function for the quantification

of the measurement. He/she also has to define weights

to each of the edges of the MRG, so that appropriate

fuzzy aggregation operators may be later applied. These

weights signify the importance of each sibling metric to

the composition of the parent metric. At this moment,

fuzzy quantification and weights assignment is done

manually by the evaluator or a domain expert.

The complete set of steps that an evaluator is required to

follow is summarized in Table I.

TABLE I

SUMMARIZATION OF METHODOLOGICAL STEPS

1. Traverse MRG and select metrics

2. Provide domain specific metrics (optionally)

3. Determine metrics parameters

4. Specify measurement method and parameters

5. Execute experiments

6. Define weights in the graph

7. Define fuzzy variables and convert measurements accordingly

8. Select and apply aggregation operators on the collected measurements

IV. EVALUATOR AGENT FRAMEWORK

In this section, we describe the general architecture and

components of the Evaluator Agent Framework, a development framework for producing an agent structure that automates most parts of the proposed evaluation methodology.

This framework is targeted to developers who need to evaluate

the performance of their systems, either existing or under

development. The basic requirements of this framework are

the minimum modifications of the tested system at hand, as

well as the minimum effort in writing evaluation specific code.

A. Purpose and Benefits

The purpose of the proposed framework is to guide the researcher/developer through the methodological steps of Table

I by providing:

• visualization and manipulation of the MRG and the

corresponding parameters of simple or composite metrics

• interactive specification of the fuzzy variables, membership functions and weights that correspond to the MRG

• automatic generation of the skeleton code of the Evaluator Agent that will undertake the task of monitoring the

system for evaluation and collect all necessary information with respect to the selected metrics

• visualization of the performance evaluation results

At this point, a few of the abovementioned tasks still remain

at the developers hands, as he/she is required to fill in the

skeleton code of the Evaluator Agent with actual domainspecific parameters. Additionally, the developer is currently

required to manually load and manipulate the MRG for the

application domain at hand. At a latter phase, this will also be

automated, as discussed in Section VI.

Applying the Evaluator Agent Framework to actual evaluation processes includes the realization of our primary motivation: the generalization of the evaluation process. By following

a standardized step-wise approach for any given systems, two

evaluators are expected to reach to the same conclusion. The

framework will also reduce the time burden for evaluators, to

tune parameters and manually monitor themselves the results.

Additionally, readily available Application Programming Interfaces (APIs) for aggregating and visually presenting evaluation

results may be used within any evaluation context. Fnally, this

framework is envisioned to become a forum for collecting

and utilizing knowledge for metrics from different domains.

Developers within a domain-specific community will ideally

be able to share MRGs and other experiences. Newcomers

245
in any field will be provided with an MRG, accompanying

weights and fuzzy sets from domain experts and will proceed

to evaluation of their systems.

B. General Architecture

The general architecture of the proposed framework is

depicted in Figure 2. The generic framework comprises five

distinct components that are sequentially linked with user

actions. The evaluation process initiates with the domainspecific MRG import action by the user. The MRG is presented

through a interactive Graphical User Interface - GUI (Component A). After editing and manipulating the MRG, the user is

provided with the skeleton of the Evaluator Agent (Component

B). The user subsequently writes some domain specific code

to produce the run-time Evaluator Agent and connects it to the

system via the Evaluator Agent Middleware (Component C).

The produced agent is then appended to the runtime of a MAS

and starts monitoring and collecting measurement information

(Component D). Upon user request or upon an end system

event, the Evaluator Agent processes the logged information

and produces graphs and other evaluation results (Component

E), according to user preferences. Each of the components of

the framework is further described in the next paragraph.

C. Components

In this subsection, the components of Figure 2 are further

analyzed.

1) Component A - MRG GUI: This component is responsible for presenting the user with a visual representation of the

MRG. The initiation of this process is done either by loading

an existing MRG (which, ideally, is readily available from the

domain communities) or by creating a new one. Node and edge

manipulation tasks -such as edit and delete- are provided. For

each simple or composite metric, the user may define a set of

characteristic properties that are provided in drop-down menus

and options. For example, for simple metrics, a user may define metric scales, units of measurement and metric types (e.g.

range, boolean, nominal, ordinal etc). For composite metrics,

the connection of a specific node to a set of other nodes

(simple or composite metrics) with explicit edges declares that

the parent node is composed of the children nodes. Through

another MRG GUI option, users may also define weights for

each edge, so that the participation importance of children

metrics to the parent metric is determined. The set of weights

will be later utilized in the fuzzy aggregation process.

After defining the structure and parameters of the MRG,

the MRG GUI also provides a wizard for the determination of

fuzzy variables that are necessary for the fuzzy quantification

process. Appropriate fuzzy variables and corresponding membership functions are defined by the user and are correlated

to each simple metric of the MRG. For example, a user may

define the fuzzy variable fastResponse and correlate it to the

simple metric Response Time. He/she must also provide a

fuzzy membership function that maps actual measurements

to the [0,1] range, as illustrated in Figure 3. It is evident that

fuzzy variables and membership functions are heavily dependent on the application domain and are, currently, subjective

specifications that are carried out manually by the user. The

MRG GUI completes its execution by producing, upon user

demand, the skeleton code of the Evaluator Agent.

The MRG GUI has been implemented as a plug-in for the

Prot ́eg ́e Ontology Editor and Knowledge Acquisition System

[4]. MRGs are represented in XML-RDF format and are

loaded into the main Prot ́eg ́e platform. Readily available

ontology visualization functions have been incorporated for

the presentation of MRGs, and have been further enriched with

metric-specific functionality for metric parameter, weight and

fuzzy variable manipulation.

2) Component B - Skeleton Code Generator: Based on the

fully defined MRG, the Skeleton Code Generator component

is initiated in order to automatically produce the outline of an

abstract, general Evaluator Agent, using the Java language.

The resulting skeleton code consists of both complete and

abstract classes. Complete classes implement the necessary infrasrtucture for processing, communication and logging tasks,

whereas abstract classes are declared only to guide users

through the domain specific addition to the Evaluator Agent..

Overall, the Skeleton Code Generator produces complete and

abstract classes for:

• MRG specific manipulation functions

• metrics representation and processing functions

• tasks for collection of run-time data that correspond to

the simple metrics of the selected MRG

• logging collected data into XML format

• processing XML log files

• aggregating measurement data

• communication primitives and message handling

The Skeleton Code Generator component essentially translates all the concepts that are represented in the MRG into

specific or abstract code. If, for example, the user has selected

metrics for the agent level of granularity, then the resulting

code will be adjusted so that it efficiently addresses relevant

single agent performance issues, such as accuracy, autonomy

or timeliness. In a similar manner, if the user has selected the

MAS level of granularity, the code will reflect performance

issues, such as (possibly in addition to some of the above)

scalability and modularity.

3) Component C - Evaluator Agent Middleware: The Evaluator Agent Middleware component serves as a connection

API between the newly produced Evaluator Agent and the

system under evaluation, as illustrated in Figure 4.

Since our evaluation methodology is based on the assumption that only observable behavior contains information on performance, the Evaluation Agent Middleware specifies the functions that manage observable events and observable messages,

at different levels of granularity. The API provides functions

for declaring, initiating, labeling and recording information on

observable events and messages. Based on this API, the user

is responsible to fill in the necessary code to the Skeleton

Evaluator Agent, in order to produce a running Evaluator

Agent. On the other hand, if it is not already implemented,

246
Skeleton

Code

Generator

B

MAS

Run-time

Logger

D

Results

Presentation

GUI

E

Evaluator

Agent

Middleware

C

MRG

GUI

MRG A

Load

MRG

Specify

metrics

Provide

domain

code

Append

Evaluator

agent at 

run-time

Terminate

experiment

Display

results

20%

20%

20%

20%

20%

Fig. 2. Architecture of the Evaluator Agent Framework

Fig. 3. Example fuzzy membership function for Response Time metric

he/she may be required to provide some additional code to

the original system in order to adhere to the Evaluator Agent

Middleware. After the provision of the necessary code, the

Evaluator Agent is ready to be appended to the run-time of

the system.

The Evaluation Agent Middleware could be implemented

in any existing programming language or platform. However,

for testing purposes, we have implemented this component

using the Java Agent Development Environment (JADE) [1].

Jade provides a comprehensive API for agent construction,

behavior specification, communication management, as well as

a few very useful tools, including the Sniffer API for tracking

Evaluator API 

Middleware

MAS Run-time

Evaluator

Agent

Agent1 Agent2

AgentN

Log

Fig. 4. Evaluator Agent Middleware

message content and events at runtime.

4) Component D - MAS runtime logger: This component

undertakes the actual task of conducting the experimental

measurement of the system’s performance. The newly constructed Evaluator Agent participates as an observer agent in

the MAS. On each declared event or sniffed message, the

247
Evaluator Agent calls the appropriate class or method in order

to record performance-related information in XML format.

This information may range from event or message timing to

domain-specific parameter assessment, as for example the bid

value in an electronic auction. The logging of this information

is initiated at the designated staring-event and continues

throughout execution until the designated ending-event. Both

events, as well as iteration parameters, are defined by the user

through the Evaluator Agent Middleware API.

5) Component E - Results Presentation GUI: For aggregation and presentation purposes, the Results Presentation

GUI has been developed. This GUI loads one or more XML

log files with all the performance information that has been

recorded at runtime, as well as with the corresponding XML

representation of the MRG. The user is then requested to

select aggregation and presentation methods from a library

of statistics, drawing, and fuzzy aggregation functions. Thus,

for simple metrics a number of graphs and figures can be

exported, while for composite metrics, fuzzy characterizations

of parts or of the system as a whole are provided.

V. TEST CASE

In order to demonstrate the applicablity and validate the

efficiency of the Evaluator Agent Framework, we have selected

Symbiosis as a MAS for evaluation. In this section, a brief

description is provided, and then we organize a selected set

of metrics into a new instance of the MRG, apply the fuzzy

quantification process and execute the experiments, using the

generated Evaluator Agent.

A. Description of Symbiosis

Symbiosis [16] is mutli-agent simulation framework that

follows the animat approach, as proposed by [7]. Animats

represent autonomous, adaptive, learning entities that live and

evolve in complex environments, in competition or collaboration with other animats. Symbiosis constitutes a virtual

ecosystem, where two competing species of animats co-exist

and share the environment’s limited resources. Additionally,

one of the two groups assumes the role of a group of preys,

whereas the other is a group of predators. In addition to

consumption of natural resources, predators may also consume

preys. The goal of Symbiosis is to provide a simulation

environment for testing and validating a number of emergent

learning and adaptation techniques and the consequent effect

of behavioral strategies.

The environment of Symbiosis is a x × y grid, where each

cell can either be empty or occupied by:

• a natural resource, namely food, obstacle, trap

• a predator agent, or

• a prey agent

While natural resources are static, preys and predators are

free to move in any neighbouring cell, aiming to maximizing

their energy, either by visiting energy enhancing cells (food

cells for preys and prey cells for predators) or by avoiding

energy reducing cells (predator cells for preys and obstacles

and traps for both species). Each agent is born with an initial

amount of energy, certain vision and communication capabilities, a decision-making mechanism and reproduction abilities.

The decision-making mechanisms employs genetic algorithms

for the classification and evaluation of a set of action rules,

based either on previous experiences or communicated from

a neighbouring entity of the same species. Finally, in order to

reproduce conditions that occur in real-world environments,

uncertainty hs been introduced in Symbiosis, in the form of a

parameterised vision error probability.

B. MRG Instance

The experimental measurement of the Symbiosis performance is based on a set of simple, measurable metrics that

have been proposed and analyzed in [16]. These metrics are:

• energy (en): the energy balance of an agent

• age (ag): the number of epochs an agent lives

• resource consumption rate (rcr): the ratio of energy

enhancing cells that an agent has visited, to the number

of total moves

• trap collision rate (tcr): the ratio of trap cells that an

agent has collided upon, to the number of total moves

• unknown situation rate (usr): the ratio of the total unknown situation (no suitable rule applied), to the number

of total moves

• reproduction rate (rr): the ratio of the total offspring of

an agent, to the number of total moves

• effectiveness (e): the energy uptake rate minus the energy

loss rate, to the energy availability rate

• net effectiveness (eNET ): the effectiveness of preys, without taking into account losses caused by their interaction

with predators

After specifying the simple metrics, we need to aggregate

them into composite metrics that are more comprehensible by

the human evaluator. The resulting MRG is depicted in Figure

5. At depth 2, we have concluded on the following composite

metrics: adaptability, scalability, durability and robustness. All

these metrics are further composed to produce both preyEfficiency and predatorEfficiency at depth 1. Finally, the root

composite metric, MASEfficiency is naturally composed by the

two aforementioned metrics, with the addition of the stability

simple metric, which corresponds to the deviation of total

energy imbalance between the two species.

It must be noted that the weights presented in Figure 5

have been determined by a domain expert and are therefore

subjective.

C. Fuzzy quantification

The next step is to determine a fuzzy variable and the

corresponding membership value for each of the simple metrics defined in the previous paragraph. For simplicity and

conciseness, we assign a * high fuzzy variable for each simple

metric, where * is the name of the metric. For example,

the fuzzy variable for rcr is named rcr high and implies

a high degree of resource consumption rate. Based on the

knowledge provided by the domain expert, we have defined

the membership function for rcr high, which is depicted in

248
MAS

Efficiency

equilibrium

distribution

0.4

preyEfficiency predatorEfficiency stability

adaptability

scalability

durability

robustness

energy

age

rcr

usr

tcr

rr

e

population eNET

rr

adaptability

scalability

durability

robustness

energy

age

rcr

usr

tcr

rr

e

population

rr

0.4

0.4 0.2

1

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.33

0.33

0.33

0.4

0.6

0.33

0.33

0.33

0.4

0.6

1

0.5

0.5

0.33

0.33

0.33

0.33

0.33

0.33

Fig. 5. An instance of the MRG for Symbiosis

Fig. 6. Fuzzy membership function for rcr high

Figure 6. We follow a similar approach for the rest of the

simple metrics.

D. Experiments

Having completed the above steps, we continue with the

automatic generation of the Skeleton Code and the actual

implementation of the Evaluator Agent. Since the content of

the exchanged messages between agents is not of importance

to performance, the resulting agent is restricted to observe

and record designated events, including food (or prey) consumption, trap collision, reproduction and unknown situations.

The only burden assigned to the developer was to modify

the original code in order to trigger new events in the above

situations.

Two series of experiments were conducted in order to asses

performance issues related to the behavior of the animats with

respect to the classification mechanism and the environmental

variety, respectively. In the first series of experiments, for certain environmental parameters, the impact of the classification

mechanism to the system efficiency was examined. For varying

values of the employed genetic algorithm invocation step (in

the range of [50,500]), each of the selected simple metrics

was measured. After the fuzzification and aggregation, it was

determined that the highest system efficiency is a result of the

genetic alogirhm step that equals to 100.

The above optimal value was then provided to the second

series of experiments, which focus on prey’s efficiency. A

taxonomy of environments was created, as described in more

detail in [16]. For each of these environments, the preys used

the classification mechanism with the optimal value for the

genetic algorithm step, whereas the predetators either used the

same mechanism or did not use any learning mechanism at

all. It was easily confirmed that Experiment B7 of the original

paper provided the best results for preyEfficiency in total, as

well as for adaptability, durability and robustness metrics.

Overall, the testing of the proposed evaluation methodology

and the Evaluator Agent proved to be useful for carrying out

preformance evaluation tasks for an already developed system.

As expected, the results of this evaluation process adhere to

the experimental findings of the original paper, a fact that was

a principal goal of our system. Moreover, the performance of

the system was analyzed in many more composite metrics,

that were examined and compared in isolation of the rest

249
of the system. This way, the evaluator may easily identify

defective parts or modules of his/her system that affect the

performance of the entire system. The only shortcoming of the

entire experimentation process was the fact that the presence

of a domain expert (in our case, the developer of the system)

was necessary, both for the definition the MRG, as well as the

provision of domain specific code for the Evaluator Agent.

VI. CONCLUSIONS

Driven by the urging need to provide general methodologies

and tools for IS performance evaluation, we presented a novel

methodology and accompanying tools for evaluating the unpredictable, emerging behavior of agents and MAS in dynamic

environments. The proposed methodology provides concise

methodological steps, which the evaluators may follow to

guarantee a standardized and repeatable evaluation procedure.

Focused on the observalble aspects of agent behavior, such as

messages and events, the methodology provides representation

tools for organizing, categorizing and aggregating performance

metrics. Fuzzy sets have been incorporated to represent higherlevel composite metrics that are more meaningful to the human

evaluator.

The Evaluator Agent Framework was also described and

demonstrated. The goal of this framework is to automate

most of the steps of the above methodology, by providing

GUIs for metrics and results manipulation, as well as a code

generating component for automatic monitoring of observable

behaviors at runtime. The produced Evaluator Agent monitors

messages and events, while recording all performance related

information for posterior processing. The Evaluator Agent

Framework was successfully tested on Symbiosis, a MAS

simulation framework for adaptive autonomous agents.

The most important future direction that emerges from

this work is the automation of the MRG definition process.

Currently, domain experts are being mobilized to specify the

simple and composite metrics, the corresponding fuzzy variables, the fuzzy membership values and finally the weights in

the MRG. Some of these tasks may be automated by exploiting

information on previous evaluation efforts and historical data.

It is also feasible to use this information as training datasets

in order to train predefined, domain-specific MRGs. It is

our vision that this effort will initiate the sharing of domain

knowledge, metrics and practices towards more standardized

evaluation procedures.

ACKNOWLEDGMENT

This paper is part of the 03ED735 research project, implemented within the framework of the “Reinforcement Programme of Human Research Manpower” (PENED) and cofinanced by National and Community Funds (25% from the

Greek Ministry of Development-General Secretariat of Research and Technology and 75% from E.U.-European Social

Funding).

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(PERMIS), 13-15 August 2002.

250
Information Management for High Performance 

Autonomous Intelligent Systems

Scott Spetka 

SUNY Institute of Technology 

and ITT Corp. 

Utica and Rome, NY, USA 

scott@cs.sunyit.edu

Scot Tucker 

ITT Corp. 

775 Daedalian Drive 

Rome, NY, USA 

Scot.Tucker@itt.com 

George Ramseyer 

Richard Linderman 

Air Force Research Laboratory 

Rome, NY, USA 

George.Ramseyer@rl.af.mil

Abstract— The publish/subscribe model for information 

management is particularly well suited for use in intelligent 

autonomous systems, ranging from robots to tactical communication 

systems. Information management systems that support pub/sub 

inherently provide a high degree of autonomy for users and 

communicating systems. The pub/sub paradigm can allow 

autonomous intelligent systems to communicate without requiring 

connection to a centralized brokering system. Each system is 

responsible for part of the overall brokering function, which imposes 

a cost for local system resources and proportionally diminishes the 

intelligence that can be expressed by each node. This raises the 

question of whether there exist controls that each intelligent 

autonomous system can use to avoid over-committing resources for 

publication brokering, such that node intelligence is uncompromised. 

Issues which affect autonomy in a pub/sub system that is currently 

under development are addressed. 

Keywords: Quality of Service (QoS), High Performance Computing 

(HPC), Autonomy, Broker, Pub/Sub, Intelligent

I. INTRODUCTION

 The main advantage of publish/subscribe (pub/sub) 

information management systems for autonomous intelligent 

systems is the decoupling of senders and receivers [1]. Instead 

of listening to particular publishers, subscribers can specify 

publications they want to receive by content, based on 

meta-data associated with publications. Similarly, publishers 

submit publications without regard to exactly which 

subscribers will receive them or whether they are currently 

listening for new publications. A broker performs the key 

function of matching publications with subscribers. Brokering 

depends on subscription information from end users 

(subscribers) and knowledge of structure for performing 

matching functions. 

 Optimal brokering for pub/sub information management 

systems that support quality of service (QoS) constraints 

requires simultaneously optimizing parameters that measure a 

range of criteria, including: bandwidth, latency, jitter and 

error rates. The problem is similar to the problem of optimal 

routing in a multicast system, except that routing is 

content-dependent for pub/sub systems. Because of the 

Nondeterministic Polynomial-time (NP) hard nature of the 

problem, intelligent and heuristic approaches to routing for 

multi-constrained QoS multicast systems have been proposed 

[2][3]. 

 The central issue for intelligent autonomous systems 

participating in a pub/sub brokering system while preserving a 

maximum degree of autonomy is the requirement that 

decisions be made based on a global state that can only be 

known through cooperation among participating brokers. But 

this places a requirement on the brokers to share their 

information and also to collect and maintain the information 

regarding remote systems that is needed locally. Requirements 

for storage, bandwidth and processing resources to support 

execution of intelligent algorithms and exchange of state 

information are generally proportional to a loss of autonomy 

due to participation in the system. 

An intelligent autonomous pub/sub system is being developed 

at the Air Force Research Laboratory Information Directorate 

(AFRL/IF) [4]. Issues that affect autonomy and intelligence 

are surfacing in the system, and are being explored. 

 Implementing a distributed brokering service that scales 

well for increasing numbers of publications requires 

dynamically increasing resource usage as the number of 

publications being brokered increases. To meet QoS 

requirements for robustness, variable degrees of redundancy 

can be implemented. In addition, intelligent approaches to 

brokering must be considered, due to the complexity of the 

brokering problem in large systems and QoS constraints. 

Scalability for high performance information management 

systems provides the ability to add resources to handle 

increasing brokering loads on the system. Fairness issues must 

be considered and, when possible, measured, due to varying 

demands for resources to support cooperating brokers for 

pub/sub systems. 

 In the next section intelligent autonomous systems are 

introduced in the context of pub/sub information management 

systems. Then brokering architecture issues are discussed. 

The succeeding two sections present autonomy issues for this 

pub/sub architecture and for other architectures. The interplay 

251
of intelligent brokering and autonomy is discussed for each 

approach. Related pub/sub applications that could also be 

implemented in a high performance computing (HPC) 

environment are described. Ideas for future research are 

presented, and consideration of whether scalable HPC pub/sub 

systems can support a high degree of autonomy for 

participating systems is presented in the conclusion. 

II. INTELLIGENT AUTONOMOUS SYSTEMS

 Intelligent autonomous pub/sub systems rely on brokering 

functions to match publications with subscribers (Figure 1). 

Some of the factors that can affect the performance of 

brokering include: buffer space, queued messages, message 

input rates, bandwidth among brokers and bandwidth between 

brokers and system end users (publishers and subscribers). 

Intelligent algorithms that manage brokering functions predict 

and plan for future processing requirements. 

Fig. 1. Pub/Sub Information Management System 

 Intelligent systems are increasingly characterized by 

higher-level communication with understanding of content. 

Distributed system components receive inputs without 

specifying where those inputs should come from. Publications 

are sent without regard for the exact destinations. Some of the 

problems that system users face in formulating processing 

requests that are brokered by the system are similar to 

problems encountered when formulating requests to submit to 

an Internet search engine. For example, it may take several 

queries at a hardware store’s Web site to find a water heater. 

 In a pub/sub-based system user inputs that specify a 

search for a local minimum or maximum on a surface could 

be published as a service request. There may be several 

subscribing services that could handle the request, depending 

on the degree of precision specified in the publication when 

the request is published. Results from each run can help the 

user to narrow a request, possibly by refining the required 

precision or by varying the search region. 

 In a pub/sub system, several subscriber HPC’s that provide 

the requested service may receive the request and process it. 

In this case, several different responses may be received by 

the client that publishes the request, depending on the 

algorithm used for processing. It may be easy to choose the 

best result from the set of responses, eliminating the need for 

additional requests. After publishing a request for service, the 

user would normally wait for all processing sites to reply, to 

see if a good result has been returned, before sending in any 

further requests to refine the processing. However, lower 

latency can be achieved if an acceptable result is found, even 

if it is not optimal, so that processing can continue. 

 Our pub/sub architecture already implements a similar 

concept for reliable low-latency subscriptions. Subscribers 

always receive three subscriptions, through independent 

brokers. In this case, we know that all three will be identical, 

so we return the first publication received and ignore those 

that arrive later. 

III. BROKERING ARCHITECTURE ISSUES 

 Our brokering architecture is designed to support the Joint 

Battlespace Infosphere (JBI) reference architecture [5]. The 

JBI specifies a common application programming interface 

(CAPI) for the interaction of end users, publishers and 

subscribers, with the system. The brokering function uses 

XML metadata, at least conceptually, to route publications 

from publishers to subscribers. As system load, measured by 

publications passing through the system, increases, demands 

on the brokering services increase. A parallel design provides 

scalability, which allows increasing the number of brokering 

nodes supporting the system. 

 An efficient pub/sub system that can operate across HPC 

systems is desirable, to allow load balancing and support 

processing for jobs that require more processors than may be 

available on any one HPC systems. Computations can also be 

distributed across hybrid HPC platforms when part of the 

computation may be performed more efficiently on particular 

architectures. For example, some parts of HPC codes perform 

better on shared memory systems, like the IBM P5, while 

other parts of the computation can take advantage of message 

passing on Linux clusters. 

 Resources that are contributed by a system to support 

distributed brokering activities on behalf of remote systems 

have the greatest impact on autonomy. Autonomous systems 

may be supporting brokering services even when there are no 

local publishers or subscribers. System performance will be 

degraded due to the support for other communicating systems 

that share the common pub/sub infrastructure. 

252
 Intelligent brokering systems generally require increased 

distributed state information at finer granularity, leading to 

increased storage, bandwidth and processing costs for each 

broker. In parallel broker designs, increased load can cause 

additional brokers to be dynamically added to the system. The 

HPC broker, implemented on a cluster computer, provides a 

capability for offloading processing, thereby enhancing 

autonomy for brokers and improving QoS processing. 

IV. AUTONOMY IN HPC BROKER IMPLEMENTATIONS 

 Autonomy for brokers can be measured in terms of local 

storage, bandwidth and processing costs demanded of 

participating systems and also the degree to which individual 

systems can control their own resources. In an intelligent 

brokering system, cooperating brokers can offload work to 

other brokers, thereby improving overall system performance. 

However, forcing work on a broker may impact its ability to 

meet agreed upon QoS requirements. Of course, if the group 

of brokers as a whole agreed to a request for QoS, it would 

less affect the reputation of the underperforming broker. But, 

cooperative negotiations limit autonomy, by moving the 

decision to support a level of QoS for a publisher/subscriber 

from an individual broker to a committee. 

 The main advantage of our HPC brokering system is 

scalability. Within each HPC in our pub/sub environment, 

processing nodes may be dedicated to either brokering or 

other HPC applications. When additional brokering nodes are 

needed, due to increasing demands, in order to meet QoS 

requirements, they can be added at the HPC where the 

additional load will be supported. The decision to assign the 

load to a particular HPC, and whether the assigned processing 

load must be accepted, certainly impacts the autonomy of the 

system. 

 If HPCs make local decisions to voluntarily add brokering 

resources to the local broker pool, other HPCs could maintain 

smaller pools of broker nodes, giving them an unfair 

advantage. However, adding additional intelligence into 

decisions to increase the number of broker nodes increases 

overhead and can ultimately lead to committee decisions to 

allocate additional brokers at a given HPC, again resulting in 

the erosion of autonomy for the HPC which must contribute 

resources. 

 Defining and measuring autonomy for brokers in an 

intelligent Pub/Sub system is the key to providing QoS 

controls and assurance. In our HPC pub/sub implementation, 

increased communication requirements are gracefully 

supported by gradually reducing available processing 

resources to maintain an appropriate level of communications 

support for applications where processing is distributed across 

HPC systems. Figure 2 shows four HPC centers sharing 

resources to provide an execution environment for three 

parallel programs. One of the programs is performing digital 

signal processing, another is performing cryptanalysis and 

another is executing the Modtran atmospheric analysis 

program. All three applications depend on the pub/sub system, 

which is shown spanning all four HPC’s, for their 

communications needs. Each of the HPC centers is making 

some processors available for use by the pub/sub system in 

supporting system-wide communications. 

