ARMY SCIENCE BOARD

AD HOC STUDY ON 

HUMAN ROBOT INTERFACE ISSUES

FINAL REPORT

SEPTEMBER 2002

Distribution: Approved for public release; distribution is unlimited
DISCLAIMER

This report is the product of the Army Science Board (ASB). The ASB is an

independent, objective advisory group to the Secretary of the Army (SA) and the Chief of 

Staff, Army (CSA). Statements, opinions, recommendations and/or conclusions contained 

in this report are those of the 2002 Ad Hoc Study Panel on “Human-Robot Interface

Issues” and do not necessarily reflect the official position of the United States Army or the 

Department of Defense (DoD).

CONFLICT OF INTEREST

Conflicts of interest did not become apparent as a result of the Panel’s 

recommendations.
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1. AGENCY USE ONLY (Leave Blank) 2. REPORT DATE

September 2002

3. REPORT TYPE AND DATES COVERED

Army Science Board – 2002 Ad Hoc Study

4. TITLE AND SUBTITLE

AD HOC STUDY ON HUMAN ROBOT INTERFACE ISSUES

6. AUTHOR(S)

Study Chairs: 

Dr. Prasanna Mulgaonkar – Chair; Dr. Herbert Dobbs – Co-chair

Panel Members:

Dr. John Blair Prof. Ka C. Cheok

Mr. Robert Dodd Dr. Mark Hofmann

Mr. David Martinez Dr. Christine Mitchell (AFSAB)

Dr. Roberta-Diane J. Perna

5. FUNDING NUMBERS 

N/A

7. PERFORMING ORGANIZATION NAMES(S) AND ADDRESS(ES) 

Executive Secretary

Army Science Board 

SAAL-ASB

2511 Jefferson Davis Highway

Arlington, VA 22202-3911

8. PERFORMING 

ORGANIZATION REPORT NUMBER

 N/A

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

HON Claude Bolton, Assistant Secretary of the Army for Acquisition, Logistics and Technology

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 Approved for Public Release

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13. ABSTRACT (Maximum 200 words)

The Army Science Board Panel was tasked to: (1) Examine Army, DARPA, Navy, Air Force and NASA unmanned

ground vehicle (UGV) and unmanned aerial vehicle (UAV) research and development efforts focused on human-machine 

interfaces, command and control of robots and supervisory control; (2) Project technologies and capabilities into the 2015-

2020 timeframe and assess technology voids that may remain; (3) Determine the availability issues for applicable

commercial systems and technologies; and (4) Propose cost-effective options or strategies for addressing identified

technology voids.

The Panel's findings discuss the absence of a systematic study of human-robot interface design and a disconnect 

between end-users and the development process. The Panel's overall recommendations include: (1) development of an 

operational architecture with support from experimental data collected in operationally relevant scenarios in realistic 

environments; (2) Formulation of an FCS Block 1 human-robot interaction architecture consistent the FCS Operational 

Requirements Document in time for the FCS Milestone B decision; and (3) Establishment of a Science and Technology 

program aimed at developing a technical architecture for human robot interactions focused on autonomous ground robots.

14. SUBJECT TERMS 15. NUMBER OF PAGES

Robotics, technical architecture, operational architecture, human-robot interactions, autonomy, unmanned systems, 

human engineering

16. PRICE CODE

N/A

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CLASSIFICATION OF REPORT

 Unclassified

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Unclassified

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Unclassified

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None

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89

Prescribed by ANSI std 239-18

296-102

TABLE OF CONTENTS

 

EXECUTIVE SUMMARY...........................................................................................................I

1 TERMS OF REFERENCE ................................................................................................1

2 INTRODUCTION ..............................................................................................................5

3 ROBOTICS AND ITS PLACE IN TRANSFORMATION ...................................................7

4 SOLDIER INTERACTIONS AND INTERFACES WITH UNMANNED SYSTEMS..........17

5 PANEL FINDINGS..........................................................................................................36

6 PANEL RECOMMENDATIONS......................................................................................41

APPENDIX A: PARTICIPANTS LIST ................................................................................. A-1

APPENDIX B: ACRONYMS................................................................................................ B-1

APPENDIX C: ARMY UGV ROADMAP.............................................................................. C-1

APPENDIX D: EXAMPLES, LESSONS LEARNED, AND BENEFITS ............................... D-1

APPENDIX E: STUDY REPORT DISTRIBUTION.............................................................. E-1

i

Executive Summary

The Army transformation initiative, and the drive toward lighter, more lethal, and highly 

deployable formations, and the parallel evolution of the battlefield towards a dynamic, nonlinear, 

and highly dispersed environment, leads to one clear conclusion: intelligent unmanned systems will 

not just be required, but indeed will be critical to the success of our future forces. The defense 

community has recognized the value of even the first-generation fielded, unmanned systems. For 

example Predator, the unmanned aerial vehicle, proved its value as a sensor and weapons platform 

in battlefields as diverse as Bosnia and Afghanistan. This has led to a call for increased use of 

unmanned systems from the highest levels of our military establishment. President Bush stated at 

The Citadel on December 11, 2001 that 

“We’re entering an era in which unmanned vehicles of all kinds will take on greater 

importance—in space, on land, in the air, and at sea.”

Further the Department of Defense (DoD) 2003 budget submission quotes Secretary Donald 

Rumsfeld: 

“The growing use of unmanned aerial vehicles, the effective utilization of real-time 

intelligence, and the coordination among special operations and allied forces all 

demonstrate the cutting edge of what military transformation can achieve and offer a 

glimpse of a future transformed joint force.” 

The Army Science Board (ASB) has also recognized and acknowledged the value of 

unmanned systems in several recent studies focused on Army transformation, including the 

Summer Studies in 1999 and 2000. The clear recognition in these studies was that human 

elements of the force will require and use unmanned systems in ways that involve close 

interaction. Further, because the technology required for full autonomy will not be available in the 

abbreviated time frame for the initiation of Army transformation, efforts should focus on 

controlled, semiautonomous operations. These two observations lead naturally to the question of 

how humans and unmanned systems interact. In this study, we focused primarily on the issues 

surrounding the interactions of humans with unmanned ground systems. The ground environment 

presents significant challenges to autonomous systems, in large part due to the navigation 

requirements created by the wide variability of the terrain, and the close proximity between the 

autonomous entities and humans in the environment. These challenges to automated systems 

operation make the task of effective human-robot interactions particularly important to mission 

success. Further, airborne vehicles alone cannot efficiently perform a large number of tasks that 

are important to the Army necessitating the use of unmanned ground vehicles. However, the 

general conclusions we reached and the recommendations that follow are applicable to airborne 

systems as well.

During our study, the Human-Robot Interface panel and its government advisors either 

visited or were briefed on various science and technology programs within the Army, Air Force, 

Navy, National Aeronautics and Space Administration (NASA), and the Defense Advanced 

Projects Agency (DARPA). We examined these programs to identify the research and 
ii

development activities being carried out to allow effective interactions1 between humans and

(semi)autonomous systems, and to identify how effectively such systems were being transitioned 

into operational use. 

Our key findings provide significant reasons for optimism as well as significant concern. 

First, we conclude that the technology for autonomous robotics has matured significantly. In 

particular, the Army Research Laboratory (ARL) lead robotics program, Demo III, has made 

significant progress in developing the perception technology essential to autonomous crosscountry navigation. While much research and development remains, the progress made in the last 

2 years (since the ASB last viewed the program) is remarkable. Nonetheless, we conclude that 

no existing programs systematically approach the challenges of interactions between humans and 

complex unmanned systems. Existing literature contains numerous examples that show that the 

lack of rigor in the design of interactions and interfaces between humans and complex systems can 

lead to catastrophic results (e.g., Three Mile Island, the USS Vincennes shoot-down of the Iran 

Air Airbus). If the human-robot interaction issue is not systematically addressed, we are 

concerned that similar catastrophic problems could arise in the application of robotic platforms in 

the Army. This, in turn, would result in severe setbacks to the induction of robotics into the force. 

Finally, we observed that no consolidated programmatic drivers are providing the “user pull” for 

semiautonomous and autonomous platforms that couple user needs directly with fundamental 

research. The 6.1, 6.2, 6.3, and 6.4 robotics communities are fragmented, with no single visible 

advocate or manager for these technologies in the Army. We are concerned that without such a 

driver, there is no motivation for hands-on experimentation with evolving unmanned systems. This 

in turn will restrict the development of effective interaction modalities, tactics, techniques, and 

procedures that will delay the induction of usable robotic entities into the force. 

Our recommendations are divided into three parts. First, we recommend that the 

requirements community, led by TRADOC and the schools, establish an operational architecture 

for autonomous robots, and validate the architecture through an aggressive program of hands-on 

usage and experimentation with available robots in the field; (e.g., by the Army National Guard, 

by the Opposing Force (OPFOR) at the National Training Center). Second, we recommend the 

creation of a new systems-oriented program for the analysis, understanding, development, and 

improvement of human-robot interactions. We recommend that ARL, in cooperation with 

DARPA and other technology and system developers be the steward for such a program. This 

should facilitate technology insights and lessons learned from the field use of robots and the real 

time feedback establish the baseline for future developments; a process that should promote spiral 

development. Finally, we recommend that the Army insist that the Future Combat System (FCS) 

Block 1 program have, at a minimum, follower robots with a significant level of autonomy and 

surveillance and reconnaissance robots that can operate in limited environments—capabilities that 

can developed by maturing the technology that exists today. 

 

1

 In this report, we use the terms unmanned systems and robots interchangeably. Further, we use 

the term interface and interaction interchangeably for reasons that are described later in the text.
1

1 TERMS OF REFERENCE

3
4
5

2 INTRODUCTION

Figure 1. Participants

Robotics TF

Page 3

Study Sponsorship and Participants

• Government advisors specifically selected to ensure:

– Views from stakeholder DoD organizations carefully considered in study

– Deliberations of panel coordinated with parent organizations

Task Force Members

Dr. Prasanna Mulgaonkar (Chair)

Dr. Herbert Dobbs (Co-Chair)

Dr. John Blair

Prof. Ka C Cheok 

Mr. Bob Dodd

Dr. Mark Hofmann

Mr. David Martinez 

Dr. Christine Mitchell (*)

Dr. Robbi Perna

SPONSOR: ASAALT

Executive Secretary

Dr. Patrick Eicker

Government Advisors

Mr. Jack Taylor (OSD)

Dr. Rene Dupontbriand (ARL)

ASB Secretariat Representative

MAJ Robert Grier

Other participants

Dr. Greg Canavan

Dr. Michael Krause

(*) Member AFSAB
6

Table 1. Meeting Schedule

Location Program(s) Reviewed Date Study Panel Attendees

Washington DC • Pat Eicker —Robotics Roadmap

• Weatherington (OSD-ATL) —UAV

• Deitchman (ONR)—Navy perspective

8/31/2001 Blair, Hofmann, Mulgaonkar, 

Dobbs, Mitchell, Krause, Perna

Ft Knox • LTC Abbott —Battle Lab 9/27-

9/28/01

Mulgaonkar, Dodd, Eicker, Blair, 

Perna, Hoffman, Martinez, 

Cheok, Dobbs, Mitchell

T. Indiantown 

Gap, PA

• ARL Demo III XUV demonstration Week of 

11/12/01

Blair, Mulgaonkar, Mitchell, 

Dobbs, Hoffmann, Taylor

Ft Leonard 

Wood –

Maneuver 

Support Center

• Chem school

• MP school

• Mini-flail demo

11/15/01 Blair, Dodd, Eicker, Hofmann

Huntsville • Ground robots

• Software engineering

• System simulation

• Comm and others

11/29-

30/01

Mulgaonkar, Dodd, Eicker, Blair, 

Perna, Hofmann, Martinez, 

Cheok, Dobbs, Mitchell

DARPA • Scott Fish – Perceptor, UGCV

• Doug Gage, Jean Scholtz – HRI

• Alan Rudolph

• Sam Wilson – MAV 

• Mike Leahy – UCAV

• John Bay

12/14/01 Mulgaonkar, Dodd, Eicker, Blair, 

Mitchell, Hofmann, Dobbs

NIST Jim Albus – robot architectures 12/14/01 Mulgaonkar, Dodd, Eicker, Blair, 

Mitchell, Hofmann, Dobbs

Natick Soldier 

Systems Center

Robotics for Objective Force Warrior 

Workshop

12/18-

19/01

Blair, Eicker

SRI 

International

Greg Myers—human robot interface issues; 

speech recognition, CommandTalkTM

02/05/02 Mulgaonkar, Dodd, Blair, Perna, 

Hofmann, Martinez

NASA Ames A. Vera & M Matessa—collaboration 

research at Ames

02/05/02 Mulgaonkar, Dodd, Blair, Perna, 

Hofmann, Martinez

Jet Propulsion 

Lab

HRI, cooperative behavior of robots, smart 

robot nav, robot software architecture, modsim

02/06/02 Mulgaonkar, Dodd, Eicker, Blair, 

Perna, Hofmann, Martinez, 

Cheok, Dobbs

Sandia National 

Labs

UGV programs, physics-based mod-sim, 

remote manipulation of hazardous objects 

02/07/02 Mulgaonkar, Dodd, Eicker, Blair, 

Perna, Hofmann, Martinez, 

Cheok, Dobbs

TACOM Jeff Jaczkowski — Vetronics Overview, 

Crew Integration & Automation Testbed 

ATD, Robotic Follower ATD & Vetronics 

Technology Testbed Demo

3/28/02 Mulgaonkar, Blair, Dobbs, 

Eicker, Cheok, Mitchell

At SRI

Arlington
7

3 ROBOTICS AND ITS PLACE IN TRANSFORMATION

One might ask, “why is the military interested in unmanned vehicles?” The obvious 

motivation for this interest is the benefit of using unmanned vehicles to keep soldiers out of 

harm’s way and increase soldiers’ effectiveness by providing access to areas that are otherwise 

inaccessible. A secondary benefit is the reduction in the weight of the unmanned platforms 

because of the reduced need to protect an occupant (e.g., by armor or environmental controls). 

Given these general military objectives, unmanned vehicles should have several general 

capabilities: to

1. Go somewhere

2. Do something

3. Report to someone or something 

These capabilities correspond to the basic and enduring requirements that soldiers be able to 

move, shoot, and communicate.

They also imply that unmanned vehicles include:

1. Locomotion and navigation systems—to get somewhere 

2. Mission equipment packages—to do something

3. Communication Links—to report to someone or something.

What is meant by locomotion and navigation in this context? Does it mean the ability to 

negotiate all types of terrain in all types of weather? Does it mean that continuous interaction 

with a soldier is required for locomotion control and navigation, or does mean that less is required 

of the soldier? 

What does a mission equipment package mean in this context? Does it mean that the vehicle 

carries sensors only, or weapons as well as sensors? How much must the soldier interact with the 

vehicle to use the mission equipment? Will different operators be needed for the unmanned 

system and the mission equipment? Do vehicles accept different mission packages, depending on 

the mission?

What is meant by communication links in this context? Do communication links connect the 

soldier to one vehicle or to multiple vehicles? Do communication links connect multiple vehicles 

for intervehicle collaboration? What types and amounts of communication flow over the links, 

and how many interactions take place between soldiers?

Lastly, to what extent should we permit unmanned vehicles do the above things without 

humans in the loop? What should they do by themselves or in combination with other unmanned 

vehicles, and what should be the role of soldiers? Answers to these questions will profoundly 

influence the design of unmanned vehicles as well as the force structures that will operate and 

maintain them.

To understand why unmanned systems are a critical enabler for the Objective Force2, it is 

instructive to examine a few relevant scenarios. In this section, we briefly discuss three aspects of 

unmanned systems, based on an examination of three sources. The first aspect is the “ensemble” 

 

2

 The Objective Force is the conceptual Army structure of the 2020 era.
8

concept articulated in many sources but brought to the forefront in the ASB 1999 Summer Study. 

The second is a blend of the operational concepts under discussion by the current FCS 

community, including sources such as ARL, the FCS Lead Systems Integrator (LSI), and the U.S. 