V. AUTONOMY IN OTHER BROKER 

IMPLEMENTATIONS 

 Peer-to-peer networks can be used to implement pub/sub, 

but they naturally infringe upon the autonomy of workstations 

participating in distribution of messages. Increased activity for 

brokering on behalf of publication streams that pass through a 

peer system which is neither their origination nor their 

destination impose a load that may not be particularly 

welcome. The more intelligent the brokering scheme, the 

more processing and storage overhead are imposed on the 

cooperating peer. Similar concerns for autonomy have been 

studied for mobile peer-to-peer networks [6]. 

 Serving as a broker for an open peer-to-peer system could 

also have implications for autonomy such as the loss of ability 

to filter messages based on content. Administrators may be 

responsible for transporting messages without ever approving 

of the users sending them or of the types of messages that they 

are sending. The Freenet [7] is an example of a dissemination 

system that is not exactly a pub/sub system. Participating 

Freenet sites must relinquish some of their control, especially 

over content. In the Freenet, "Users contribute to the network 

by giving bandwidth and a portion of their hard drive (called 

the 'data store') for storing files." Part of the mechanism which 

ensures the privacy of Freenet users is based on encrypting 

messages that are routed through the Freenet.. 

Fig. 2. Distributed Broker Architecture for HPC

253
 Some distributed broker architectures implement 

agent-based approaches. These approaches usually assume 

that agents can be decoupled from the entities that they 

represent. However, as in the peer-to-peer case, increases in 

processing, storage and communication, associated with 

increasingly intelligent algorithms, reduce the autonomy of 

participating systems that support brokering functions. 

Enhancing autonomy for perceptive middleware and 

intelligent agents is considered by Dimakic [8]. 

VI. RELATED WORK 

 There is a lot of work on QoS in pub/sub systems, but most 

of it pays little attention to autonomy issues. The SIENA 

publish/subscribe event notification service [9] is dynamically 

reconfigurable to adapt to the processing requirements of 

brokers using feedback from the on-line evaluation of 

performance models. SIENA routers can be dynamically 

added when they are needed, and routing functions can be 

redistributed. The idea is similar to our approach to scaling, 

explained above. 

 The Object Management Group (OMG) [10] Distributed 

Data Service for Real-Time Systems (DDS) standard [11] is 

an open international middleware standard directly addressing 

publish-subscribe communications for real-time and 

embedded systems. The DDS standard has been partially 

implemented with The Ace Orb (TAO) by several companies, 

including; Object Computing, Inc. [12], Real-Rime 

Innovations, Inc. [13], Prism Technologies, Inc. [14]. While 

autonomy is not a primary consideration for DDS, it places 

content filtering functions close to the end uses and brokers 

based on "topics". Users subscribe and publish to topics. 

Brokering topics minimizes the need for intelligent brokering, 

but increases communication costs for publications in topics 

which are filtered when they arrive at the subscriber. 

VII. FUTURE RESEARCH

 Distributed architectures afford the opportunity to assign 

brokering for incoming subscriptions fairly among 

participating brokers. In systems where acceleration 

techniques are used to enhance brokering services, it may be 

both fair and efficient to concentrate new subscriptions for 

implementation at a single broker, but assign batches of new 

subscriptions to brokers in a round robin manner. 

 This approach would be effective in systems where fieldprogrammable gate arrays (FPGAs) are used to support 

brokering. Since it is expensive to synthesize and load a new 

FPGA design, the cost should be shared evenly among all 

brokers. It should have a minimal overall effect on publication 

throughput rates during update cycles, when enough new 

subscriptions have been received to warrant the cost of 

rebuilding the FPGA. 

 Over a longer time frame, our intelligent autonomous 

pub/sub-based system will need to implement a new paradigm 

for distributed computing that goes beyond SOAP [15] and 

Grid [16] protocols currently implemented for distributed 

computing. All routing in our system will intelligently find 

dynamically changing destinations for services that may help 

to find a solution to a problem, similar to the way that humans 

solve problems today. 

VII. CONCLUSION 

 Our HPC cluster broker architecture shows that autonomy 

and scalability share similar characteristics, making scalable 

HPC architectures appear to be a good approach to implement 

autonomous pub/sub information management systems. The 

more brokers we have, the less they have to cooperate. In 

general, when functions are bound to particular locations, it 

limits autonomy by making it difficult to decide locally that a 

service should migrate to another system, to recover local 

resources. Scalability assures that additional processing 

resources can be used effectively and that applications are 

designed with component granularity that supports component 

migration. 

 We have shown that approaches to autonomy are feasible 

for pub/sub information management systems. More 

intelligence requires more knowledge of what is happening at 

remote brokers, loads on specific queues, etc. Scaling the 

brokering support provides the needed resources to support 

increasing intelligence in systems. Although this architecture 

is proven for general-purpose information management 

systems, we believe that it is well suited to support 

information management functions in other areas of 

autonomous intelligent distributed systems as well. 

REFERENCES 

[1] Combs, V., Linderman, M., "A Jini-Based Publish and Subscribe 

Capability", Proceedings of SPIE -- Volume 4863, Java/Jini 

Technologies and High-Performance Pervasive Computing, June 

2002, pp. 59-69. 

[2] Yuan, Xin; Liu, Xingming; "Heuristic Algorithms for Multi-constrained 

Quality of Service Routing" INFOCOM 2001. Twentieth Annual 

Joint Conference of the IEEE Computer and Communications 

Societies. Proceedings. IEEE Volume 2, 22-26 April 2001 Page(s):844 - 

853 vol.2 

[3] Wang, Junwei; Wang,Xingwei; Huang, Min, "Hybrid Intelligent 

QoS Multicast Routing Algorithm in NGI", Proceedings of the Sixth 

International Conference on Parallel and Distributed Computing, Applications 

and Technologies (PDCAT'05) 

[4] Madhavan , R., Messina,I, E., “Performance Metrics for Intelligent 

Systems (PerMIS) 2006 Workshop: Summary and Review”, Applied Imagery 

Pattern Recognition (AIPR) Workshop: Theory and Application of 

Model-based Image Analysis, Cosmos Club, Washington DC, October 11-13, 

2006. 

254
[5] Linderman, M., Combs, V.T., Hillman, R.G., Muccio, M.T., McKeel, 

R.W., "Joint Battlespace Inforphere (JBI): Information Management in a 

Netcentric Environment, AFRL-IF-RS-TR-2006-178, May 2006. 

[6] Jari, Veijalainen, "Autonomy, Heterogeneity and Trust in Mobile P2P 

Environments", International Conference on Multimedia and Ubiquitous 

Engineering, 2007. MUE '07. April 2007 Page(s):41 - 47 

[7] The Freenet Project - http://freenetproject.org/ 

[8] Dimakis, N.; Soldatos, J.; Polymenakos, L.; Schenk, M.; Pfirrmann, 

U.; Burkle, A.; "Perceptive Middleware and Intelligent Agents Enhancing 

Service Autonomy in Smart Spaces" IEEE/WIC/ACM International 

Conference on Intelligent Agent Technology, 2006. IAT '06. Dec. 2006 

Page(s):276 - 283 

[9] Caporuscio, M., Di Marco, A., Inverardi, P., "Run-time Performance 

Management of the Siena publish/subscribe Middleware", Proceedings of the 

5th international workshop on Software and performance WOSP '05, July 

2005 

[10] Object Management Group - omg.org 

[11] Data-Distribution Service for Real-Time Systems (DDS) - 

http://portals.omg.org/dds 

[121] Object Computing, Inc., http://www.ociweb.com/products/dds 

[13]Real-Rime Innovations, Inc., 

http://www.rti.com/products/data_distribution/dds_leader.html

[14] Prism Technologies, Inc. http://www.prismtechnologiesinc.com

[15] http://www.w3.org/TR/soap12-part0/

[16] http://www.ogf.org/ 

255
Efficient Monte Carlo Computation of Fisher

Information Matrix using Prior Information

Sonjoy Das

University of Southern California

Los Angeles, California, USA

Sonjoy.Das@usc.edu

James C. Spall

The Johns Hopkins University

Laurel, Maryland, USA

james.spall@jhuapl.edu

Roger Ghanem

University of Southern California

Los Angeles, California, USA

ghanem@usc.edu

Abstract—The Fisher information matrix (FIM) is a critical

quantity in several aspects of mathematical modeling, including

input selection, model selection, and confidence region calculation. For example, the determinant of the FIM is the main

performance metric for choosing input values in a scientific

experiment with the aims of achieving the most accurate resulting

parameter estimates in a mathematical model. However, analytical determination of the FIM in a general setting, especially

in nonlinear models, may be difficult or almost impossible due

to intractable modeling requirements and/or intractable highdimensional integration.

To circumvent these difficulties, a Monte Carlo (MC)

simulation-based technique, resampling algorithm, based on the

values of log-likelihood function or its exact stochastic gradient

computed by using a set of pseudo data vectors, is usually

recommended. This paper proposes an extension of the current

algorithm in order to enhance the statistical characteristics of

the estimator of the FIM. This modified algorithm is particularly

useful in those cases where the FIM has a structure with some

elements being analytically known from prior information and

the others being unknown. The estimator of the FIM, obtained

by using the proposed algorithm, simultaneously preserves the

analytically known elements and reduces the variances of the

estimators of the unknown elements by capitalizing on the

information contained in the known elements.

Keywords: Fisher information matrix, Monte Carlo simulation.

I. INTRODUCTION AND MOTIVATING FACTORS

The precision matrix, a measure of accuracy of the estimates

(based on a set of input values) of model parameters (to be

denoted by θ1, · · · , θp) of a scientific model, plays a key role in

the field of optimal design [1, Chapter 17] in which the input

values to the model are selected such that θ = [θ1, · · · , θp]

T

is estimated with maximum possible accuracy. Here, the

superscript, T, is transpose operator. The Fisher information

matrix (FIM) is often chosen as the precision matrix in

the field of experimental design involving nonlinear models

and, the determinant of the FIM is the most popularly used

performance measure in this context. In particular, optimal

design is determined as a set of inputs to the model by

maximizing the determinant of the FIM over all possible

inputs. Subsequently, the model parameter, θ, is updated based

on the resulting set of optimal inputs.

However, the analytical determination of the FIM may be

a formidable undertaking in a general setting, specially in

nonlinear models, due to intractable modeling requirements

and/or intractable high-dimensional integration. To avoid this

analytical problem, a computational technique based on Monte

Carlo (MC) simulation technique, called resampling approach

[1, Section 13.3.5], [2], may be employed to estimate the FIM.

There may be also instances in practice when some elements

of the FIM are analytically known from prior information

while the other elements are unknown (and need to be

estimated). In a recent work [3], a FIM of size 22 × 22

was observed to have the structure as shown in Fig. 1.

1 4 8 12 16 20 22

1

4

8

12

16

20

22

Known elements = 54

Fig. 1. Fisher information matrix with

known elements as marked; void part consists of unknown elements.

In such cases, the

above resampling

approach, however,

still yields the full

FIM without taking

any advantage of the

information contained

in the analytically

known elements. The

prior information

related to the known

elements of FIM is not

incorporated while employing this algorithm for estimation of

the unknown elements. The resampling based estimates of the

known elements are also “wasted” because these estimates

are simply replaced by the analytically known elements. The

issue yet to be examined is whether there is a way of focusing

the averaging process (required in the resampling algorithm)

— on the elements of interest (unknown elements that need to

be estimated) — that is more effective than simply extracting

the estimates of those elements from the full FIM estimated

by employing the existing resampling algorithm.

The current work presents a modified and improved (in

the sense of variance reduction) version of the resampling

approach for estimating the unknown elements of the FIM

by “borrowing” the information contained in the analytically

known elements.

II. FISHER INFORMATION MATRIX: DEFINITION AND

NOTATION

Consider a set of n random data vector (to be treated as

column vector) {Z1, · · · , Zn} and stack them in Zn, i.e.,

256
Zn = [Z

T

1

, · · · , Z

T

n ]

T

. Let the multivariate joint probability

density or mass (or hybrid density/mass) function (pdf) of

Zn be denoted by pZn(·|θ) that is parameterized by θ.

The likelihood function of θ is then given by `(θ|Zn) =

pZn(Zn|θ) and the associated log-likelihood function, L, by

L(θ|Zn) ≡ ln `(θ|Zn).

Let us define the p×1 gradient vector, g, of L by g(θ|Zn) =

∂L(θ|Zn)/∂θ and the p×p Hessian matrix, H, by H(θ|Zn) =

∂

2L(θ|Zn)/∂θ ∂θ

T

. Then, the p × p FIM, Fn(θ), is defined

[1, Section 13.3.2] as follows,

Fn(θ) ≡ E

£

g(θ|Zn) · g

T

(θ|Zn)

 ̄

 ̄ θ

¤

= −E [H(θ|Zn)|θ] ,

(1)

provided that the derivatives and expectation (the expectation

operator, E, is with respect to the probability measure of Zn)

exist. The equality ‘=’ in (1) is followed [1, p. 352 − 353]

by assuming that L is twice differentiable with respect to

θ and the regularity conditions [4, Section 3.4.2] hold for

the likelihood function, `. The Hessian-based form above is

more amenable to the practical computation for FIM than the

gradient-based form that is used for defining the FIM.

III. CURRENT RESAMPLING ALGORITM — NO USE OF

PRIOR INFORMATION

The current resampling approach is based on producing a

set of large (say N) number of Hessian estimates from either

the values of the log-likelihood function or (if available) its

exact stochastic gradient both of which, in turn, are computed

from a set of pseudo data vector, {Zpseudo(1), · · · , Zpseudo(N)},

with each Zpseudo(i), i = 1, · · · , N, being digitally simulated

from pZn(·|θ) and statistically independent of each other. The

set of pseudo data vector acts as a proxy for the observed data

set in the resampling algorithm. The average of the negative

of these Hessian estimates is reported as an estimate of the

Fn(θ).

For i-th pseudo data, let the k-th estimate of the Hessian matrix, H(θ|Zpseudo(i)), in the resampling algorithm, be denoted

by Hˆ

(i)

k

. Then, Hˆ

(i)

k

, as per resampling scheme, is computed

as [2],

Hˆ

(i)

k =

1

2







δG

(i)

k

2 c

h

∆

−1

k1

,· · ·,∆

−1

kpi

+

Ã

δG

(i)

k

2 c

h

∆

−1

k1

,· · ·,∆

−1

kpi

!T







, (2)

in which c > 0 is a small number, δG

(i)

k ≡ G(θ +

c∆k|Zpseudo(i))−G(θ −c∆k|Zpseudo(i)) and the perturbation

vector ∆k = [∆k1, · · · , ∆kp]

T

is a user-generated random

vector statistically independent of Zpseudo(i). The random

variables, ∆k1, · · · , ∆kp, are mean-zero and statistically independent and, also the inverse moments, E[|1/∆km|], m =

1, · · · , p, are finite.

The symmetrizing operation (the multiplier 1/2 and the

indicated sum) as shown in (2) is useful in optimization

problems to compute a symmetric estimate of the Hessian

matrix with finite samples [2]. This also maintains a symmetric

estimate of Fn(θ), which itself is a symmetric matrix.

Depending on the setting, G(·|Zpseudo(i)), as required in

δG

(i)

k

, represents the k-th direct measurement or approximation of the gradient vector, g(·|Zpseudo(i)). If the direct measurement or computation of g is feasible, G(θ ±

c∆k|Zpseudo(i)) represent the direct k-th measurements of

g(·|Zpseudo(i)) at θ ±c∆k. Otherwise, G(θ ±c∆k|Zpseudo(i))

represents the k-th approximation of g(θ ± c∆k|Zpseudo(i))

based on the values of L(·|Zpseudo(i)).

If the direct measurements or computations of g are not

feasible, G in (2) can be computed by using the classical

finite-difference (FD) technique [1, Section 6.3] or the simultaneous perturbation (SP) gradient approximation technique [5],

[1, Section 7.2] from the values of L(·|Zpseudo(i)). For the

computation of gradient approximation based on the values

of L, there are advantages to using one-sided [1, p. 199] SP

gradient approximation (relative to the standard two-sided SP

gradient approximation) in order to reduce the total number of

function measurements or evaluations for L. The SP technique

for gradient approximation is quite useful when p is large

and usually superior to FD technique when the objective is to

estimate Fn(θ) by employing the resampling algorithm. The

formula for the one-sided gradient approximation using SP

technique is given by,

G(1)(θ±c∆k|Zpseudo(i))= (1/c ̃)

h

L(θ+ ̃c∆ ̃

k±c∆k|Zpseudo(i))

−L(θ ± c∆k|Zpseudo(i)) i







∆ ̃ −1

k1

.

.

.

∆ ̃ −1

kp





 , (3)

in which superscript, (1), in G(1) indicates that it is one-sided

gradient approximation (G = G(1)), c > ̃ 0 is a small number

and ∆ ̃

k = [∆ ̃k1, · · · , ∆ ̃kp]

T

is generated in the same statistical

manner as ∆k, but otherwise statistically independent of ∆k

and Zpseudo(i). It is usually recommended that c > c  ̃ .

At this stage, let us also formally state that the perturbation vectors, ∆k and ∆ ̃

k, satisfy the following condition,[1,

Chapter 7].

C.1: (Statistical properties of the perturbation vector) The random variables, ∆km (and ∆ ̃

km), k =

1, · · · , N, m = 1, · · · , p, are statistically independent and almost surely (a.s.) uniformly bounded for

all k, m, and, are also mean-zero and symmetrically distributed satisfying E[|1/∆km|] < ∞ (and

E[|1/∆ ̃

km|] < ∞).

Let us also assume that the moments of ∆km and 1/∆km (and,

of ∆ ̃

km and 1/∆ ̃km) up to fifth order exist (this condition

will be used later in Section IV-C). Since ∆km (and ∆ ̃

km)

is symmetrically distributed, 1/∆km (and 1/∆ ̃

km) is also

symmetrically distributed implying that,

I: (Statistical properties implied by C.1) All the

odd moments of ∆km and 1/∆km (and of ∆ ̃

km

and 1/∆ ̃

km) up to fifth order are zeros, E[(∆km)

q

]

= 0 and E[(1/∆km)

q

] = 0 (E[(∆ ̃

km)

q

] = 0 and

E[(1/∆ ̃

km)

q

] = 0) , q = 1, 3, 5.

257
The random vectors, ∆k (and ∆ ̃

k), are also independent

across k. The random variables, ∆k1, · · · , ∆kp (and ∆ ̃

k1, · · · ,

∆ ̃

kp), can also be chosen identically distributed. In fact,

independent and identically distributed (i.i.d.) (across both k

and m) mean-zero random variable satisfying C.1 is a perfectly

valid choice for ∆km (and ∆ ̃

km). In particular, Bernoulli ±1

random variable for ∆km (and ∆ ̃

km) is a valid — but not

the necessary — choice among other probability distributions

satisfying C.1.

The current resampling algorithm is schematically shown

in Fig. 2. As shown in this figure, it is preferred to generate

(1) Zpseudo

Pseudo data

Input

H1

^ ( )1 ^H( )

M

1

, . . . . .,

^H1

( ) N ^H

( )

M

N

(1) H

−

( ) H

− N

.

.

.

.

.

.

.

.

.

−H( ) i Negative average of

Zpseudo( ) N , . . . . .,

Hessian estimates H i

k

^ ( ) Average

H

^

k

( )i of =i H

− ( )

FIM estimate,

FM,N

−

Fig. 2. Schematic model for forming estimate, F ̄M,N , of Fn(θ); adapted

from [2].

several Hessian estimates by generating more than one (say,

M > 1) perturbation vectors, ∆1, · · · , ∆M, for each pseudo

data vector, Zpseudo(i), if the pseudo data vectors are expensive

to simulate relative to the Hessian estimate. However, it should

be noted that M = 1 has certain optimality properties [2] and

it is assumed throughout this work that for each pseudo data

vector, only one perturbation vector is generated and, thus,

only one Hessian estimate is computed. The current work

can, however, be readily extended to the case when M > 1.

Therefore, from now on, the index of the pseudo data vector

will be changed from i to k. Consequently, the pseudo data

vector will be denoted by Zpseudo(k), k = 1, · · · , N, the difference in gradient and the Hessian estimate in (2), respectively,

will simply be denoted by δGk and Hˆ

k, k = 1, · · · , N, and

the notation of the one-sided gradient approximation in (3)

will take the form of G(1)(θ ± c∆k|Zpseudo(k)). Finally, the

following simplification of notation for the estimate of Fn(θ)

will also be used from now on,

Fˆ

n ≡ F ̄1,N . (4)

In the next section, the main idea of the current work

with a brief highlight of the relevant theoretical basis is

presented. The proposed scheme, that is similar in some sense

to the one for Jacobian/Hessian estimates presented earlier [6],

modifies and improves the current resampling algorithm by

simultaneously preserving the known elements of the FIM and

yielding better (in the sense of variance reduction) estimators

of the unknown elements.

IV. IMPROVED RESAMPLING ALGORITHM — USING PRIOR

INFORMATION

Let the k-th estimate of Hessian matrix, H(θ|Zpseudo(k)),

per proposed resampling algorithm be denoted by H ̃

k. In

this section, the estimator, H ̃

k, is shown separately for two

different cases: Case 1 :– when only the measurements of

the log-likelihood function, L, are available and, Case 2 :–

when the measurements of the exact gradient vector, g, are

available. To contrast the two cases, the superscript, (L), is

used in H ̃

(L)

k

and Hˆ

(L)

k

to represent the dependence of H ̃

k

and Hˆ

k on measurements of L for Case 1 and, the superscript,

(g), in H ̃

(g)

k

and Hˆ

(g)

k

for Case 2.

A. Additional Notation

Denote the (i, j)-th element of Fn(θ) by Fij (θ) (non-bold

character and suppressing the subscript, n, in the symbolic

notation representing the element of Fn(θ)) for simplification

of notation. Let Ii, i = 1, · · · , p, be the set of column indices

of the known elements of the i-th row of Fn(θ) and I

c

i

be the

complement of Ii. Consider a p × p matrix, F

(given)

n , whose

(i, j)-th element, F

(given)

ij , is defined as follows,

F

(given)

ij =

1⁄2

Fij (θ), if j ∈ Ii

0, if j ∈ I

c

i

, i = 1, · · · , p. (5)

Consider another p × p matrix, Dk, defined by,

Dk = ∆k [∆−1

k1

, · · · , ∆

−1

kp ]. (6)

together with a corresponding matrix, D ̃

k, obtained by replacing all ∆ki in Dk with the corresponding ∆ ̃

ki (note that

Dk is symmetric when the perturbations are i.i.d. Bernoulli

distributed).

B. The Step-by-Step Description of the Proposed Resampling

Algorithm

The new estimate, H ̃

k, is extracted from H ̃k0 that is defined

below separately for Case 1 and Case 2.

Case 1: only the measurements of the log-likelihood function, L, are available,

H ̃

(L)

k0 =Hˆ

(L)

k −

1

2

h

D ̃

T

k

(−F

(given)

n

)Dk+(D ̃

T

k

(−F

(given)

n

)Dk)

T

i

.

(7)

Case 2: measurements of the exact gradient vector, g, are

available,

H ̃

(g)

k0 = Hˆ

(g)

k −

1

2

h

(−F

(given)

n )Dk+((−F

(given)

n )Dk)

T

i

. (8)

The estimates, H ̃

(L)

k

and H ̃

(g)

k

, are readily obtained from,

respectively, H ̃

(L)

k0

in (7) and H ̃

(g)

k0

in (8) by replacing the

(i, j)-th element of H ̃

(L)

k0

and H ̃

(g)

k0 with known values of

−Fij (θ), j ∈ Ii, i = 1, · · · , p. The new estimate, F ̃

n, of

Fn(θ) is then computed by averaging the Hessian estimates

H ̃

k, and taking negative value of the resulting average. For

convenience and, also since the main objective is to estimate

the FIM, the matrix, F ̃

n0, can be first obtained by computing

the (negative) average of the matrices H ̃

k0, and subsequently,

258
replacing the (i, j)-th element of F ̃

n0 with the analytically

known elements, Fij (θ), j ∈ Ii, i = 1, · · · , p, of Fn(θ) would

yield the new estimate, F ̃

n.

The matrices, Hˆ

(L)

k

in (7) or Hˆ

(g)

k

in (8), need to be

computed by employing the existing resampling algorithm that

is based on (2) and Fig. 2. Note that F

(given)

n as shown in the

right-hand-sides of (7) and (8) is known by (5). It must be

noted as well that the random perturbation vectors, ∆k in

Dk and ∆ ̃

k in D ̃k, as required in (7) and (8) must be the

same simulated values of ∆k and ∆ ̃

k used in the existing

resampling algorithm while computing the k-th estimate, Hˆ

(L)

k

or Hˆ

(g)

k

.

Next a summary of the salient steps, required to produce

the estimate, F ̃

n (i.e., F ̃

(L)

n or F ̃

(g)

n with the appropriate

superscript) of Fn(θ) per modified resampling algorithm as

proposed here, is presented below. Figure 3 is a schematic of

the following steps.

Step 0. Initialization: Construct F

(given)

n as defined by (5)

based on the analytically known elements of the

FIM. Determine θ, the sample size (n) and the

number (N) of pseudo data vectors that will be

generated. Determine whether log-likelihood, L(·),

or gradient vector, g(·), will be used to compute

the Hessian estimates, H ̃

k. Pick a small number,

c, (perhaps c = 0.0001) to be used for Hessian

estimation (see (2)) and, if required, another small

number, c ̃ (perhaps c ̃ = 0.00011), for gradient

approximation (see (3)). Set k = 1.

Step 1. At the k-th step perform the following tasks,

a. Generation of pseudo data: Based on θ, generate the k-th pseudo data vector, Zpseudo(k),

by using MC simulation technique.

b. Computation of Hˆ

k: Generate ∆k (and also

∆ ̃

k, if required, for gradient approximation)

by satisfying C.1. Using Zpseudo(k), ∆k or/and

∆ ̃

k, evaluate Hˆk (i.e., Hˆ

(L)

k

or Hˆ

(g)

k

) by using

(2).

c. Computation of Dk and D ̃

k: Use ∆k or/and

∆ ̃

k, as generated in the above step, to construct

Dk or/and D ̃

k as defined in section IV-A.

d. Computation of H ̃

k0: Modify Hˆk as produced

in Step 1b by employing (7) or (8) as appropriate in order to generate H ̃

k0 (i.e., H ̃

(L)

k0

or

H ̃

(g)

k0

).

Step 2. Average of H ̃

k0: Repeat Step 1 until N estimates,

H ̃

k0, are produced. Compute the (negative) mean

of these N estimates. (The standard recursive representation of sample mean can be used here to avoid

the storage of N matrices, H ̃

k0). The resulting

(negative) mean is F ̃

n0.

Step 3. Evaluation of F ̃

n: The new estimate, F ̃n, of Fn(θ)

per modified resampling algorithm is simply obtained by replacing the (i, j)-th element of F ̃

n0 with

the analytically known elements, Fij (θ), j ∈ Ii,

i = 1, · · · , p, of Fn(θ). To avoid the possibility of

having a non-positive semi-definite estimate, it may

be desirable to take the symmetric square root of

the square of the estimate (the sqrtm function in

MATLAB may be useful here).

(1) Zpseudo H1

^

H

^

N

Current

re−sampling

algorithm

H

Hessian

estimates, k

^

Pseudo data

Input

(given)

F

n

Use 

Hessian

estimates, Hk

~

H1

~

HN

~

~Hk Negative average of

.

.

.

.

.

.

.

.

.

Zpseudo( ) N

New FIM

estimate, Fn

~

Fig. 3. Schematic of algorithm for computing the new FIM estimate, F ̃ n.

The new estimator, F ̃

n, is better than Fˆn in the sense that

it would preserve exactly the analytically known elements of

Fn(θ) as well as reduce the variances of the estimators of the

unknown elements of Fn(θ).

C. Theoretical Basis for the Modified Resampling Algorithm

For notational simplification, the subscript ‘pseudo’ in

Zpseudo(k) and the dependence of Z(k) on k would be suppressed (note that Zpseudo(k) is identically distributed across

k). Since, ∆k is usually assumed to be statistically independent across k and an identical condition for ∆ ̃

k is also

assumed, their dependence on k would also be suppressed in

the forthcoming discussion. Let also the (i, j)-th element of

Hˆ

k and H ̃k be, respectively, denoted by Hˆij and H ̃ij with the

appropriate superscript. The two cases as described earlier by

(7) and (8) are considered next in the following two separate

subsections.

1) Case 1 - only the measurements of L are available:

The main objective here is to compare variance of Hˆ

(L)

ij and

variance of H ̃

(L)

ij to show the superiority of H ̃

(L)

k

, which leads

to the superiority of F ̃

(L)

n .

It is assumed here that the gradient estimate is based on

one-sided gradient approximation using SP technique given

by (3). Based on a Taylor expansion, the i-th component of

G(1)(θ|Z), i = 1, · · · , p, that is an approximation of the i-th

component, gi(θ|Z) ≡ ∂L(θ|Z)/∂θi, of g(θ|Z) based on the

values of L(·|Z), can be readily shown to given by,

G

(1)

i

(θ|Z) = L(θ +  ̃c∆ ̃ |Z) − L(θ|Z)

c ̃∆ ̃

i

(9)

=

X

l

gl(θ)

∆ ̃

l

∆ ̃

i

+

1

2

c ̃

X

l,m

Hlm(θ)

∆ ̃ m∆ ̃

l

∆ ̃

i

+

1

6

c ̃

2 X

l,m,s

∂Hlm(θ)

∂θs

∆ ̃

s∆ ̃ m∆ ̃l

∆ ̃

i

, (10)

in which Hlm(θ|Z) ≡ ∂

2L(θ|Z)/∂θl ∂θm is the (l, m)-th

element of H(θ|Z), θ = λ(θ +  ̃c∆ ̃ ) + (1 − λ)θ = θ +  ̃cλ∆ ̃

259
(with λ ∈ [0, 1] being some real number) denotes a point on

the line segment between θ and θ+  ̃c∆ ̃ and, in the expression

after the last equality, the condition on Z is suppressed for

notational clarity and, also the summations are expressed in

abbreviated format where the indices span their respective and

appropriate ranges.

Given Gi(·|Z) ≡ G

(1)

i

(·|Z) by (10), the (i, j)-th element of

Hˆ

(L)

k

can be readily obtained from,

Hˆ

(L)

ij =

1

2

h

Jˆ

(L)

ij + Jˆ

(L)

ji i

, (11)

in which Jˆ

(L)

ij (J is to indicate Jacobian for which the

symmetrizing operation should not be used) and its expression

based on Taylor expansions of the associated terms, Gi(θ ±

c∆|Z), are shown below (A third order Taylor expansion is

applied on the first group of summand of Gi(θ ± c∆|Z) ≡

G

(1)

i

(θ ±c∆|Z), that is obtained by replacing θ with θ ±c∆

in (10), and first order Taylor expansions are applied on the

second and third group of summand.),

Jˆ

(L)

ij ≡

Gi(θ + c∆|Z) − Gi(θ − c∆|Z)

2 c ∆j

=

X

l,m

Hlm(θ|Z)

∆m

∆j

∆ ̃

l

∆ ̃

i

+ O∆ ̃ ,∆,Z(c

2

)

+ O∆ ̃ ,∆,Z( ̃c) + O∆ ̃ ,∆,Z( ̃c

2

). (12)

The subscripts in the ‘big-O’ terms, O∆ ̃ ,∆,Z(·), in the above

equation explicitly indicate that they depend on ∆ ̃ , ∆ and

Z. In these random ‘big-O’ terms, the point of evaluation,

θ, is suppressed for notational clarity. By the use of C.1 and

further assumptions on the continuity and uniformly (in k)

boundedness conditions on all the derivatives (up to fourth

order) of L, it can be shown that |O∆ ̃ ,∆,Z(c

2

)/c2| < ∞ almost

surely (a.s.) (a.s. with respect to the joint probability measure

of ∆ ̃ , ∆ and Z) as c −→ 0 and, both |O∆ ̃ ,∆,Z( ̃c)/c ̃| < ∞

a.s. and |O∆ ̃ ,∆,Z( ̃c

2

)/c ̃

2

| < ∞ a.s. as c ̃ −→ 0. The effects

of O∆ ̃ ,∆,Z( ̃c

2

) are not included in O∆ ̃ ,∆,Z( ̃c). The reason

for showing O∆ ̃ ,∆,Z( ̃c) separately in (12) is that this term

vanishes upon expectation because it involves either E[∆ ̃

r] or

E[1/∆ ̃

r], r = 1, · · · , p, both of which are zero by implication

I and rest of the terms appeared in O∆ ̃ ,∆,Z( ̃c) do not depend

on ∆ ̃ . The other terms, O∆ ̃ ,∆,Z(c

2

) and O∆ ̃ ,∆,Z( ̃c

2

), do not

vanish upon expectation. Subsequently, by noting the fact that

∆ ̃ , ∆ and Z are statistically independent of each other and

by using the condition C.1, it can be readily shown that,

E[Jˆ

(L)

ij |θ] = E [Hij (θ|Z)| θ] + O(c

2

) + O( ̃c

2

). (13)

Note that the ‘big-O’ terms, O(c

2

) and O( ̃c

2

), satisfying

|O(c

2

)/c2| < ∞ as c −→ 0 and |O( ̃c

2

)/c ̃

2

| < ∞ as c ̃ −→ 0,

are deterministic unlike the random ‘big-O’ terms in (12). In

the context of FIM, E [Hij (θ|Z)| θ] = −Fij (θ) by (Hessianbased) definition using which (along with the symmetry of

the FIM, Fn(θ)) E[Hˆ

(L)

ij |θ] follows straight from (11)-(13)

as below,

E[Hˆ

(L)

ij |θ] = −Fij (θ) + O(c

2

) + O( ̃c

2

). (14)

The variance of Hˆ

(L)

ij is to be computed next. It is given

by,

var[Hˆ

(L)

ij |θ]=1

4

3

var[Jˆ

(L)

ij |θ]+var[Jˆ

(L)

ji |θ]+2cov[Jˆ

(L)

ij ,Jˆ

(L)

ji |θ]

 ́

. (15)

The expression of a typical variance term, var[Jˆ

(L)

ij |θ], in (15)

would now be determined followed by the deduction of the

expression of covariance term, cov[Jˆ

(L)

ij , Jˆ

(L)

ji |θ].

By the use of (12), it can be shown after some simplification

that,

var[Jˆ

(L)

ij |θ] = X

l,m

alm(i, j)var [Hlm(θ|Z)| θ]

+

X

l,m

lm6=ij

alm(i, j) (E [Hlm(θ|Z)| θ])2

+ O(c

2

) + O( ̃c

2

) + O(c

2

c ̃

2

), (16)

in which alm(i, j) = E[∆2

m/∆2

j

]E[∆ ̃ 2

l

/∆ ̃ 2

i

]. The expression

in (16) is essentially derived by using the mutual independence

of ∆ ̃ , ∆ and Z, the condition C.1 and the implication I.

In addition, it is also assumed that all the combinations

of covariance terms involving Hlm(θ|Z), Hlm,s(θ|Z) and

Hlm,rs(θ|Z), l, m, s, r = 1, · · · , p, exist around θ that indicates the point of evaluation of these functions.

Next, the expression of cov[Jˆ

(L)

ij , Jˆ

(L)

ji |θ], j 6= i, can

be deduced by using identical arguments to the ones that

are used in deriving the expression of var[Jˆ

(L)

ij |θ] above.

Further simplification based on the use of mutual statistical

independence among ∆ ̃ , ∆ and Z and, also on the Hessianbased definition and symmetry of FIM as well as on condition

C.1 and implication I yields the following, j 6= i,

cov[Jˆ

(L)

ij , Jˆ

(L)

ji |θ]

= 2 nvar[ (Hij (θ|Z))| θ] + (E [ (Hij (θ|Z))| θ])2

o

+2E[Hii(θ|Z)Hjj(θ|Z)|θ]−(Fij(θ))2+O(c

2

)+O( ̃c

2

)+O(c

2

c ̃

2

).(17)

Now, the variance of Hˆ

(L)

ij , var[Hˆ

(L)

ij |θ], for j 6= i, can

be readily obtained from (15) by using (16) and (17). Note

that var[Hˆ

(L)

ii |θ] is same as var[Jˆ

(L)

ii |θ] that can be directly

obtained from (16) by replacing j with i. The contributions

of the variance and covariance terms (as appeared in (15)) to

var[Hˆ

(L)

ij |θ] are compared next with the contributions of the

respective variance and covariance terms to var[H ̃

(L)

ij |θ].

Consider the (i, j)-th element of H ̃

k associated with (7) that

is given by,

H ̃

(L)

ij =

1

2

3

J ̃

(L)

ij + J ̃

(L)

ji  ́

, ∀j ∈ I

c

i

, (18)

and

H ̃

(L)

ij = −Fij (θ), ∀j ∈ Ii

. (19)

In (18), J ̃

(L)

ij is defined as,

J ̃

(L)

ij = Jˆ

(L)

ij −

X

l

X

m∈Il

(−Flm(θ))∆m

∆j

∆ ̃

l

∆ ̃

i

, ∀j ∈ I

c

i

. (20)

260
Note that E[

P

l

P

m∈Il

(−Flm(θ))(∆m/∆j )(∆ ̃

l/∆ ̃i)|θ] =

0 in (20), ∀j ∈ I

c

i

, implying that E[J ̃

(L)

ij ] = E[Jˆ

(L)

ij ], ∀j ∈ I

c

i

.

By using this fact along with identical arguments, that are used

in deducing (14), immediately results in, ∀i = 1, · · · , p,

E[H ̃

(L)

ij |θ] = 1⁄2

−Fij (θ) + O(c

2

) + O( ̃c

2

), ∀j ∈ I

c

i

,

−Fij (θ), ∀j ∈ Ii.

(21)

Noticeably, the expressions of the ‘big-O’ terms both in (14)

and in the first equation of (21) are precisely same implying

that E[H ̃

(L)

ij |θ] = E[Hˆ

(L)

ij |θ], ∀j ∈ I

c

i

.

While var[H ̃

(L)

ij |θ] = 0, ∀j ∈ Ii

, by (19) clearly implying

that var[H ̃

(L)

ij |θ] < var[Hˆ

(L)

ij |θ], ∀j ∈ Ii

, the deduction of

expression of var[H ̃

(L)

ij |θ], ∀j ∈ I

c

i

, is the task that will be

considered now. In fact, this is the main result associated

with the variance reduction from prior information available

in terms of the known elements of Fn(θ).

The first step in determining this variance is to note that the

expression of Jˆ

(L)

ij in (12) can be decomposed into two parts

as shown below,

Jˆ

(L)

ij =

X

l





X

m∈Il

Hlm(θ|Z)

∆m

∆j

∆ ̃

l

∆ ̃

i

+

X

m∈I

c

l

Hlm(θ|Z)

∆m

∆j

∆ ̃

l

∆ ̃

i





+ O∆ ̃ ,∆,Z(c

2

) + O∆ ̃ ,∆,Z( ̃c) + O∆ ̃ ,∆,Z( ̃c

2

). (22)

The elements, Hlm(θ|Z), of H(θ|Z) in the right-hand-side

of (22) are not known. However, since by (Hessian-based)

definition E[Hlm(θ|Z)|θ] = −Flm(θ), approximation of the

unknown elements of H(θ|Z) in the right-hand-side of (22),

particularly those elements that correspond to the elements of

the FIM that are known a priori, by the negative of those

elements of Fn(θ) is the primary idea based on which the

modified resampling algorithm is developed. This approximation introduces an error term, elm(θ|Z), that can be defined

by, ∀m ∈ Il, l = 1, · · · , p,

Hlm(θ|Z) = −Flm(θ) + elm(θ|Z), (23)

and this error term satisfies the following two conditions that

directly follow from (23), ∀m ∈ Il, l = 1, · · · , p,

E[elm(θ|Z)|θ] = 0, (24)

var[elm(θ|Z)|θ] = var[Hlm(θ|Z)|θ]. (25)

Also, introduce Xlm, l = 1, · · · , p, as defined below,

Xlm(θ|Z) = 1⁄2

elm(θ|Z), if m ∈ Il,

Hlm(θ|Z), if m ∈ I

c

l

.

(26)

Now, substitution of (23) in (22) results in a known part in

the right-hand-side of (22) involving the analytically known

elements of FIM. This known part is transferred to the lefthand-side of (22) and, consequently, acts as a feedback to

the current resampling algorithm yielding, in the process, an

expression of J ̃

(L)

ij . By making use of (26), it can be shown

that J ̃

(L)

ij can be compactly written as,

J ̃

(L)

ij =

X

l,m

Xlm(θ|Z)

∆m

∆j

∆ ̃

l

∆ ̃

i

+ O∆ ̃ ,∆,Z(c

2

) + O∆ ̃ ,∆,Z( ̃c)

+ O∆ ̃ ,∆,Z( ̃c

2

), ∀j ∈ I

c

i

. (27)

The variance of J ̃

(L)

ij , ∀j ∈ I

c

i

, can be computed by

considering the right-hand-side of (27) in an identical way

as described earlier for Jˆ

(L)

ij . The expression for var[J ̃

(L)

ij |θ],

∀j ∈ I

c

i

, follows readily from (16) by replacing Hlm with Xlm

because of the similarity between (12) and (27). Use of (26)

first and subsequent uses of (24)-(25) on the resulting expression finally yields an expression of var[J ̃

(L)

ij |θ]. Subtracting the

finally yielded expression of var[J ̃

(L)

ij |θ] from (16), it can be

shown by having recourse to the Hessian-based definition of

FIM, E[Hlm(θ|Z) |θ] = −Flm(θ), that, ∀j ∈ I

c

i

, i = 1, · · · , p,

var[Jˆ

(L)

ij |θ] − var[J ̃

(L)

ij |θ] = X

l

X

m∈Il

alm(i, j) (Flm(θ))2

+ O(c

2

) + O( ̃c

2

) + O(c

2

c ̃

2

) > 0. (28)

The inequality above follows from the fact that alm(i, j) =

(E[∆2

m/∆2

j

] E[∆ ̃ 2

l

/∆ ̃ 2

i

]) > 0, l, m = 1, · · · , p, for any given

(i, j) and assuming that at least one of the known elements,

Flm(θ), in (28) is not equal to zero. It must be remarked

that the bias terms, O(c

2

), O( ̃c

2

) and O(c

2

c ̃

2

), can be made

negligibly small by selecting c and c ̃ small enough that are

primarily controlled by users. Note that if ∆1, · · · , ∆p and

∆ ̃

1, · · · , ∆ ̃p are both assumed to be Bernoulli ±1 i.i.d. random

variables, then alm(i, j) turns out to be unity.

At this point it should be already clear that var[H ̃

(L)

ii |θ] <

var[Hˆ

(L)

ii |θ], if j = i ∈ I

c

i

, by (28).

Next step is to compare cov[J ̃

(L)

ij , J ̃

(L)

ji |θ] with

cov[Jˆ

(L)

ij , Jˆ

(L)

ji |θ], j 6= i, ∀j ∈ I

c

i

, in order to conclude

that var[H ̃

(L)

ij |θ] < var[Hˆ

(L)

ij |θ]. As var[J ̃

(L)

ij |θ] is deduced

from var[Jˆ

(L)

ij |θ] by the similarity of the expressions between

(12) and (27) along with other arguments, following identical

line of arguments, the expression of cov[J ̃

(L)

ij , J ̃

(L)

ji |θ] can be

deduced and, finally, by keeping in mind that j ∈ I

c

i

and by

using (26), it can be shown that,

cov[Jˆ

(L)

ij , Jˆ

(L)

ji |θ] − cov[J ̃

(L)

ij , J ̃

(L)

ji |θ]

= 2(E [Hii(θ|Z)Hjj (θ|Z)| θ]−E [Xii(θ|Z)Xjj (θ|Z)| θ])

+ O(c

2

) + O( ̃c

2

) + O(c

2

c ̃

2

), j 6= i, ∀j ∈ I

c

i

. (29)

Note that Xii and Xjj must take the form from one of following four possibilities: (1) eii and ejj , (2) eii and Hjj , (3) Hii

and ejj , (4) Hii and Hjj . While E[Hii(θ|Z)Hjj (θ|Z)|θ] −

E[Xii(θ|Z) Xjj (θ|Z)|θ] is 0 for the fourth possibility, it can

be shown by using (23) and the Hessian-based definition of

FIM that for the other possibilities, this difference is given by

Fii(θ)Fjj (θ) that is greater than 0 since Fn(θ) is positive

definite matrix.

Therefore, using (28) and the fact that

E[Hii(θ|Z)Hjj (θ|Z)|θ] − E[Xii(θ|Z) Xjj (θ|Z)|θ] ≥ 0,

261
that appeared in (29), and also noting that the bias terms,

O(c

2

), O( ̃c

2

) and O(c

2

c ̃

2

), can be made negligibly small

by selecting c and c ̃ small enough, the following can be

concluded immediately from (15) and an identical expression

of var[H ̃

(L)

ij |θ],

var[H ̃

(L)

ij |θ] < var[Hˆ

(L)

ij |θ], i, j = 1, · · · , p.

In deducing the expressions of mean and variance, several

assumptions related to the existences of derivatives of L with

respect to θ and also the existences of expectations of these

derivatives are required as ‘hinted’ earlier sporadically. For a

complete list of assumptions and rigorous derivation of these

expressions, readers are referred to [7].

Since Case 2 is simpler than Case 1 that has already been

considered in full possible detail within the limited space, the

next section simply presents the final and important points for

Case 2 that highlight the fact that the variance of H ̃

(g)

ij is less

than the variance of Hˆ

(g)

ij .

2) Case 2 - measurements of g are available: In this

section, it is assumed that the measurements of exact gradient

vector, g, are available. The (i, j)-th element, Hˆ

(g)

ij , (the

dependence on k is suppressed) of Hˆ

(g)

k

is then given by,

Hˆ

(g)

ij =

1

2

h

Jˆ

(g)

ij + Jˆ

(g)

ji i

, (30)

in which Jˆ

(g)

ij and its expression based on third-order Taylor

expansion of the associated terms, gi(θ ± c∆|Z), are shown

below,

Jˆ

(g)

ij ≡

gi(θ+c∆|Z)−gi(θ−c∆|Z)

2 c ∆j

=

X

l

Hil(θ|Z)

∆l

∆j

+O∆,Z(c

2

). (31)

The expectation of Hˆ

(g)

ij follows straight from (31) by carefully using the identical arguments as described in previous

subsection for E[Hˆ

(L)

ij |θ],

E[Hˆ

(g)

ij |θ] = −Fij (θ) + O(c

2

). (32)

On the other hand, the (i, j)-th element of H ̃

k associated

with (8) is given by,

H ̃

(g)

ij =

1⁄2 1

2

(J ̃

(g)

ij + J ̃

(g)

ji ), ∀j ∈ I

c

i

,

−Fij (θ), ∀j ∈ Ii,

with J ̃

(g)

ij being given by,

J ̃

(g)

ij = Jˆ

(g)

ij −

X

l∈Ii

(−Fil(θ)) ∆l

∆j

, ∀j ∈ I

c

i

. (33)

As shown for H ̃

(L)

ij earlier in Case 1, it can shown in an

identical way that ∀i = 1, · · · , p,

E[H ̃

(g)

ij |θ] = 1⁄2

−Fij (θ) + O(c

2

), ∀j ∈ I

c

i

,

−Fij (θ), ∀j ∈ Ii.

(34)

Again, the expressions of the ‘big-O’ terms both in (32) and

in the first equation of (34) are precisely same implying that

E[H ̃

(g)

ij |θ] = E[Hˆ

(g)

ij |θ], ∀j ∈ I

c

i

.

Next, the difference between var[Hˆ

(g)

ii |θ] and var[H ̃

(g)

ii |θ]

can be deduced by carefully following the identical arguments

as used for Case 1 in the previous subsection and this

difference can be shown to be given by ∀j ∈ I

c

i

,

var[Jˆ

(g)

ij |θ] − var[J ̃

(g)

ij |θ] = X

l∈Ii

bl(j) (Fil(θ))2 + O(c

2

) > 0.

(35)

Here, bl(j) = E[∆2

l

/∆2

j

] > 0, l = 1, · · · , p, and it turns out

to be unity if ∆1, · · · , ∆p is assumed to be Bernoulli ±1 i.i.d.

random variables and, as always, the bias term, O(c

2

), can be

made negligibly small by selecting the user-controlled variable

c small enough.

The difference between the contributing covariance terms

can also be shown to be given by,

cov[Jˆ

(g)

ij ,Jˆ

(g)

ji |θ]−cov[J ̃

(g)

ij ,J ̃

(g)

ji |θ]=E[Hii(θ|Z)Hjj(θ|Z)|θ]

− E [Xii(θ|Z)Xjj (θ|Z)| θ]+O(c

2

), j 6= i, ∀j ∈ I

c

i

. (36)

Therefore, it can be immediately concluded by using identical arguments as used for Case 1 that

var[H ̃

(g)

ij |θ] < var[Hˆ

(g)

ij |θ], i, j = 1, · · · , p.

Finally, since Hˆ

k, k = 1, · · · , N, are statistically independent of each other and H ̃

k, k = 1, · · · , N, are also

statistically independent of each other, it can be concluded

straightway that (i, j)-th element of Fˆ

n = −(1/N)

PN

k=1Hˆ

k

and F ̃

n =−(1/N)

PN

k=1H ̃

k (with appropriate superscript, (L)

or (g)) satisfy the following relation, i, j = 1, · · · , p,

var[F ̃

ij |θ] = var[H ̃

ij |θ]

N

< var[Fˆ

ij |θ] = var[Hˆ

ij |θ]

N

. (37)

Therefore, we conclude this section by stating that the

better estimator, F ̃

n, (vis-a-vis ` the current estimator, Fˆn) as

determined by using the modified resampling algorithm would

preserve the exact analytically known elements of Fn(θ) as

well as reduce the variances of the estimators of the unknown

elements of Fn(θ).

Next section presents an example illustrating the effectiveness of the modified resampling algorithm.

V. NUMERICAL ILLUSTRATIONS AND DISCUSSIONS

Consider independently distributed scalar-valued random

data zi with zi ∼ N(μ, σ2 +ciα), i = 1, · · · , n, in which μ

and (σ

2 +ciα) are, respectively, mean and variance of zi with

ci being some known nonnegative constants and α > 0. Here,

θ is considered as θ = [μ, σ2, α]

T

. This is a simple extension

of an example problem already considered in literature [1,

Example 13.7]. The analytical FIM, Fn(θ), can be readily

determined for this case so that the MC resampling-based

estimates of Fn(θ) can be verified with the analytical FIM. It

can be shown that the analytical FIM is given by,

Fn(θ) =





F11 0 0

0 F22 F33

0 F33 F33



 ,

262
in which F11 =

Pn

i=1(σ

2 + ciα)−1

, F22 = (1/2)Pn

i=1(σ

2 +

ciα)

−2

and F33 = (1/2)Pn

i=1 ci (σ

2+ciα)−2

. Here, the value

of θ, that is used to generate the pseudo data vector (as a proxy

for Zn = [z1, · · · , zn]

T

) and to evaluate Fn(θ), is assumed

to correspond to μ = 0, σ

2 = 1 and α = 1. The values of

ci across i are chosen between 0 and 1, which are generated

by using MATLAB uniform random number generator, rand,

with a given seed (rand(‘state’,0)). Based on n = 30

yields a positive definite Fn(θ) whose eigenvalues are given

by 0.5696, 8.6925 and 20.7496.

To illustrate the effectiveness of the modified MC based

resampling algorithm, it is assumed here that only the upperleft 2 × 2 block of the analytical FIM is known a priori.

Using this known information, both the existing [2] and the

modified resampling algorithm as proposed in this work are

employed to estimate the FIM. For Hessian estimation per (2),

c is considered as 0.0001 and, for gradient-approximation per

(3), c ̃ is considered as 0.00011. Bernoulli ±1 random variable

components are considered to generate both the perturbation

vectors, ∆k and ∆ ̃

k.

The results are summarized in Table I. The mean-squared

error (MSE) of Fˆ

n and F ̃n are first computed; for example,

in the case of Fˆ

n, MSE(Fˆn) is computed as MSE(Fˆ P n) =

ij (Fˆ

ij − Fij (θ))2

. The relative MSE are computed, for

example, in the case of Fˆ

n, as relMSE(Fˆn) = 100 ×

MSE(Fˆ

n)/

P

ij (Fij (θ))2

. The effectiveness of the modified

resampling algorithm can be clearly seen from the fourth

column of the table that shows substantial MSE reduction.

The relative MSE reduction in the table is computed as

100×(MSE(Fˆ

n)−MSE(F ̃n))/MSE(Fˆn). In this column also

shown within parentheses are variance reduction. The relative

variance reduction are computed as 100×(A−B)/A, in which

A =

P

ij var[Fˆ

ij |θ] and B =

P

ij var[F ̃

ij |θ].

It would also be interesting to investigate the effect of the

modified resampling algorithm on the MSE reduction in the

estimators of the unknown elements of the FIM in contrast

to a rather ‘naive approach’ in which the estimates of the

unknown elements are simply extracted from Fˆ

n. To see the

improvement in terms of MSE reduction of the estimators of

the unknown elements of the FIM, the elements corresponding

to the upper-left 2 × 2 block of Fˆ

n obtained from the current

resampling algorithm are simply replaced by the corresponding known analytical elements of the FIM, Fn(θ). Therefore,

the results (shown in Table II) only display the contributions

of the MSE from the estimators of the unknown elements

of FIM. The relative MSE reductions are reported as earlier

by showing 100 × (MSE(Fˆ

n) − MSE(F ̃n))/MSE(Fˆn). This

table clearly reflects the superiority of the modified resampling

algorithm as presented in this work over the current resampling

algorithm. In this table, similar results on variance are also

reported within parentheses.

Table I-II essentially highlight the substantial improvement

of the results (in the sense of MSE reduction as well as

variance reduction) of the modified MC based resampling

algorithm over the results of the current MC based resampling

Error in FIM estimates MSE

relMSE(Fˆ n) relMSE(F ̃ n) (variance)

Cases [MSE(Fˆ n)] [MSE(F ̃ n)] reduction

Case 1 0.3815 % 0.0033 % 99.1239 %

[1.9318] [0.0169] (97.7817 %)

Case 2 0.0533 % 0.0198 % 62.8420 %

[0.2703] [0.1005] (97.5856 %)

TABLE I

MSE AND MSE REDUCTION OF FIM ESTIMATES (N = 2000).

MSE(Fˆ n) MSE(F ̃ n) MSE

(and A): (and B) (variance)

Cases naive approach reduction

Case 1 0.1288 0.1021 20.7235 %

(0.0159) (0.0006) (95.9179 %)

Case 2 0.0885 0.0878 0.7930 %

(0.0030) (0.0002) (94.4222 %)

TABLE II

MSE COMPARISON FOR Fˆ n AND F ̃ n ONLY FOR THE UNKNOWN

ELEMENTS OF Fn(θ) ACCORDING TO F

(GIVEN)

n (N = 100000) (SIMILAR

RESULTS ON VARIANCE ARE REPORTED WITHIN PARENTHESES,

A =

Pp

i=1

P

j∈I

c

i

VAR[Fˆ

ij |θ] AND B =

Pp

i=1

P

j∈I

c

i

VAR[F ̃

ij |θ]).

algorithm. Of course, this degree of improvement is controlled

by the values of the known elements of the analytical FIM;

see (28) and (29) for Case 1 and (35) and (36) for Case 2.

VI. CONCLUSIONS

The present work re-visits the resampling algorithm and

computes the variance of the estimator of an arbitrary element of the FIM. A modification in the existing resampling

algorithm is proposed simultaneously preserving the known

elements of the FIM and improving the statistical characteristics of the estimators of the unknown elements (in the sense

of variance reduction) by utilizing the information available

from the known elements. The numerical example showed

significant improvement of the results (in the sense of MSE

reduction as well as variance reduction) of the proposed

resampling algorithm over that of the current resampling

algorithm.