Army Tank-Automotive and Armaments Command (TACOM) Vetronics Technology Integration 

(VTI) communities. The third is the long-term operational concepts that set the roadmap for the 

tight integration of robots and soldiers into organic teams that fight together instead of soldiers 

who use the robots merely as tools with which they fight.

1999 Summer Study Vision

In the 1999 Summer Study, one of the key technology recommendations for producing a 

lighter fighting vehicle based force with the lethality and survivability of today’s heavy ground 

platforms was the concept of an ensemble of manned and unmanned systems (air and ground). An 

ensemble is a notional way to deconstruct the functions of a heavy platform (lethality, 

survivability, mobility, etc.) into individual elements that could physically disperse into 

components of less than 20 tons each. This concept is similar to the way in which a Navy battle 

group uses specialized components to perform specialized functions (carrier, anti-submarine 

screen, etc.) Figure 2 is a graphic representation of this concept.

Figure 2. Ensemble Vision

The success of such a scheme requires that unmanned systems constitute the majority of the 

ensemble components. In addition, the smaller size of the crew controlling an ensemble 

(compared to the crews that control current platforms) will require ensemble components that are 

essentially autonomous. Note also that the ensemble consists of autonomous air and ground 

platforms that operate together in concert with the manned elements of the force. The concept 

articulated in the summer study was that ensemble components be networked into a common 

Robotics TF

Page 8

The Ensemble Vision (From ASB SS 1999)

• Autonomous robotics is integral to the FCS/Objective force concept

• Interactions between humans and robots operating as teams is critical
9

C4ISR3 infrastructure, such that they would provide electronic sensor shields around the 

ensemble. The ensemble elements would have both ground and air mobility and would move and 

fight as a collective entity to achieve the mission characteristics required by the Force 

Commander.

The FCS vision

The vision of the unmanned system for the FCS that is the stepping-stone for the Objective 

Force is still evolving. Different organizations within the Army and its contractor base have 

developed concepts in which robotic platforms are used with a variety of different capabilities. 

Figure 3, a graphic from the FCS LSI envisions the use of a number of small (5 T) reconnaissance 

platforms similar to the ARL Demo III XUV, robotic mules for providing logistical support to 

dismounted units, and air vehicles of various sizes. While the various scenarios may have different 

details, they share several common features. They all postulate the use of both ground and air 

vehicles; they postulate the organic nature of the unmanned entities (i.e., control and ownership at 

lower echelons, even down to the squad); and they use robots as a screen around the manned 

entities. Finally, they postulate a lethal capability for the robots (i.e., they do more than 

reconnaissance, surveillance, and target acquisition [RSTA] missions). On the other hand, they 

differ in their assumptions about the robustness and availability of robotic capabilities.

 

3

 C4ISR: Command, control, communications, and computers, intelligence, surveillance, and 

reconnaissance.
10

Figure 3. The FCS is envisioned as having several robotic platforms

Given the uncertain availability of autonomy technology, and with a view to accelerate the 

adoption and use of unmanned systems in the Army, the ASB 2000 Summer Study recommended 

that the Army focus on semiautonomous robots for logistics. For example, in a convoy operation, 

humans could compensate for the limitation of today’s robots. The TACOM Robotic Follower 

program that adopted similar goals has developed scenarios where human operators use a 

combination of inputs to define the path that the robotic vehicles would follow. Figure 4 

graphically shows the TACOM vision, and their technical challenges.
11

TRL Milestone Chart Accelerated RF ATD

FY01 FY02 FY03 FY04 FY05 FY06

TRL= 3

Demo III Baseline Perception

METRIC

•Obstacle Detection

Positive .5 m (H)

Negative 1 m (W)

TRL= 3

Demo III Baseline Intelligence

METRIC

•Planning Capabilities - Plan 

around 5 m W obstacles using 

onboard perception only

•6 Operator Interventions/km

TRL= 3

XUV Follower Demo w/GPS 

Waypoints

METRIC

•Speed 

On road 30 kph

X-Country 15 kph

•Separation 500 m

SoldierRobot

Interface

Semiautonomous 

Perception

Intelligent 

Situational

Behavior

LeaderFollower

Technology

TRL= 6

Improve Obstacle Detection 

Algorithms

METRIC

•Obstacle Detection

Positive .3 m (H)

Negative .5 m (W)

TRL= 6

Vehicle Interface Testing

METRIC

•Workload/vehicle - 50% 

Reduction over Demo III

TRL= 6 

Intelligent Situational Behavior

METRIC

•Planning Capabilities - Plan to 

prevent communication loss or 

mobility kill.

•1 Operator Intervention/km

TRL= 6

•Improved Mobility Follower

•Waypoints Augmented with 

Terrain Intelligent Navigation

METRIC

•Speed

On road 65 kph

X-Country 40 kph

•Separation 5 km

TRL= 6

CAT Interface Testing

METRIC

•Workload/vehicle - 50% 

Reduction over Demo III

TRL= 6

Road Following and Collision 

Detection (Pedestrian & 

Vehicles)

METRIC Lane maintenance

Lateral error 0.1 m max

•Collision avoidance 100%

TRL= 6

Dismounted controller

METRIC

•Workload/vehicle - 50% 

Reduction over Demo III

TRL= 6

Incorporate onboard terrain 

database

METRIC

•Limited planning capability 

since LOS operation only

•1 Operator Intervention/km

TRL= 6

On-road LOS convoying

METRIC

•Speed 

On road 65 kph

X-Country N/A 

•Separation 20 - 100 m

TRL= 6 

Incorporate onboard terrain 

database

METRIC

•Planning Capabilities - Plan 

around 10 m W obstacles using 

onboard database 

•1 Operator Intervention/km

TRL= 6

•Dismounted follower using 

waypoints augmented with 

terrain intelligent navigation

METRIC

•Speed

On road 0-20 kph

X-Country 0-20 kph

•Separation 5 m - 1 km

TRL= 6

Improve Obstacle Detection 

Algorithms; subset of Demo 

III Sensor Suite.

METRIC Obstacle Detection

Positive .3 m (H)

Negative .5 m (W)

Figure 4. Goals of TACOM Robotic Follower Program

Objective Vision

We believe that a greater integration of humans and robots will characterize the battlefield in 

the Objective Force era (2020 and beyond). We anticipate that robots will operate in a team, 

where each team member (whether human or robotic) takes appropriate initiatives as 

opportunities present themselves. To this end, robots not only must have autonomy and reasoning 

capabilities about events in the world, but they also must communicate with each other and with 

their human teammates in ways that are natural for humans. No graphic depictions of such 

scenarios exist outside the realm of science fiction and movies such as the “Star Wars” series. 

However, these fictional scenarios do indicate that focusing on the natural modes of humanrobotic interactions will be a key driver to the development of both autonomy and interfaces.

Interactions vs. Interfaces

The terms of reference for this study use the word “interfaces” to define the subject of the 

study. The usage rationale for this derives from prior ASB observations that humans will control 

robots for the foreseeable future. Consequently, interfaces that humans will use to perform these 

control functions must be developed.

Early in our study, it became clear that the problem we were asked to study was actually 

broader than interfaces. As the scenarios just indicate, the character of the interaction between the 

manned and unmanned systems changes. In an ensemble interaction, the unmanned components 

are tasked by the manned components with which they are associated. In the FCS scenarios, 

soldiers and commanders interact with the unmanned systems by issuing tasks (i.e., RSTA 

missions) that support the force commander’s guidance. In the TACOM Robotic Follower vision, 

humans interact with unmanned systems by simply performing a component of the task; the 

unmanned systems then “follow.” In a Star Wars scenario, humans and robots interact in a fluid 
12

manner, each observing and responding to the actions, behaviors, and outcomes of prior actions 

(regardless of whether the actor is human or robotic). 

The key insight resulting from the panel’s deliberations was that one must first define the 

interactions, and then build interfaces that (optimally) enable the desired interactions to take place. 

In fact, one must understand the overlap between the environments in which a robot operates, the 

robot missions, and the robot’s capabilities. It is the interplay between these elements that defines 

the interactions that must occur between the humans and the robots. The interactions, in turn, 

drive the interfaces. This interplay is showed schematically in Figure 5. Other fields that 

successfully demonstrate this interaction definition approach for human-complex automation 

systems include aircraft flight decks, air traffic control and nuclear power plants. The lessons 

learned from these environments are critical to the unmanned systems community, but, as our 

findings show, are not currently understood or applied.

In the balance of this report, we will preferentially use the term interaction except in the 

specific cases where we discuss the particular interfaces used to embody an interaction.

Figure 5. Interactions drive interfaces 

Status of Autonomous Systems

From the previous discussion we can see that a critical driver for human-robot interactions is 

the level of autonomy. Clearly, autonomy will improve over time, but it is useful to evaluate its 

current status.

Assessing the maturity of autonomy technologies is subjective, especially when applied to 

ground robots. Further, autonomy in air vehicles is different from autonomy in ground vehicles. 

One of the key research areas in the field of autonomous unmanned ground systems has been the 

development of a type of mobility often called A-B autonomy.

Robotics TF

Page 9

From Tasks to Interfaces

Mission Needs

Environment

Robot System

Capabilities

Required

Interactions

Interfaces

Applies to all 

types of robots 

(ground, air)

Applies to 

human-robot 

teams

$$

$

$
13

Robotics TF

Page 10

Relationship of Tasks to Autonomy

Requisite 

Average 

Velocity 

to Mission 

Point

Distance to Mission Point

RSTA/BDA

MOUT 

Recon

Medevac

Non 

Lethal

Countersniper NBC 

Detection

Physical 

Security

Direct 

Fire

Mule Indirect 

Fire

Logistics 

Delivery

Material 

Handling

EOD/UXO

Obstacle 

Breaching

Engineer/ 

Construct

Smoke/ 

Obscurants

Countermine

Cave 

Clearing

A-to-B Autonomy

Intelligent 

Human Assist 

for Mobility

Simple Remote Control

Marked area indicates laboratory technology that is robust today

Figure 6. Relationship of Unmanned Ground Vehicle (UGV) Tasks to Autonomy.

Some assert that A-B autonomy for unmanned ground vehicles (UGVs) rests in large 

measure on mission distance and velocity requirements. From the chart in Figure 3, one can see 

that many useful functions can be and are being performed by unmanned vehicles without A-B 

autonomous capability. 4 On the other hand, some missions might well benefit from this 

capability. It is clear that many of the technologies needed for a full spectrum of unmanned 

vehicles are in place. None of these technologies are “show stoppers” per se. We believe that 

there are no programs in place that have a high confidence for developing A-B autonomy. The 

fundamental thesis underlying the Army’s A-B autonomy program, Demo III, is the experimental 

evidence that shows the inability to teleoperate semiautonomous UGVs cross country at high 

speeds. There are two key phrases in the latter sentence: cross country and high speed. If Force 

Capabilities exist that do not require cross-country or high-speed teleoperation, then the A-B 

autonomy requirement may not exist. The axes on the chart in Figure 6 are distance (traveled 

cross-country) to mission point and average velocity. We made a rough estimate of where each of 

the Force Capabilities falls on the chart, then coded the Force Capabilities with our estimate of

their overall technological readiness. Some comments:

 

4

 See the Army UGV roadmap, attached to this document as Appendix C. Section 1.1.4 is 

particularly relevant to the point made here. 
14

• Many capabilities exist. We estimate that all the needed component technologies 

are at a sufficient level of technical readiness that only the will remains to develop 

and deploy such capabilities 

• A number of Force Capabilities do not require A-B autonomy. 

• We estimate that the capabilities on the top right of the chart do require A-B 

autonomy. The Army Science and Technology (S&T) community must decide 

whether these capabilities are critical to the success of FCS

• The shaded region in the middle is a zone on one side of which A-B- autonomy is 

clearly required, and on the other side of which teleoperation of some sort is 

acceptable.5

Figure 7. Estimated Maturity of Autonomous Land Navigation

Figure 7 shows our estimate of the maturity of the autonomous land navigation capability 

demonstrated by and extrapolated from the ARL Demo III program. It shows that under certain 

bounded conditions, autonomous operations are feasible. For example, we believe that 

autonomous cross country surveillance could be carried out by XUV type robots over rolling 

terrain, using the technology available today. Similarly, robotic followers could be built in a robust 

fashion today.

While additional research in robotic autonomy is clearly required, we believe that an 

appropriate focus and investment in maturing the available technology is not only justified but 

critical to the success of transformation efforts.

 

5

 We recognized the need for a similar matrix that addresses air vehicles, but time constraints for 

the study prevented its development.

Robotics TF

Page 11

Technology Maturity

Performance

Cross-country mobility (day) 40 MPH 10 MPH 30 MPH M* 40 MPH* M

Cross-country mobility (night) 25 MPH 5 MPH 20 MPH M* 25 MPH* M

Physical

Mobility module size 10 ft3 14 ft3 10 ft3 L 10 ft3 L 

Mobility module weight 180 lbs 180 lbs 180 lbs L 180 lbs L 

Environmental 

Temperature Max/Min. -50°,+125°F +40°,+105°F +40°,+105°F L +40°,+105°F L 

Programmatic

Test Environment Field Test Limited Field Field Field

Unit Cost (By calculation) $370K/unit $370K/unit $370K/unit

*Demonstrated/Evaluated on 

larger platform, e.g., NAC

8X8 Hybrid Electric or new

DARPA UGV.

Attributes

FCS Best 

Estimated Need Current

April 2003

Status Risk

April 2004

Status Risk

Overall TRL Level NA 3-4 5 6

Objectives
15

It is important to ask whether any of the functions shown in Figure 7 would be better 

performed by aerial vehicles instead of ground vehicles. Table 2 shows the potential distribution 

of functions between UAVs and UGVs. For example, engineering and construction must be 

performed on the ground. On the other hand, indirect fire can be provided by air vehicles, as has 

already been demonstrated in Afghanistan by Predator UAVs carrying Hellfire missiles.

Table 2. UAV and UGV Functionality.

Shaded areas represent tasks that could be performed by respective vehicles.

Tasks (from Figure 6) UGV Low-Flying UAV High-Flying UAV

Logistics Delivery

Mule

Engineering/Construction

Obstacle Breaching

Physical Security

Material Handling

Nonlethal

Cave Clearing

EOD/UXO* (Detection only)

Medevac

Countersniping

Countermining (Detection only)

MOUT+Reconnaissance

NBC‡ Detection

RSTA/BDA

Direct Fire

Indirect Fire

*EOD: Explosive ordnance disposal; UXO: unexploded ordnance.

+ MOUT: military operations in urban terrain

‡ NBC: nuclear, biological, and chemical

The robotics community clearly recognizes that the challenges to autonomy for UAVs are 

different (and some would argue, simpler) from those for UGVs. What is not widely recognized in 

the community, is that even when certain functions could be assigned to UAVs, high-flying UAVs 

differ from those that operate close to the ground. Low-flying UAVs must contend with 

challenges such as trees, air currents, buildings, dirt and flying debris, which might make 

autonomous versions as difficult to build as autonomous UGVs. In addition, UAVs expend 

significant energy to lift their own weight and that of their payloads, so they are inefficient in 

applications that require long periods of stationary operations and those that require them to 

move significant weight.

We believe that the FCS LSI should focus effort on understanding this trade space and create 

a roadmap for the optimal distribution of functionality among various classes of autonomous and 

semiautonomous platforms.
16

Robotics TF

Page 12

Levels of Autonomous Behavior

TRUSTED

INDEPENDENT

SELF GOVERNING

MINIMAL INTERVENTION

COMPLEXITY 

CAPABILITY 

FLEXIBILITY 

ADAPTABILITY

1 Simple Automation

2 Auto 2 Autommatated T ed Task asks & Functions s & Functions 

0 M 0 Manual anual -Remot Remote Control e Control

3 Scripted Mission

4 Semi- Automated Missions w/Simple 

Decision Making

4 Semi- Automated Missions w/Simple 

Decision Making

Complex Missions Specific Reasoning 5 soning 5

Dynamically Mission Adaptable 6

Syn Synerg ergiistic Multi stic Multi--MMisisssiion on RReeaassoning 7 oning 7

HuHummanan--liklike Autonomy in a Mixed Team 8 e Autonomy in a Mixed Team 8

Autonomous Teams with Unmanned 9 

Leader/Mission Manager

Autonomous Teams with Unmanned 9 

Leader/Mission Manager

Autonomous - Conglomerate 10

Demo III

Panther

Mini Flail

Figure 8. Levels of Autonomous Behavior

As noted above, we must understand what a robot can do if we are to define human 

interactions that are required. In order to add some rigor to the discussions, we defined levels of 

autonomy in order to parameterize robot capability. These levels are shown in Figure 8 and listed 

below.

0. Manual Remote Control, like a remote controlled toy

1. Simple Automation

2. Automated Tasks and Functions, like a Hunter

3. Scripted Mission, like an Shadow or Predator UAV

4. Semiautomated Missions with Simple Decision Making, like an Cruise Missile

5. Complex Missions-Specific Reasoning

6. Dynamically Mission Adaptable 

7. Synergistic Multimission Reasoning 

8. Human-like Autonomy in a Mixed Team 

9. Autonomous Teams with Unmanned Leader or Mission Manager

10. Autonomous Conglomerate. 
17

In the UGV world, fielded systems such as the Panther and the Mini-Flail are at Level 0. They are 

simple remote-controlled devices in which the operator visualizes the actions of the robot through 

direct observation, and controls the robot through low-level commands. Demo III’s XUV is at 

Level 3 or 4, where the robot performs a significant amount of sensor-based, self-directed 

operation. There are no extant systems at Levels 9 and 10, which represent only vague concepts 

more akin to the “droids” of science fiction than any real system under development today.

4 SOLDIER INTERACTIONS AND INTERFACES WITH UNMANNED 

SYSTEMS

Once we accept the notion that interaction precedes interfaces, it is useful to step back from 

the technology and look at how humans interact with (or use) “things”—tools, weapons, other 

people in general.

Considered from that viewpoint, robots are simply a class of things with potential capabilities 

ranging from those of ordinary power tools to those of humans and possibly beyond. This 

spectrum of potentials confuses the discussion. A robot is not a clearly defined thing like a rifle or 

an entrenching tool. A robot’s use depends upon its intended function and capabilities.

In the near- and mid-term, humans will closely control most military robots (ASB Summer 

Study 2001). The robot’s ability to perform autonomously in the unstructured military 

environment will still be limited. Systems like the teleoperated Panther minefield proofing unit 

used in Bosnia will be relatively common. However, the ARL Demo III RSTA robot already has 

demonstrated significant ability to go where it is told in the field, gather information, and find its 

way home without further human control. Using a robot like the Panther differs only marginally 

from driving a vehicle. Using the Demo III robot would leave a soldier free to do other tasks 

while the robot carried out its own mission.

The performance of the Demo III robot is quite amazing to those of us who have watched the 

program progress over the past 20 years. The next 20 years predictably will show far greater

success. The concept of a robotic mule (RoboMule) for the Objective Force soldier was clearly

supported by the ASB 2001 Summer Study. In the far term, after 2010, such RoboMules will

have far more capability than the Army leadership now believes possible.

RoboMules will not require self-awareness but will require enough intelligence to know the 

soldiers to whom they are assigned and be able to communicate verbally with them. They also 

will need to communicate electronically with other machines and with soldiers. They will need to 

find their way to where they are told to go and to take care of their own refueling. They must 

know enough to ask permission before leaving their assigned soldiers to get fuel or pick up 

routine supplies from some other area. They should have more capable sensors than a human and 

the ability to travel at least 50 mph on improved roads. Off-road capabilities should include a 

travel speed of 20 mph, 60% forward and side slope negotiation, and the ability to cross a ditch 4 

feet wide. In addition, they should have the intelligence necessary to recognize impassable terrain 

at a level at least similar to human ability.

A RoboMule will require arms—two or more—for loading and unloading itself or other 

vehicles (and possibly other tasks). It will require all-around awareness, despite the visual 

obstruction of its cargo area; naturally, it will not be limited to one pair of eyes like a human. It 

will need the ability to open and close the top of its cargo area for loading and unloading as well 
18

as to help with the wounded, and safely load and unload litters.

The RoboMule is on the other end of the robot spectrum from the “dumb” Panther. The 

basic point here is that soldiers will interact with the semi-intelligent RoboMule much as they do 

with other soldiers. One of the more challenging tasks that a human performs when interacting 

with robots is that of analyzing and reacting to information. The Rotocraft Pilot’s Associate 

(RPA) demonstrates how to automate such tasks successfully. In the RPA program, automated 

decision tools coupled with mission execution and planning modules reduce the cognitive 

workload for humans. The RPA system has allowed AH-64D Apache helicopter pilots to control 

another UAV (Hunter) in addition to their own helicopters in real time and in a real operating 

environment (Fort Polk JCF-AWE, and at the NTC with the 101st Air Assault Division).

In sum, soldiers will use robots in many different ways, just as they use the tools, weapons, 

and fellow soldiers with whom they work today. The task and the tools at hand, whether robots 

or ratchet wrenches, will determine how the soldier uses them. Unless robots can perform tasks 

better than humans, and the resulting human-robotic combination produces a more capable 

fighting force, there is no value in fielding them. Initially, the human-robotic interface will likely 

involve simple jobs not significantly different from those accomplished with current military 

vehicles. As robotic capabilities increase, soldiers will in general give robots direction rather than 

control them directly. Ultimately, soldiers will work with robots in the same way as they do with 

other soldiers.

 

Robotics TF

Page 14

Impact of Autonomy Levels on Workload

• Workload does not 

necessarily decrease 

with automation

• Workload 

characteristics change

• Decision automation 

(Associates 

technology) may help

Autonomy Levels (Task Complexity & Independence)

Direct Control

Observation

Sensing

Supervision

Error Recovery

Exact shape of workload curve unknown

 Figure 9. Impact of Autonomy Levels on Workload

It is important to understand the way in which assumptions about autonomy and robot 

system capability factor into the design of human robot interfaces.

As Figure 9 shows, the requirement for robots to execute certain functions autonomously 

varies. Even when autonomy may be required, the environment may be complex enough that full 

autonomy is not achievable. Further, in complex environments with semiautonomous robots, the 
19

level of supervision that is required may actually put a heavier cognitive load on the human than 

simpler environments or simpler robots. 

In a systems-engineering approach to designing complex systems, we would have to start by 

understanding the tasks that they have to perform, the capabilities of the autonomous systems, 

and the failure modes. This analysis then defines the interactions required between the humans and 

the robots either to control a system’s capabilities or to react to its deficiencies. The required 

interactions are then mapped to the interfaces that allow the humans to perform the proper 

interactions. 

In this context, there are three general forms of interaction modes, which parallel autonomy 

levels: remote control, teleoperation, and telerobotics of the soldier/robot interface.

Remote control interactions are usually accomplished with simple operator interface 

designs. Operators have line of sight to the unmanned vehicle, so their eyes provide the sensory 

feedback needed for vehicle navigation and locomotion control. Feedback to control specific onboard payloads also comes from direct sensory feedback or through simple displays. Control 

interfaces for these vehicles usually are simple in design, given the constrained nature of the 

interaction. Examples of such vehicles are remote-controlled model airplanes and cars. Military 

ground examples are the Mini-Flail and Panther. These types of systems usually require one 

operator per vehicle. The components of these systems are much the same as those found in any 

vehicle-based system. What is unique to the world of unmanned vehicles is the dominance of 

electronic information flow between the soldier and the unmanned vehicle. There is the vehicle 

that, depending upon the mission, is more or less “smart.” There is the soldier-vehicle interface 

consisting of display and controls. Finally, the soldier will oversee or direct the system to 

accomplish the mission or task to the maximum of its capability.

We cannot overemphasize the importance of the soldier getting required information in a 

timely manner and in a useful form. On some missions, as unmanned vehicles become even more 

capable, soldiers will command several robots and robots will communicate among themselves to 

solve problems. These factors require the development of interfaces that assure timely information 

flow at various levels of employment where changes occur rapidly. 

Teleoperation interactions require more sophisticated display and control interfaces because 

navigation, locomotion control, task execution, and vehicle status monitoring are not performed 

by line of sight. Therefore, soldier interface displays must be capable of providing appropriate and 

timely information feedback that is formatted in ways that allow operators to successfully control 

unmanned vehicles and their mission equipment packages. Controller interface designs must 

permit operators to send commands to the vehicle, based on display information feedback, that 

are timely, appropriate, and accurate enough to accomplish the mission successfully. Some believe 

that unmanned vehicles of this type evolved from manned vehicles. More specifically, these 

include manned vehicles designed to achieve near daytime operational capability 24 hours a day in 

nearly all weather conditions, and those designed to operate in a “closed hatch” mode during 

NBC operations. To achieve the above goals, many of these manned vehicles employ advanced 

sensors that provide visual and auditory feedback about the environment. These sensors often 

operate in ranges of the electromagnetic spectrum (I2, IR, RF, MMWR, and LADAR6

) that 

 

6

 I2: image intensification; IR: infrared; RF: radio frequencies; MMWR: millimeter-wave radar; 

LADAR: laser radar.
20

exceed the direct sensing capability of the vehicle operator’s eyes and ears. Therefore, 

operational capability additional to that of the unaided human sensory system can be achieved. 

The visual displays used in conjunction with these sensors include helmet-or panel-mounted 

displays. Operator control interfaces to slew sensors help to overcome sensor field-of-view 

limitations. Helmet-mounted displays, the sensors are often slaved to the movement of the helmet 

to provide slewing control. Vehicles having the above sensor capabilities often integrate 

locomotion, navigation, target acquisition, fire control and vehicle status feedback information 

into a limited number of operator interface displays. In many cases, soldier interfaces designed to 

control these vehicles and perform mission tasks are also integrated into a common module. 

Many of the control system outputs are electrical or electro-optical signals that go to powerassisted actuators. Therefore, a transition from a manned to unmanned vehicle fundamentally 

involves providing links for sending signals from sensors on board the vehicle to soldier display 

interfaces in remote operator stations. Links also must be provided to send signals generated by 

the operator control interface back to vehicle actuators, thus enabling soldiers to control the 

vehicle while they remain out of harm’s way. It is no surprise that remote-control stations often 

look like in-vehicle control stations. The assumption is that whether a soldier is in the vehicle or 

remote to the vehicle, the display/control interfaces and layout should be similar. Military ground 

examples of this would include the Man-Portable Remote System (MPRS). 

The teleoperation interaction approach usually requires one operator per system. Operator 

data requirements for effective control and display usually demand large bandwidth for 

transmission. Additionally, locomotion and navigation control of these systems, especially ground 

systems, is difficult because of sensor and/or display limitations and lack of operator motion cues.

The bulk of the current requirements for unmanned ground systems in the Army are in 

applications of remote-controlled or teleoperated systems. The Joint Program Office (JPO) at 

Huntsville is focused on satisfying these requirements.

Telemanagement (aka telerobotics) interaction approaches require a teleoperation 

capability, but provide more capability for autonomous action. The goal is to provide as much as 

possible of the autonomous capability at the vehicle level in order to reduce the data transmission 

requirements between vehicles and remote display and control interface sites. Reducing 

transmission requirements to reduce bandwidth requirements is critical. In this design approach, 

adding more autonomy or off-loading actions reduces operator workload and provides increased 

performance over teleoperation. It can also enable the operator to operate more than one vehicle, 

i.e., one on many (force multipliers). When these systems become smart enough, operators will 

assume higher-level roles and interact only on functions and tasks that a priori are determined to 

require soldier input. Examples might include planning a mission that would entail defining its 

destination, identifying targets to kill, and intervening when emergencies occur. For operational 

effectiveness, however, highly autonomous robots may require operator control and display 

interfaces more complex than those required by teleoperation. This statement is based on the 

assumption that successful intervention in emergency situations may require telepresence or 

perceptual immersion. In addition, the assurance of timely and appropriate inputs may require 

sophisticated decision-aiding displays and their associated software. A current ground vehicle 

with a rudimentary version of what we call telemanagement properties is the ARL XUV vehicle 

that uses the Real-Time Control System (RCS) architecture. RCS is one of the most promising 

architectures for providing “smart” vehicle control systems. (James Albus and Alexander Meystel 
21

described RCS in Engineering of Mind, [2001]). This hierarchical planning and control 

architecture is compelling from a number of perspectives. For example, it is isomorphic with 

general stereotypes regarding intelligent behavior generation, management and organization. 

These features have significant implications for soldier-machine interface design. The RCS version 

currently employed in the XUV vehicle is capable of navigation and locomotion control. This 

capability is advanced enough that it elicits anthropomorphic comments from soldiers. Example 

statements include “Watch, he (referring to the vehicle) is going to make a turn,” or “He is trying 

to make up his mind.” The development and fielding of “smart” and “smarter” unmanned vehicles 

becomes more feasible each year as the weight, power, cost, and size of computing power, 

displays, and sensors decreases.

Figure 9 shows that as the level of autonomy for any task increases, the nature of the 

interaction between the human and the unmanned system changes. The human goes from being a 

detailed task-level controller, requiring full-time interaction, to more of a supervisory role; a role 

that is more instructional, more involved with what to do than with how to do it. Feedback comes 

more by notification and query than by continuous monitoring, unless there is an emergency or 

highly complex or risky task.

It is interesting to correlate the information in Figure 9 with that in Figure 7. In Figure 9, the 

area between the teleoperation functions and the autonomous functions is a critical region—one 

where some autonomy is possible but is not robust enough for full-time reliance. Humans must 

take control and teleoperate the robots whenever things do not go according to expectations. The 

interactions that enable the humans to understand the context of the failure and the potential 

remedies are significant challenges. Very little work has been done on computational 

representations of such spatio-temporal context and on the mixed-initiative nature of the 

interactions.
22

Robotics TF

Page 13

Human Robot Interactions Depend on Autonomy

• Mixed Teams (Humans 

and Robots)

• Full Autonomy (Interaction 

at the level of missions and 

commander’s intent)

• Telemanagement

(Infrequent interaction, 

usually exception-based)

• Teleoperation (Interaction 

at a distance, sensor and 

communications mediated)

• Remote control (direct 

visibility of the robot)

Low Level 

Robot

Hardware

Mac ros

Waypoint Nav

Miss ion Models

Behaviors

Team Dynamics

Local sensor

loops

Autonomy Levels

• Gross 

behavior

• Environmental 

parameters

• Task 

completion

• Position, 

attitude, 

velocity

• Direct visual 

observation

Figure 10. Interactions and Autonomy.

For force multiplication, it is desirable that individual humans control multiple robots. Today, 

Predators require a crew of 24 humans to manage all aspects of a mission. In future, the hope is 

that with appropriate autonomy and interaction modalities a single soldier would control multiple, 

perhaps four, robots. If this is achievable, the value to the Army should be significant. 

The question “how many robots can one person control” is often asked, but is extremely 

challenging to answer. The answer depends upon all of the factors we have discussed in the 

previous sections: the level of autonomy assumed, the level of intelligence in the robot (ability to 

deal with unexpected situations), the complexity of the environment, and the criticality of the task. 

At the lowest level of autonomy (remote control), the connection between a controller and a 

robot is only 1:1. As autonomy increases, a soldier might be able to task a robot to initiate a 

mission and then devote attention to other robots, returning periodically to check up on the 

progress of individual robot mission. In an environment where everything works as planned, there 

is no theoretical limit to the number of robots a soldier could control. Air traffic controllers in the 

U. S. airspace routinely manage the positions and movement of dozens of aircraft (primarily 

through effectively structuring what is otherwise an unstructured environment). However, in 

environments that one cannot structure and where things do go wrong, the commander or 

operator has to keep a mental map of what is happening—which limits the span of control. For 

example, in a standard military environment, the commander’s span of control is usually 3 to 5 

subordinates. If we assume the existence of robots with the same level of capability as humans, 

the span of control is unlikely to be much greater than that. This span of control is similar to what 

one finds in typical civilian organizations.