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Monte Carlo approach using prior information,” Master’s thesis,

Department of Applied Mathematics and Statistics, The Johns

Hopkns University, Baltimore, Maryland, USA, May 2007,

http://dspace.library.jhu.edu/handle/1774.2/32459.

263
Performance of 6D LuM and FFS SLAM — An

Example for Comparison using Grid and Pose

Based Evaluation Methods

Rolf Lakaemper, Andreas Nuchter, Nagesh Adluru, and Longin Jan Latecki  ̈

The focus of this paper is on the performance comparison

of two simultaneous localization and mapping (SLAM) algorithms namely 6D Lu/Milios SLAM and Force Field Simulation (FFS). The two algorithms are applied to a 2D data set.

Although the algorithms generate overall visually comparable

results, they show strengths & weaknesses in different regions

of the generated global maps. The question we address in

this paper is, if different ways of evaluating the performance

of SLAM algorithms project different strengths and how can

the evaluations be useful in selecting an algorithm. We will

compare the performance of the algorithms in different ways,

using grid and pose based quality measures.

I. INTRODUCTION

The simultaneous localization and mapping (SLAM) problem is one of the basic problems in autonomous robot navigation. In the past many solutions of the simultaneous localization and mapping (SLAM) problem have been proposed [16].

However, it is difficult for engineers and developers to choose

a suitable algorithm, due to a lack of true benchmarking

experiments. In the well-known Radish (The Robotics Data

Set Repository) repository [10] algorithms and results as

bitmapped figures are available, but the algorithms have not

been compared against each other. A valuable source for state

of the art performance are competitions like RoboCup [7],

Grand Challenge [4] or the European Land Robotics Trial [8].

However, the aim of such competitions is to evaluate whole

systems under operational conditions, but are not well suited

for the performance evaluation of vital components like perception. This paper presents two methodologies for comparing

the results of state of the art SLAM algorithms, namely 6D

LuM [3] and FFS [11].

LuM and FFS SLAM, treat the mapping problem as an optimization problem, that is a maximal likelihood map learning

method. The algorithms seek to find a configuration ξ

∗

, i.e.,

scan poses that maximizes the likelihood of observations and

could be written as

ξ

∗ = argmax

ξ

F(ξ),

Rolf Lakaemper, Nagesh Adluru and Longin Jan Latecki are with

the Department of Computer and Information Sciences, Temple

University, Philadelphia, U.S.A. lakamper@temple.edu,

nagesh@temple.edu, latecki@temple.edu

Andreas Nuchter is with the Knowledge Systems Research Group of  ̈

the Institute of Computer Science , University of Osnabruck, Germany.  ̈

nuechter@informatik.uni-osnabrueck.de

where F is a function measuring the map quality or likelihood.

This paper is organized as follows: After an overview

of related work, section III will give a brief description of

the compared SLAM algorithms. Section IV presents our

evaluation methodology, followed by the results. Section VI

concludes.

II. RELATED WORK

A. Robot Mapping

State of the art for metric maps are probabilistic methods, where the robot has probabilistic motion models and

perception models. Through integration of these two distributions with a Bayes filter, e.g., Kalman or particle filter,

it is possible to localize the robot. Mapping is often an

extension to this estimation problem. Besides the robot pose,

positions of landmarks are also estimated. Closed loops, i.e.,

revisiting a previously visited area of the environment, play a

special role here: Once detected, they enable the algorithms

to bound the error by deforming the mapped area to yield

a topologically consistent model. For e.g. [15] addresses the

issues in loop-closing problems. Several strategies exist for

solving SLAM. Thrun [16] surveys existing techniques, like,

maximum likelihood estimation, Expectation Maximization,

Extended Kalman Filter, Sparsely Extended Information Filter

SLAM. FastSLAM [18] and its improved variants like [9] use

Rao-Blackwellized particle filters.

SLAM in well-defined, planar indoor environments is considered solved. However, little effort has been spent in comparing the performance evaluation of the SLAM algorithms.

Given vast literature and various successful approaches for

SLAM, such comparative studies are needed to choose appropriate SLAM algorithms for specific applications.

B. Performance Evaluation

Most research in the SLAM community aims at creating

consistent maps. Recently, on the theoretical side of SLAM,

Bailey et al. proves that EKF-SLAM fails in large environments [1] and FastSLAM is inconsistent as a statistical filter:

it always underestimates its own error in the medium to longterm [2] that is it becomes over-confident. Besides focusing

on such consistency issues, little effort has been made in

comparative studies of SLAM algorithms.

Comparing two or more SLAM algorithms needs quantitative performance metrics like robustness, rate of convergence,

quality of the results. Though the metrics used for comparison

264
in this paper are not completely new, the use of them in this

context has not been done before, to the best of our knowledge.

In this paper we mainly focus on the rate of convergence and

quality of results of the two algorithms. They are measured in

two different ways: occupancy grid based and pose based, as

described in section IV.

III. DESCRIPTION OF MAPPING ALGORITHMS

A. FFS

FFS [11] treats map alignment as an optimization problem.

Single scans, possibly gained from different robots, are kept

separately but are superimposed after translation and rotation

to build a global map. The task is to find the optimal rotation

and translation of each scan to minimize a cost function

defined on this map. FFS is a gradient descent algorithm,

motivated by the dynamics of rigid bodies in a force field.

In analogy to Physics, the data points are seen as masses, data

points of a single scan are rigidly connected with massless

rods. The superimposition of scans defines the location of

masses, which induces a force field. In each iteration, FFS

transforms (rotates/translates) all single scans simultaneously

in direction of the gradient defined by the force field under

the constraints of rigid movement; the global map converges

towards a minimum of the overlying potential function, which

is the cost function. FFS is motivated by physics, but is adapted

to the application of map alignment. It differs in the definition

of the potential function, and in the choice of the step width

of the gradient descent. The potential is defined as

P =

1

2

X

pi∈P

X

pj∈P\pi

Z r

∞

m1m2 cos(∠(pi, pj ))

σt

√

2π

e

μ

− z

2

2σ2

t

¶

dz (1)

with r =

p

(X − x)

2 + (Y − y)2,pi = (X, Y ),pj = (x, y) ∈

P, P is the set of all transformed data points.

The potential function measures the probability of visual

correspondence between all pairs of data points based on

distance, direction and visual importance of data points:

in (1) m1, m2 denote the visual importance of two data

points, ∠(pi, pj ) the difference of direction of two points.

Defining the visual importance of points dynamically is a

simple interface to incorporate low or mid level perceptual

properties (e.g. shape properties) into the of the global map

into the optimization process. In contrast to algorithms like

ICP, FFS does not work on optimization of nearest neighbor

correspondences only, but (theoretically) takes into account all

pairs of correspondences. Different techniques built into FFS

drastically reduce the computational complexity.

FFS is steered by two parameters, σt in eq. 1 and the

step width ∆t of the gradient descent. σ steers the influence

of distance between points. Initially set to a big value to

accumulate information from a large neighborhood, it linearly

decreases over the iterations to focus on local properties. The

step width ∆t in the FFS gradient descent is defined by

an exponentially decreasing cooling process, similar to techniques like simulated annealing. Initially set to a high value

it allows for significant transformations to possibly escape

local minima. Decreasing the step enables local adjustment

in combination with a low σt.

To conclude, the basic properties of FFS are

1) Data point correspondences are not made by a hard

decision, but an integral between pairs of points defines

the cost function instead of hard ’nearest neighbor’

correspondences

2) FFS is a gradient approach, it does not commit to an

optimal solution in each iteration step

3) The iteration step towards an optimal solution is steered

by a ’cooling process’, that allows the system to escape

local minima

4) FFS transforms all scans simultaneously thus searching

in 3n space of configurations with n scans.

5) FFS easily incorporates structural similarity modeling

human perception to emphasize/strengthen the correspondences

B. 6D LuM

To solve SLAM, a 6D graph optimization algorithm for

global relaxation based on the method of Lu and Milios [12] is

employed, namely Lu and Milios style SLAM (LUM). Details

of the 6D optimization, i.e., how the matrices have to be filled,

can be found in [3]:

Given a network with n + 1 nodes X0, ..., Xn representing

the poses V0, ..., Vn, and the directed edges Di,j , we aim to

estimate all poses optimally to build a consistent map of the

environment. For simplicity, we make the approximation that

the measurement equation is linear, i.e.

Di,j = Xi − Xj .

An error function is formed such that minimization results in

improved pose estimations:

W =

X

(i,j)

(Di,j − D ̄

i,j )

T C

−1

i,j (Di,j − D ̄

i,j ). (2)

where D ̄

i,j = Di,j + ∆Di,j models random Gaussian noise

added to the unknown exact pose Di,j . The covariance matrices Ci,j describing the pose relations in the network are

computed based on the paired points of the ICP algorithm. The

error function Eq. (2) has a quadratic form and is therefore

solved in closed form by Cholesky decomposition in the order

of O(n

3

) for n poses (n ¿ N). The algorithm optimizes

Eq. (2) gradually by iterating the following five steps [3]:

I) Compute the point correspondences (n closest points)

using a distance threshold (here: 20 cm) for any link

(i, j) in the given graph.

II) Calculate the measurement vector D ̄

ij and its covariance

Cij .

III) From all D ̄

ij and Cij form a linear system GX = B.

IV) Solve for X

V) Update the poses and their covariances.

For this GraphSLAM algorithm the graph is computed as

follows: Given initial pose estimates, we compute the the

number of closest points with a distance threshold (20 cm). If

there are more than 5 point pairs, a link to the the graph is

added.

265
To summarize, the basic properties of 6D LuM are

1) Data point correspondences are made by a hard decision

using ’nearest neighbor’ point correspondences

2) 6D LuM computes the minimum in every iteration

3) 6D LuM transforms all scans simultaneously

4) This GraphSLAM approach has been extended successfully to process 3D scans with representation of robot

poses using 6 degrees of freedom.

In this paper, we process 2D laser range scans with the

6D LuM algorithm, i.e., in the range data the height coordinate

is set 0. In this case the the algorithms shows the behaviour

of the original Lu and Milios [12] GraphSLAM method.

IV. EVALUATION

Evaluation of SLAM algorithms applied to real world data

often faces the problem that ground truth information is hard

to collect. For example, in settings of Search and Rescue

environments, data sets are scanned, which usually have no

exact underlying blue print, due to the nature of the random

spatial placement of (sparse) landmarks and features. Hence

map inherent qualities, like entropy of the distribution of data

points, must be used to infer measures of quality that reflect

their ability to map the real world. In the experiments, we will

compare the performance of 6D LuM and FFS SLAM using

a grid based and a pose based approach. Especially the grid

based approach will be compared to visual inspection, which

in this setting could be seen as a subjective ground truth of

the performance evaluation. The reason for the choice of 6D

LuM and FFS SLAM are the following:

• Both, 6D LuM and FFS SLAM, are state of the art algorithms to simultaneously process multiple scans, which

is needed in settings of multi-robot mapping, which is

a problem that currently has stronger focus in robot

mapping.

• By visual inspection, 6D LuM and FFS perform, intuitively spoken, alike, although differing in details. Evaluation of the algorithms should be able to report this

behavior.

It should be noted that 6D LuM is applied here to a 2D

dataset, to compare it to the currently available version of the

FFS algorithm, which works on 2D scan data only. Hence the

LuM performance is only evaluated on three dimensions.

A. Occupancy Grid Based

Occupancy grids are used to represent the environment by

discretizing the space into grid cells that have probabilistic

occupancy values accumulated by sensor readings. They were

introduced by [13] and are very popular in SLAM community.

Learning occupancy grids is an essential component of the

SLAM process. Once built they can be used to evaluate the

likelihood of the sensor readings and also be used for guiding

the exploration task as they are useful for computing the

information gain of actions.

The likelihood of sensor readings is computed usually

using different sensor models like beam-model, likelihoodfield model or map-correlation model [17]. The information

gain of actions can be computed using change in entropy of

the grid. We use these basic ideas to compare the outcome of

the two SLAM algorithms.

We use the beam penetration model described in [6] to

compute likelihood of the sensor readings. Entropy of the

grid is computed as described in [14]. Once the final map is

obtained we compute the log-likelihood of all sensor readings

with trajectory given out by the algorithm as

L(m, x1:n) = Xn

i=1

X

K

j=1

log(p(zij )|xi, m))

where m is the final occupancy grid, x1:n is the final set of

poses, K is the number of sensor readings at each pose and

p(zij |xi, m) is computed using beam-penetration model as in

[6]. The log-likelihood ranges from −∞ to 0 and the higher

it is the better the algorithm’s output.

The entropy of the map is computed based on the common

independence assumption about the grid cells. Since each grid

cell in the occupancy grid is a binary random variable the

entropy of H(m) is computed as follows as described in [14].

H(m) = −

X

c∈m

p(c) log p(c) + (1 − p(c)) log(1 − p(c))

Since the value of H(m) is not independent of the grid

resolution it’s important either to use same resolution or to

weight the entropy of each cell with its size when comparing

output from two algorithms. The lower the entropy of the

map the better the outcome is. It is important to note that

the entropy of the map and the likelihood-scores are not

completely uncorrelated.

B. Pose Based

The occupancy grid based evaluations are very useful in

the sense that they do not need ”ground truths” to compare

the results. But their memory requirements are proportional

to the dimensionality and size of the environment. The pose

based evaluations have an advantage in terms of memory

requirements but require ”ground truth” data to compare to.

Here we present the technique that can be used to measure the

quality of the output of SLAM algorithm assuming ground

truth trajectory is available. The ground truth data can be

obtained by surveying the environment as done in [5].

The SLAM algorithm gives out a final set of poses x1:n.

Let the set of ground truth poses be x

G

1:n

. Since each pose

in 2D mapping has three components viz. x, y, θ we compute

the average error in each of the components. It is important

that both the output of SLAM algorithm and the ground truth

poses are in the same global frame. This could be done by

rotating and translating the set of poses such that the first

corresponding pose in each set is (0, 0, 0). Once the poses are

in same global frame the average error in each component is

266
computed as:

E(x) = 1

n

Xn

i=1

|xi − x

G

i

|

E(y) = 1

n

Xn

i=1

|yi − y

G

i

|

E(θ) = 1

n

Xn

i=1

cos−1(cos(θi − θ

G

i

))

E(θ) is computed as shown above so that the difference

between the orientations is always between 0 and π.

V. EXPERIMENTS

A. The Data, Visual Inspection

Both algorithms will be evaluated based on their performance on the NIST disaster data set with the same initial set

of poses, see fig. 1. The data set consists of 60 scans and

is especially complicated to map, since the single scans have

minimal overlap only, and no distinct landmarks are present

in the single scans. For this data set, no reliable ground truth

pose data exists. This configuration was gained by random

distortion of a manually gained global map.

Fig. 1. Initial configuration of the NIST data set. The data consists of 60

scans. The scale is in centimeters.

Fig. 2. 6 example scans of the NIST data set. In fig. 1, they can be located

on the left side.

Six sample scans are shown in fig. 2. The final results

of LuM and FFS respectively are shown in fig. 3. Visual

inspection of 3 shows the following properties:

• The overall appearance of both approaches is equal.

• The mapping quality in different details is different: while

FFS performs better in the left half, especially in the top

left quarter, LuM shows a more visually consistent result

in the right half, especially the top right corner.

To test if the evaluation does reflect these properties, we

performed the following tests:

• First, entropy (and additional, likelihood-score) of the

entire global maps (global evaluation) of both algorithms

over all iterations are computed. This should reflect the

behavior of both algorithms to converge towards optimal

values, which should be in the same order of magnitude

for both metrics.

• To check the evaluation of the different quality of

mapping details in different areas, we split the result

maps into four quarters and evaluated separately (regional

evaluation).

In the LuM algorithm, 500 iterations were performed. FFS

stopped automatically after 50 iterations, detecting a condition

of changes in poses below a certain threshold. To compare all

iteration steps, we extended the final result (iteration 50) to

iterations 51 − 500.

B. Grid Based Global Evaluation

The entropies and the likelihood-scores of the maps as

the algorithms progress are shown in the fig. 4(a) and 4(b)

respectively.

Please note the different scale on the iteration axis in

the intervals [1 − 50]and(50 − 500], in the first interval the

iterations increase in units of 1, whereas in the second they

increase in units of 10. This holds for all following figures.

You can see that the entropy decreases non-monotonically in

case of FFS while in case of LuM it tends to be monotonically

decreasing. This is based in the different nature of both

algorithms: FFS is gradient based approach that has a built in

”cooling strategy” for the step width to possibly escape local

minima. In the beginning, FFS takes bigger steps, yielding

a non monotonic behavior in its target function, which is

also visible in the entropy. LuM optimizes its pose in each

iteration, leading to a more smooth behavior, bearing the risk

of being caught in local minima. This is also reflected in the

convergence behavior in terms of speed: since LuM commits to

optimal solutions earlier, it converges faster in the beginning,

slowing down afterwards. FFS is slower (or more positively:

more careful) in the first steps, due to the choice of step

width that causes a jittering behavior. After the step width is

balanced, FFS reaches its optimum very quickly. Interestingly,

in both cases the near optimum value is reached after about

50 iterations.

The entropy score of both algorithms is comparable, which

fulfills the expectations based on the visual inspection.

Similar behavior is observed in the likelihood scores. Hence

the grid based evaluation is able to reflect the properties of

both algorithms in the case of global evaluation.

C. Regional Evaluation

The maps are split into four regions, being North-West,

North-East, South-West, South-East. Only the results for entropies are shown here, the likelihood scores did not lead to

267
Fig. 3. Result of FFS (left) and LuM (right) on NIST data set, initialized as in 1. Evaluated by the overall visual impression, both algorithms perform

comparably. Differences in details can be seen especially in the top left, where FFS performs better, and the top right, where LuM is more precise.

(a) (b)

Fig. 4. (a): The entropy of the map H(m) at various stages of FFS and LuM. (b): The likelihood-score L(m, x1:n) at various stages of the algorithms.

Please note the different scale on the iteration axis in the intervals [1 − 50]and(50 − 500].

additional further information. We expect better results for FFS

in the North-West region, whereas LuM should outperform

FFS in the North-East region, results for the southern regions

should not vastly differ from each other.

The results are presented in fig. 5. fig. 5(a) shows the

behavior for the North-West region of the map while 5(b)

shows for North-East, 5(c) for South-West and 5(d) for SouthEast.

In accord with visual inspection, FFS is evaluated to perform better on the North-West region (fig. 5(a)) while LuM

performs better in other regions. However, looking at the

difference in final values, we can see that they always differ

in ranges between ∼ 30 and ∼ 80 units: (a) ∼ 430 − 480,

(b) ∼ 3200 − 3280, (c) ∼ 278 − 309, (d) ∼ 950 − 1000.

Hence, although the tendency in the north regions is correct,

the comparison to the southern regions, which should yield a

smaller distance in values, does not clearly verify the correct

estimation.

D. Global Pose Based Estimation

Pose based estimation needs a ground truth reference pose,

see section IV-B. Since a ground truth for the NIST data set

is not available, we just use the final set of poses of each

algorithm. This necessarily leads to a graph that converges

to an error of zero. Hence it does not give any information

about the actual mapping quality, but it shows the behavior of

the algorithms in terms of rate of convergence. Fig. 6 shows

the behavior of the algorithms using error-metrics presented

in section IV-B.

With respect to path to convergence, the pose based evaluation also shows the same properties of LuM and FFS as the

grid based: LuM is ”more monotonic”, while FFS has jittering

behavior. Interestingly the pose based evaluation shows FFS

converging faster, which is in contrast to the result using grid

based evaluation. While reasons for this different result will

be topic of future discussion, it again shows that the choice of

evaluation method has an influence on the property description

of the algorithms.

VI. CONCLUSIONS AND FUTURE WORK

This paper has presented a performance evaluation of two

simultaneous localization and mapping (SLAM) algorithms

namely 6D Lu/Milios SLAM (6D LUM) and Force Field

Simulation (FFS). These two algorithms have been applied

to a 2D data set, provides by NIST. The results have been

compared using two different metrics, i.e., an occupancy grid

268
(a) (b)

(c) (d)

Fig. 5. (a): H(m) for North-West (top-left quadrant) region of m. (b): H(m) for North-East (top-right quadrant). (c): For South-West (bottom-left). (d):

For South-East (bottom-right)

(a) (b) (c)

Fig. 6. (a): E(x) for FFS and LuM. (b): E(y). (c): E(θ) for FFS and LuM. The errors E(x) and E(y) are given in meters, E(θ) is given in radians.

based method and a pose based method. In addition these

metrics have checked by visual inspection for plausibility. 6D

LUM and FFS show similar performances on the data set

considered in this paper.

Needless to say a lot of work remains to be done. The two

algorithms have been on one data set. However, in robotic

exploration task the environment is the greatest element of

uncertainty. Mapping algorithms might fail in certain environments. In future work we plan to benchmark mapping

algorithms using more suitable standardized tests and evaluate

on automatically generated test cases. The grid and pose based

evaluation methods will be used for these evaluations.

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270
Smart Assembly: Industry Needs and Challenges

John A. Slotwinski, Ph.D. 

National Institute of Standards 

and Technology 

100 Bureau Drive, Mail Stop 8200 

Gaithersburg, MD USA 

john.slotwinski@nist.gov

Robert B. Tilove, Ph.D. 

General Motors 

30500 Mound Road 

Warren, MI, USA 

robert.tilove@gm.com 

Abstract— In recent years globalization has radically changed the 

nature of manufacturing (including manufacturing engineering), with 

an increasing emphasis on the management of complex, dynamic, 

interconnected supply chains. Manufacturing has become 

information and knowledge intensive, requiring the sharing of 

information accurately, inexpensively, and seamlessly throughout the 

extended enterprise and supply chain. Vertical integration is 

declining as a competitive advantage, and Original Equipment 

Manufacturers (OEMs) are instead focusing on managing core 

technologies and critical assets, emphasizing systems integration, 

assembly, service and marketing. To an increasing extent, part 

fabrication is globally outsourced, but there remains a business case 

for placing final assembly close to the customer. 

 

“Smart Assembly” is about re-inventing assembly processes 

(engineering and operations) to succeed in this new environment. 

Smart Assembly may be a key aspect of future, thriving 

manufacturing enterprises. 

General Motors (GM), in collaboration with the National Institute of 

Standards and Technology’s (NIST) Manufacturing Engineering 

Laboratory (MEL), is developing a broad industry definition and 

vision for Smart Assembly, and is beginning to develop the 

technology and business process “roadmaps” that will provide a 

framework to focus and prioritize both current and future research 

and development (R&D) in this area. 

In this paper, we will: 

• Present a high-level business case for the importance of 

manufacturing in general, and Smart Assembly in particular. 

• Present a working definition of Smart Assembly, and a vision of 

what Smart Assembly might look like in the future 

• Describe efforts to increase awareness of Smart Assembly, through 

the creation of a smart assembly working group, which is hoping to 

refine the vision, scope, business case scenarios and roadmaps for 

what we hope will ultimately be a National Smart Assembly activity. 

• Describe how Smart Assembly is being considered in the context of 

MEL’s strategic planning. 

Keywords: Assembly, Assembly Processes, Intelligent 

Manufacturing, Smart Assembly

I. INTRODUCTION

The United States Council for Automotive Research 

(USCAR) is the umbrella organization of Chrysler, Ford and 

General Motors, which was formed in 1992 to further 

strengthen the technology base of the domestic auto industry 

through cooperative research. 

On December 9, 2004, USCAR and the U.S. Department of 

Commerce’s Technology Administration, through the 

National Institute of Standards and Technology (NIST), 

announced a new partnership to facilitate technological 

research and technology policy analysis focused on improving 

the manufacturing competitiveness of the U.S. automotive 

industry. Since then, the authors have been collaborating on a 

specific USCAR project related to the interactive modeling of 

assembly operations involving flexible parts such as hoses and 

cables. 

In an effort to identify opportunities for expanding the 

collaborative research portfolio between General Motors 

(GM) Manufacturing Systems Research Laboratory (MSR) 

and NIST (perhaps, but not necessarily under the USCAR 

umbrella), the second author visited NIST’s Manufacturing 

Engineering Laboratory (MEL) in 2005 to present a GM 

Research and Development (R&D) perspective on Virtual 

Manufacturing, and to gain a better understanding of MEL’s 

mission, capabilities, and current projects. The primary 

observations/conclusions from this visit were: 

• MEL technical capabilities in information technologies, 

metrology (including sensing & perception), controls, 

interoperability, and standards are world class. 

• Current MEL programs of most relevance to MSR appeared 

to be Intelligent Control of Mobility Systems, Manufacturing 

Interoperability, and Smart Machining Systems. 

• While these activities exhibit significant technical synergies 

with MSR interests in next generation automotive assembly 

systems and technologies, with a few notable exceptions (e.g. 

Virtual Manufacturing Environments, Next Generation 

Robots), the specific applications and context for the work at 

MEL were considerably different (military vehicles, product 

design & engineering, and machining). 

• MEL researchers and management expressed a strong 

interest in better understanding industry needs in “smart 

assembly systems and technologies” in relation to their 

271
mission and capabilities, and in further exploring 

opportunities for re-aligning and/or focusing their work to 

better address these needs. 

The second author was invited to join the Manufacturing 

Engineering Laboratory at NIST for a six month appointment 

as a Visiting Scientist to (1) produce a review paper defining 

the state of the art and industry needs in “Smart Assembly”, 

and (2) initiate the development and documentation of a 

conceptual framework, including information models and 

architecture, for “Smart Assembly”, working in collaboration 

with NIST scientists in three MEL divisions (Manufacturing 

Metrology, Intelligent Systems, and Manufacturing Systems 

Integrartion). GM R&D approved a Domestic Temporary 

Assignment for this purpose from September 2006 – February 

2007. This paper, and other documents referenced within, 

comprise the key deliverables and results of this special 

assignment. 

The remainder of this paper is organized as follows: 

• First, we present a view of manufacturing in today’s “flat 

world” environment. 

• Second, we review the vision and industry needs in “Smart 

Assembly”. This material is drawn primarily from a 

workshop conducted at NIST in October 2006. 

• Finally, we present an “Grand Challenge,” a visionary 

example of what might be accomplished in practice should 

certain aspects of smart assembly be realized. 

Taken together, these sections provide the starting point for 

the development and documentation of information models 

and architectures for “Smart Assembly.” 

II. MANUFACTURING IN THE “FLAT WORLD” 

Manufacturing in today’s “flat world” is network-centric, and 

employs dynamic, complex, interconnected supply chains. It 

is information and knowledge intensive, and requires the 

capability to share information accurately, inexpensively, and 

seamlessly. Original Equipment Manufacturers (OEMs) are 

focusing on core technologies and critical assets, and are 

transitioning their focus towards systems integration, 

assembly, service, and marketing, as vertical integration 

declines as a competitive strategy. Components are 

fabricated globally, and there is an emerging business case for 

locating final assembly close to customers. Smart Assembly 

is about reinventing assembly processes to succeed in this 

new environment. 

Successful, correct assembly requires that many other things 

are first done successfully. Responsive, efficient assembly 

of high quality products with a high degree of product 

variation is the result of doing many things right, and these 

things are highly interdependent. These include: design for 

assembly, virtual simulation and validation, flawless launches, 

highly trained workers, knowledge asset management, supply 

chain management, real-time decision making, fast response 

to problems, maintenance, and line balancing. 

III. VISION AND INDUSTRY NEEDS IN “SMART ASSEMBLY” 

On October 3-4, 2006, approximately 60 researchers, software 

and equipment suppliers, and end users convened at NIST to 

discuss the next generation Smart Assembly (SA) capability. 

The team developed a vision for SA, determined basic needs 

and gaps, defined key enabling technologies, assessed 

interests in establishing an industry-led SA initiative, and 

defined next steps. [1] 

A. Business Case and State of the Art in “Smart Assembly” 

Manufacturing operations involve the preparation and 

processing of raw materials, the creation of components, and 

the assembly of components into subassemblies and finished 

products. The broader scope of manufacturing includes 

innovation, design, engineering, and management of life cycle 

performance. 