In the future, humans will have to work with multiple robots and the robots will have to 

work with each other. Therefore, some argue that the design of the human control and display 
23

interface with “really smart” unmanned machines should eventually duplicate the same modalities 

as human-human interactions, on the assumption that one simply tells the machine what to do and 

when to do it. The machine then figures out how to comply and verbally reports back when it 

completes the assigned task or mission. It also provides interim progress reports if they are 

desired. Only if an autonomous unmanned vehicle(s) encounters a situation beyond its capacity 

will it ask for external human or machine assistance. This capability to interact and dialog in a 

human-like modality is particularly important when multiple soldiers and robots work together in 

integrated teams. Each needs to understand intuitively what all of the others are doing. 

4.1 HUMAN-ROBOT INTERFACES AND TODAY’S ROBOTIC SYSTEMS

Figure 11. Remote-Controlled Robots

Figure 11 graphically shows the remote-controlled ground robots being developed by the 

Army. Of these, only the Panther and the Mini-Flail are operational today. 

Robotics TF

Page 16

APPLIQUE SYSTEMS DEDICATED SYSTEMS

APPLIQUE - Robotics applied to existing systems DEDICATED - Built specifically as a robotic system

MATILDA

URBOT

URBIE

MAN-PORTABLE

VEHICLETRANSPORTABLE

MINI-FLAIL

SARGE

GLADIATOR

In operational use today

In development

Concept

Remote Controlled Robots

MEERKAT HMMWV

PANTHER I ABRAMS PANTHER 

DEUCE

D7G T3

ACE
24

Figure 12. Interfaces for Remote-Controlled Robots

Figure 12 indicates the fairly simple level of the interfaces required to operate such remote 

controlled robots. The key challenges in such displays are engineering in nature: building the 

displays cost effectively and making them rugged and survivable.

Robotics TF

Page 17

HRI for Remote Controlled Robots

Human-Machine Interface (HMI)

Baseline / Block I (FUE 07) Upgrade / Block II (FUE 14) Upgrade / Block III (FUE 17)

OCU displays positions

 of MBU, OCU, friendly and 

 enemy elements in UTM

Modular payloads mountable & Modular payloads mountable &

 dismountable in 10 minutes dismountable in 5 minutes

OCU not visually detectable by OCU not visually detectable by OCU not visually detectable by

 unaided eye at night beyond unaided eye at night beyond unaided eye at night beyond 

 20 meters* 15 meters* 10 meters*

Capable of operation while Capable of operation while 

 wearing cold weather gloves wearing mittens

video recording capability at UCU

OCU weight

 20 lbs. including batteries 15 lbs. including batteries 10 lbs. including batteries

earpiece to hear MBU sounds

OCU power without battery OCU power without battery OCU power without battery 

 change - 2 hours change - 4 hours change - 8 hours

OCU display digital map display standard military Rugged, robust devices symbols and overlays

Key issues are engineering

Courtesy AMRDEC
25

Robotics TF

Page 18

Telerobotic/Autonomous Human Robot 

Interfaces

• Simple menu driven, waypoint navigation

• Specialized interfaces for mission packages (e.g., RSTA)

• Used by soldiers with nominal training

Camera 1 Camera 3

Asset Status

Camera 2

90° 22 MI

Courtesy ARL (Demo III)

Courtesy T ARDEC (CAT/RF ATD) Not shown: Ft. Knox Simulator interfaces

Each program has its own preferred HRI

Focus is on demonstrations, not 

evaluations

•

• Multimodal soft programmable

• Multiple displays

• Specialized tools 

(steering-column)

• Speech interface at 

the simple command level

Figure 13. Human-Robot Interfaces for Intelligent Semiautonomous Robots

Figure 13 shows the interfaces that have been developed on Army programs dealing with 

intelligent semiautonomous robots. The photograph on the top left shows the Demo III flat-panel 

display in use. Demo III robots are tasked by using a point-and-click interface to provide the 

robots with waypoints, destinations, and simple constraints (such as “hug the tree-line”).

The three screen captures in the bottom half of the figure show the visualization interface 

under development for the Crew Automation Testbed (CAT) ATD program. This interface will be 

used to task the Robotic Follower (RF). The interface consists of three panels with a full suite of 

software reprogrammable buttons, as well as a hardware “steering-wheel” interface with hardware

buttons and controls. 

Not shown in the figure is the robotic interface under development at Fort Knox for use with 

the robotic simulators in the Battle Lab. 

The panel observed that even though there was overlap between the work of contractors on 

the Demo III and the CAT/RF programs, the user interfaces were different. There had been no 

experimentation on Demo III to identify the effectiveness of the interface, and therefore, no 

lessons learned that could be transferred to the CAT/RF program. Further, the Demo III interface 

seemed to be primarily a mechanism to task the robot and to allow the demonstration of the 

autonomous capability of the XUV vehicle. Similarly, the CAT interface was an exercise in 

building a highly reprogrammable, flexible crew workstation, rather than a robot controller per se. 

A key panel finding was that the stovepiped nature of development within the S&T 

community and the lack of user pull, allowed the various communities to focus only on their own 

narrow technical goals. Even the demonstrations that each community was planning were 

uncoordinated and not focused on a common user-defined problem (i.e., FCS Block I scenarios).
26

Figure 14. Human-Robot Interfaces Developed at JPL

This problem is illustrated in Figure 14 by three human-robot interfaces developed at the 

NASA Jet Propulsion Laboratory (JPL). The interface on the top left was developed by JPL 

researchers for the DARPA Tactical Mobile Robots (TMR) program. The interface on the bottom 

left is the tool used to control Sojourner, the first Mars rover. The figure on the top right shows 

the interface for the Wide Area Telerobotics System (WITS) that will be used to control the nextgeneration Mars rover in a worldwide Web-enabled distributed manner. 

Again, no metrics were used to evaluate the efficacy of each of the interfaces. It appeared 

that each development team had its own human-robot interface subteam that “invented” its own 

interface tool from scratch. Without appropriate metrics, no mechanism can evaluate aspects of 

the interface that operate well, or aspects that need to be further developed.

Robotics TF

Page 19

Human Robot Interfaces at JPL

Compass Ring

True Robot Heading

Arm Position Controls

Chosen driving arc.

Dashed lines reflect

available arcs.

TMR Interface

• Teleoperation

• Visualization

Blue Path is

User Drawn

Red Path is

Robot Drawn

Purple Path is

Active Path

Available Paths

Sojourner Interface

• Significant time delay

• 3-D visualization

• Model and data overlay

WITS Interface

• Collaborative

• Web-based

• Worldwide

Courtesy NASA JPL

No 

sharing/ 

evaluation 

of HRI
27

Figure 15. Advanced Multimodal Interfaces

Figure 15 shows two research interfaces more sophisticated than simple mouse-windows 

mechanisms. The left-hand figure shows a multimodal interface developed by researchers at SRI 

International on a recent DARPA program. The interface uses speech, gestures, and contextual 

references to objects in a scene (as seen by the on-board robot camera) to command the robots. 

The figure on the right shows an eye-tracker developed at Sandia National Laboratories that 

allows the wearer to refer to robot commands by simply glancing at appropriate commands 

projected into a heads-up display. 

Since neither of these programs is tied to any real application, it is impossible to ascertain 

whether such interaction modalities are useful, or even necessary. While they represent significant 

advances in terms of human-robot “dialog,” value can only be determined within the context of 

real applications. For example, will ambient noise affect speech recognition adversely? What will 

be the results of the interaction if commands have to be constantly repeated? Such questions can 

be answered only if the community develops a core set of metrics and performs a series of 

experiments in realistic environments.

What is common to all these examples is that they focus on interfaces, not interactions; they 

lack a systematic evaluation of the relationships between missions, robot capabilities and 

interactions; they have no metrics for the evaluation of performance; and they constitute 

technology demonstrations, not solutions.

For example, the Demo III vehicle “resorts to full teleoperation” if it cannot reason about the 

path on its own in three attempts. So, while there are interaction modes in the interface to provide 

the robot with waypoints and goals, there is no effective teleoperation mode. Users have to resort 

to remote control, direct observation of the robot from a chase vehicle, to perform the error 

recovery tasks.

Robotics TF

Page 20

Advanced Multimodal Interfaces

DARPA Urban Robot Multimodal Interface

(Courtesy SRI International)

• Speaker independent continuous speech

• Gesture recognition

• Robot-centric command language

Advanced interfaces 

are only in the 

research stage

Eye-Tracked Robot Interface (Courtesy Sandia)

• Gaze detection using eyeball tracking

• Can be coupled with speech detection

• Provides a hands-free approach to robot control
28

The research community has recognized this problem. A recent joint DARPA/National 

Science Foundation (NSF) workshop on human-robot interactions concluded that 

multidisciplinary research was needed in the area. They recognized, as we did, that suitable 

metrics, and focused experimentation in defined environments were critical to the success of the 

field. For further information on the conclusions of the workshop, please refer to: 

http://www.aic.nrl.navy.mil/hri/nsfdarpa/

4.2 INTERACTIONS IN OTHER COMPLEX SYSTEMS

Systematic experimental work should be conducted to identify what Army functions and 

tasks can benefit most from the use of unmanned vehicles; determine which class of interaction 

should be used for the identified tasks and functions; and specify the design of the soldier control 

and display for the selected classes and functions/tasks. This effort should embrace the full 

repertoire of developmental tools including analytical efforts, simulation, field experiments, and 

prototype development. The failure to perform systematic experimental work to gain effective 

design guidance for interfacing humans with advanced semi-autonomous systems will lead to 

undesirable outcomes. Researchers in a number of fields learned this lesson the hard way. For 

example, commercial airlines have achieved enhanced system performance by using advanced 

flight management systems that are electronically or electro-optically linked to actuators that 

control aerodynamic surfaces and engine performance. Such systems make an airplane “smarter,” 

in that they enable the airplane to perform many tasks that previously required the full time 

attention of the crew. The introduction of these technologies, when correctly designed and 

interfaced with the crew, has not only led to safer, more efficient flight, also has done so at a 

reduced cost. On the other hand, when the crew interface is incorrectly designed catastrophic 

outcomes can occur. For example, a “smart” airplane may take some “not so smart” actions 

under certain conditions that are unknown by the crew. When the crew finally becomes aware of 

the problem, appropriate corrective actions may be too late. In some cases, crew interfaces are 

designed so as to induce input instruction errors. In the worst case, these errors lead to 

catastrophic results and in the best cases; they result in losses of time and fuel. The medical 

equipment field has also experienced its share of catastrophic results due to inadequate control 

and display interface design. For example, during cancer therapy, remotely controlled “smart” 

machines administered radiation overdoses to patients because of faulty human control and 

interface design. Some of these overdoses killed patients. Three Mile Island, and many other 

complex human-machine environments have resulted in adverse consequences. The message is 

clear: attention must be paid to the human control/display interfaces and the underlying 

architectures of complex smart systems if humans are to successfully interact with them and they 

are to achieve their design objectives. 

During this study we tried to determine two things: first, whether the Army has work in 

progress to identify the tasks and functions that would benefit most from the application of 

manned vehicles; and second, whether work to develop effective soldier interfaces with unmanned 

vehicles was also in progress. Emerging results seem to indicate that for the most part this was 

not the case. Inarguably, much good work is in progress in a number of places. Unfortunately, 

we found very little quantifiable data from systematic efforts to identify the tasks and functions 

that could most benefit from the use of unmanned vehicles with various capabilities. Similarly, we 

found very little data to support the design of soldier interfaces with unmanned vehicles designed 

to execute military tasks. To some extent, these findings are understandable, given the maturity of 
29

some levels of unmanned-vehicle-enabling technologies. However, history shows that success 

depends on the early introduction of the ultimate operator and maintainer (the human) into the 

design loop. Designs that do not heed human control interface issues produce results that can 

have catastrophic consequences or at the very least will degrade system performance.

We surveyed a number of automation-related research efforts concerned with crew-station 

design in the aviation field. The record shows that making the appropriate task assignments 

between the pilot and the machine as well as correctly designing the pilot-computer interface are 

essential to efficient operations. We posit that the same is true of the soldier–robot system 

interface. 

The factors that drive unmanned system interactions are

• The mission/operational environment and overall task complexity

• The desired ratio of soldiers to unmanned systems

• Line-of-sight vs. non-line-of-sight operation

• Communication bandwidth availability

• Technology availability and cost

• Force/team OPTEMPO and degree of operation risk or threat.

These factors drive systems design and dictate the level of soldier-system interaction as well 

as the technology design tradeoffs that in turn determine interface requirements. The interface, 

regardless of the technology and degree of automation, must provide a soldier with the 

information that he or she needs, when they need it, and in a format that allows them to respond 

so as to accomplish the mission. Figure 16 summarizes the importance of the interface between a 

human user and a robotic system.
30

Figure 16. Importance of Human-Robot Interactions

4.3 THE VALUE OF HUMAN-ROBOT INTERACTIONS 

We can only evaluate the value of human-robot interactions by examples from the field of 

Human Engineering (HE). HE looks at the issues of integrating humans into complex systems in 

an effective manner.

There are two major ways to illustrate the value of sound HE efforts. One is to show the 

negative results from lack of appropriate HE involvement, and the other is to demonstrate 

positive results of HE activities. The following sections examine the value of the HE effort from 

both perspectives.

4.3.1 Historical Problems from a Lack of HE Efforts

Lack of appropriate HE involvement in design can result in system shortcomings that require 

costly redesign, produce substandard system performance, or in extreme cases, precipitate system 

failures endangering life and equipment. Many problems found during testing and evaluation are 

evidence of the lack of a good HE effort during the design and development phase. While some of 

the problems are resolvable, it typically costs more to make these changes in the later stage of a 

program development.. Problems found during the operational phase are even more costly to 

resolve. Sometimes such problems are identified only after critical incidents. Two examples are 

provided to illustrate the problems that can occur when insufficient attention is paid to the HE 

aspects of design. The first set consists of several well-known disasters that, though they have 

multiple causes, resulted at least partly from lack of adequate HE. The second set is a sampling of 

specific lessons learned in a variety of HE-related areas. These examples are provided in the hope 

that future system designs will benefit from previous system design failures.

Robotics TF

Page 24

Why Focus on HRI?

• It is important to the success of the long-term FCS vision

– Humans and automated systems will have to be integrated 

and work seamlessly together to achieve the goals

• It can reduce development and fielding costs

• It will increase the speed of development

• It can reduce human interface related failures 

Literature from other complex systems

shows the value of HRI

Failure of HRI can be costly
31

One of the technology areas in which systematic HE work is beneficial is the effective 

integration of humans with automated systems in flight-deck automation and glass-cockpit design. 

In this area a community of scientists, engineers, operators, and regulators works closely together 

to study, experimentally evaluate, and iteratively improve the interactions between pilots and the 

automated technology. Research shows that in general, improvements in automation significantly 

reduce pilot workload and stress. On the other hand, improper interactions and poor interface 

designs can lead to failures: “Since these aircraft [glass cockpits] were introduced in the early 

1980s, hundreds of incidents and a few fatal accidents have occurred in which pilot-computer 

interface was a factor.” [AWST Jan 1995].

Some of the “few” accidents in which automation was a possible contributing factor are 

listed below.

• Boeing 707, Jamaica, New York, 1962

• Lockheed L1011, Miami, Florida, 1972

• Douglas DC10-30, Luxembourg, 1979

• Douglas DC-10-30, Boston, Massachusetts, 1982

• Douglas DC-10, New York, New York, 1984

• Boeing 747-SP near San Francisco, California, 1985

• Airbus 320, Mulhouse-Habsheim, France, 1988

• Airbus 320, Bangalore, India, 1990

• Boeing B767-300, Suphan Buri Province, Thailand, 1991

• Airbus 320, near Strasbourg, France, 1992

• Douglas MD11, Alaska, 1993

• Airbus A320 Warsaw, Poland, 1993.

4.3.2 Catastrophic Accidents 

The failure to adequately consider human capabilities and limitations in system design can 

sometimes have disastrous consequences, as illustrated by the following three well-known 

incidents.

Downing of Korean Air Lines Flight 007 

Soviet air-to-air missiles shot down KAL-007 on 1 September 1983, when it strayed into 

Soviet air space. A navigational error led the aircraft approximately 365 miles off course, placing 

it over Soviet military installations at Sakhalin Island. All 269 people on board perished after a 

90-second descent into the Pacific Ocean. The most likely cause of the navigational error 

concerns the inertial navigation system (INS) installed in this large passenger aircraft. The aircraft 

had three INS systems: one primary system and two backups. Each INS could be programmed 

separately, or a “remote” mode could be chosen in which the crew programmed only the primary 

INS and the information was then automatically passed to the two backup units. To ensure that 

the proper coordinates have been placed in the system, the INS checks the primary INS 

coordinates against the coordinates entered into the two backup units. It is hypothesized that the 

crew, to save time and energy, chose the “remote” mode when programming the INS units and 
32

incorrectly entered the flight path coordinates. This error would not have been detected when in 

this mode, because a copy of the incorrect coordinates would have been used to check the original 

incorrect coordinates. This INS was designed to reduce workload and stress to the aircrew. 

Unfortunately, the system was so automated that it caused inactivity, boredom, and complacency. 

Due to the defective interface of its INS, KAL-007 found itself off course and in unfriendly 

airspace, which led to tragedy [Stein, 1983; Malone, 1990; Time, 1992]. 

Three Mile Island Incident. 

On 28 March 1979, operators at Three Mile Island, a nuclear power plant in Pennsylvania, 

made a series of mistakes that led to a near meltdown of the plant's reactor core. A series of 

equipment failures also contributed to this accident. The result was a release of approximately 

1200 millirems per hour of radiation into the environment, forcing the evacuation of several 

thousand residents of the surrounding area. Fortunately, there were no deaths as a direct result of 

the incident. The near meltdown of the reactor occurred when a pilot-operated relief valve at the 

top of the pressurizer failed to close, resulting in the loss of a pressurizer steam bubble and the 

loss of reactor control system pressure and quantity. The indicator light on which the operators 

relied to determine the position of the relief valve led them to believe that the valve was closed, 

but the light was not displaying the actual system state—rather, it showed the presence of a signal 

commanding the valve to close. In other words, the operators believed the relief valve was closed 

when in reality the valve was open, despite the command to close. This belief led the system 

operators to conclude falsely that a leak had occurred, and they began to act accordingly. 

However, they continued to make errors that increased the volatility of the system, such as 

confusing reactor B with reactor A (a problem directly attributable to the control panel layout). 

Two hours later an operator who had recently arrived realized that the relief valve was at fault 

and initiated proper actions to correct the problem. In the end, an investigation by the Nuclear 

Regulatory Commission into the human factors aspects of the accident determined that “the 

human errors which occurred during the incident were not due to operator deficiencies but rather 

to inadequacies in equipment design, information presentation, emergency procedures, and 

training” [Malone 1990]. 

Crash of a Passenger Airliner into the Florida Everglades.

 In 1972, a Lockheed L-1011 descended at night into the swamp of the Florida Everglades, 

killing all 99 passengers and crewmembers on board. The ensuing investigation by the National 

Transportation Safety Board (NTSB) revealed that one small, burned-out light bulb in the landing 

gear indicator set in motion a sequence of events that ended in a completely avoidable tragedy. 

More precisely, the response of the flight deck crew to the inoperative bulb ultimately hardened 

the last links in the chain of errors that led to the eventual crash of the aircraft. While in flight, 

each of the three crew members (flight engineer, first officer, and captain) fixated on solving the 

same problem, an aberrant “landing gear down and locked” bulb, while neglecting to notice that 

the autopilot had become disengaged. Quietly, while all crew members were attending to the 

same non emergency condition, the aircraft descended, under neither human nor automatic 

control, until it finally came to rest in the swamp below [NTSB, 1973]. 

4.3.3 Lessons Learned

The following lessons learned summarize in condensed form the experiences of users and 

managers of systems whose designers failed in some way to adequately consider human 

capabilities and limitations. It is often difficult to obtain detailed data directly related to such 
33

problems, since these could be used to indicate an error or tarnish the image of a contractor. To 

avoid legal ramifications, the lessons learned are stated in very general terms, and references to 

specific defense systems and manufacturers have been omitted. In some cases, human engineering 

was involved late in the design process and the problems were turned into success stories. One 

major lesson learned is that costly engineering changes could have been prevented if HE had been 

involved earlier in the systems acquisition process. We present three examples.

Landing Gear Visibility at Night 

Failure to design a helicopter landing gear so that it remains visible to the landing signal crew 

at night can result in a wheels-up landing, causing damage to aircraft, safety hazards to aircrew 

and ground personnel, and operational loss of a valuable fleet asset. [Department of the Air Force 

1996].

Attitude Directional Indicator (with no Velocity Vector 

Heads-up display without velocity vector indicators do not provide a flight path marker, 

leading to possible situational awareness problems for the pilot [Air Force HSI Office 1995.

Night-Vision Goggles and Exterior Lighting.

A failure to provide fleet aircraft with exterior lighting compatible with the use of nightvision goggles for night missions can result in mid-air collisions with aircraft that are not 

operating within the flight formation [Department of the Air Force 1996]. (See appendix D for 

more examples)

4.3.4 Benefits from Human Engineering 

As with most worthwhile efforts, HE requires an investment of money and time to gain 

eventual savings, increased total system performance, safety, and user satisfaction. Typically, an 

investment in HE is relatively small compared to those in other system creation activities, while 

the return is relatively high (see Table 3). 

 Table 3. Example Benefits from HE 

System Type Investment

($)

Total Savings

($)

Time 

(Years)

Reconnaissance & Light 

Attack Helicopter

74.9M 3.29B 20

Attack Helicopter 2.3M 268.8M 20

Nuclear/Biological/Chemical 

Reconnaissance Vehicle

60K $2–4M l

 Source: Booher [1997]

HE efforts strive to optimize a system to (1) permit operator and maintenance personnel to 

achieve the required performance levels; (2) minimize manpower, personnel and training 

requirements; (3) achieve the required reliability and effectiveness of personnel-system 

combinations; and (4) enhance operational safety by avoiding human error. These benefits can be 

seen in overall system and HE testing and evaluation reports. Success stories such as the 

following help illustrate the importance and value added of HE efforts. 
34

Center High-Mounted Brake Lights. 

In 1985, after extensive HE studies showing positive results, high-mounted center brake 

lights became standard equipment on all new passenger cars in the U.S. Vehicles equipped with 

the new brake lights are involved in 50% fewer rear-end collisions, and when an accident does 

occur, the costs of repairs are 50% less. It was estimated that 126,000 reported crashes would be 

avoided annually from 1997 on, resulting in savings of $910 million a year. These benefits 

stemmed from a $5 million investment in HE [Hendrick, 1996].

Redesign of Aircraft Throttle Module 

An oversensitivity problem (an unacceptably large output in response to a small input) was 

discovered in the use of the throttles of a large transport aircraft during aerial refueling. After 

engineers unsuccessfully redesigned the throttles without HE input, the HE group was asked to 

develop a solution. The HE practitioners collected and analyzed data and identified critical 

component failures. They worked with designers to modify the throttles by reducing the spring 

force, smoothing the cam action, and adding helper handles. The resdesign was greeted with 

overwhelming pilot acceptance [Air Force HSI Office 1995].

Modification of a Manufacturing Facility 

In the first year, following an HE evaluation and modification of a manufacturing facility, 

worker's compensation losses dropped more than 75%, from $400,000 to $94,000. The changes 

that resulted from this HE evaluation saved the manufacturer $1.48 million in the period 1990–

1994 [Hendrick 1996].

Transceiver Operator Panel 

Sound HE involvement often goes unnoticed because of the flawless way a system operates. 

A transceiver operator panel for the control of an airborne computerized communications sending 

and receiving processor was designed according to HE principles. A task analysis fleshed out six 

major system requirements, and fit the system into an existing slot on the flight deck. The design 

was integrated into the operator's concept of operation so well that upon first questioning during 

T&E, the field-test engineers could not recall using the system at all [Shafer 1976].

4.3.5 The Pervasive Impact of Windows and GUIs.

The previous examples in this section have focused on the problems that occur when user 

interaction design is not done in a rigorous or systematic manner. The computer industry presents 

one significant example of the positive value of developing interactions and interfaces in an 

organized manner—the development of graphical user interfaces (GUIs), leading to the present 

day WindowsTM operating system.

In the early sixties, the US Air Force funded a series of research contracts for the 

examination of human interactions with time-shared computer systems that were then starting to 

come into being. One of these contracts was to a visionary computer scientist, Douglas Englebart,

who was then with the Stanford Research Institute in Menlo Park, CA. Doug focused his energies 

on understanding how humans would interact with computer information, and in doing so, 

developed concepts such as multimedia, hypertext, windows, icons, and the computer mouse. In 

the fall of 1968, Doug demonstrated the first “personal computing” platform with a mouse 

controlling a networked computer system with hypertext linking, real-time text editing, multiple 

windows, and teleconferencing.
35

These concepts, evolving through the lineage of Xerox, and Apple, became widely adopted 

as the preferred mechanisms for interacting with computer information. Arguably, the windowstype interfaces map readily into the cognitive models that underlie common office actions such as 

filing information, searching, and indexing. It is this mapping that makes windows-type interfaces 

"intuitive". Our challenge to the robot human interaction community can be summed up in one 

short sentence: "What is the analog of the desktop metaphor, in the field of robotics?"

We feel that if such an intuitive model could be devised, it would provide a significant boost 

to the usability, and hence deployment of robots in the Army.
36

5 PANEL FINDINGS

Figure 17. Findings

5.1 SIGNIFICANT ADVANCES IN FUNDAMENTAL ROBOTICS TECHNOLOGY

Significant progress has been made in the Demo III program since the ASB last observed the 

capabilities. The final capabilities demonstration at Fort Indiantown Gap in October 2001 showed 

a convincing capability of unmanned, supervised autonomous systems to perform a meaningful set 

of RSTA tasks. Although full A-B mobility at speed and across all terrain is not yet possible, 

ground vehicle robotics technology has matured sufficiently that useful military systems can be 

fielded. Robotic ground vehicle development has reached a point where user input is needed to 

carry the work forward to fully useful robotic combat systems. The Real-time Control System 

(RCS) is one of the best control system architectures to achieve greater unmanned vehicle 

autonomy. The Army Vetronics Technology Integration (VTI) program and the associated Crew 

Automation Testbed/Robotic Follower (CAT/RF) Advanced Technology Demonstration (ATD) 

program are starting to address the issue of integrating Demo III mobility and perception 

capabilities into a transitionable system.

5.2 NO SYSTEMATIC STUDY OR APPLICATIONS OF HUMAN-ROBOT 

INTERFACE DESIGN

There is no systematic application of basic principles to the matter of human-robotic 

interfaces. All robotic systems have human interfaces, which leads to an assumption in the 

Robotics TF

Page 26

Findings

• No focused effort on the human-robot interaction issues anywhere

– Ad-hoc interfaces, built by engineers, for engineers

– Even NASA has multiple competing approaches 

(programmatics driven)

• No push to get autonomous robots into actual use

– No clear roadmap for FCS Block II and beyond

– Sandbox mentality

– No cross-branch proponent or technology advocate

– No end-to-end simulation capability that compensates

• Significant progress in the Demo III program since ASB last 

observed the capabilities

– However, full A-B mobility at speed and across all terrain is not 

yet possible

– NASA (JPL), Sandia, and others, have significant programs 

underway that can be leveraged for Army use
37

research community that the problem is being addressed. Most interfaces that we observed (Army 

programs, NASA programs, etc.) have interfaces designed primarily by robotics engineers for 

their own use. Even interfaces (such as those on the NASA space programs) that have been 

designed by committees with end-user participants have not evolved according to a systematic 

hands-on analysis of the actual use of the interfaces in real-world conditions. Other technical areas 

that have systematically applied HE processes in the past (such as flight deck automation) have 

reported significant measurable value from the application of systematic techniques.

There is a lack of systematic analytical and/or experimental work to identify the combat 

functions and tasks that could have the highest payoff from the use of unmanned vehicles; and to 

define the soldier interface design required for unmanned vehicles that will perform combat 

functions and tasks. This is especially true with regard to telemanaged systems

Every project approaches the human-robotic interface problem in its own way. The usual 

programmatic drivers for this are principal investigators who are focused on the core robotic or 

platform technologies rather than interfaces (for example, Demo III’s main focus is on developing 

perception technology and not on the robotic system per se); the lack of a systematic 

understanding of the goals of the system and the tasks expected to be carried out by the elements 

of the system (including the human elements); and the lack of hands-on experience with the 

existing robotic systems in the field to catalog and document interaction needs. Acceptable 

metrics for human-robot interfaces are also lacking.

5.3 DISCONNECT BETWEEN END USERS AND DEVELOPMENT PROCESSES

Basic robotics technology has progressed to where it is possible to incorporate some 

elementary ground robotics capabilities into FCS Block I if the acquisition system were structured 

to move technology rapidly into the field. Robotic ground vehicles still do not have a “sponsor” in 

the way that aviation, trucks, guns, missiles, and tanks do. To be successful, FCS program 

management has to be able to find needed, useable military or commercial technology and force 

its rapid maturation, but the “normal” processes of the acquisition system cannot deliver the 

needed technology in a timely fashion. The fielding of existing developmental robotic ground 

vehicles for training purposes also would speed up combat development work for the FCS, but 

there does not appear to be any clear path by which the (robotic vehicle) work being done within 

the Army R&D community can find its way into the FCS development program.

The Army acquisition system appears to be disconnected at every transition point in the 

developmental process. The system is focused on formalism, on organizational structure, on 

funding categories—and on a zero defects philosophy that is totally intolerant of fielding lessthan-perfect equipment. An output-oriented, field-and-upgrade approach is at the heart of the 

spiral development concept, which focuses on moving new technology through the developmental 

process and into the field as quickly as it can be made useful. A recent Defense News article 

points out, however, that Congress does not want to buy anything until it has been completely 

tested and proven [Kaufman 2002]. In that regard, the Army Acquisition System is in step with 

Congress, which may be very unwilling to accept the notion of spiral development. The Army 

often is accused of stovepiping development on the assumption that this is a bad way to manage 

R&D, when the perceived stovepipe development was actually quite logical and served very well. 

Stovepiping was an effective way to develop the equipment the Army needed when there was 

relatively little technology crossover between the systems concerned. The large systems 
38

developed in the 1970s and 1980s—M1 Abrams, HMMWV, HEMTT, Apache, Blackhawk, 

etc.—were products of that type of development, as were their many predecessors developed 

from 1940 to1970. 

Over the past 60 years, however, as technology became more complex and system costs rose 

astronomically, local control over the development process has continually devolved toward a 

centralization of decision-making in the Department of the Army (DA) and the DoD. At the 

same, time much formerly in-house Army R&D work has migrated to industry, with a consequent 

degeneration of government “engineering muscle” into “bureaucratic gristle.” The time needed to 

develop and field a new major item has at least doubled since 1960. There still are many pockets 

of solid technical capability in the Army system, some of which we encountered in the course of 

this study. But, like a species being driven toward extinction by piecemeal habitat destruction, 

they increasingly are isolated from the interaction with major system developments that is 

essential both for them to contribute and to survive. 