Globalization is redefining the distribution of these 

manufacturing functions and operations. In recent years, 

many manufacturing sectors have gone offshore to produce, 

except for high-end, specialty products. Lower offshore 

labor costs make it difficult for U.S. manufacturers to produce 

cost competitive components in the United States. When 

one also considers that manufacturing costs of a product 

produced in China is 30 % to 50 % lower than the same 

product produced in the U.S., the near term threat to 

America’s manufacturing base is very real. 

The importance of manufacturing to America’s economic well 

being remains very high. Manufacturing is the backbone of 

our economy and the cornerstone behind our national defense. 

It is the major source of our high economic leverage, well 

paying jobs, and R&D investment and innovation. 

Manufactured products account for over $900 billion worth of 

U.S. exports -- nearly two-thirds of all U.S. exports -- and 

manufacturing’s value-added to the U.S. economy is 

approximately $2 trillion per year, contributing 12 % of the 

U.S. Gross Domestic Product (GDP). Every dollar invested in 

manufacturing spawns another $1.43 for the economy, and in 

the automotive industry, for example, every job results in 6.6 

spin-off jobs in other industries (electronics, financial, 

materials, etc.) [2], [3]. 

To remain strong in the global marketplace, the U.S. must 

maintain its ability to produce excellent products cost 

effectively. This is not an easy challenge. Manufacturing is 

evolving in the “Flat World”. Today’s manufacturing is 

network centric and requires the effective management of 

dynamic, complex, and interconnected supply chains. 

Manufacturers have become information & knowledge 

intensive by necessity, demanding the ability to share 

272
information accurately, inexpensively, and seamlessly. 

There is an emerging business case that considers service, 

logistics, shipping costs, regulatory and policy issues, and 

market intelligence for placing final assembly operations 

close to the customer. Focusing resources on improving 

technologies and processes associated with assembly of 

components into final products could conservatively achieve a 

$100 Billion/yr productivity increase for the U.S. [1].

Today, the vast majority of assembly operations are manual. 

In a typical automotive plant, at least 95% of assembly 

operations are manual, with automation being used only on 

simple tasks. Some manual tasks have been improved by 

providing operators with machine-assisted processes to help 

with ergonomics, productivity, and quality. Still, the trend is 

outsourcing of non-critical tasks to the most competitive 

supplier. The increasing reliance on information technology 

(IT) to optimize and operate the supply chain has become an 

integration nightmare for many companies, especially small 

and medium sized manufacturers who do not have the 

resources to develop customized integration solutions. 

Assembly efficiency and capability is a competitive 

discriminator in every product manufacturing sector. Time is 

a key driver for successful assembly operations. Companies 

that can move an innovative new product from the drawing 

board to the loading dock before anyone else gain a huge 

advantage in profitability. Several case studies have 

demonstrated dramatic results of applying best practices and 

technologies to assembly operations. For example, Toyota’s 

V-Comm Digital Mockup program1

 validates both product 

and manufacturing processes through digital assembly, 

reducing lead time for production by 33 %, design changes by 

33 %, and development costs by 50 %. Boeing’s 

advancements in assembly and supply chain integration on 

their 777 aircraft program have reduced product cycle 

development time by 91 % and reduced labor costs by 71 %. 

B. Vision and Definition of Smart Assembly 

Following the workshop, various yet similar definitions of 

Smart Assembly evolved. For the purpose of this paper, we 

adopt the following definition currently being developed by 

NIST’s MEL: 

Smart Assembly is the incorporation of learning, 

reconfigurability, human-machine collaboration, and 

 

1 Certain commercial equipment, instruments, or materials are identified in 

this paper in order 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.

model-based techniques into assembly systems to 

improve productivity, cost, flexibility, responsiveness 

and quality. 

SA goes well beyond traditional automation and 

mechanization to exploit the effective collaboration of man 

and machine in engineering and in operations. It integrates 

highly skilled, multi-disciplinary work teams with 

self-integrating and adaptive assembly processes. It unifies 

“virtual” and “real-time” information to achieve dramatic 

improvements in productivity, lead time, agility, and quality. 

The vision for Smart Assembly is a system consisting of the 

optimal balance of people and automation interacting 

effectively, efficiently, and safely. People work in 

knowledgeable, empowered work teams that utilize best 

assembly practices and technologies. Virtual optimization and 

validation of assembly processes are used to ensure the best 

designs work the first time. Effective integration of 

automation and information technology into the human 

assembly process maximizes total system performance on a 

consistent basis. And finally, flawless execution of supply 

chain and product life cycle processes successfully 

synchronizes the entire assembly. 

To summarize the key characteristics of Smart Assembly 

Systems: 

• Empowered, knowledgeable people: A multi-disciplined, 

highly skilled workforce is empowered to make the best 

overall decisions. 

• Collaboration: People and automation working in a safe, 

shared environment for all tasks. 

• Reconfigurable: Modular “plug and play” system 

components are easily reconfigured and reprogrammed to 

accommodate new product, equipment, and software 

variations, and to implement corrective actions. 

• Model and Data Driven: Modeling and simulation tools 

enable all designs, design changes, and corrective actions to 

be virtually evaluated, optimized, and validated before they 

are propagated to the plant floor. The “virtual” models and 

real-time plant floor systems are synchronized. 

• Capable of Learning: Self integrating and adaptive 

assembly systems prevent repeated mistakes and avoid new 

ones. 

C. Enabling Technologies for Smart Assembly 

We can partition the significant enabling technologies for 

Smart Assembly into four inter-dependent thrust areas as 

illustrated below. The rows correspond to technology areas 

273
(described below) for which R&D roadmaps can be 

developed. The columns correspond to industry-specific 

application (or “grand challenge”) scenarios involving a high 

degree of integration across the technology areas. These 

use-case scenarios outline the significant milestones, 

deliverables, and capability demonstrations that could be 

included in a future focused Smart Assembly R&D program 

(e.g. a national testbed). 

Technology Roadmaps

“SA Grand Challenges”

(Potential focus for a future “Smart Assembly Testbed”?)

Intelligent, flexible assembly 

processes, equipment, tools

Pervasive and persistent

virtual capability

Actionable real-time data

Infrastructure:

Standards and interoperability

Automotive 

Aerospace 

Additional Industry Sectors

Fig. 1 Smart Assembly Enabling Technologies

1) Intelligent Flexible Assembly Processes, Equipment, and 

Tools 

Smart Assembly processes, equipment, and tools must be 

modular, low cost, and reusable. R&D in this area focuses on 

next generation robotics, sensors, controls, effectors, material 

handling, and assembly concepts. 

Intelligent, safe cooperative robots will eliminate costly, hard, 

restrictive safety fences. Modular, multifunctional assembly 

system components will be readily reconfigurable – ultimately 

autonomously reconfigurable – allowing rapid changeover to 

initiate production of new products. These assembly systems 

will be self-integrating and self-configuring, negotiating their 

respective “roles and responsibilities” based on digital 

knowledge of product, process, and business requirements as 

defined by applicable product and process models. The 

assembly process will ensure sequenced material delivery to 

point-of-use to eliminate inventory waste and unnecessary 

material handling. 

2) Accurate, easy-to-use, pervasive, persistent, virtual 

capability 

We must be able to virtually launch a factory, optimize its 

operation and eliminate errors prior to production. R&D in 

this area focuses on next generation virtual modeling and 

simulation technologies to enable changes to the assembly 

system to be emulated in a cost effective manner before 

deployment, both to ensure they work, and to minimize 

disruption to on-going production operations. 

A virtual capability will drive collaborative systems 

engineering. Product requirements and manufacturing 

capabilities and infrastructure will drive the creation of 

product models for Smart Assembly. The product models will 

support the definition and development of best assembly 

processes, with optimization and evaluation done in the 

“model space” of the virtual environment. The process 

models will be the foundation for intelligent closed-loop 

process control and will be robust enough to transfer directly 

to operations. Because the virtual capability is integrated 

into the manufacturing information infrastructure and business 

planning process, the models will be continuously updated so 

that the virtual plant floor accurately synchronizes with the 

real plant floor throughout the product life cycle. 

3) Real time actionable information for man and machine

Real time, actionable information provides timely and 

accurate decision support for people and automation to keep 

operations, maintenance, and fault recovery activities 

optimized. R&D in this area focuses on wireless and 

web-enabled monitoring, prognostics, and intelligent 

maintenance. 

The assembly system will be a sense-, analyze-, 

advise-and-respond environment. Sensors will monitor every 

parameter that is important to the operation, and control limits 

will be set for all parameters. The human-in–the-loop will be 

aided by excellence-in-information, instructions on what and 

how to perform, and monitor assurance of acceptable 

completion of tasks. The state of the assembly will be 

evaluated at all times, and any deviations will be made known. 

The assembly environment will function in a manner that is 

similar to the immune system of the human body, wherein 

anomalies that have no obvious symptoms are responded to in 

a very effective manner. 

This mindset is giving birth to a new discipline called immune 

systems engineering. It is an environment wherein there is 

sufficient intelligence to monitor key parameters and 

determine, mandate, and ensure the best response. 

Self-diagnosing and self-healing will be attributes of the 

systems. The intelligent closed loop assembly environment 

will be achieved through advances in control and 

manufacturing diagnostics & prognosis technologies that 

embrace open architecture and modular functionality. The 

control & diagnostic function will be linked to the 

model-based environment to support the application of 

knowledge with data to enable automated generation of the 

necessary information to drive, control, monitor, and maintain 

assembly operations. 

4) Real time actionable information for man and machine

274
The Smart Assembly environment must be interoperable at all 

levels (e.g. tool, cell, zone, line, plant, enterprise), with 

plug-and-play hardware and software (both virtual and 

physical) that communicate seamlessly across domains and 

different commercial toolsets. Both sending and receiving 

devices will speak the same language or have integral 

real-time translation incorporated into the communication 

systems. R&D on the development of harmonized standards 

for sufficient coverage of all assembly functions is the 

minimum requirement of the future strategy, and on 

interoperability, conformance, and performance testing 

relative to these standards. 

IV. SMART ASSEMBLY GRAND CHALLENGES

The enabling technologies for Smart Assembly (rows in 

Figure 1) have been the subject of active R&D for many years 

and are not “new ideas.” The visionary elements of “smart 

assembly” are not within the enabling technologies per se, but 

rather on the unique and substantial opportunities enabled by 

a “deep integration” across technologies. The current focus 

of the Smart Assembly activity is to develop and document 

“Smart Assembly Grand Challenges” that could provide the 

basis for a funded R&D initiative, and that involve significant 

integration across the rows of Figure 1. 

Work on defining an appropriate set of Grand Challenge 

scenarios is underway, but to illustrate the idea, we shall 

outline one potential example. Although the example was 

developed from an automotive perspective, we suspect that it 

may apply (with perhaps minor modifications) in other 

industry sectors. 

1) Example Challenge Scenario: Hybrid Emulation for 

Reconfigurable Automotive Body Shop 

Jim is a Body Manufacturing Engineer for General Motors. 

Last year, GM launched a new product in at the plant in 

Arlington Texas. Jim was responsible for the 

design/configuration of all of the weld guns, clamps, and 

fixtures in the body shop, as well as the robot programs. In 

the past, this work would have been done by several 

groups/engineers. However, using a new generation of 

computer tools, Jim was able to optimally configure the body 

shop tooling from libraries of modular components, 

minimizing the number of tooling variations and maximizing 

flexibility, and he was able to develop and validate the robot 

programs completely in a virtual environment. 

Today, from the web browser on his personal computer, Jim is 

able to monitor every aspect of the body shop in Arlington as 

it is running. He can navigate from the body shop, to any 

zone or line, to any workcell, or any robot or programmable 

logic controller (PLC). For example, if he is looking at a 

particular workcell, his screen displays a 3-D visualization of 

the robots doing the work. His screen looks remarkably like 

the one he was working with when he designed and validated 

the tooling and robot programs for the cell prior to production, 

only now, the robot positions and other information on his 

screen are being continuously updated by reading information 

from the plant floor network in real time, rather than by a 

simulation. He can pause the display at any time, and replay 

information from the past. Jim used this tool during the 

launch of the Arlington plant to “fine tune” the workcell to 

optimize performance and thruput of the line. He did this from 

his office in China where he was on special assignment at the 

time; he did not travel to Arlington during the launch of the 

plant. 

Today, there is a problem at the plant. One of the robots in 

the workcell has gone down, and it will take four hours to 

repair. Jim receives an urgent message on his Blackberry, 

and immediately acknowledges the message, and launches the 

operations monitor to investigate from his office in Michigan. 

(Had Jim not been available, this message and the work 

described below could have been performed at any one of 

GM’s Body Manufacturing Engineering centers globally. 

Jim is the first choice for the work because he is so familiar 

with the operation.) 

Although the workcell is down, Jim can replay exactly what 

was happening in the workcell prior to the failure. From his 

screen, he selects all of the weld points that the failed robot 

was responsible for, and he selects several other robots, and 

requests each one to report whether or not they are capable of 

performing any of the welds that had been assigned to the 

robot requiring repair. He finds that it would be feasible to 

continue operations by temporarily reassigning weld points to 

other robots. 

He selects the points to be reassigned and the robots to which 

they will be assigned. Each robot integrates the new points 

into their respective programs. Jim then simulates the 

operation of the line. Now, for the robots, weld controllers, 

and PLCs whose programs are being modified, the motions 

and information are being provided by a simulation tool; for 

the rest of the line, the motions and information are being 

provided by re-playing the real-time data collected before the 

line went down. In this way, Jim is able to verify that the 

new programs will work properly without interferences. He 

is able to make whatever modifications are necessary to insure 

proper operation. In fact, this is exactly the type of work 

Jim was doing when the plant was initially launched and he 

was fine tuning the operation. 

 When he is satisfied, he “releases” the modified programs to 

the robots, weld controllers, and PLCs, and informs the plant 

personnel that they may re-start the line with the new 

programs. It has taken Jim 15 minutes to complete this work. 

Later that day, when the robot has been repaired, plant 

personnel momentarily stop the line, restore the programs to 

275
their prior state, and resume normal operation. In the interim, 

the line is operating at 80 % of its normal thruput, and 20 

minutes of production have been lost. In the past, 4 hours of 

production would have been lost while the failed robot is 

repaired. 

V. CONCLUSIONS

Globalization has radically changed the nature of 

manufacturing, and Smart Assembly is about re-inventing 

assembly processes to succeed in this new environment. If 

successfully realized, Smart Assembly may be a key aspect of 

future, thriving manufacturing enterprises. Efforts are 

currently underway to increase awareness of Smart Assembly, 

through the creation of a Smart Assembly working group, 

which is hoping to refine the vision, score, business case 

scenarios, and roadmaps for what is hoped to ultimately be a 

national Smart Assembly activity. 

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the contributions of all of 

the participants in the NIST sponsored Smart Assembly 

Workshop conducted on October 3-4, 2007 (see 

http://smartassembly.wikispaces.com/) and specifically the 

following individuals who contributed substantially to the 

content of this report: 

From the Manufacturing Engineering Laboratory at NIST: 

Dale Hall, Steve Ray, Al Wavering, Kevin Jurrens, Alkan 

Donmez, Fred Proctor, Rob Ivester, Ranchuri Sudarsan, and 

numerous other team members. 

From the Manufacturing Systems Laboratory at GM 

Research: Roland Menassa, Stephan Biller, Jon Owen, Jeff 

Alden, Jeff Tew and members of their groups. 

From ARC Advisory Group: Jim Caie, 

From the Integrated Manufacturing Technology Initiative: 

Richard Neil, 

From Oneida: Allan Martel, 

From Rolls-Royce: Parker Sykes. 

REFERENCES

[1] Draft workshop report is available at 

http://smartassembly.wikispaces.com/ 

[2] "Manufacturing in America", US Department of Commerce, 2004 

[3] NAM, based on US Bureau of Labor Statistics, US Bureau of Economic 

Analysis, US Census Bureau 

276
Science based Information Metrology for Engineering Informatics 

Sudarsan Rachuri 

George Washington University & 

Design and Process Group 

Manufacturing System Integration Division 

National Institute of Standards and Technology, USA 

sudarsan@cme.nist.ogv 

 

Abstract— Engineering informatics is the discipline 

of creating, codifying (structure and behavior that 

is syntax and semantics), exchanging (interactions 

and sharing), processing (decision making), storing 

and retrieving (archive and access) the digital 

objects that characterize the cross-disciplinary 

domains of engineering discourse. It is absolutely 

critical that a sharing mechanism should preserve 

correctness (semantics), be efficient (for example, 

representation, storage and retrieval, interface), 

inexpensive (for example, resources, cost, time), 

and secure. In order to create such a sharing 

mechanism, we need a science-based approach for 

understanding significant relationships among the 

concepts and consistent standards, measurements, 

and specifications. To develop this science, it is 

essential to understand the interactions among the 

theory of languages, representation theory, and 

domain theory. Creating the science of information 

metrology will require a fundamental and formal 

approach to metrology, measurement methods and 

testing and validation similar to the physical 

sciences. 

Keywords: Engineering informatics, product lifecycle, standards, 

interoperability, metrics, semantics

I. INTRODUCTION

A prerequisite for competitive advantage in 

manufacturing is a good and sustained investment 

in Engineering Informatics to describe a common 

product description that is shared among all 

stakeholders throughout the lifecycle of the product. 

Informatics is a conceptual synthesis of 

mathematics, computing science, and applications 

as implemented by information technology. 

Engineering informatics is the discipline of 

creating, codifying (structure and behavior that is 

syntax and semantics), exchanging (interactions 

and sharing), processing (decision making), storing 

and retrieving (archive and access) the digital 

objects that characterize the cross-disciplinary 

domains of engineering discourse. This is a 

relatively hard problem, as it requires combining a 

diverse set of emerging theories and technologies: 

namely, information science, information 

technology and product engineering, and many 

different cross-disciplinary domains. 

The environment in which products are designed 

and produced is constantly changing, requiring 

timely identification and communication of failures, 

anomalies, changes in technology and other 

important influences. For such an adaptable 

organization to function, an information 

infrastructure that supports well-defined 

information exchange processes among the 

participants is critical. The IT industry that 

supplies engineering informatics support systems 

is currently vertically integrated. Vertically 

integrated support systems do not provide for 

opportunity of full diffusion of new innovations 

across the entire community of users. A study of 

engineering informatics support provided by a 

representative set of major software vendors shows 

that the availability of support tools is partial and 

incomplete. Some vendors cover several areas, 

while there are areas that are poorly covered or not 

covered at all by any vendor. Relying on a single 

vendor to cover all areas of support for engineering 

informatics would not provide the kind of 

innovation needed by the customers. There is a 

lack of interoperability across tools and that there 

are barriers to entry for software developers that 

could provide a plug and play approach to 

engineering informatics support. Currently only a 

few IT companies with vertically integrated tool 

sets are able to provide facilities that are even 

partially integrated. 

The Product Lifecycle Management (PLM) concept 

holds the promise of seamlessly integrating all the 

information produced throughout all phases of a 

277
product's life cycle to everyone in an organization 

at every managerial and technical level, along with 

key suppliers and customers. PLM systems are 

tools that implement the PLM concept. As such, 

they need the capability to serve up the 

information referred to above, and they need to 

ensure the cohesion and traceability of product 

data. 

A critical aspect of PLM systems is their product 

information modeling architecture [1]. Here, the 

traditional hierarchical approach to building 

software tools presents a serious potential pitfall: if 

PLM systems continue to access product 

information via Product Data Management (PDM) 

systems which, in turn, obtain geometric 

descriptions from Computer-Aided Design (CAD) 

systems, the information that becomes available 

will only be that which is supported by these latter 

systems. 

II. PRODUCT REPRESENRATION AND INTEROPERABILITY 

Interoperability is pervasive problem in today’s 

information systems and the cost of the problem of 

managing interoperability is a major economic 

drain to most industries. The problem of 

supporting interoperability requires the 

development of standards through which different 

systems would communicate with each other. 

These standards vary from purely syntactical 

standards to standards for representing the 

semantics of the information being exchanged. 

However, for multiple systems to interoperate 

these systems will have to be tested for 

conformance, implementation and inter-operability 

among each other. These tests will have to 

encompass syntactic, content and semantic aspects 

of exchange between these systems. 

A standardized exchange behavior within a 

specified set of conventions has a form (syntax), 

function (scope) and the ability to convey as 

unambiguously as possible an interpretation 

(semantics) when transferred from one participant 

to the other. The design of a standardized exchange 

in the context of information metrology is dictated 

by: 

1. Language: the symbols, conventions and rules 

for encoding content with known 

expressiveness. Examples include First Order 

Logic [2], Knowledge Representation [2], OWL 

[3], UML [4], SysML [5], and EXPRESS [6]. 

2. Processible Expressiveness: the degree to which 

a language mechanism supports machine 

understanding or semantic interpretation. 

Expressiveness is closely connected to the scope 

of the content that can be expressed and to the 

precision associated with that content. 

Support of standardized exchange requires a 

set of complementary and interoperable 

standards. 

3. Content: the information to be communicated. 

Content includes the model of information in 

the domain and the instances in the domain 

and explicates the relationship between the 

message and the behavior it intends to elicit 

from the recipient. Examples of content, 

include Standard for the Exchange of Product 

model data (STEP) [7], NIST Core Product 

Model (CPM) [8] and its extensions, the Open 

Assembly Model (OAM) [9], the 

Design-Analysis Integration model (DAIM) and 

the Product Family Evolution Model (PFEM). 

4. Interface: User interface concerns efficiency of 

communication between the system and 

humans. Software interface concerns 

accurateness and completeness of 

communication between systems. 

III. LONG TERM KNOWLEDGE RETENTION AND ARCHIVAL

These digital objects in engineering need to be 

preserved and shared in a collaborative and secure 

manner across the global enterprise and its 

extended value chain. The problem of digital 

preservation is very complex and open-ended 

(dynamic situations or scenarios that allow the 

individual users to determine the outcome). To 

understand the problem of digital archiving we 

need to define a taxonomy of usage scenarios as an 

initial guide to categorize different end-user access 

scenarios. The scenarios, which we call the “three 

Rs”, are: (i) reference, (ii) reuse and (iii) rationale. 

The primary driver for the above categorization is 

the special retrieval needs for each of these 

scenarios. For example a collection intended 

primarily for reference may need to be organized 

differently than one intended for reuse, where not 

only the geometric aspects of the product are 

sought but also other information regarding 

manufacturing, part performance, assembly and 

278
other aspects. In a similar vein, rationale 

information may have to be packaged differently in 

that it may include requirements information along 

with other performance data on the part or the 

assembly. Given the range of uses and perspectives 

of the end-users will have large impact on the 

process of archiving and retrieval. 

IV. SCIENCE-BASED INFORMATION METROLOGY

It is absolutely critical that a sharing mechanism 

should preserve correctness (semantics), be 

efficient (for example, representation, storage and 

retrieval, interface), inexpensive (for example, 

resources, cost, time), and secure (such as 

Role-Based Access Control). In order to create 

such a sharing mechanism, we need a sciencebased approach for understanding significant 

relationships among the concepts and consistent 

standards, measurements, and specifications. To 

develop this science, it is essential to understand 

the interactions among the theory of languages, 

representation theory, and domain theory. Creating 

the science of information metrology will require a 

fundamental and formal approach to metrology, 

measurement methods and testing and validation 

similar to the physical sciences. The effort 

involved will be cross-disciplinary in nature 

because 1) supply chain and engineering 

informatics are complex endeavors involving 

artifacts in several business areas 2) the industry 

does not have an established interoperability 

testing approach at the semantic level and 3) 

testing can consume a lot of time and there is no 

clear methodology to suggest what kinds of 

testing are essential. 

Even though several disparate attempts were made 

in the past to understand this problem, the 

primary reasons that we can succeed now are: (1) 

The new sets of technical ideas that have emerged 

like the semantic web technologies, (2) better 

collaborative tools, and new models of software and 

standards development that have become 

dominant in the IT world, and (3) advanced 

mathematics, computer science, and logic-based 

systems. Besides, these developments the IT 

industry in large parts is moving away from 

products with only proprietary software 

components to a mixture of open source and 

proprietary components. The awareness and 

benefit of open source models are being embraced 

by large parts of the industry. The timing and 

attitude towards open standards and open source 

models have gained currency. The primary reason 

for the call for open standards is the current 

environment in the IT industry and the rise of the 

global network-based manufacturing. Both 

economic efficiency of global firms and design and 

manufacturing capabilities of these firms in the 

future will depend on the smooth functioning of the 

design and manufacturing information network 

especially for the small and medium enterprises 

(SMEs) to take advantage of the global and local 

markets. 

V. CONCLUSIONS

The potential impacts of information metrology for 

engineering informatics include: (1) assistance to 

manufacturing industry end users and software 

vendors in ensuring conformance to information 

exchange standards; (2) creation of science of 

information metrology [10], (3) development of a 

fundamental and formal approach to information 

metrology, and (4) measurement, testing and 

validation methods similar to the physical sciences. 

Disclaimer: No approval or endorsement of any commercial product 

by the National Institute of Standards and Technology or by the 

Syracuse University is intended or implied. 

 1. Sudarsan, R., Fenves, S. J., Sriram, R. D., and 

Wang, F., "A product information modeling 

framework for product lifecycle management," 

Computer-Aided Design, Vol. 37, No. 13, 2005, 

pp. 1399-1411. 

 2. Sowa, J. F., Knowledge representation, Logical, 

Philosophical, and Computational Foundations, 

Brooks/Cole1998. 

 3. Web Ontology Language (OWL). 

http://www.w3.org/2004/OWL/ . 2005. 

 4. OMG. UML 2.0 OCL Specification. 

http://www.omg.org/cgi-bin/doc?ptc/03-10-14 . 

2004. 

 5. SysML - Open Source Specification Project. 

www.sysml.org . 2007. 

 6. Schenck, D., and Wilson, P. R., Information 

279
modeling: the EXPRESS way, Oxford 

University Press, New York,1994. 

 7. Kemmerer, S., "STEP: The Grand Experience, 

(Editor)," NIST Special Publication 939, 

National Institute of Standards and Technology, 

Gaithersburg, MD 20899, USA, 1999. 

 8. Fenves, S., Foufou, S., Bock, C., Bouillon, N., 

and Sriram, R. D., "CPM2: A Revised Core 

Product Model for Representing Design 

Information ," National Institute of Standards 

and Technology, NISTIR 7185, Gaithersburg, 

MD 20899, USA, 2004. 

 9. Sudarsan, R., Baysal, M. M., Roy, U., Foufou, S., 

Bock, C., Fenves, S. J., Subrahmanian, E., 

Lyons K.W, and Sriram, R. D., "Information 

models for product representation: core and 

assembly models," International Journal of 

Product Development, Vol. 2, No. 3, 2005, pp. 

207-235. 

 10. Carnahan, L., Carver, G., Gray, M., Hogan, M., 

Hopp, T., Horlick, J., Lyon, G., and Messina, E., 

"Metrology for Information Technology (IT)," 

National Institute of Standards and Technology, 

Gaithersburg, MD 20899, USA, NISTIR 6025, 

1997. 

280
Abstract —The focus of this paper is: how can standards for

manufacturing machine control languages be evaluated? What

is required of a standard defining one of these languages so that

implementations will interoperate? The paper provides a set of

specific questions to ask about a control language standard.

Reasons why the questions should be asked are given. Four

machine control languages are used as examples:

EIA-274-D, BCL, DMIS, and STEP-NC.

Keywords: control, language, machine, standard, AP 238, BCL,

DMIS, EIA-274-D, ISO 10303, ISO 14649, STEP-NC

I. INTRODUCTION

The focus of this paper is: how can standards for

manufacturing machine control languages be evaluated? What

is required of a standard defining one of these languages so that

implementations will interoperate?

The paper deals only with languages intended to be used

in control program files. There are also manufacturing machine

control languages (such as the I++ DME Interface

Specification and DMIS Part 2) intended to be used for

transmitting individual commands one at a time. The issues for

those languages are similar, but they are not addressed here.