The FCS development program forces all of the Army Acquisition System’s faults and 

problems out into the open. It demands the rapid fielding of new technology, and, being a 

“system of systems” it chops across the “stovepipes.” The Army’s response is to bypass FCS 

development by turning to industry for a “lead system integrator” (LSI) to integrate the disparate 

technologies involved into a coherent combat system. It remains to be seen how well this 

approach is going to work. The gold standard for such a development program is the Navy’s 

1950s Polaris program, a tough standard to meet with regard to both quick response and useful 

output. The Army materiel acquisition organization is highly compartmentalized, and putting 

together a project management structure that can quickly mobilize its resources for the FCS will 

be challenging at best. Technologies such as UGVs may get lost in the shuffle. In the course of 

its deliberations over the past 10 months, the Human-Robot Interface panel saw considerable 

reason to believe that is a likely outcome for the very promising robotics work currently going on

in the Army laboratories and Research, Development and Engineering Centers (RDECs). The 

FCS program management structure must be set up to prevent this outcome since the FCS cannot 

become a successful fighting system without its robotic subsystems. In fairness, however, we must 

point out here that it is easier to apply spiral development and still exercise adequate configuration 

control over a fleet of less than 50 large, very expensive capital ships [none of which are ever 

exactly alike, anyway!] than it is to control the configuration of a fleet of tens of thousands of 

combat and tactical vehicles. Anyone who has ever dealt with serial production and maintenance 

of equipment in the field should be a serious believer in rigorous configuration control. This is 

one of the fundamental weak points of the spiral development approach. In the Army, spiral 

development could easily lead us to the German Werhmacht's WWII problem of ground vehicle 

populations that were riddled with more or less undocumented running production changes, to 

mention only one problem. Modern computer systems should enable us to cope with spiral 

development today, but no one should think it will be easy or foolproof.

The FCS concept of a system of manned and unmanned vehicles, ground and air, is not new. 

Dr. James Albus of NIST7 proposed the basic idea at the start of the 1980s in an address to an 

ADPA Combat Vehicles Conference. Dr. Albus referred to the concept as a system of “swarm 

 

7

 NIST: National Institute of Standards and Technology; ADPA: American Defense Preparedness 

Association.
39

vehicles”: the unmanned air and ground vehicles in the swarm would both protect the manned 

control vehicle at the center of the swarm and serve, collectively, as its primary weapon. He was 

not the only person to suggest this general approach to future combat vehicles.

W. J. Whelan [1982] laid out a novel concept for a future fighting vehicle system of small 

robotic fighting vehicles under the direction of a larger manned vehicle. The concept, while perhaps 

prophetic, realistically was not testable at the time it was proposed, nor was it possible then to 

predict with credibility when developments in computing technology might make it possible to 

build such a system. When H. H. Dobbs proposed a similar approach [1993] the technology 

picture had cleared considerably, and the explosive growth of computational power over the 

proceeding decade pointed to such military robotic vehicle systems becoming practical in the 

foreseeable future. The article, however, received little comment. At that time the Armor 

community was still in the afterglow of its brutally decisive success in Operation Desert Storm. 

Not one U.S. tank crewman was lost to enemy action during the 100 hours of the Desert Storm 

land battle, while literally every Iraqi force that tried to fight them was totally destroyed and 

suffered enormous loss of life. The Army focus then was on a “Super M1” as the future successor 

to the already dominant M1 Abrams series of Main Battle Tanks (MBTs). The ASB Tank 

Modernization Study of 1995-1998 reflected that focus. Robotic vehicles were discussed only in 

an appendix. However, military robotic vehicle research did continue throughout the 1980s under 

the direction of the DoD land warfare systems specialists and DARPA. Congress created the 

DoD Joint Robotics Program (JRP) in 1989 to consolidate all such efforts. The Unmanned 

Ground Vehicle/Systems Joint Project Office (UGV/S JPO) was established at Redstone Arsenal 

in Huntsville, Alabama to work with ground robotic vehicle systems. Members of this panel were 

briefed on some of their work during our visit to the U.S. Army Aviation and Missile Command 

(AMCOM). The Board on Army Science and Technology (BAST), an element of the National 

Research Council, stated in its 1992 report on the Army of the 21st century that “the core weapon 

of the 20th century has been the tank. The core weapon of the 21st century may well be the 

unmanned system operating mostly under computer control [i.e., autonomously] with human 

supervision.”

The Demo III work that some study group members observed at Fort Indiantown Gap began 

in 1998 [Bornstein and Shoemaker 1998]. At that point in time management of the work shifted 

from the DoD and DARPA to the Army, and ARL and the U.S. Army Tank Automotive and 

Armaments Command (TACOM) became involved. The involvement of TACOM, which is 

responsible for the development of vehicle and armament systems for the Army was a clear 

indication that robotic ground vehicles were approaching the point of practical application in the 

field.

By the end of the 1990s it also had become possible to predict with reasonable accuracy 

when a given level of computational capability would become available at a chosen cost level. 

The Impact Matrix for Computational Technology [Dobbs 2002] shows such estimates at 

nominally current PC price levels. Professor Moravec, one of the most knowledgeable people 

working on robotic intelligence and sensing, estimates that machines will reach human-level speed 

(108 MIPS) by the year 2040 [Moravec 1999]. Current supercomputers already process data at 

about 4 ? 106 MIPS, and the goal of the IBM Corporation Blue Gene project [DeCusatis 2001], 

first announced in 2000, is to build a supercomputer with a speed of over 1010 MIPS by 2005, one 

hundred times Professor Moravec's estimate of human-level speed. This supercomputer will need 
40

approximately 1013 bytes of memory. The price of supercomputer speed usually falls to PC levels 

by about 20 years after its introduction; the price of computer memory capability has always 

closely followed that of computational speed. Professor Moravec’s estimates of the time when 

robots will reach human mental capability thus may appear conservative. However, note that 

although computational speed and memory capacity are necessary conditions for intelligence, 

they are not necessarily sufficient conditions! No one yet knows what the sufficient conditions 

are. A great deal of work also remains to be done in the areas of sensors and the interpretation of 

their outputs before fully autonomous machines become practical.

Nevertheless, the U.S. military is moving strongly toward robotic vehicle systems for many 

missions, including combat, in the near to mid term. Congressional pressure to move in that 

direction also has developed [Wolfe 2000]. Army leadership now appears to understand the 

necessity of robotic vehicle systems, a capability inherent in the FCS concept. Whether the Army 

in the field understands yet is less clear, and whether the acquisition system can successfully 

develop and field a fightable initial version of the FCS in the appropriate time frame is open to 

question. Fielding even one of the robotic units derived from the Demo III work with the Block I 

version of the FCS would both improve the system’s survivability and let the soldiers begin to 

learn to use the FCS in the ways that will make it a successful weapon system. The HRI panel 

believes that the Army can accomplish this goal.

For the present (until the FCS comes on the scene), the panel believes that, with a minimum 

of changes, the Demo III robots they observed in November at Fort Indiantown Gap are suitable 

for issue to troops in well-defined environments for testing and training. These activities would 

provide essential feedback to both the FCS developers and the combat development community 

and would ensure that the FCS was far more capable when initially fielded. The benefits of this 

approach should greatly outweigh the moderate cost of the equipment involved. The interim 

brigades might be the best choice for the initial fielding of this equipment.
41

6 PANEL RECOMMENDATIONS

Figure 18. Recommendations

6.1 SUMMARY RECOMMENDATION

The panel recommends that the Army move rapidly to create a strong, technology focused 

program for understanding and developing the technologies for human robot interactions, with 

particular emphasis on autonomous ground robots. We recommend that such a program be 

grounded in well-articulated operational needs, and be oriented towards the transformation and 

objective force requirements. The proposed efforts should augment and build on the successful 

robotic ground vehicle work that has been done in the Army laboratories (particularly, ARL) and 

RDECs—most specifically the Demo III and related ground robotics programs. 

To this end, we make three specific recommendations.

6.2 DETAILED RECOMMENDATIONS

1. Ft. Knox and TRADOC should specify an operational architecture for the 

use of robots, particularly followers, teleoperated systems, and autonomous

vehicles. 

• To be useful, the operational architecture must be baselined with experimental 

data gathered in operationally relevant scenarios, and in realistic operational 

environments. 

Robotics TF

Page 27

Recommendations

• Ft. Knox and TRADOC should specify an operational architecture for the use 

of followers, teleoperated, and autonomous ground robots

– Experiments with users should form the basis of the operational architecture

– Field experience with existing autonomous robots (such as Demo III), in 

experimental environments such as the NTC, and with the National Guard 

would provide a rigorous baseline (ground truth) for the operational 

architecture

• PEO FCS, and the OFTF, supported by the FCS LSI should formulate a Block 1

human-robot interaction architecture consistent with the FCS ORD in time for 

the FCS Milestone B decision

– Robotic followers and autonomous RSTA robots are technically feasible in 

the FCS SDD timeframe

• ARL should immediately create and lead a S&T program aimed at developing a 

technical architecture for human-robot interactions (focused on autonomous 

ground robots)

– HRED’s MANPRINT activity could be delegated to take on this role

– Collaboration with DARPA may be appropriate to develop and mature 

enabling technologies (Potential synergies with IXO, ATO, and IPTO)

– Collaboration with application-focused RDECs is critical
42

• To this end, we suggest that the Army apply additional resources to initiate 

accelerator low rate initial production (LRIP) fielding of the Demo III RSTA 

robots with organized feedback to the supporting R&D programs, and constant 

upgrades to the fielded robots—total deliberate integration on a real-time basis 

of (1) the material development process, (2) the combat development process, 

(3) troop training, and (4) lessons learned from combat. The Human-Robot 

Interface panel is not aware that the Army has done this before except on a very 

limited basis during wartime. In this environment the difference in the nature of 

robots from previous equipment should surface. 

• Research should continue in improving the core robotic technologies 

(particularly perception, navigation, and mobility). The most important aspect 

of a robotic weapon system, its (obviously still limited) ability to “think”, will 

improve in the field, due to its “experiences” in dealing with its environment 

(soldiers and missions) and to relatively low-cost software and hardware 

upgrades to its ability to learn. 

• Focus robotic R&D on making the following improvements in robot capability 

and self-sustainment ability 

1. Increased temporary memory (RAM) and processor speed 

(a factor of 10? is available now)

2. Permanent memory (must be rugged)

3. Sensory capability to “see” and understand

4. World model software (its local world: terrain, weather, 

mission execution, “its soldiers,” etc.)

5. Ability to manipulate its environment, support its own 

needs

6. Ability to communicate with humans, electronically and 

verbally

7. Improved automotive capability

2. PEO FCS, and the OFTF, supported by the FCS LSI should formulate a 

Block 1 human-robot interaction architecture consistent with the FCS ORD in 

time for the FCS Milestone B decision

• Having a user community “pulling” the autonomous robotics technology is an 

invaluable motivator for the future development of the field. As we have 

indicated in the report, autonomous operation is a key need for FCS. FCS and 

the Objective Force Task Force are therefore, the most appropriate 

“customers” who should drive for technology adoption, and define an 

architecture for human-robot interactions.
43

3. ARL should immediately create and lead a S&T program aimed at developing 

a technical architecture for human-robot interactions (focused on 

autonomous ground robots)

• HRED’s MANPRINT activity could be delegated to take on this role. ARL 

should establish an R&D program to systematically review the possible forms 

of human-robotic interfaces and the work that has been done in this area, and 

develop guidelines for the development of future military robotic systems. The 

program should consist of an analytical/experimental effort to:

A. Identify the combat functions/tasks that could benefit most from the 

application of unmanned vehicles 

B. Determine the interaction class that best fits the identified combat 

functions and tasks that have the highest payoff if unmanned vehicles 

are used 

C. Provide design guidance for the unmanned vehicle and the operator 

control and display interfaces that will provide the desired 

operational capability

• Collaboration with DARPA may be appropriate to develop and mature enabling 

technologies (Potential synergies with IXO, ATO, and IPTO)

• Collaboration with application-focused RDECs is critical.
44

REFERENCES

Air Force HSI Office (1995). HSI examples. In Human systems integration history book. 

Brooks AFB, TX: Human Systems Center, Developmental Plans (HSC/XR), Human Systems 

Integration Office.

Boeing Product Safety Organization (1993). Statistical summary of commercial jet aircraft 

accidents: Worldwide operations, 1959-1992. Seattle, WA: Boeing Commercial Airplane 

Company. 

Booher, H. R. (1997). Human factors integration: Cost and performance benefits on Army 

systems. Aberdeen Proving Ground, MD: Army Research Laboratory. (DTIC No. AD-A330 

776)

Bornstein, Jon, and Chuck Shoemaker, U.S. Army Research Laboratory. "The Demo III UGV 

Program." Unmanned Systems, Vol. 16, No. 4 [Fall 98].

Casey (1993). Set phasers on stun and other true tales of design, technology, and human error. 

Santa Barbara, CA: Aegean Publishing Company.

DeCusatis, Casimer. “When Light Speed Isn’t Enough: Creating Superluminal Supercomputers,” 

ANALOG, Vol. CXXI, No. 9 [Sep 2001].

Department of Energy. (1998). Lessons learned home page [On-line]. Available: 

http://llis.gsfc.nasa.gov/

Department of the Air Force. (1996). Automated Lessons Learned Collection and Retrieval 

System (ALLCARS) [CD-ROM].

Dobbs, COL Herbert H., USA (Ret.). "Impact Matrix for Computational Technology. Prepared 

for an Oakland University task force workshop on "Creating the Future." Rochester, MI 

[Original version, March 1998 – 2nd Revision, July 2002].

Dobbs, COL Herbert H., USA (Ret.). "Planning for a Future Tank Must Consider Technology 

Leaps, Robotic 'Crews'," ARMOR, Vol. CII, No. 1 [Jan-Feb 93].

Department of Defense, Human Engineering Program Process and Procedures, MIL-HDBK46855A, 17 May 1999 

Gentner, F. C., Wourms, D. F., Hunn, B., & Farrell, J. L. (1996). AC-130U Gunship AFTScanner prone workstation redesign improved mission effectiveness: A continuing process. In 

The IEEE 1996 National Aerospace & Electronics Conference (NAECON) (pp. 435-442). 

Piscataway, NJ: IEEE.

Gorman, M.C. (1990). Ergonomics in concurrent engineering. In CSERIAC Gateway, Vol. I, No 

3. Wright-Patterson AFB, OH: Crew System Ergonomics Information Analysis Center. 

Hendrick, H. (1996). The ergonomics of economics is the economics of ergonomics: 1996 

Human Factors and Ergonomics Society (HFES) presidential address. Santa Monica, CA: HFES

Kaufman, Gail. “Air Force Office to Test Spiral Arms Development,” Defense News [April 1-7, 

2002].

Malone, T. B. (1990). Human factors and human error. In Proceedings of the Human Factors 

and Ergonomics Society 34th Annual Meeting (pp. 651-654). Santa Monica, CA: The Human 

Factors and Ergonomics Society.
45

McDaniel, J. W. (1998). Design requirements for human strength: Asking the right question. 

Wright-Patterson AFB, OH: Air Force Research Laboratory.

Moravec, Hans P. ROBOT. Oxford Press, New York, ISBN 0-19-511630-5 [1999].

National Transportation Safety Board. (1973). Aircraft accident report: Eastern Airlines, Inc. 

L-1011, N310EA Miami Florida (Report No. NTSB/AAR-73/14). Washington, DC: Author.

Ninety seconds of terror. (1992, 26 October). Time.

Reed, F. (1988). Stinger defies its critics. (1988, 4 January). The Washington Times

Shafer, J. B. (1976). Enhanced VERDIN panel design rationale. IBM-Owego 76-535-001. 

Skelton, I (1997). MANPRINT for the U.S. Army: Reprint of Congressman Ike Skelton's speech 

in the Congressional Record dated October 1, 1997. MANPRINT Quarterly, 6, 2-6.

Stein, K. J. (1983). Human factors analyzed in 007 navigation error. Aviation Week & Space 

Technology

Whelan, W. J. "New Concepts for U.S. Combat Vehicle Systems," Rand Corporation Report, R2933-ARPA/RC, ISBN 0-8330-0462-X [Dec 82].

Wolfe, Frank. "Warner: Speed Development of Unmanned Combat Systems," Defense Daily, 

Vol. 205, No. 25 [Feb 9, 2000].
46
A-1

Appendix A: Participants List
A-2
A-3

PARTICIPANTS LIST

ARMY SCIENCE BOARD 2001 AD HOC STUDY

HUMAN ROBOTICS INTERFACE ISSUES

ASB Panel

Dr. Prasanna Mulgaonkar 

Study Chair

SRI International 

Dr. Herbert Dobbs 

Study Co-Chair

Torvec, Inc.