In this paper, “manufacturing machines” means things

such as machining centers, turning centers, and coordinate

measuring machines that are run by computer numerical

control systems.

The interfaces served by the standards are those between

program generators and program execution systems. The

programs that travel over these interfaces need to be capable of

exercising the full functionality required by the task at hand (in

general, all or almost all of the functionality of the receiving

system). Hence, a manufacturing machine control language

must provide a suite of program statements or commands that

exercise that functionality.

The systems on the two sides of the interface must

interoperate in the sense that a program passing across the

interface must be executable by the receiving system and must

do what the generator of the program intended it to do.

Moreover, the intended meaning must be as described in the

standard.

A standard for a language should describe how program

statements should be executed in enough detail that if the

standard is implemented in a number of sending systems and a

number of receiving systems and all systems conform to the

standard, any receiving system will do what is intended in any

program generated by any sending system.

II. WHY AN IMPLEMENTER’S VIEW?

An implementer is a person who programs the software on

one side or the other of an interface so that the software either

generates programs or reads and executes programs. Because

building an implementation requires understanding all the

details of a language, even the most minute, implementers are

the people in the best position to judge whether a standard is

complete and unambiguous. Standards are usually written with

the implementer as the primary audience. One World Wide

Web Consortium web page, for example, says “Specifications

are aimed at people writing software to implement them” [15].

The implementer’s view of a control language standard is

different from the standard writer’s view in the same way that a

file reader is different from a file generator. The standard writer

has concepts in mind and puts down statements in the formal

language used to define the standard, statements in natural

language, and diagrams to represent those concepts. The

implementer reads the formal language, natural language, and

diagrams and forms concepts from them. Just as parsing from

characters into structures is much more difficult than

generating character strings from structures, so implementing a

standard is much more difficult than writing it.

In implementing a standard, the implementer is

continually asking, “Is there more than one way in which this

can reasonably be interpreted?”, and “Do I have to make some

assumption about the meaning in order to implement it?” If the

answer to either question is yes, the implementer can be

confident that some other implementer will make the other

choice or assumption, so that the other implementation will not

interoperate.

Evaluating Manufacturing Machine Control

Language Standards: An Implementer’s View

Thomas R. Kramer

National Institute of Standards and Technology

MS8230

Gaithersburg, MD 20817, USA

kramer@cme.nist.gov

281
III. EXAMPLES OF STANDARDS FOR

MANUFACTURING MACHINE CONTROL LANGUAGES

The following manufacturing machine control language

standards will be used as examples. The author has direct

experience with all of them by having built an implementation

and/or studied the standard and submitted detailed comments

to the committee responsible for the standard.

A. EIA-274-D

EIA-274-D, dated February 1979, [4] is a standard of the

Electronics Industry Association (EIA) and is a low-level

language designed for execution on the controller of a

machining center or turning center. Another name for it is RS274-D, since it is also an ANSI standard under that name. It is

informally called a “G and M code” standard, since it consists

primarily of codes starting with G or M. In this respect and in

meaning, it is similar to ISO 6983.

B. BCL

BCL [13] is a low-level language designed for execution

on the controller of a machining center or turning center. It is a

language whose acronym outlived its original name. The name

started in 1983 as “Binary CL” (EIA 494 A). The “CL”

probably stood for Cutter Location. The standard did not say

what CL stood for. By the February 1997 proposal for EIA 494

C, BCL had changed into “Basic Control Language”. The

language itself changed also over that period from a terse

gobbledegook of letters and digits into human-readable

abbreviated English command names accompanied by

parameter values having primitive data types (keyword, string,

number, etc.), all represented using ASCII (American Standard

Code for Information Interchange) characters.

C. 3. The STEP-NC milling family: ISO 14649 and STEP

AP238

ISO is the International Organization for Standardization.

STEP is the STandard for the Exchange of Product model data.

ISO 14649 and STEP AP 238 are high-level languages for

various types of numerically controlled machines. These

standards are still being developed. The most mature parts

(subdivisions of a standard) are applicable to machining,

specifically Parts 10 and 11 of ISO 14649 [8], [9]. Only those

two parts are discussed in this paper.

AP 238 [7] is largely a recasting of ISO 14649 semantics

into the terms of the STEP “integrated generic resources” so

that machine control programs may be processed (to a certain

extent) by any system that can handle the STEP integrated

generic resources. STEP itself is a series of several dozen parts

designed for product data representation and exchange. All the

STEP parts are part of ISO 10303. AP 238 encompasses

multiple parts of ISO 14649. Only the portion of AP 238

relevant to machining centers is covered here.

These languages are “high-level” in the sense that they are

designed to communicate geometry, machining operation data,

and machining strategies and to leave other decisions (the

generation of toolpaths, in particular) to the controller.

However, they also include facilities for sending low-level

commands that give tool paths in detail.

D. DMIS (Part 1)

DMIS (Dimensional Measuring Interface Standard) is a

mid-level language for programs for coordinate measuring

machines (CMMs) and other dimensional measuring

equipment [2]. Since it must do numerical data analysis while

it executes, DMIS requires much more complex execution

software than do the EIA-274-D and BCL languages, but it

does not require the use of strategies that STEP-NC requires.

DMIS has been updated through five versions, starting in 1986.

DMIS 5.0 is the most recent standard.

DMIS defines both a programming language (the input

format) and a format for data reporting (the output format).

Only the programming language is covered in this paper. There

is a DMIS Part 2, which is an object interface specification. It

is not further discussed here.

IV. QUESTIONS FOR EVALUATING A

MANUFACTURING MACHINE CONTROL LANGUAGE

STANDARD

To evaluate a manufacturing machine control language

standard, the following questions should be asked.

A. Is the standard complete for the intended use?

B. Is the standard clear and unambiguous?

C. Is the standard defined using a high-level information

modeling language for which processors (readers and code

generators) are readily available?

D. If the standard is defined using a high-level information

modeling language, is there a well-defined file representation

that works with the high-level language?

E. If the standard is defined directly as a file format (i.e. not by

using high-level language), is the method used to define the file

format clear and unambiguous?

F. Is special software required for reading and writing program

files or for assembling the file data into meaningful structures?

If so, is it widely available, and is it free or costly?

G. Has the standard been tested? How? What were the results?

H. Is there a continuing committee devoted to maintaining the

standard? What is the committee’s track record of dealing with

proposed changes?

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I. Are there intellectual property issues that may make using

the standard impossible or expensive in the future?

J. Is there a critical mass of conforming implementations of the

standard? Does it appear there will be a critical mass in the

future?

K. Does the standard have conformance classes? Are they part

of the standard?

L. Is it necessary to follow a set of usage rules additional to the

standard in order to build an interoperating implementation?

M. Are there mechanisms in place (formal or “natural”) to

insure that implementations conform to the standard?

V. DISCUSSION OF THE QUESTIONS

How much weight to give to the various questions depends

on one’s point of view. Most of the people to whom the

performance of a manufacturing machine control language

standard is important are in one of two groups: (A) end users

trying to decide whether to acquire and use a system that

implements the standard, or (B) systems developers trying to

decide whether to implement the standard (particularly those

working for systems vendors or for users building their own

systems). End users who want to buy a system rather than to

build one, for example, may not care how hard it is to build an

implementation. A person building an implementation, as

another example, may not care whether there are other

implementations of the same sort.

The discussion of examples in this section reflects the

opinion of only the author.

A. Is the standard complete for the intended use?

From a user’s point of view, in order to determine whether

a standard is complete enough, the user should make a list of

the required machine functions and then determine whether the

standard supports those functions. This may be done by

studying the standard, by getting information from users of the

standard, by observing conforming implementations in action,

or by some combination of those.

There is no global answer to this question because the

meaning of “the intended use” is dependent on who is doing

the intending. There are no fixed boundaries on the set of

people who might be users of a standard.

All of the examples are complete for simple use on

machines of the sort for which they were originally developed,

but all of them could be extended for more advanced

functionality or for control of similar but different machines.

As an example of additional functionality on a target

machine, EIA-274-D, which was designed to be used on

machining centers, specifies that codes G36 to G39 are

“permanently unassigned” (meaning revisions of the standard

should not use them) and available for “individual use”.

Kearney and Trecker built a machining center with a broken

tool detector, and extended EIA-274-D by using G38 to

operate the detector [11].

As an example of a machine that falls a little out of the

ballpark from the original target machines, DMIS was

designed for doing dimensional measurements on a coordinate

measuring machine using a touch probe, so DMIS has a

“measure a point” command. Dimensional measurement can

be done using a theodolite, but a point cannot be located all at

once with a theodolite. It is necessary to take at least two

measurements of angles to the same point and then calculate its

location. DMIS does not provide a command to do that.

B. Is the standard clear and unambiguous?

For clarity and unambiguousness, there are two largely

separable areas requiring attention: syntax and semantics.

Syntax covers what tokens (i.e words, numbers, and special

symbols), statements, and sequences of statements can legally

be written. Semantics covers what a token, statement, or

sequence of statements means.

Modern formal languages exist (EBNF, for example) that

make it possible to specify the syntax of a control language

very precisely. It is also possible to be precise about syntax in

natural language, but that is more difficult.

There are no formal languages that make it possible to

specify semantics. Only natural language and diagrams are

available for conveying the meaning of a standard, and it is

very difficult to specify semantics by these methods.

The level of being clear and unambiguous of most of the

machine control standards the author has seen is not very high.

Ambiguity in a standard may be intentional or

unintentional, but in either case ambiguity defeats

interoperability. EIA-274-D, for example, is filled with

intentional ambiguity. Appendixes A.1, A.2, and A.3 provide

that each implementer can specify a host of things (such as

how numbers can be written and whether dimension values are

absolute or incremental) that need to be agreed between a

program generator and a program executor.

STEP NC contains many instances of unintentional

ambiguity (for example, the location of most open profiles is

undefined). NIST has submitted 153 suggestions for technical

changes in Part 10 of ISO14649 and 70 for Part 11. Many of

these suggestions aim to eliminate ambiguity.

C. Is the standard defined using a high-level information

modeling language for which processors (readers and code

generators) are readily available?

Standards that are defined using a high-level information

modeling language have a large advantage over those that are

not. Examples of high-level information modeling languages

include:

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• EXPRESS (not an acronym) — developed as part of

STEP [5],

• XML Schema (Extensible Markup Language Schema)

developed by the World Wide Web Consortium [14].

These languages all contain primitive data types (integers,

strings, lists, etc.) and provide for defining the sorts of

interlinked data structures that are needed in a machine control

language.

When a control language standard is written using one of

these high-level languages, a certain amount of automatic

processing may be done. Software is available that will read

the file defining the control language, check its syntax, and

generate source code in a computer language. The source code

defines computer language structures corresponding to the

structures in the control language and contains functions for

accessing (extracting and inserting) the data in those structures.

If a machine control language standard is written using a highlevel language, the standard will probably have been checked

for good syntax using an automatic checker, and a potential

user of the standard who has a checker can use it to check the

standard.

If a machine control language standard is written using a

standard lower-level formal language such as EBNF (Extended

Backus Naur Form) [10], it can be checked for syntax

automatically, but utilities for generating computer code are

not readily available. Moreover, since the lower level formal

languages do not define structures, there is not enough

information in the control language definition file to produce

code useful for building an implementation. Only structures

that mirror the syntax can readily be built by an automatic

system working from EBNF, and the syntax structure is not

likely to be the structure an implementer would like to use.

If a machine control language is written using an ad hoc

description method, neither automatic syntax checking nor

automatic code generation is feasible.

Since the semantics of a machine control language

standard cannot currently be described in a formal language, it

is never possible to generate an implementation automatically

that does anything more than read program files, rewrite

program files, allow browsing, and generate statistics, even if a

high-level language has been used to define the control

language.

D. If the standard is defined using a high-level information

modeling language, is there a well-defined file representation

that works with the high-level language?

With a high-level information modeling language, it is

feasible to define a generic file format that combines with any

information model defined in the language so that a specific

file format exists for the model. Thus, when a manufacturing

machine control language is modeled using the high-level

information modeling language, general-purpose software

designed to be used with the high-level language will read or

write a file containing an executable machine control program

without any work on the part of the implementer other than

making a single library function call in a program. This saves

an enormous amount of work an implementer would otherwise

have to do and makes it much less likely that the reader or

writer will not conform to the standard. Of course, when

writing, a model of the program must be built before a “write

this model to a file” function can be called.

EXPRESS and XML Schema have well-defined generic

file formats of the sort just described. EXPRESS works with

the STEP Part 21 format [6], while XML Schema works with

XML [1]. A high-level language can work with more than one

file format. An EXPRESS model can be used with XML, for

example.

High-level information modeling languages generally are

also built to support implementations that use databases (or

persistent objects) and application programming interfaces

rather than files for exchanges across an interface, but this

paper does not deal with that. It is not common for stored

machine control programs to be implemented that way.

The STEP-NC standards are all modeled using EXPRESS.

None of the other three examples uses a high-level information

modeling language.

E. If the standard is defined directly as a file format (i.e. not by

using high-level language), is the method used to define the file

format clear and unambiguous?

EIA-274-D uses English to define the file format. The

English descriptions are generally hard to follow. There are

several unintentional ambiguities and many intentional ones.

Section 3 of BCL (as proposed for EIA 494 C) defines

overall file structure in English. Section 4 defines in English

what the fields of a BCL record (a single statement) may be

and what characters constitute a valid field (such as a text field,

parameter separator field, or numerical field). Most of the

descriptions in Sections 3 and 4 are clear and unambiguous,

but the definition of “numerical field” is ambiguous. Sections

8.0.1 and 8.0.2 of BCL define a higher level syntax notation for

defining what sequences of fields constitute valid commands.

The higher level notation is clear and unambiguous and is used

consistently in the succeeding parts of the standard.

DMIS 5.0 defines its file format two ways. First, much of

Section 5 describes syntax in English, and a syntax notation

defined briefly at the beginning of Section 6 is used in the

remainder of Section 6 (over 400 pages) to define what

sequences of fields constitute valid commands. Second, Annex

C gives a definition of DMIS syntax in EBNF (although the

lowest level, such as what sequence of characters makes a real

number, is omitted). The file format of DMIS is thus generally

clear and unambiguous. The use of EBNF has enabled the

automatic construction of DMIS input file syntax checkers

[12].

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F. Is special software required for reading and writing program

files or for assembling the file data into meaningful structures?

If so, is it widely available, and is it free or costly?

In all four examples, there are at least two levels of

encoding. All of the examples use ASCII code at the lower

level to interpret bits as characters. Managing this level takes

no special software. Every common programming language

reads and writes ASCII. At the upper level (where reading

means to convert a stream of ASCII characters into meaningful

structures and writing means to convert structures into a

character stream) all the examples except STEP-NC require

special software for reading and writing. Typically:

• data structures must be designed,

• a parser must be built to receive a character stream and

build and populate a hierarchy of structures, and

• a writer must be built to traverse a hierarchy of structures

and generate a character stream.

In STEP-NC, ISO 14649 EXPRESS models can be used

directly with STEP Part 21, and no special software is needed

for reading and writing files beyond that which can be

generated automatically. AP 238, however introduces a third

level of encoding. Special software is needed not for reading

and writing but for dealing with this third level of encoding.

Most of the data in AP 238 is encoded at a level between a

character stream and structures meaningful to an application

programmer. The middle level is built in terms of entities from

the STEP integrated resources, and Part 21 files contain

representations of these structures. The structures in this

middle level are utterly unintelligible to programmers

conversant with machine control. In order to use AP 238 it is

currently necessary to have special software that either (1)

converts the integrated resources structures into structures like

those that may be created directly from ISO 14649 and

provides access functions for the 14649-like structures or (2)

provides access functions for the integrated resources

structures with semantics similar to those that may be created

directly from ISO 14649. Currently, only the second method

has been implemented, and there is only one provider of this

type of special software. Building software of this sort has a

high and steep learning curve.

G. Has the standard been tested? How? What were the results?

A manufacturing standard is like a piece of complex

software. As with software, mistakes may be made in syntax or

logic, and the functionality may not be what is intended by the

authors. There is no chance that complex software will be bug

free as it comes from the programmer. It must be compiled,

tested, and debugged before release. There may be bugs in

syntax, bugs in operation (writing beyond the end of an array,

for example), and bugs in what the program does. This is

universally acknowledged. Commercial software houses

always have testing procedures in place. As with software,

there is no chance that a complex manufacturing machine

control language standard will be bug free as it comes from the

authors. It should be implemented, tested, and debugged before

final release. This is rarely acknowledged. Most standards

development organizations do not have standards testing

methods in place that must be applied before a proposed

standard may be approved. STEP has testing and conformance

procedures, but they are too little and too late to insure that a

standard is of high quality at the time of first release.

Computer languages have compilers that make

executables that can be tested. Standards do not. The best that

can currently be done automatically with a standard, if it is

defined using a high-level language, is to build a system that

can read program files, rewrite program files, allow browsing,

and generate statistics.

EIA-274-D allows so many choices (i.e. is so ambiguous)

that only testing one of the many billions of legal variants is

feasible. The extent to which a variant has been tested is

dependent on the creator or vendor of the variant.

BCL appears to have been well-tested by the large

organizations that used it, in close collaboration with the

vendor that provided the implementations. If it had been

widely implemented (correctly), it could have provided a high

degree of interoperability.

There has been no formal testing program for DMIS.

Some vendors appear to have implemented DMIS in

conformance with the standard and tested carefully. Other

vendors have not.

STEP-NC has been tested to a modest extent, but it is far

from being fully tested. There are enough ambiguities in the

standard that the notion of “conforming implementation” is

tenuous. Further implementation tests are under way.

H. Is there a continuing committee devoted to maintaining the

standard? What is the committee’s track record of dealing with

proposed changes?

Technology advances rapidly so that additional

functionality is needed periodically in any standard dealing

with machine control. If a standard is not updated when new

technology appears, implementers will extend the language in

non-standard ways in order to use the technology.

Even if new technology does not appear, machine control

language standards are so complex that it takes years to

eliminate all the ambiguities and bugs.

To accomplish updating a standard, it is necessary to have

a group in place that understands the standard and can judge

the merits of proposed changes. Determining what updates are

needed works most smoothly if there is an established, well

documented process for updating the standard. The process

should include consideration of requests from anyone for

changes.

Of the examples, DMIS has a very good method of

handling updates, STEP-NC is just getting started on its first

285
round of updates, and EIA-274-D and BCL appear to have no

currently active group.

In the DMIS system, a web site is open for Standard

Improvement Requests (SIRs) from anyone [3]. Each request

is logged and proceeds through several status states until

consideration is complete. The web site shows the disposition

of all of the hundreds of SIRs proposed since 1996. Once a SIR

is entered in the system, anyone can submit a statement for or

against the suggested change. No formal system will force the

group in control of a standard be open to suggestions for

change. In the case of DMIS, the group (now the DMIS

Standards Committee) has been open to change and appears to

treat all suggestions fairly. Other groups for other standards are

often said to be less open and fair.

I. Are there intellectual property issues that may make using

the standard impossible or expensive in the future?

Intellectual property right problems related to standards

are not unusual. Potential users of a standard should look out

for existing and foreseeable problems.

It is possible to get intellectual property rights (patents and

copyrights) related to standards. If rights are granted, the

owner may try to prevent others from using a standard or try

to charge a fee for using it. A ploy the owner might use is to

allow inexpensive usage at first and later, once the user has a

substantial investment in using the standard, increase the fees.

With patents, the owners rights are likely to be unclear, so that

users may become involved in expensive litigation.

In 1993, U. S. Patent 5198990 was issued in which a claim

was allowed for executing DMIS directly on a control system.

The point of having a control language is to execute it, the idea

of doing so directly is completely obvious, and patenting the

obvious is not supposed to occur. Thus, it is disheartening that

the U.S. Patent Office allowed the claim. The effect of the

patent is said to have been that no one implemented DMIS for

a few years. Eventually, it is said, an agreement was reached

that the patent rights would not be used, and DMIS came into

common use.

In 2004, U.S. Patent 6795749 was issued for a method of

using ISO 14649. It is possible that this may have a chilling

effect on the implementation of ISO 14649.

J. Is there a critical mass of conforming implementations of the

standard? Does it appear there will be a critical mass in the

future?

There need to be enough systems on each side of the

interface that useful work can be done.

With EIA-274-D the very notion of conformance fails

because the standard is so ambiguous. There are dozens of

dialects of the language. Most computer aided manufacturing

(CAM) systems have many different post-processors so that

they will produce files in most dialects. Thus, it is feasible to

use EIA-274-D, but programs are not portable from one

machine to another except in some cases when the same

company built both controllers. To be fair, EIA-274-D

apparently never intended to support interoperability. EIA-274-

D is analogous to the concept of “romance language” in that a

speaker of one romance language will have a much easier time

learning another romance language than will a speaker of

Chinese or Swahili.

BCL is, perhaps, the saddest case. BCL is the clearest and

least ambiguous of the standard languages for milling

machines. Its usefulness, including the portability of programs,

was proven by implementations in a few large installations

(Rock Island Arsenal, in particular) but there are currently no

known commercially available implementations. It seems to

have died out. The better mousetrap did not make it in the

marketplace.

STEP-NC - Commercially available implementations do

not yet exist. There may or may not be a critical mass of

implementations in the future.

DMIS - There are said to be several commercially

available conforming implementations, but there are also said

to be several commercial implementations that purport to

implement DMIS but do not conform. There are also

commercially available packages that include “DMIS” in their

names but are not DMIS and do not claim to be. It is a “buyer

beware” situation.

K. Does the standard have conformance classes? Are they part

of the standard?

A conformance class is a subset of the specifications of a

standard that is approved in some way for some type of use.

For example, DMIS has prismatic and thin walled

conformance classes. A conformance class may be defined by

specifying which commands must be implemented and for

each command, which parameters must be implemented.

There are at least three reasons to have conformance

classes. First, for large languages, implementing the entire

language may be beyond the capability of a vendor or the

vendor may decide that it is not economically justified.

Second, there may be some class of jobs which requires only a

subset of the capabilities of the language. Third, there may be

some set of machines which share a subset of the capabilities

for which the language has commands.

If conformance classes have been defined for a standard

but not incorporated in the standard itself, the status of the

classes is in doubt (for example, it may not be clear under what

circumstances the definition of the classes might change).

EIA-274-D does not define conformance classes.

DMIS defines two main conformance classes (prismatic

and thin walled — meaning sheet metal) plus seven addenda

for special capabilities such as rotary table and contact

scanning. Moreover, there are three levels for each class and

addendum. The DMIS conformance classes are not yet part of

the standard.

286
BCL divides its commands into 32 groups called function

sets. This is done in the standard. The intended use of the

function sets is not described in text, but Appendix F, which

suggests what the contents of machining process plans should

be, says that a machining process plan should include a list of

required function sets.

STEP-NC defines conformance classes for milling in

section 5 of ISO 14649-11 and in section 6 of AP 238. These,

however, are not the same. ISO 14649-11 defines six

conformance classes by first dividing its entities into eight

“data sets” (same idea as BCL’s function sets) and then saying

which combination of data sets must be included in which

conformance class. AP 238 section 6 defines four conformance

classes by providing a checklist of entities with a column for

each class.

L. Is it necessary to follow a set of usage rules additional to the

standard in order to build an interoperating implementation?

There may be communities of users of a standard that

agree to follow a set of usage rules. In such communities, it is

usually expected that if both the standard and the usage rules

are followed, implementations will interoperate, but if the

usage rules are not followed, implementations will not

interoperate even if they conform to the standard. In some

cases there is a fee to join the group, and the usage rules are not

publicly available. Potential users of a standard should look out

for this situation.

Usage rules may be desirable in several circumstances,

such as:

• The standard is large and conformance classes have not

been defined, so the rules serve to define a de facto

conformance class.

• The standard is ambiguous.

It is much more desirable, however, to fix the standard so

as to formalize the conformance classes and fix the bugs.

Of the examples, the author is aware of usage rules only in

the case of AP 238 testing, and these rules do not seem to be

intended to continue in the long run.

M. Are there mechanisms in place (formal or “natural”) to

insure that implementations conform to the standard?

Where there are many vendors on each side of a data

interface and many users on only one side of a data exchange

(readers and writers of HTML, for example), there is a

“natural” mechanism for insuring that implementations

conform. Any product that does not conform will not be used.

No company can produce its own non-conforming flavor of

HTML and coerce customers into using it.

Machine control languages never have the benefit of

natural pressure for conformance. The markets are too small.

Also, both sides of a machine control language interface (i.e.,

the programmer and the machine controller) are usually in the

same customer company, so that the writer is able to adjust to

whatever the reader expects. The customer rarely is able to

insist on conformance to a standard in this situation. Vendors

want to be able to claim to use a standard, but also want users

to be unable to use products from other vendors. Thus, vendors

may claim to implement a standard without actually

conforming to the standard.

Thus, for machine control languages, formal procedures

for ensuring conformance are needed in order to get

conformance.

For machine control languages, conformance tests strict

enough to ensure interoperability if passed are extremely

difficult and time-consuming to devise. Once devised, they are

difficult and time-consuming to apply. The details of this are

enough to fill another paper.

The author is aware of no conformance mechanisms for

EIA-274-D, and close conformance to the standard appears to

be rare or non-existent.

BCL did not seem to have formal conformance

mechanisms, but conformance (in the late 1990’s) appeared to

be excellent for two reasons. First, there was one primary

vendor for BCL controllers. Second, the users were mostly

large organizations (including the Rock Island Arsenal) with

many machining centers who made the same parts many times

and wanted to be able to use the same program on different

machines.

For many years, DMIS did not have conformance

requirements in the standard or conformance tests or

conformance testing services. All manner of non-conforming

implementations that claimed to use the standard came into

existence. Commercial systems that were only generally

similar to DMIS were built. Programs called DMIS programs

were rarely interoperable between vendors. It became clear

that something needed to be done to help achieve

interoperability. In 2001, conformance requirements were

included in DMIS 4.0. However, no conformance classes,

conformance tests, or conformance testing services were

defined at that time. Since then, conformance classes have been

defined as discussed earlier, and modest conformance tests

have been provided [12]. There is still no conformance testing

service.

VI. CONCLUSION

This paper has presented thirteen questions the potential

user of a machine control language standard might want to ask

in order to decide whether to use the standard. Reasons why

the questions should be asked have been given. As examples,

partial answers to the questions have been provided for four

machine control language standards.

287
REFERENCES

[1] Bray, T., et al., (editors), “Extensible Markup Language

(XML) 1.0 Fourth Edition”, World Wide Web Consortium,

http://www.w3.org/TR/2006/REC-xml-20060816, 2006.

[2] Consortium for Advanced Manufacturing - International,

“Dimensional Measuring Interface Standard Part I, Revision

05.0”, Consortium for Advanced Manufacturing -

International, 2004.

[3] Dimensional Metrology Standards Consortium,

http:www.dmisstandard.org/content/blogsection/6/55, 2007.

[4] Electronic Industries Association, “EIA Standard EIA-274-D

Interchangeable Variable Block Data Format for

Positioning, Contouring, and Contouring/Positioning

Numerically Controlled Machines”, EIA, 1979.

[5] International Organization for Standardization, “ISO

International Standard 10303-11, Industrial automation

systems and integration — Product data representation and

exchange — Part 11: Description method: The EXPRESS

language reference manual”, International Organization for

Standardization, 2003.

[6] International Organization for Standardization, “ISO

International Standard 10303-21, Industrial automation

systems and integration — Product data representation and

exchange — Part 21: Clear text encoding of the exchange

structure”, International Organization for Standardization,

2002.

[7] International Organization for Standardization, “ISO

International Standard 10303-238, Industrial automation

systems and integration — Product data representation and

exchange — Part 238: Application protocol: Application

interpreted model for computerized numerical controllers”,

International Organization for Standardization, 2007.

[8] International Organization for Standardization, “ISO

International Standard 14649-10, Industrial automation

systems and integration — Physical device control — Data

model for computerized numerical controllers — Part 10:

General process data, second edition”, International

Organization for Standardization, 2004.

[9] International Organization for Standardization,

“International Standard ISO 14649-11, Industrial

automation systems and integration — Physical device

control — Data model for computerized numerical

controllers — Part 11: Process data for milling, second

edition”, International Organization for Standardization,

2004.

[10] International Organization for Standardization,

“International Standard ISO/IEC 14977, Information

technology — Syntactic metalanguage — Extended BNF”,

International Organization for Standardization, 1996.

[11] Kearney & Trecker Corporation, “Part Programming and

Operating Manual, KT/CNC Control Type C”, Pub 687D,

Kearney & Trecker, 1979.

[12] National Institute of Standards and Technology, “DMIS

Test Suite”, http://www.isd.mel.nist.gov/projects/

metrology_interoperability/dmis_test_suite.htm, 2007.

[13] Numerical Control BCL Standards Association, “NCBSA

Standard Proposal for EIA 494 C, Basic Control Language

(BCL) An ASCII Data Exchange Specification for

Computer Numerical Control Manufacturing”, Numerical

Control BCL Standards Association, 1996.

[14] Walmsley, P. (editor), “XML Schema Part 0: Primer

Second Edition”, World Wide Web Consortium, http://

www.w3.org/TR/2004/REC-xmlschema-0-20041028,

2004.

[15] World Wide Web Consortium, http://www.w3.org/XML/

Core/#IPR, 2007.

288
Interoperability Testing for 

Shop Floor Measurement

Fred Proctor 

NIST 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

frederick.proctor@nist.gov

Bill Rippey 

NIST 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

John Horst, Joe Falco and 

Tom Kramer 

NIST 

100 Bureau Drive, Stop 8230 

Gaithersburg, MD 20899 

Abstract— Manufactured parts are typically measured to 

ensure quality. Measurement involves equipment and software 

from many different vendors, and interoperability is a major 

problem faced by manufacturers. The I++ Dimensional 

Measuring Equipment (DME) specification was developed to 

solve interoperability problems and enable seamless flow of 

information to and from dimensional metrology equipment. 

This paper describes validation testing of the I++ DME 

specification. The testing was intended to improve the 

specification and also to speed up its adoption by vendors. 

Testing issues are described, and a software test suite is 

detailed. Interoperability testing with real equipment was done 

over several years, and lessons learned from the testing will 

be presented. The paper concludes with recommendations for 

improving this type of testing. 

Keywords: interoperability, measurement, software testing

I. INTRODUCTION

Automated geometric inspection of parts is done using 

coordinate measuring machines (CMMs). Traditionally, CMM 

vendors have sold a tightly-coupled software-hardware system 

for programming and controlling the inspection process. The 

last 15 years have seen large manufacturers acquire CMMs 

from many different vendors and endure the overhead of 

supporting multiple software applications. Further, 3rd party 

software vendors have been offering high quality products 

that often cannot be used because they are incompatible with 

some CMMs. 

Automakers are major users of measurement equipment, and 

suffer from the cost and time to work around these 

incompatibilities. They have responded by supporting a 

specification for dimensional measurement equipment 

interoperability, called the I++ Dimensional Measuring 

Equipment Interface specification (I++ DME). The goal of 

I++ DME is to allow automakers, and any other 

manufacturers, to select the best software and equipment for 

their purposes and budgets and ensure that they work together 

seamlessly out of the box. 

Specifications, like any result of a human endeavor, are 

never perfect and need to be tested (validated) to make sure 

they fulfill their requirements. For I++ DME, this means 

answering the questions, “Does I++ DME handle all of 

today’s measurement activities, or are important types of 

measurements or equipment left out? Is the specification 

written clearly and unambiguously, or will implementers have 

to make assumptions?” Likewise, products that claim to 

support I++ DME are never perfect and need to be tested 

(verified) to make sure they comply with the specification. 

This means answering the questions, “Does the product send 

only valid I++ DME messages? Does it respond appropriately 

to both valid and invalid messages?” 

NIST has written an I++ DME test suite designed to help the 

specification writers make a better specification and the 

product vendors make better products. The test suite includes 

a simulated client that acts as the software that runs 

measurement plans, and a simulated server that acts as the 

equipment that makes the measurements. Test scripts cover all 

measurement activities, from startup through measurement 

and shutdown, including error conditions. A logging feature 

allows for later analysis of test results. 

The I++ DME has undergone testing in a series of 

demonstrations involving real software and equipment at 

several important international quality technology expositions, 

including the 2004 International Manufacturing Technology 

Show (IMTS), the 2005 Quality Expo, and the 2005 – 2007 

Control Shows. These multivendor demonstrations have 

included combinatorial testing of several software packages 

with several measurement machines. Comments from the 

participants, and their continuing participation, show that this 

level of testing rigor is valuable and helps to ensure quality 

products that meet customer requirements. 

II. THE MEASUREMENT PROCESS

Before parts can be measured, they must be designed and at 

least partially manufactured. Design is normally done using 

computer-aided design (CAD) workstations that generate 

electronic design files that define the product requirements for 

subsequent downstream manufacturing operations. From the 

point of view of measurement, the design files contain 

dimensions and tolerances, and other requirements such as 

surface finish. A standard for the output of CAD information 

is ISO 10303, “Standard for the Exchange of Product Model 

Data,” also known as STEP [1]. STEP Application Protocol 

289
(AP) 203 deals with design data; the second edition includes 

geometric dimensioning and tolerancing. 

Although not part of the measurement process, computeraided manufacturing (CAM) and computer- numerical control 

(CNC) are steps that define how the part is to be 

manufactured. It is worth noting that manufacturers would 

like to inspect as much as possible on the equipment used to 

manufacture the parts, in order to save the time it takes to 

move parts between equipment. Supporting this flexibility is 

one goal of interoperability specifications like I++ DME. 

Given a part design, measurement plans are then developed 

which guide how specialized equipment or human experts are 

to inspect the part. A standard for the output of measurement 

planning is the Dimensional Measuring Interface Standard 

(DMIS) [2]. DMIS plans define the measurement sensors to 

be used (typically touch probes), features to be measured 

(such as surfaces and holes), and reports to be made. 

Measurement plans are executed by software that connects to 

measurement equipment such as coordinate measuring 

machines. During this phase, commands are directed toward 

the equipment to select sensors, capture points of interest and 

return the results. Measurement plans may consist of 

thousands of individually acquired points, with coordinate 

systems set and branch points taken depending on 

intermediate results. The I++ DME specification covers the 

exchange of data between the execution software and the 

measurement equipment. 

Once measurement data has been acquired, an analysis phase 

is performed in which the raw results are compared against 

the design requirements (e.g., dimensions and tolerances) so 

that quality conclusions can be made. A draft standard for 

reporting results is the Dimensional Markup Language (DML), 

being prepared by the Automation Industry Action Group. 

While interoperability between these different phases of 

measurement is the overall goal, this paper focuses on 

validation testing of the I++ DME specification. The authors 

are conducting similar testing on STEP, DMIS and DML. 

III. CHALLENGES FOR STANDARDS-BASED MEASUREMENT

A challenge for any standards-based activity is constraining 

the data exchange to a set that can be documented and thus 

standardized, while enabling vendors to innovate their 

products and thereby benefit manufacturers. For measurement, 

this challenge is made more difficult by the wide range of 

equipment used for measurement, and the many types of 

measurements done. For example, measurement equipment 

includes sensors such as touch-trigger probes, capacitance 

gages, lasers and other optical sensors; and machines ranging 

from small hand-moved portable arms through large 

granite-based fully automatic coordinate measuring machines. 

This technology continually evolves, and defining a set of 

capabilities to be used as the basis for a standard is difficult 

and requires compromise. In any case, there must be a process 

in place to revise the standard as technology improves and 

new sensors and measurement capabilities become available. 

IV. THE I++ DME SPECIFICATION

The I++ committee is comprised of measurement equipment 

end users primarily from the automobile manufacturing sector. 

The I++ Dimensional Measuring Equipment (DME) 

specification [3] was written by I++ members and targeted 

toward equipment and software vendors. The goal was to 

enable manufacturers to pick best-in-class equipment and 

software reflecting their particular needs for sensor type, part 

size and measurement tasks. 

I++ DME is a messaging protocol between measurement 

plan executors and measurement equipment. It uses TCP/IP 

sockets as the communication mechanism, and defines a 

message set and a client-server architecture. Clients are 

measurement plan executors, and servers are the equipment 

that carries out the measurements. For example, a client could 

read DMIS measurement plans produced by some upstream 

application, interpret the DMIS statements, send I++ DME 

messages to the measuring equipment, accumulate the 

measurement results that return as I++ DME messages from 

the server, and output a DMIS or DML measurement report. 

This is shown in Figure 1. 

I++ DME consists of Unified Modeling Language (UML) 

descriptions of the messages, accompanied by natural 

language (English) that describes the semantics. Production 

rules in Backus-Naur Form (BNF) are provided that define the 

syntax of message composition. Numerous examples are 

provided as guidance to implementers. A sample I++ DME 

session is shown below, with messages from the client not 

underlined and responses from the server underlined. 

00002 StartSession() 

00002 &

00002 %

00003 GetDMEVersion() 

00003 &

00003 # DMEVersion(1.4.2)

00003 %

00027 ChangeTool("ProbeB") 

00027 &

00027 %

00078 SetProp(Tool.GoToPar.Speed(25.0)) 

00078 &

00078 %

00079 GoTo(X(2.626), Y(-4.656), Z(-4.100)) 

00079 &

00079 %

00094 PtMeas(X(2.47), Y(-4.13), Z(-5.10), 

IJK(-0.01,-0.99,-0.00)) 

00094 &

00094 # X(2.44), Y(-4.64), Z(-5.99), 

IJK(-0.019,-0.997,0.074)

00094 % 

V. I++ DME TESTING

As a product of a human endeavor, the I++ DME 

specification inevitably contains errors. The purpose of 

validation testing is to find the errors and suggest changes to 

the specification that fix the errors, before the specification is 

published and implementations are released. Validation 

ensures that the specification is complete, correct and 

290
unambiguous. “Complete” means that it covers all the 

requirements set forth by the I++ members. Due to 

compromises, these may not completely satisfy the 

requirements of everyone. Nevertheless, it is the job of

validation testing to discover any requirements that are not

expressible in I++ DME. “Correct” means that there are no 

factual errors, including typographical errors but also 

inconsistencies in descriptions and conflicts with stated

requirements. “Unambiguous” means that two readers of the

specification will agree what is meant. This is difficult to 

achieve in practice, if for no other reason that the authors do 

not all speak the chosen natural language (English) as their 

native language. Ambiguity can be mitigated through the use

of pictures or figures, and good examples. 

Another objective of testing was to speed the 

commercialization of products that support I++ DME. This 

was achieved as a side effect of including vendors in the 

testing activities. 

Testing can also lead to product conformance, if the testing 

tools persist after validation testing has concluded. In this case, 

all the hard work of testing can benefit newcomers, who can

run the tests themselves privately and improve their products 

before releasing them. 

The approach to testing taken by the authors was to provide a 

software test suite that enables controlled, comprehensive 

testing, in source code, paired with a series of public 

interoperability tests and demonstrations at trade shows that 

included real products and real measurement tasks. 

VI. THE I++ DME TEST SUITE

The I++ DME Test Suite [4] was written by the authors as a 

utility to enable internal testing of conformance to the 

specification. It is comprised of two applications, a server and

a client, many test scripts, and source code for a C++ class 

library and parsers that parse client and server messages. The 

source code is free and intended to help newcomers

implement I++ DME without having to incur the tedium of

developing message handling code. 

Figure 2 shows the I++ Server Utility. The server simulates 

the response of measurement equipment to I++ commands,

maintaining a coarse world model and simulation of a 

coordinate measuring machine and responding plausibly to

requests from a client. Developers of client software typically

use the Server Utility as a stand-in for real servers (e.g., 

coordinate measuring machines) that are expensive to obtain. 

Developers of client software can use the Server Utility to 

verify that their commands are valid, and to see what 

responses they should be prepared to receive. The Server first 

opens up a socket on a port specified by the user, and awaits

connections from a client. Every message received or sent by

the Server is logged, displayed in a window and written to a 

file. Some attributes of the simplified models are configurable, 

for example the radius of the probe. 

Fig. 1. The I++ DME activity model. 

Figure 3 shows the I++ Client Utility. The client simulates 

the actions of plan execution software, sending requests to the 

server to select sensors and measure attributes of the part, and

collecting responses back for later analysis. Developers of

server equipment typically use the Client Utility as a stand-in

for execution software. This allows them to see what

commands they are expected to handle, and to check that their 

responses are valid. The client connects to a running server on

a socket specified by the user, who then loads a script file for 

reading and execution, similar to the excerpt shown below:

Fig. 2. The I++ DME Server Utility used is a surrogate for 

measuring equipment, used for testing client software.

291
AlignPart(1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 2.0) 

AlignTool(0, 0, 1, 30) 

CenterPart(2.0, 3.0, 4.0, 0.1) 

ChangeTool("Probe1") 

Each script file of I++ DME commands has an associated 

response file that is compared against what is received from

the server. If responses don’t match what is expected, errors

are noted in the log file. These errors are not necessarily true

errors, since the server messages in general include data

points that vary depending on the actual sensed values of

probe points. Strict comparisons against a pre-written

response file may not match exactly yet still be valid. This is a 

challenge for automated testing, and one that requires

balancing the difficulty of building an intelligent automated 

analysis tool against the value it provides, given that people

will eventually be viewing the results and can be expected to 

make more difficult determinations of acceptability. 

VII. PUBLIC DEMONSTRATIONS

The I++ Test Suite allows developers to build compliant

applications within their companies and test them before 

releasing them to their customers. At some point, applications

will be run in production at customer facilities, and will 

interface with compliant applications from other vendors. It is 

important to have some experience with production

interoperability prior to full release. This is the purpose of

public demonstrations. 

Three I++ public demonstrations have taken place, during 

the Control Shows in 2005, 2006 and 2007. The participants 

varied during each show, with the intent to include some

number of client providers (e.g., measurement plan execution

software developers) and some number of server providers 

(e.g., coordinate measuring machine builders). In 2007, the 

public demonstration included six clients and four servers, for 

24 combinations possible for testing. 

Fig. 4. Representative automobile part used for public 

demonstrations. 

Fig. 3. The I++ DME Client Utility is a surrogate for measuring 

plan execution software, used for testing measuring equipment.

Unlike private testing with the I++ Test Suite, public 

demonstrations used real measurement plans (e.g., DMIS or

some vendor proprietary plan formats) and real parts. A

representative automobile part was selected, as shown in 

Figure 4. No test scripts were used, and thus no pre-written

response files were written. Tests were done point-to-point, 

client-to-server, with people observing the measurement

process on the machines and determining if the results of the 

measurement were acceptable. 

The burden on the test judges was lessened somewhat by

their experience with the test part. It was usually obvious 

when failures occurred, and where the source of the problem

lay. If each test took place with a randomly-generated part, 

understanding what constitutes correct measurement would

have been more difficult. The challenge is therefore to select a 

part with enough features to cover what is required by most

manufacturers, simple enough to machine easily. 

VIII. RECOMMENDATIONS

Practical experience with the I++ Test Suite and the series of

public demonstrations has led to some recommendations for

others who are undertaking similar validation efforts. 

• Pre-testing components with simulated “mates” uncovers

many simple errors that can be fixed early, saving time at 

the more expensive public demonstrations or installations

on plant floors. 

• Misinterpretation of specifications by people is to be

expected. Formal methods of describing syntax and if

possible semantics are preferred over natural language,

especially when the audience members do not all speak the 

natural language natively.

• Examples should be provided where possible. Forgo the 

temptation to write all examples in the same style. For

292
example, if the specification allows variations in white 

space, examples should show this variation. 

• Where the specification is ambiguous, expect that two 

developers will each interpret it differently. In cases where 

the resolution is a choice between two arbitrary options, 

each vendor will argue that their choice is the right one. 

There must be an arbiter whom all parties agree has the 

final word, and everyone must be prepared to go back to 

their benches and change. 

• Standards validation is expensive, and should include 

line-by-line reading of the specification by experts; 

ongoing meetings to discuss revisions to the specification; 

development of testing tools to be shared by all 

participants; and commitment to a series of public 

interoperability testing under real-world conditions. 

REFERENCES

[1] S. Kemmerer, Editor, “STEP: The Grand Experience.” NIST Special 

Publication 939, July 1999. 

[2] Consortium for Advanced Manufacturing - International, “Dimensional 

Measuring Interface Standard,” Revision 3.0, ANSI/CAM-I 101-1995. 

[3] International Association of CMM Vendors, “I++ DME,” Version 1.5. 

Available: www.isd.mel.nist.gov/projects/ 

metrology_interoperability/specs/idmespec.1.5.pdf 

[4] J. Horst, T. Kramer, J. Falco, W. Rippey, F. Proctor and A. Wavering, 

“User's Manual for Version 3.0 of the NIST DME Interface Test Suite for 

Facilitating Implementations of Version 1.4 of the I++ DME Interface 

Specification,” October 4, 2002. 

Available: www.isd.mel.nist.gov/projects/metrology_interoperability/ 

NISTI++DMEtestSuite3.0UsersManual.pdf 

293
Virtual Mentor: A Step towards Proactive User 

Monitoring and Assistance during Virtual 

Environment-Based Training 

Maxim Schwartz 

Energetics Technology Center 

P.O. Box 601 

La Plata, MD, USA 

mschwartz@etcmd.org

S.K. Gupta and 

D.K. Anand 

Center for Energetic Concepts 

Development 

University of Maryland 

College Park, MD, USA 

skgupta@eng.umd.edu 

dkanand@umd.edu 

Robert Kavetsky 

Energetics Technology Center 

P.O. Box 601 

La Plata, MD, USA 

bob.kavetsky@verizon.net 

Abstract—This paper describes a component of the Virtual Training 

Studio called the Virtual Mentor, which is responsible for interacting 

with the trainees in the virtual environment and proactively 

monitoring their progress. The Virtual Mentor is a component that is 

embedded in the Virtual Workspace. Some of the tasks it performs 

are driving the interactive simulation code generated by the Virtual 

Author, executing user testing, logging user actions in the virtual 

environment, detecting errors and providing detailed messages and 

hints, and assisting the instructor in tailoring the generated training 

material to increase training effectiveness. This paper presents some 

of the technical challenges and solutions as well as the rationale 

behind the Virtual Mentor design. 

Keywords: Virtual environment-based training, assembly modeling 

and simulation, proactive user monitoring and assistance in virtual 

training 

I. INTRODUCTION

Due to the rapid inflow of new technologies and their 

complexities, accelerated training is a necessity in order to 

maintain a highly productive manufacturing workforce. We 

believe that existing training methods can be improved in 

terms of cost, effectiveness and quality through the use of 

digital technologies such as virtual environments. Personal 

virtual environments (PVEs) offer new possibilities for 

building accelerated training technologies. 

We are developing a virtual environment-based training 

system called Virtual Training Studio (VTS) [1]. The VTS 

aims to improve existing training methods through the use of 

a virtual environment-based multi-media training 

infrastructure that allows users to learn using different modes 

of instruction presentation while focusing mainly on cognitive 

aspects of training as opposed to highly realistic 

physics-based simulations. 

The VTS system has two main goals. The first goal is the 

quick creation of virtual environment-based instructions for 

training personnel in the manufacturing industry so that an 

overall training cost reduction can potentially be realized by 

the use of our system. The second goal is to accelerate the 

training process through the use of adaptive, multi-modal 

instructions. With VTS, training supervisors have the option 

of employing a wide variety of multi-media instructions such 

as 3D animations, videos, audio, text and interactive 

simulations to create training instructions. The virtual 

environment enables trainees to practice instructions using 

interactive simulation and hence reduces the need for 

practicing with physical components. Our current system is 

designed mainly for training of cognitive skills: training 

workers to recognize parts, learn assembly sequences, and 

correctly orient the parts in space for assembly. The VTS is 

designed to be an affordable training tool. Hence we 

developed a low-cost wireless wand and use an off-the-shelf 

head mounted display (HMD). 

The VTS system consists of the following three modules: 

• Virtual Workspace: The objective of this component of 

the VTS is to provide the infrastructure for multimodal 

training and to incorporate the appropriate level of 

physics-based modeling that is suitable for the operation 

of a low-cost PVE. Virtual Workspace contains the 

necessary framework to allow manipulation of objects, 

collision detection, execution of animations, and it 

integrates the software with the hardware in order to give 

the user an intuitive, easy to use interface to the virtual 

environment. Virtual Workspace offers three primary 

modes of training: 3D animation mode, which allows 

users to view the entire assembly via animations; 

interactive simulation mode, which is a fully user-driven 

mode that allows users to manually perform the assembly 

tasks; and video mode, which allows users to view the 

entire assembly via video clips. Trainees can switch 

between these modes at any time with the click of a 

button. 

294
• Virtual Author: The goal of the Virtual Author is to 

enable the user to quickly create a VE-based tutorial 

without performing any programming [2]. The Virtual 

Author package includes a ProEngineer assembly import 

function. The authoring process is divided into three 

phases. In the first phase, the author begins with a 

complete assembly and detaches parts and subassemblies 

from it, creating an assembly/disassembly sequence. In 

the process of doing this, the instructor also declares 

symmetries and specifies the symmetry types. In the 

second phase, the instructor arranges the parts on a table. 

In the third and final phase, the instructor plays back the 

generated assembly/disassembly sequence via animation. 

During this final phase, text instructions are generated 

automatically by combining data about collision 

detection and part motion. 

• Virtual Mentor: The goal of the Virtual Mentor is to 

simulate the classical master/apprentice training model 

by proactively monitoring the actions of the user in the 

Virtual Workspace and assisting the user at appropriate 

times to enhance the user’s understanding of the 

assembly/disassembly process. If users make repeated 

errors, then the system will attempt to clarify instructions 

by adaptively changing the level of detail and inserting 

targeted training sessions. The instruction level of detail 

will be changed by regulating the detail of text/audio 

instructions and regulating the detail level of visual aids 

such as arrows, highlights, and animations. This paper 

describes the Virtual Mentor module in detail. 

II. BACKGROUND

Development of the Virtual Mentor came about because of 

the need for an intelligent agent to operate inside the Virtual 

Workspace. Virtual Workspace was designed to be the basic 

infrastructure for running Virtual Author generated tutorials. 

It is capable of running animations, playing video clips, 

playing audio, and allowing the trainee to interact with objects 

in the virtual environment. It was also meant to give the 

trainee the capability to communicate with the Virtual 

Training Studio by manipulating virtual buttons on the virtual 

control panel and using wand commands by pressing buttons 

on the wand. Running interactive simulation, analyzing logs, 

and making intelligent decisions when generating tests, 

however, takes more complicated logic. Using a separate 

module to accomplish these tasks makes it easier to upgrade 

and tailor the intelligent behavior of the system. It also makes 

it easier to plug in the same functionality into other VTS 

components like the Virtual Author, if, for example, the 

instructor wants to simulate the training session on the fly 

within Virtual Author. The tasks of the Virtual Mentor can be 

divided into two categories: support for interactive simulation 

and adapting training material based on the performance of 

users. 

A good amount of work has been done in this area in the 

past. Some have worked on techniques to detect errors made 

by trainees during training sessions and generate hints to 

provide them meaningful feedback. An example of a system 

that uses these techniques is the Georgia Tech Visual and 

Inspectable Tutor and Assistant, a tutoring system designed to 

teach satellite control and monitoring operations [3]. Lessons 

can be assigned one of many styles of tutoring ranging from 

demonstration via animation with little control of the lesson 

by the user-to-system monitoring of trainee progress with only 

occasional intervention by the system. In effect the tutor 

“fades” as the trainee progresses through the curriculum. Each 

lesson specifies performance requirements, which the student 

must satisfy to proceed to the next lesson. Another example of 

this type of system is Steve, an animated agent who helps 

students learn to perform procedural, physical tasks in a 

virtual environment [4]. Steve can demonstrate tasks, monitor 

students, and provide basic feedback when prompted by 

trainee. Steve signals mistakes with shaking of the head and 

saying “No”. Yet another good example is a system designed 

by Abe et al., which teaches novices assembly and 

disassembly operations on mechanical parts inside a virtual 

environment by showing a technical illustration to trainees 

with lines representing assembly paths [5]. The hand motions 

of trainees are tracked and errors are detected. Trainees are 

alerted when they grasp wrong parts or move parts in the 

wrong direction. Monitoring errors and user actions in spatial 

manipulation tasks and providing highly descriptive feedback 

will require development of new types of algorithms. 

Some work has also been done on intelligent adaptive 

tutorials. Various researchers have developed next generation 

tutorials that can adapt their instructions based on a user’s 

capability and progress. Such systems, which adapt 

instructions to specific users, often use machine learning 

techniques from the artificial intelligence community. An 

example of this is AgentX, which uses reinforcement learning 

to cluster students into learning levels [6]. AgentX chooses 

subsets of all hints for a problem (instead of showing all 

possible hints) based on student’s learning level. Students are 

grouped into levels based on pretests and their subsequent 

performance. If pretest data are not available for a student, 

then that student is automatically placed in level L4, which 

represents students who perform in the 50th percentile of the 

performance distribution. 

Subsequent sections will explain the techniques used by 

Virtual Mentor and the rationale for those features. Section III 

presents all aspects of running interactive simulation. These 

include handling of part and assembly symmetries, detecting 

and reporting errors based on the symmetries, and using 

symmetry data to improve the quality of dynamic animations. 

Section IV discusses the initial testing that lead to the 

development of Virtual Mentor and the idea of an intelligent 

agent. Section V explains the technical details of logging, log 

analysis, and generating tests tailored to trainees. Finally, 

Section VI presents some concluding remarks and presents the 

future path of Virtual Mentor to achieving more autonomy in 

custom tailoring of tutorials. 

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III. HANDLING OF SYMMETRIES AND ERROR DETECTION

A. Use of Part Symmetries to Check for Correct Placement 

According to the case studies and the system testing 

conducted to the date invloving VTS, interactive simulation, 

involving manual assembly, turned out to be a popular system 

capability among users. An important aspect of a 

well-designed interactive simulation is the proper handling of 

symmetries. In real world mechanical assemblies, very often 

there are parts that are highly symmetric along certain planes 

or axes. Such symmetries often mean that there is more than 

one correct insertion position and insertion orientation. The 

challenge of this problem is that the system is not aware of 

any symmetries and the only information it has access to is 

the single position and single orientation of each part within 

the overall assembly. This position and orientation were 

declared when the assembly was put together by the instructor 

in the virtual environment. The challenge for VTS is to find 

out what types of symmetries exist and to calculate other 

possible positions and orientations during interactive 

simulation. This allows a user to place a part that is symmetric 

in some way at one of the alternate insertion locations as it 

could be done in real life without the system giving an error. 

It also allows the user to use one of many clones of a part in 

the assembly process at a particular step without the system 

requiring the use of a particular clone. Proper implementation 

of symmetries speeds up the training process by not forcing 

the user to attempt various correct insertion locations or 

orientations until the user finally uses only those that were 

declared during assembly sequence demonstration inside the 

Virtual Author run virtual environment. 

Another reason why part symmetries need to be properly 

handled are animations. After the instructor demonstrates the 

assembly process in the Virtual Author monitored virtual 

environment, Virtual Author automatically generates the 

initial animation code, in the form of Python script, which will 

later be executed by Virtual Workspace where users train to 

create dynamic animations. The initial code, which does not 

take symmetries into account, will not produce efficient 

animations for parts that have symmetries. This is because the 

generated code will always instruct Virtual Workspace to 

animate the movement of a part to one particular position and 

orientation – declared by the instructor during the 

demonstrated attachment. In many cases, it would be better to 

animate the movement of a part to the nearest symmetric 

orientation or position. This speeds up the animation and 

reduces risk of confusing the trainee. 