Dr. John Blair

JBX Technologies, Inc.

Dr. Greg Canavan

Los Alamos National Laboratories

Prof. Ka C. Cheok

Oakland University

Mr. Robert Dodd

Quality Research, Inc.

Dr. Mark Hofmann

COLMAR-L.L.C.

Dr. Michael Krause

KRI

Mr. David Martinez

MIT Lincoln Laboratories

Dr. Christine Mitchell

Air Force Scientific Advisory Board 

(AFSAB)

Dr. Robbi Perna

MezzoGiorno Consulting

Gov't Advisors

Mr. Jack Taylor

Office of the Secretary of Defense (OSD)

Dr. Rene Dupontbriand

Army Research Laboratory (ARL)

Staff Assistant

Dr. Patrick Eicker

Sandia National Laboratories

Sponsor

HON Claude M. Bolton

Assistant Secretary of the Army for Acquisition, Logistics and Technology

[ASA(ALT)]
B-1

Appendix B: Acronyms
B-2
B-3

ADI Attitude Directional Indicator

ADPA American Defense Preparedness Association

AMCOM Aviation and Missile Command

ARL Army Research Laboratory

ARL-XUV Army Research Laboratory—Experimental Unmanned Vehicle

ASB Army Science Board

ATD Advanced Technology Demonstration

ATO Advanced Technology Office (DARPA)

BDA Battle Damage Assessment

BAST Board on Army Science Technology (National Academies of Science, 

Department of Military Science and Technology)

C4ISR Command, Control, Communications, and Computers, Intelligence, 

Surveillance and Reconnaissance

CAT/RF Crew Automation Testbed/Robotics Follower

DA Department of the Army

DARPA Defense Advanced Project Research Agency

DoD Department of Defense

FCS Future Combat System

GUI Graphical User Interface

HE Human Engineering

HF Human Factors

HFE Human Factors Engineering

HRI Human-Robot Interface

HSI Hyperspectral Imaging

INS Inertial Navigation System

IPTO Information Processing Technology Office (DARPA)

IR Infrared

IXO Information Exploitation Office (DARPA)

JPL Jet Propulsion Laboratory

JPO Joint Program Office 

JRP Joint Robotics Program

KAL Korean Air Lines

LADAR Laser detection and ranging
B-4

LRIP Low-rate initial production

LSI Lead System Integrator

MAV Manned Aerial Vehicle

MBT Main Battle Tank

MIC Methyl isocyanate

MIPS Million Instructions per Second

MMWR Millimeter Wave Radar

MOUT Military Operations in Urban Terrain

MPRS Man-Portable Remote System

MPT Military potential test

NASA National Aeronautics and Space Administration

NBC Nuclear, Biological and Chemical

NIST National Institute of Standards and Technology

NRC National Research Council

NTC National Training Center

NTSB National Transportation Safety Board

OFTF Objective Force Task Force

OFW Objective Force Warrior

ONR Office of Naval Research

OPFOR Opposing Forces

OSD-ATL Office of Secretary of Defense – Acquisition, Technology and Logistics

PC Personal Computer

PEO Program Executive Office

PI Principal Investigator

Psig Pounds per square inch gauge

R&D Research and Development

RAM Random access memory

RCS Real-time Control Systems (other contexts, Radar Cross Section)

RDEC Research, Development, and Engineering Center

RSTA Reconnaissance, Surveillance, and Target Acquisition

S&T Science and Technology

T&E Test and Evaluation

TACOM U. S. Army Tank Automotive and Armaments Command

TIC Tactical Information Coordinator

TMR Tactical Mobile Robotics (DARPA Program)
B-5

TOR Terms of Reference

TRADOC Training and Doctrine Command

UAV Unmanned Aerial Vehicle

UCAV Unmanned Combat Air Vehicle

UGCV Unmanned Ground Combat Vehicle

UGV/S Unmanned Ground Vehicle/System

UMV/S Unmanned Vehicle/System

VTI Vetronics Technology Integration

WITS Wide-area Telerobotics Systems
C-1

Appendix C: Army UGV Roadmap
C-2
C-3
C-4
C-5
C-6
C-7
C-8
D-1

Appendix D: Examples, Lessons Learned, and Benefits
D-2
D-3

Downing of Iranian Air Lines Flight 655 (IAL-655). 

On July 3, 1988, the U.S.S. Vincennes shot down a commercial airliner over the Persian 

Gulf. These circumstances, combined with the fact that an Iranian F-14 had been observed taking 

off from an Iranian military base in the same general location and at approximately the same time 

as the commercial airliner, led to the accident. With such intense expectations of an attack, and 

given previous observations allowing for the possibility of an F-14 cloaking itself as a commercial 

airliner, the crew was falsely led to believe they were under attack. The official investigation of 

the incident by Admiral Fogarty used the phrase "scenario fulfillment" to describe the tactical 

information coordinator’s (TIC’s) distortion of the situation. The investigation states, "Stress, 

task fixation, and unconscious distortion of data may have played a major role in this incident." 

(Based on Malone, 1990)

 The accident occurred in part because a TIC mistakenly reported the aircraft as descending 

over the ship, based on the seaman's perception of the information gathered from his display, 

when in actuality the aircraft was ascending. Two other crew concurred with the TIC's 

assessment of the situation. To understand why this error happened, we must examine the events 

leading up to this incident. The accident is a classic example of the influence of expectancy in 

causing human error. Already in a stressful environment, naval personnel in the Gulf were told to 

expect an attack from the Iranians on Independence Day. In addition, specific warnings were 

issued concerning possible attacks from Iranian F-14 Tomcats. From June 2 - July 2, 1988, there 

were 150 challenges issued by warships to aircraft. Of these, 83 percent were to Iranian military 

aircraft (7 of which were F-14s), while only 1.3 percent were issued to commercial aircraft. 

Moreover, Iranian F-14s were previously observed flying commercial traffic routes for cover, 

emitting radar squawk signals of commercial aircraft in tandem with their military squawk.

Industrial Accident at Union Carbide. 

On December 2, 1984, the accidental release of a toxic chemical from a Union Carbide 

subsidiary in Bhopal, India, killed at least 2,500 people (official count) and quite possibly as many 

as 10,000. In addition, over 150,000 people (from a population of 672,000 in this urban area)

sought medical attention resulting from the accident. The incident occurred at night when the 

pressure relief valve blew out on a tank containing 45 tons of methyl isocyanate (MIC), releasing 

its contents and bathing the city of Bhopal in suffocating fumes. MIC is a highly volatile, 

unstable, flammable chemical that becomes a gas at approximately 100 degrees F and is highly 

reactive with water at any temperature. In this accident, the MIC in the tank became 

contaminated with water when the connecting lines were flushed during a routine maintenance 

operation. Within a period of less than two hours, the pressure in the tank had risen from its 

normal acceptable level of between 2 and 25 psig to well in excess of 55 psig, and the temperature 

had soared to at least 392 degrees F, causing the relief valve to open. The accident was attributed 

to human error caused by carelessness, poor training, and improper maintenance, as well as design 

shortcomings in the control room. Control room instrumentation supplied no record of previous 

values for important system parameters such as tank pressure to supply a historical trace for an 

operator who was taking over a shift from another (the accident occurred within a few hours of 

shift change). The upper limit on the displays for the tank temperature and pressure gauges was 

too low to adequately reflect the conditions in the tank (for example, the pressure gauge topped 
D-4

out at 55 psig). The tank temperature display was not even located in the control room. In 

Bhopal after the accident, Union Carbide's chairman, Warren M. Anderson, expressed his concern 

for the loss of life, but maintained that the "safety standards in the U.S. are identical to those in 

India.... Same equipment, same design, same everything" (Casey, 1993). Unfortunately, that was 

part of the problem. The designers of the plant did not anticipate the cultural differences between 

the operators in India and their American counterparts. Nor did they take into consideration the 

fact that requiring the Indian operators to keep their logbooks in English, a second language for 

the Hindi personnel impeded the transfer of information between operators. Finally, two safety 

mechanisms that could have contained the accident failed. The neutralizing soda scrubber system 

was grossly inadequate; it was designed to absorb 190 pounds of gas per hour at 15 psig and 95 

degrees F, but, at the time of the accident, the MIC was flowing at 40,000 pounds per hour at a 

temperature of over 392 degrees F. The flare tower that could have harmlessly burnt off the gas 

was not in operation. (Based on Malone, 1990; Casey, 1993)

Lessons Learned

Effects of joint range of motion limits on strength. 

When the angle of a fully deflected aircraft rudder/brake pedal is beyond the limit of ankle 

mobility, the pedal will seem to have excessive resistance. In addition, this will prevent the pilot 

from fully utilizing the brakes of the aircraft. (McDaniel, 1998)

Component accessibility. 

Failure to design aircraft components and equipment for easy accessibility causes excessive 

maintenance time for repair and decreased aircraft availability. (Department of the Air Force, 

1996)

Cross-connected hydraulic lines. 

Inadvertent cross-connection of hydraulic lines can easily occur when similar fittings are used 

for all lines, leading to extremely high human and equipment cost. (Department of the Air Force, 

1996)

Command ejection option. 

Lack of a command ejection option in a multiple-ejection-seat system can have two major 

negative effects: seats can collide because ejections are not coordinated; and one or more people 

may be left within the vehicle if they are unable to eject themselves. Either of these situations 

could result in loss of life. (Department of the Air Force, 1996)

Cargo-loading procedures. 

When a winch operator loading cargo aboard an aircraft cannot see the cargo being loaded 

and does not have communications with the rest of the loading crew, safety is adversely impacted. 

(Department of the Air Force, 1996)

Overhead power lines and tall vehicles. 

It is important to identify overhead hazards such as low power lines when planning and 

organizing work involving tall vehicles. Without proper identification of workplace hazards, the 

ability to minimize exposure to and protect personnel from hazards is significantly reduced. (Dept. 

of Energy, 1998)
D-5

Tactical altitude director system audio alarms. 

When a low-altitude warning system sounds frequent false alarms, pilots become accustomed 

to the audio alarm and tend to ignore it. This can result in loss of the aircraft and aircrew through

failure to respond to a valid alarm. (Department of the Air Force, 1996)

Life-raft deployment. 

Life rafts not properly designed for quick and easy deployment can result in death to 

passengers and crew. (Department of the Air Force, 1996)

Human error in aircraft accidents. 

Studies of commercial jet aircraft accidents attribute over 70 percent of accidents to crew 

error. (BOEING, 1993)

Benefits from HE

Nuclear/Biological/Chemical Reconnaissance Vehicle.

 Redesigning interfaces utilizing subjective workload assessment, this system, originally 

requiring a four-member crew, was successfully reduced to three members. As a part of crew risk 

reduction, HE design principles enabled incorporation of standoff detection capability, thereby 

reducing exposure risk to crewmembers. In addition, HE involvement in overall system design 

integration allowed for considerable reductions in maintainability costs. (Booher, 1997)

Forward Area Artillery Resupply Vehicle. 

During the beginning stages of system development, subjective workload analysis and 

computer modeling were used to determine optimal vehicle crew size for maximum mission 

performance. During the T&E phase of the design process, task analysis, and field observation 

techniques were utilized to identify critical areas of interaction between warfighters, equipment 

and environment during battle scenarios. Throughout the course of the project, these HE 

principles targeted key issues that were subsequently resolved by the design team leading to a 

more effective product. (Booher, 1997)

Transport aircraft redesign to accommodate parachutists

A joint Air Force-Army working group helped redesign the fuselage and door position of a 

large transport aircraft using HE principles to improve the airflow for parachutes. The vehicle is 

now considered the best aircraft for parachute jumping. (Air Force HSI Office, 1995)

Screen display redesign. 

The CRT screen display used by the directory assistants at a regional telephone company was 

reconfigured in keeping with HE recommendations. After the redesign, the average time to 

process a call dropped by 600 milliseconds. This reduction saves the company $2.94 million a 

year across the five-state region served by the system. (Hendrick, 1996)

Training system redesign. 

At the same telephone company, HE was applied in redesigning the systems used to train 

directory assistants. These revisions reduced the time needed to train an operator from five days 

to one and a half days. (Hendrick, 1996)

Shoulder-launched missile system. 

A shoulder-launched missile system with challenging operational requirements received 
D-6

extensive human engineering during its concept, design, test, and fielding, closely following the 

tasking in Section 4. Not only did it meet operational requirements, but also the design of the 

user-system interface and operational procedures enabled what might have otherwise been a 

complicated system, to facilitate effortless field use by third-world freedom fighters. Its original 

critics now praise the system for its simplicity. (Reed, 1988)

Gunship aft-scanner workstation redesign. 

Because gunship aft-scanners (who look for missile launches and other threats from the tail 

of their aircraft) had to remain in an "unnatural" prone position for hours over hostile territory,

they suffered back and neck pain. To remedy this problem, HE practitioners identified alternative 

design solutions for the aft-scanner workstation. The program office was able to fund several of 

these recommendations. As a result of the HE effort, weight distribution, body posture, 

positioning for mission tasks, functional reach, and external visibility were all improved, and neck 

and back pain complaints declined. (Gentner, Wourms, Hunn, & Farrell, 1996)

Development of an efficient helicopter tool kit. 

A striking example of the benefits of HE involvement is the design of the tool kit for 

helicopter mechanics. Historically, aircraft maintenance tool kits have been large, awkward, and 

heavy to transport on deployments. Based on HE recommendations, the organizational tool kit 

for one helicopter was reduced from 134 tools to only 6 tools. This redesign reduced what is 

usually a trunk-sized kit to a canvas pouch that is approximately half the size of a rolled-up 

newspaper. (Skelton, 1997)

Antisubmarine Warfare System. 

Using mission task and function analysis methods the HE practitioner shaped the design 

process of this system. The designers were able to meet mission objectives while incorporating 

many off-the-shelf components, lowering overall system cost. During T&E, the system 

substantially exceeded customer expectations, and subsequently the design lead to a highly 

successful deployment. (J. B. Shafer, personal communication, May 7, 1998)

Experimental Helicopter Technological Integration. 