The Virtual Mentor is responsible for enforcing correct 

attachments and insertions involving part/assembly 

symmetries, though the Virtual Author is used to declare and 

categorize the symmetries. When creating tutorials via the 

Virtual Author, the instructor specifies for each part that 

exhibits symmetry the main symmetry axis of the part. The 

main symmetry axis is the axis around which the assembly 

has the greatest number of allowable orientations. By 

allowable orientations, we mean that the assembly looks the 

same and can be attached to the receiving assembly with that 

orientation. If we use a tube as an example, the main 

symmetry axis would be the axis of the cylinder because the 

tube can be rotated around that axis infinite number of ways 

and will still look the same. The instructor also specifies the 

number of different permissible orientations around this axis. 

We call this type of symmetry type A. In addition to this 

information, the instructor declares a second type of 

symmetry for each step, which we call type B. In type B 

symmetry, the instructor specifies one secondary symmetry 

axis, which is perpendicular to the main symmetry axis and 

also specifies a sub-type. By declaring the secondary 

symmetry axis, the instructor states that the assembly being 

attached may be flipped 180 degrees around this axis and the 

attachment would still be correct. In addition to declaring a 

secondary symmetry axis, the instructor also specifies a 

sub-type. The specified assembly sub-type informs the system 

about what types of rotations are allowed around the 

secondary symmetry axis and whether an alternate insertion 

position may be used for a particular step. The current version 

of the Virtual Mentor simplifies the problem by allowing only 

one alternate attachment location for the part being attached to 

an assembly and only one alternate orientation around the 

secondary axis. Sub-types for symmetry type B in the current 

version are: 

• Sub-type B1: Allow primary position and primary 

orientation only 

• Sub-type B2: No alternate position allowed, but alternate 

orientation for primary position is allowed 

• Sub-type B3: Alternate position allowed but with 

primary orientation only (no alternate orientation for 

primary position) 

• Sub-type B4: All combinations of (alternate/primary) 

positions and orientations are allowed 

• Sub-type B5: Alternate position allowed but with 

alternate orientation only (no alternate orientation for 

primary position) 

We came up with a method to handle placement of parts at 

alternate locations that is not computationally expensive. Our 

current method causes the animation to always attach parts to 

their unique, designated locations and orientations, which 

were declared during instructor’s assembly sequence 

specification. This strategy simulates the placement of parts at 

their alternate locations and orientations, by rotating, 

swapping, and repositioning parts in a way that is least 

noticeable to the trainee before activating the animation 

mechanism, which is part of the Virtual Workspace 

infrastructure. 

One example of such swapping is how identical parts are 

handled. Upon loading all the parts, the Virtual Author 

automatically detects and marks identical parts. It does this by 

comparing the number of vertices and the bounding boxes of 

the parts. At the end of interactive simulation, right before the 

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animation that completes the step is activated, the system 

swaps clones depending on which clone was originally the 

designated attachment part for that particular step. This 

strategy once again allows the Virtual Workspace animation 

to always attach parts to their unique, designated locations and 

orientations. 

After the check for clones is made, the Virtual Mentor 

checks if the position of the released part is close enough to 

the ideal position(s) relative to the receiving assembly. The 

correct position for the attaching part depends on the sub-type 

of symmetry type B. For sub-type B5, for instance, there are 

two allowed positions – primary and alternate. The primary 

position is specified by the instructor explicitly via the Virtual 

Author. The Virtual Mentor automatically ascertains the 

alternate position for sub-type B5 by first drawing a vector 

from the primary insertion location to the final location and 

then doubling that vector. A marker is placed at the tip of this 

vector. The Virtual Mentor then checks if the released part is 

close to the alternate position. An example of sub-type B5 

symmetry is shown in Fig.1, where a primer retainer is being 

inserted into the inner tube. One interesting aspect of sub-type 

B5 symmetry is that if the alternate insertion position is used 

on the other side of the inner tube, then the primer retainer 

must have the alternate orientation relative to the receiving 

assembly so that it is once again facing the inner tube. 

Alternate orientation is achieved by rotating the primer 

retainer around the secondary symmetry axis 180 degrees. If 

the trainee has placed the primer retainer at its alternate 

position, then the Virtual Mentor checks if the primer retainer 

has the alternate orientation. If that is the case, the Virtual 

Mentor flips the receiving assembly/part, in this case the inner 

tube, 180 degrees around the instructor-specified secondary 

axis before passing control to the Virtual Workspace 

animation generating mechanism. By rotating the receiving 

assembly, the attaching subassembly is now at its primary 

insertion position and orientation, and as we already 

mentioned, all parts must be placed at their primary positions 

and orientations before animation is activated and the 

attaching part is inserted into the receiving part. In most cases 

the trainee does not notice this rotation. 

The final check that the Virtual Mentor makes is the 

correctness of rotation around the primary symmetry axis. If 

the placement is correct, the Virtual Mentor rotates the 

attaching part in increments based on the number of 

permissible orientations. For example, if this number is 3, 

then the increment is 120 degrees. If the number is 4, then the 

increment is 90 degrees. The system must rotate in these 

increments to make sure the user does not notice a change in 

rotation. By rotating in these increments, the Virtual Mentor 

takes advantage of the attaching part’s symmetry to conceal 

the rotation. The reason why the attaching assembly must be 

rotated at all is because without such “setup rotation” the 

animation will be forced to rotate the part until it reaches its 

designated orientation within the assembly, slowing down the 

training in the process. 

Fig. 2 shows an example of sub-type B1 symmetry. A front 

plate assembly containing the needle and needle valve is 

being attached to the engine block. There are no alternate 

orientations or positions. The trainee must place the front 

plate assembly very close to the primary orientation and 

positions declared by the instructor within theVirtual Author. 

Otherwise, an error message is given to the trainee describing 

the flawed orientation or position. 

Fig. 2. An example of sub-type B1 symmetry where only the 

primary position and primary orientation are allowed. In 

effect, there is no symmetry. 

Fig. 3 shows an example of sub-type B2 symmetry. An 

outer tube is being attached to the rest of the rocket motor 

assembly. Sub-type B2 symmetry says, “No alternate position 

allowed, but alternate orientation for primary position is 

allowed.” This means that the outer tube can only be attached 

from one side of the rocket motor assembly – the primary 

position declared during authoring. However, the outer tube is 

symmetric along the plane that is perpendicular to the outer 

tube’s main symmetry axis. The main symmetry axis of the 

outer tube is the axis of the cylinder. This means that if the 

instructor chooses a secondary symmetry axis that is 

perpendicular to the main symmetry axis and flips the outer 

tube 180 degrees around the secondary symmetry axis, then 

the outer tube will look the same and can be attached to the 

Fig. 1. Primer Retainer with Two Correct Insertion Position 

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rocket motor assembly with that orientation. This flipped 

orientation is called alternate orientation and for sub-type B2, 

it is allowed. In this scenario, the mobile part, the part being 

attached, also has an infinite number of symmetric 

orientations around the main symmetry axis. Since this 

orientation has to do with the main symmetry axis, it is type A 

symmetry. The trainee can use any orientation around the 

main axis during placement and the Virtual Mentor will allow 

that instead of generating an error. 

The two tutorials used in the latest case study contain 

twelve steps involving symmetries out of a total of nineteen 

steps. Three steps out of nineteen also involve the use of 

clones. During the case study, we observed users placing 

symmetric assemblies and parts at both their primary and 

alternate locations. The Virtual Mentor demonstrated 100 

percent accuracy in detecting alternate correct placements and 

allowing users to proceed. One such case that we observed 

was step three of the ejection seat rocket tutorial in which one 

of the users had to place cartridge propellant grain into a 

cartridge case. The propellant grain was cylindrical while the 

case was a tube. The user placed the cartridge propellant grain 

on the other side of the cartridge case, which was not the 

original insertion location declared in the Virtual Author. The 

Virtual Mentor correctly gave the user a success message and 

correctly animated the propellant grain going into the case 

from the alternate location. 

B. Error Messages 

Detailed and precise error messages are important in the 

quick diagnosis and resolution of a problem, like for example 

an incorrect assembly attempt. In order to provide detailed 

error messages and helpful hints in the event of a mistake, the 

Virtual Mentor must first determine exactly what type of error 

was made. The current version of the Virtual Mentor is 

capable of detecting four types of error: 

• Incorrect part used for a given step in the process 

• Part was placed in an incorrect position 

• Primary axis of the part is not correctly aligned 

• Part is not correctly rotated around the primary axis of 

the part 

• Primary axis of part is correctly aligned by object facing 

in the opposite direction 

Whenever the Virtual Mentor gives the third, fourth, or fifth 

error to the user, it draws the primary axis through the part 

which the trainee attempted to assemble to another part or 

subassembly. This way the trainee knows exactly what axis is 

being referred to by the Virtual Mentor. 

In the process of testing our system using volunteers, we 

observed that when trainees paid attention to the text error 

messages, they corrected their mistakes more quickly, on 

average, in order to complete the step. Trainees who, for 

whatever reason, did not pay attention to the text errors took 

significantly longer, on average, to correct their mistakes. 

For the two tutorials used in our case studies, the Virtual 

Mentor reported a total of 146 errors during training. While 

monitoring the training of each trainee in VTS, no error 

detection or error classification mistakes on the part of the 

Virtual Mentor were observed. One of the instances of error 

detection and classification that we observed typified the 

detection and classification of an error by the Virtual Mentor. 

In the fourth step of the model airplane engine tutorial, a 

trainee had to place a cylinder head on top of the engine case. 

The cooling fins on the cylinder head had to be aligned 

parallel to the crankshaft. The user positioned the cylinder 

head correctly above the engine case but did not align the 

cooling fins with the crankshaft. After signaling to the Virtual 

Mentor to complete the assembly by pushing the “Complete” 

button, the trainee received a text error message saying, 

“Error: The object which needs to be inserted is not oriented 

correctly.” The trainee then watched an animation of the step 

and completed it correctly. 

Fig. 3. An example of sub-type B2 symmetry where the 

attachable part (outer tube) can only be inserted at the primary 

position, but with either primary or alternate orientation 

(around the secondary axis). Secondary axis is perpendicular 

to the main axis, which is the axis of the cylinder. 

IV. ANNOTATION OF AMBIGUOUS INSTRUCTIONS

Once again the first major task of the Virtual Mentor is to 

provide support for interactive simulation by using 

information about part and assembly symmetries at each step 

in the assembly process to detect correct and incorrect part 

placements, report errors, and to prepare the part being 

attached for animation by performing a series of hidden 

rotations and translations. The second major task of the 

Virtual Mentor is to assist the instructor in adapting the 

training material based on the performance of trainees. The 

need for the second task came about as a result of some 

informal testing conducted early in the development of the 

VTS. 

As the infrastructure of the VTS was built up to a certain 

level and a sample tutorial was created, we used six 

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volunteers, consisting of graduate and undergraduate 

engineering students, to test the training effectiveness of the 

system and its user interface. At the time, the Virtual Author 

was not available so all the custom code needed for the 

tutorial was written manually in Python script by a 

programmer. The custom code included the text instructions, 

video and audio files, rules for dynamic animations, code to 

run interactive simulation, and variable detail visual hints to 

be used within interactive simulation. The six volunteers were 

trained inside the VTS to assemble a navy rocket that is a 

component of an ejection seat. After the virtual environment 

training with CAD models of these devices, the trainees were 

given actual parts and asked to assemble real devices. Even 

though most volunteers felt very confident after VTS training 

and felt they could easily assemble the real devices, a good 

number of them made some mistakes during the assembly of 

the real devices. What is interesting is that the errors were 

pretty consistently being made at a certain set of points in the 

assembly process. Rocket motor tutorial included an assembly 

step where the trainee must attach a small cap to one side of a 

rocket nozzle. The cap must be attached to the side of the 

nozzle with a relief. The animation that all volunteers saw 

during training showed the cap moving toward the side of the 

nozzle with a relief. Unfortunately, the limitations of the 

virtual reality display technologies used during testing made it 

difficult to see the relief due to a low 640 X 480 resolution. 

During physical testing some trainees attempted to attach the 

cap to the wrong side of the nozzle without the relief. There 

was another point in the assembly process that caused 

problems for several volunteers. Here the trainee must slide a 

rubber o-ring onto the right rectangular o-ring groove on the 

primer retainer. Some trainees slid the real o-ring onto the 

rounded grove next to it which is not designed for o-rings. 

The trainees who did this did not notice the difference 

between rounded and rectangular groves during virtual reality 

training. 

After the initial testing, we added more detail to the 

tutorials to highlight the problem areas. The added details 

were in the form of additional text and audio instructions and 

more detailed animations. Animations were expanded in 

certain steps to include flashing 3D arrows that pointed out 

important features of the assembly. Fig. 4 shows the second 

scenario where an o-ring must be rolled on top of a 

rectangular o-ring grove. 

After the changes were made, we conducted a second round 

of training and testing with another six volunteers. During the 

second round of testing, the new volunteers made fewer errors. 

These results showed that no matter how clear the instructor 

tried to be when generating the training material, certain flaws 

in the training material could only be detected after user 

testing and analysis of the results. This spurred the need for 

development of an intelligent agent operating inside the 

virtual environment that is capable of not only logging all the 

actions of the trainees during training sessions, but also 

capable of generating targeted tests, analyzing the results, and 

later even automatically adapting the tutorials. The more such 

tasks the Virtual Mentor can perform automatically, the less 

of a burden will be placed on the instructor. Current version of 

the Virtual Mentor performs logging during training sessions 

and tests within the virtual environment, analyzes the logs, 

generates tests that are customized for each trainee based on 

that trainee’s performance, and provides recommendations to 

the instructor. We envision the Virtual Mentor not simply 

giving the instructor advice on what parts of the tutorials to 

adjust, but actually adapting the tutorials automatically with 

the instructor’s approval. 

Fig. 4. Detail in the form of flashing arrows is added to the 

animation of rubber o-ring attachment to o-ring grove. 

V. LOGGING, ANALYSIS AND GENERATION OF CUSTOM TESTS

While trainees train to assemble a device in the Virtual 

Workspace and interact with the system, the Virtual Mentor 

logs a very wide range of events. Each event is logged with a 

timestamp representing the number of expired seconds since 

the beginning of the tutorial. Some of the events that the 

Virtual Mentor logs are: 

• Activation of buttons on the virtual control panel 

• Activation of animations 

• Activation of hints 

• Activation of video clips 

• Browsing of steps in the assembly process by skipping to 

the next step or going back to a previous one 

• Pick-up of objects 

• Release of objects 

• All errors detected during interactive simulation and the 

type of error 

• Successful completions of steps 

• Use of wand functions like rotation of objects with wand 

buttons and trackball 

In addition to events, the Virtual Mentor also periodically 

logs the position of the user’s head and the position of the 

wand. This information is logged in order to analyze the range 

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of users’ movement in the virtual environment. The amount of 

movement can later be used to determine the efficiency of the 

virtual room by answering such questions as: 

• Are the parts on the table spread out too much or 

arranged inefficiently causing excessive wandering? 

• Are the users moving and rotating objects manually by 

picking them up with the virtual laser pointer or are they 

using the wand buttons and trackball to rotate and move 

objects? 

• Are the users looking at parts from a different 

perspective by walking around them or are they picking 

up and rotating them with the laser pointer? 

• Should the size of the room be increased or decreased? 

The logs are stored as text files in a format that can be loaded 

into Microsoft Excel. A new file is generated for each trainee. 

After the training session inside the Virtual Workspace is 

over, the Virtual Mentor performs some analysis on the 

trainee’s log in order to generate the appropriate test for the 

trainee. The trainee receives a message from the Virtual 

Mentor that is displayed on the projector screen that a test is 

being generated. The trainee remains inside the Virtual 

Workspace, while the Virtual Mentor analyzes the log and 

generates the test. After the Virtual Mentor finishes analyzing 

the log, it generates new random positions for all parts on the 

table and chooses a subset of the training session assembly 

steps for the trainee to perform in the Virtual Workspace. 

Certain features like text and audio instructions, hints, videos, 

and step browsing are disabled during the test mode. The 

subset of steps the trainee is tested on contains about 50 

percent of the total number of tutorial steps. A certain number 

of steps is first chosen based on log data and the rest are 

picked randomly. 

The process of choosing test steps based on log data begins 

with extraction of the following information from the log: 

number and type of errors made, number and type of hints 

used, and the number of times the animation has been played. 

Each of these pieces of data is extracted for each step in the 

tutorial. Next, the Virtual Mentor gives each step in the 

tutorial a difficulty rating. When calculating the difficulty 

rating the Virtual Mentor uses the occurrence and the weight 

of the extracted events. Errors have a weight of 3, hints have a 

weight of 2, and animations have a weight of 0 or 1. The first 

animation event has a weight of 0 while all subsequent 

animation events for a particular step have a weight of 1. The 

reason why multiple animation events are used to gauge step 

difficulty is because it was noticed during user testing and 

case studies that some trainees used animations as hints 

instead of using the hint feature in interactive simulation 

mode. Those trainees would switch to the auto mode, play an 

animation, and switch back to the interactive simulation mode. 

Next, the Virtual Mentor sorts all steps in descending order 

based on the difficulty rating. 

After the steps have been sorted the Virtual Mentor must 

rearrange some steps depending on the error type of problem 

steps. There is only one error type that requires this – 

assembly sequence error. Assembly sequence error occurs 

when the trainee forgets what step to perform next by trying 

to attach the wrong part for a particular step. In order to test 

for assembly sequence memory, the Virtual Mentor must 

present the trainee with two steps – the step where the error 

occurred and the step before it. The only exception to that is if 

the step where this type of error occurred is the first step in 

the tutorial, in which case only the step where this error 

occurred will be used. To perform the rearranging of sorted 

steps, the Virtual Mentor visits each step in the queue where 

difficulties were detected. If a problem step S contains an 

assembly sequence error, then the Virtual Mentor moves step 

S – 1 in front of step S. 

After the rearrangement has been done, the Virtual Mentor 

takes the steps in the top fifty percentile and uses them as 

steps the user will be tested on. If the number of problem 

steps makes up less than fifty percent of the total number of 

tutorial steps, then the Virtual Mentor chooses some random 

steps as a filler. This strategy ensures that all trainees are 

given tests of the same length to maintain consistency for 

future gathering of statistics. 

The trainee is then put into interactive simulation mode and 

given the chosen test steps in the right sequential order. If the 

trainee performs a particular step correctly or makes three 

errors while in that step, the Virtual Mentor loads the next 

step in the queue. While the trainee is being tested, all of his 

actions are once again monitored and logged in a separate test 

log file. At the conclusion of the test, the Virtual Mentor 

analyzes the log file associated with the test and updates the 

master log associated with the used tutorial. The master log 

contains a sorted list of tutorial steps and errors associated 

with those steps. Steps at the top of the list have the highest 

occurrences of errors for all trainees. After updating the 

master log, the Virtual Mentor checks the top thirty three 

percentile of steps for changes in position. If a particular step 

in the top thirty three percentile advances to a higher position, 

the Virtual Mentor adds it to the list of steps to bring to the 

instructor’s attention. At the end of the analysis, if the list of 

changed steps is not empty, the Virtual Mentor sends out an 

email to the instructor containing the list of steps of a 

particular tutorial which have advanced in difficulty level as 

well as the error types that caused this rise. The logging and 

testing process flow is summarized in Fig. 6. 

Fig.6. Flow of Information in the Log Analysis Process 

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VI. CONCLUSIONS

The Virtual Mentor is a software component embedded in 

the Virtual Workspace which is responsible for proactive 

monitoring of trainees, logging their progress, automatically 

generating customized tests, and sending out reports to the 

instructor. The need for the Virtual Mentor arose as a result of 

informal user testing conducted to determine the training 

effectiveness of VTS. We realized that tests given to the 

trainees at the end of a tutorial can reveal confusing areas of 

the tutorial which may need additional detail for clarification. 

Current version of the Virtual Mentor alerts the instructor of 

the detected problems of the tutorial. Future versions of the 

Virtual Mentor will take over the task of changing the level of 

detail, automatically adding more detail to tutorials when 

problems are detected, and removing details after long periods 

of good trainee performance. The Virtual Author always 

generates a maximum level of detail when it automatically 

produces text instructions. Currently, the instructor is 

responsible for removing too much detail from text 

instructions and adding arrows to animations when necessary. 

Future versions of the Virtual Mentor will automatically 

control the detail level of generated text instructions, the level 

of details in animations, and the level of details in the hints. 

ACKNOWLEDGEMENT

This research is supported in parts by the Center for 

Energetic Concepts Development at the University of 

Maryland and Naval Surface Warfare Center at Indian Head. 

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Report on Panel Discussion on

(Re-)Establishing or Increasing

Collaborative Links Between

Artificial Intelligence and Intelligent Systems

B Brent Gordon

NASA Goddard Space Flight Center

Information Science and Technology Research Group

Greenbelt, MD 20771 USA

bgordon@backserv.gsfc.nasa.gov

I. INTRODUCTION

A panel discussion on “(Re-)Establishing or Increasing Collaborative Links Between Artificial Intelligence and Intelligent

Systems” was held on August 30, 2007, as a session of

the Workshop on Performance Metrics for Intelligent Systems

(PerMIS’07), 2007. The panelists were: James Albus, Senior

Fellow, Intelligent Systems Division, National Institute of

Standards and Technology; Ella Atkins, Associate Professor,

Aerospace Engineering, University of Michigan; Henrik Christensen, KUKA Professor of Robotics in the Computer Science

Department and Director of the Center for Robotics and Intelligent Machines, Georgia Institute of Technology; Lawrence

Reeker, Computer Scientist, Information Technology Laboratory, National Institute of Standards and Technology; Alex

Zalinsky, Director, CSIRO Information Communication Technology (ICT) Centre and Group Executive, Information and

Communication Sciences and Technology. The moderator was

Brent Gordon, Computer Scientist, Computer and Information Sciences and Technology Office, NASA Goddard Space

Flight Center. Finally, audience participation, in the form of

comments, opinions, or questions, was also encouraged. The

discussion lasted 90 minutes.

The premise of the discussion was that at least some parts of

the Artificial Intelligence community and Intelligent Systems

community, as represented at the conference, i.e., mainly

intelligent controls and robotics, have some fundamental goals

in common, despite very different histories and approaches to

them. Thus the main issues were to clarify what those goals

are in a way that can be understood by all, identify the major

difficulties in getting people from the different communities

to talk to each other, and suggest ideas that might have a

good chance of increasing communication and interaction, and

perhaps lead to more collaboration.

The next section is a highly condensed summary of the

discussion, following which we address what conclusions the

discussion explicitly and implicitly supports.

II. DISCUSSION SYNOPSIS

To begin with each panelist gave a short statement of their

views on artificial intelligence and intelligent systems. (Except

for Albus’s, these statements were not prepared in advance.)

Albus suggested that comparison with humans is a good metric

against which to measure high level autonomous systems, and

that it is time that the goal of developing a computational

theory of mind be treated as a serious scientific problem.

Atkins proposed that humans have not naturally evolved to

perform well in air and space, i.e., there are some areas

where machines can do better than humans, although we might

want intelligent machines to emulate humans in their decisionmaking. Christensen emphasized real world autonomy, and

described the robotic systems the EU-funded group he leads

is working on as well as relevant examples. Reeker then

brought up machine learning, and computational linguistics,

as examples of issues more in the realm of intelligence than

autonomy, and indicated that these areas took their inspiration

from cognitive science, not strictly neuroscience. Zalinsky

suggested taking a more bottom-up approach to problems

that require intelligent behavior, and that the key ingredients

for adaptation and learning are knowing how to represent

information in a way similar to the brain, and embodiment.

A. What are the goals or questions of common interest to the

AI and intelligent systems communities?

Christensen suggested that it depends on the project’s time

scale, since in such a collaboration up to a year may be

required for everyone to become comfortable with a common

vocabulary, and again emphasized the importance of working

with embodied systems. Atkins brought up the dichotomy

of symbolic modeling on the one hand and mathematical

and physics models on the other, with the necessity, or at

least common circumstance, of reasoning under uncertainty.

Albus noted that intelligent autonomous robots need elements

of both symbolic modeling and control theory. Christensen

proposed that a systems perspective would be required for a

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perceptual system to be able to recognize chairs after seeing

a single example of a chair. Louise Gunderson suggested

that aiming at human-level intelligence directly is too high

a goal, and advocated a roadmap strategy of starting with

models of simpler vertebrates. Atkins presented the ideas of

limiting perceptual algorithms in a domain-dependent way, and

connecting perceptual problems with action problems. Reeker

noted that we still don’t know how children acquire ontologies.

Steven Kalik observed that most people do and most machine

systems don’t represent a problem in multiple ways, and

suggested building a system that would do so and then select

the best approach to solving the problem. Albus favored the autonomous driving problem, as it is fundamentally locomotion,

it requires understanding space, time, dynamics, environmental

properties, and may require symbolic processing; and can

attract funding. Zalinsky preferred to emphasize the problem

of how to represent information. Christensen thought that

manipulation might be a good problem, in that intelligent

or automation systems of potential interest even to large

manufacturing can be completely worked out in smaller scale.

B. What are the interesting scientific questions that might

attract “both sides” if formulated in the right way?

James Gunderson pointed out that all organisms build

models of the world and operate with reference to that internal

model they have built. Raj Madhavan asked that if it all

came down to a matter of systems integration, what would

be the differences between an AI approach and an intelligent

systems approach to integration? Christensen suggested that

if integration is always feedforward while the situation is

normal, and feedback is activated only when something fails,

this might have a dramatic impact on how the system is

designed. Reeker mentioned that machine learning is getting

more attention and being more widely used. Someone from

the audience suggested that there is need for a more abstract

architecture that more easily allows new components to be

plugged in. Christensen observed that we need to think about

the right level of abstraction. Albus agreed that there are many

levels of abstraction within the context of any any problem.

C. What can we do that might have the best chance of

increasing the levels of communication, interaction, or collaboration among members of different communities with similar

motivations?

Atkins suggested improving the educational system, since

students now generally can’t learn both computer science and

physics, say, in depth. Christensen offered three specific ideas:

first, projects of a nature that encourage multidisciplinary

collaboration, and are long-term enough to allow participants

to build a common vocabulary, with some mechanisms to

encourage these projects; second, educational activities that

cross traditional group lines, such as summer schools aimed

at graduate students; third, other community-building mechanisms for meeting and communicating even in the absence of

existing collaborations. Zalinsky emphasized the importance

of computer scientists taking a multidisciplinary approach and

finding challenges that appeal to politicians and the public.

Albus mentioned the problems that exist in mastering domain

vocabulary, let alone multiple domains, and expressed the need

for an overarching architecture as a means for everyone to see

how their vocabulary would fit together with everyone else’s.

Kalik pointed out that mechanical and electrical engineering

both use the same vocabulary, even if computer engineering

doesn’t, there may be core components in common. Albus said

it is a very small set of basic concepts. Atkins suggested that

robotics may offer an option, as it exhibits high-level concepts

in a real-world application. J. Gunderson indicated that other

engineering disciplines have overcome the vocabulary problem, and seem able to work together. Atkins concluded with

the observation that collaboration has physical, computational,

and informational aspects to it.

III. CONCLUSIONS, AND FUTURE WORK

As suggested in the introduction, the first important assumption underlying even the idea of having this panel discussion

was that there are certain problems of common interest to

segments of both the Artificial Intelligence and Intelligent

Systems communities, and that in the long-run those subcommunities would benefit from greater interaction with each

other. The first conclusion is that this important assumption

is valid. For the panelists, as experts in their fields, were

definitely smart enough and opinionated enough to challenge

this assumption, which was stated explicitly at the time, if

they disagreed with it. But not only did they not challenge

it, they bought into it, both in their opening statements and

throughout the discussion, in the suggestions and elaborations

they proposed.

Continuing to consider the discussion as a whole rather than

the details of what was said, the major take-away message is a

resounding endorsement of the importance and the urgent need

to do something to increase the level of interaction between the

relevant sub-communities with common interests. Concerning

what problems would draw the widest audience, what mechanisms to use, etc., there were a number of suggestions, but in

the context of this panel discussion, all at the level of initial

brainstorming.

Thus, the final conclusion is that the next step should be

one or more planning meetings involving a modest number

of experts, whose goal would be to put together some specific

proposals along the lines of the suggestions that emerged from

this panel.

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