During mid-nineteen eighties studies were conducted to determine if a single pilot could 

perform a scout/attack mission using advanced cockpit technology that previously required two 

aviators. Function analysis was used to determine which mission segments had the greatest 

workload, and pilots were interviewed regarding flight deck automation and workloads. Next, 

pilots were assessed using fixed based simulators installed with a prototype cockpit design. The 

tests and interviews led to two conclusions: (1) a highly effective pilot orientated cockpit was 

designed, and (2) although an exceptional single pilot could perform the scout/attack mission, 

under battle stress the pilot would become overloaded and, consequently, the mission 

performance would be sacrificed. Due to early HE involvement, the military had the opportunity 

to discontinue a project before any further investment was required. (J. B. Shafer, personal 

communication, May 7, 1998)
E-1

Appendix E: Study Report Distribution

ADDRESSEE COPIES

E-3

ARMY

Secretary of the Army, Pentagon, Room 3E700, Washington, DC 20310-0101 1

Under Secretary of the Army, Pentagon, Room 3E732, Washington, DC 20310-0102 1

Deputy Under Secretary of the Army (Operations Research), Pentagon, Room 2E660, Washington, DC 

20310-0102 1

Administrative Assistant to the Secretary of the Army, Pentagon, Room 3E733, Washington, DC 20310-0105 1 

General Counsel, OSA, Pentagon, Room 2E722, Washington, DC 20310-0104 1

Assistant Secretary of the Army (Civil Works), Pentagon, Room 2E570, Washington, DC 20310-0108 1

Assistant Secretary of the Army (Financial Management and Comptroller), Pentagon, Room 3E606, Washington, 

DC 20310-0109 1

Assistant Secretary of the Army (Installations and Environment), Pentagon, Room 2E614, Washington, DC 

20310-0110 1

Assistant Secretary of the Army (Manpower and Reserve Affairs), Pentagon, Room 2E594, Washington, DC 

20310-0111 1

Assistant Secretary of the Army (Acquisition, Logistics and Technology), Pentagon, Room 2E672, Washington, 

DC 20310-0103 1

Military Deputy to the ASA(ALT), Pentagon, Room 2E672, Washington, DC 20310-0103 1

Deputy Assistant Secretary for Plans, Programs and Policy, OASA(ALT), Pentagon, Room 3E432, Washington, 

DC 20310-0103 1

Deputy Assistant Secretary for Procurement, OASA(ALT), Pentagon, Room 2E661, Washington, DC 20310-0103 1

Deputy Assistant Secretary for Research and Technology, OASA(ALT), Pentagon, Room 3E374, Washington, DC 

20310-0103 1

Deputy for Systems Management and International Cooperation, OASA(ALT), Pentagon, Room 3E448, 

Washington, DC 20310-0103 1

Deputy for Ammunition, OASA(ALT), Headquarters, Army Materiel Command, 5001 Eisenhower Ave., 

Alexandria, VA 22333-0001 1

Deputy for Combat Service Support, OASA(ALT), Headquarters, Army Materiel Command, 5001 Eisenhower 

Ave., Alexandria, VA 22333-0001 1

Director, Assessment and Evaluation, OASA(ALT), Pentagon, Room 2E673, Washington, DC 20310-0103 1

Director, Army Digitization Office, DACS-ADO, Pentagon, Room 2B679, Washington, DC 20310-0200 1

Director of Information Systems for Command, Control, Communications and Computers, Pentagon, 

Washington, DC 20310-0107 1

Inspector General, Pentagon, Room 1E736, Washington, DC 20310-1700 1

Chief of Legislative Liaison, Pentagon, Room 2C631, Washington, DC 20310-1600 1

Chief of Public Affairs, Pentagon, Room 2E636, Washington, DC 20310-1500 1

Chief of Staff, Army, Pentagon, Room 3E668, Washington, DC 20310-0200 1

Vice Chief of Staff, Army, Pentagon, Room 3E666, Washington, DC 20310-0200 1

Assistant Vice Chief of Staff, Army Pentagon, Room 3D652, Washington, DC 20310-0200 1

Director of the Army Staff, Pentagon, Room 3E665, Washington, DC 20310-0200 1

Director, Program Analysis and Evaluation Directorate, Pentagon, Room 3C718, Washington, DC 20310-0200 1

Assistant Chief of Staff for Installation Management and Environment, Pentagon, Room 1E668, Washington, DC 

20310-0600 1

Deputy Chief of Staff for Personnel, Pentagon, Room 2E736, Washington, DC 20310-0300 1

Deputy Chief of Staff for Operations and Plans, Pentagon, Room 3E634, Washington, DC 20310-0400 1

Assistant Deputy Chief of Staff for Operations and Plans, Force Development, Pentagon, Room 3A522, 

Washington, DC 20310-0400 1

Deputy Chief of Staff for Logistics, Pentagon, Room 3E560, Washington, DC 20310-0500 1

Deputy Chief of Staff for Intelligence, Pentagon, Room 2E464, Washington, DC 20310-1000 1

The Surgeon General, HQDA, Skyline Place Building No. 5, Falls Church, VA 22041-3258 1

Chief, National Guard Bureau, Pentagon, Room 2E394, Washington, DC 20310-2500 1

Chief, Army Reserve, Pentagon, Room 3E390, Washington, DC 20310-2400 1

Chief, U.S. Army Center of Military History, 103 Third Avenue, Ft. McNair, DC 20319-5058 1

Chief of Engineers, HQDA, Pulaski Building, 20 Massachusetts Ave., NW, Washington, DC 20314-1000 1

Commander, U.S. Army Corps of Engineers, HQDA, Pulaski Building, 20 Massachusetts Ave., NW, Washington, 

DC 20314-1000 1

Commander, U.S. Army Concepts Analysis Agency, 6001 Goethals Rd., Ft. Belvoir, VA 22060-5230 1

Commander, U.S. Army Evaluation Center, Park Center IV, 4501 Ford Ave., Alexandria, 

VA 22302-1458 1

Commander, US Army Test and Evaluation Command (USATEC), 4501 Ford Ave., Alexandria, 

VA 22302-1458 1
ADDRESSEE COPIES

E-4

Commanding General, U.S. Army Space and Missile Defense Command, P.O. Box 15280, 

Arlington, VA 22215-0280 1

Dr. Collier, U.S. Army Space and Missile Defense Command, P.O. Box 15280, Arlington, VA 22215-0280 5

Deputy Commander for Space, U.S. Army Space Command, 1670 N. Newport Rd., 

Colorado Springs, CO 80916-2749 1

U.S. Army Space Command Forward, ATTN: MOSC-ZC, 1670 N. Newport Rd., Suite 211, Colorado Springs, 

CO 80916 1

Commander, National Ground Intelligence Center, 220 7th St., NE, Charlottesville, VA 22901 1

Director, U.S. Army Research Institute for the Behavioral Sciences, 5001 Eisenhower Ave., Alexandria, VA 

22333-5600 1

Commander, U.S. Total Army Personnel Command, Hoffman Building II, 200 Stovall St., Alexandria, VA 

22332-0405 1

Commander-in-Chief, U.S. Army Europe and Seventh Army, APO AE 09014 1

Commanding General, Eighth U.S. Army, APO AP 96205 1

Commanding General, U.S. Army South, HQ US Army South, P.O. Box 34000, Ft. Buchanan, 

Puerto Rico 00934-3400 1

Commanding General, U.S. Army Pacific, Ft. Shafter, HI 96858-5100 1

Commanding General, U.S. Army Forces Command, Ft. McPherson, GA 30330-6000 1

Commanding General, Third United States Army/Army Central Command/Deputy Commanding General, 

U.S. Army Forces Command, ATTN: AFDC, Ft. McPherson, GA 30330 1

U.S. Army Space Command Forward, ATTN: MOSC-ZC, 1670 N. Newport Rd., Suite 211, Colorado Springs, 

CO 80916 1

Commanding General, U.S. Army Signal Command, Ft. Huachuca, AZ 85613-5000 1

Commanding General, U.S. Army Special Operations Command, Ft. Bragg, NC 28307-5200 1

Commanding General, U.S. Army Intelligence and Security Command, Ft. Belvoir, VA 22060-5370 1

Commanding General, U.S. Army Medical Command, Ft. Sam Houston, TX 78234 1

Commander, U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD 21702-5012 1

Commanding General, U.S. Army Materiel Command, ATTN: AMCCG, 5001 Eisenhower Ave., Alexandria, 

VA 22333-0001 1

Commanding General, U.S. Army Materiel Command, ATTN: AMCRDA-TT, 5001 Eisenhower Ave., 

Alexandria, VA 22333-0001 1

Commander, U.S. Army Chemical and Biological Defense Command, ATTN: AMSCB-CG, Aberdeen Proving 

Ground, MD 21005-5423 1

Commander, U.S. Army Communications-Electronics Command, ATTN: AMSEL-CG, Ft. Monmouth, NJ 

07703-5000 1

Director, Army Systems Engineering Office, ATTN: AMSEL-RD-ASE, Ft. Monmouth, NJ 07703 1

Commander, U.S. Army Industrial Operations Command, ATTN: IOC-AMSIO-CG, Rock Island, IL 61299-6000 1

Commander, U.S. Army Aviation and Missile Command, ATTN: AMSMI-CG, Redstone Arsenal, AL 35898 2

Commander, U.S. Army Security Assistance Command, ATTN: AMSAC, Alexandria, VA 22333-0001 1

Commander, U.S. Army Simulation, Training and Instrumentation Command, ATTN: AMSTI-CG, 12350 

Research Parkway, Orlando, FL 32836-3276 1

Commander, U.S. Army Soldier Systems Command, ATTN: AMSSC-CG, Natick, MA 01760-5000 1

Commander, U.S. Army Tank-Automotive and Armaments Command, ATTN: AMSTA-CG, Warren, MI 

48397-5000 1

Commander, U.S. Army Test and Evaluation Command, ATTN: AMSTE-CG, Aberdeen Proving Ground, MD 

21005-5055 1

Commander, U.S. Army Armament Research, Development and Engineering Center, ATTN: SMCAR-TD, 

Picatinny Arsenal , NJ 07806-5000 1

Commander, U.S. Army Aviation Research, Development and Engineering Center, ATTN: AMSAT-R-Z, 

4300 Goodfellow Blvd., St. Louis, MO 63120-1798 1

Commander, U.S. Army Communications-Electronics Research, Development and Engineering Center, 

ATTN: AMSEL-RD, Ft. Monmouth, NJ 07703 1

Commander, U.S. Army Edgewood Research, Development and Engineering Center, ATTN: SCBRD-TD, 

Aberdeen Proving Ground, MD 21010-5423 1

Commander, U.S. Army Missile Research, Development and Engineering Center, ATTN: AMSMI-RD, 

Redstone Arsenal, AL 35898 1

Commander, U.S. Army Natick Research, Development and Engineering Center, ATTN: SATNC-T, Natick, 

MA 01760 1

Commander, U.S. Army Tank-Automotive Research, Development and Engineering Center, ATTN: AMSTA-CF, 

Warren, MI 48397 1

Director, U.S. Army Field Assistance in Science and Technology Activity, 5985 Wilson Rd., Suite 100, Ft. Belvoir, 
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VA 22060-5829 1

Director, U.S. Army Logistics Support Activity, ATTN: AMXLS, Bldg. 5307, Redstone Arsenal, AL 35898-7466 1

Director, U.S. Army Materiel Systems Analysis Activity, ATTN: AMXSY-D, Aberdeen Proving Ground, MD 

21005-5071 1

Director, U.S. Army Test, Measurement, and Diagnostic Equipment Activity, ATTN: AMXTM, Redstone Arsenal, 

AL 35898-5400 1

Commander, USAWSMR Electronic Proving Ground, ATTN: Intelligence Office, Ft. Huachuca, AZ 85613-7110 1

Director, U.S. Army Research Laboratory, ATTN: AMSRL-D, 2800 Powder Mill Rd., Adelphi, MD 20783-1145 1

Director, U.S. Army Research Office, ATTN: AMXRO-D, P.O. Box 12211, Research Triangle Park, NC 

27709-2211 1

Commanding General, U.S. Army Training and Doctrine Command, Ft. Monroe, VA 23651-5000 1

Deputy Commanding General, U.S. Army Training and Doctrine Command, Ft. Monroe, VA 23651-5000 1

Deputy Commanding General, U.S. Army Training and Doctrine Command for Combined Arms/Commander, 

U.S. Army Combined Arms Center/Commandant, Command and General Staff College, 

Ft. Leavenworth, KS 66027-5000 1

Deputy Commanding General, U.S. Army Training and Doctrine Command for Combined Arms Support/

Commander, U.S. Army Combined Arms Support Command and Ft. Lee, 

Ft. Lee, VA 23801-6000 1

Commander, U.S. Army Aviation Center and Ft. Rucker/Commandant, U.S. Army Aviation School/Commandant, 

U.S. Army Aviation Logistics School (Ft. Eustis), Ft. Rucker, AL 36362-5000 1

Commander, U.S. Army Signal Center and Ft. Gordon/Commandant, U.S. Army Signal School, Ft. Gordon, GA 

30905-5000 1

Commandant, U.S. Army War College, Carlisle Barracks, PA 17013-5050 1

Commander, U.S. Army Air Defense Artillery Center and Ft. Bliss/Commandant, U.S. Army Air Defense Artillery 

School, Ft. Bliss, TX 79916-5000 1

Commander, U.S. Army John F. Kennedy Special Warfare Center and School, Ft. Bragg, NC 28307-5000 1

Commander, U.S. Army Engineer Center and Ft. Leonard Wood/Commandant, U.S. Army Engineer School, 

Ft. Leonard Wood, MO 65473-5000 1

Commander, U.S. Army Quartermaster Center and School/Deputy Commander, U.S. Army Combined Arms 

Support Command and Ft. Lee/Commandant, U.S. Army Quartermaster School, 

Ft. Lee, VA 23801-6000 1

Commander, U.S. Army Infantry Center and Ft. Benning/Commandant, U.S. Army Infantry School, Ft. Benning, 

GA 31905-5000 1

Commander, U.S. Army Chemical and Military Police Centers and Ft. McClellan/Commandant, U.S. Army 

Military Police School, Ft. McClellan, AL 36205-5000 1

Commander, U.S. Army Ordnance Center/Commandant, U.S. Army Ordnance School, Aberdeen Proving Ground, 

MD 21005-5201 1

Commander, U.S. Army Field Artillery Center and Ft. Sill/Commandant, U.S. Army Field Artillery School, 

Ft. Sill, OK 73503-5000 1

Commander, U.S. Army Transportation Center and Ft. Eustis/Commandant, U.S. Army Transportation School, 

Ft. Eustis, VA 23604-5000 1

Commander, U.S. Army Armor Center and Ft. Knox/Commandant, U.S. Army Armor School, Ft. Knox, KY 

40121-5000 1

Commander, U.S. Army Intelligence Center and Ft. Huachuca/Commandant, U.S. Army Intelligence School, 

Ft. Huachuca, AZ 85613-6000 1

Commandant, U.S. Army Ordnance Missile and Munitions Center and School, Redstone Arsenal, AL 35897-6000 1

Commandant, Army Logistics Management College, Ft. Lee, VA 23801-6053 1

Director, U.S. Army Training and Doctrine Command Analysis Center, Ft. Leavenworth, KS 66027-5200 1

Commander, Battle Command Battle Lab, ATTN: ATZL-CDB, 415 Sherman Ave., Ft. Leavenworth, KS 

66027-5300 1

Director, Space and Missile Defense Battle Lab, P.O. Box 1500, Huntsville, AL 35807-3801 1

Commander, Battle Command Battle Lab, ATTN: ATZH-BL, Ft. Gordon, GA 30905-5299 1

Commander, Battle Command Battle Lab, ATTN: ATZS-BL, Ft. Huachuca, AZ 85613-6000 1

Commander, Combat Service Support Battle Lab, ATTN: ATCL-B, Ft. Lee, VA 23801-6000 1

Commandant, Depth and Simultaneous Attack Battle Lab, ATTN: ATSF-CBL, Ft. Sill, OK 73503-5600 1

Commandant, Dismounted Battle Space Battle Lab, ATTN: ATSH-WC, Ft. Benning, GA 31905-5007 1

Commander, Early Entry Lethality and Survivability Battle Lab, ATTN: ATCD-L, Ft. Monroe, VA 23651-5000 1

Commander, Mounted Battle Space Battle Lab, ATTN: ATZK-MW, Ft. Knox, KY 40121-5000 1

Commander, Battle Lab Integration, Technology and Concepts Directorate, ATTN: ATCD-B, Ft. Monroe, VA 

23651-5000 1

Program Executive Officer, Armored Systems Modernization, ATTN: SFAE-ASM, Warren, MI 48397-5000 1
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Program Executive Officer, Aviation, ATTN: SFAE-AV, 4300 Goodfellow Blvd., St. Louis, MO 63120-1798 1

Program Executive Officer, Command, Control and Communications Systems, ATTN: SFAE-C3S, Ft. Monmouth, 

NJ 07703-5000 1

Program Executive Officer, Field Artillery Systems, ATTN: SFAE-FAS, Picatinny Arsenal, NJ 07806-5000 1

Program Executive Officer, Intelligence and Electronic Warfare, ATTN: SFAE-IEW, Ft. Monmouth, NJ 

07703-5000 1

Program Executive Officer, Missile Defense, ATTN: SFAE-MD, P.O. Box 16686, Arlington, VA 22215-1686 1

Program Executive Officer, Standard Army Management Information Systems, ATTN: SFAE-PS, 9350 Hall Rd., 

Suite 142, Ft. Belvoir, VA 22060-5526 1

Program Executive Officer, Tactical Missiles, ATTN: SFAE-MSL, Redstone Arsenal, AL 35898-8000 1

Program Executive Officer, Tactical Wheeled Vehicles, ATTN: SFAE-TWV, Warren, MI 48397-5000 1

Program Executive Officer, Cruise Missiles Project and Unmanned Aerial Vehicles Joint Project, ATTN: PEO-CU,

47123 Buse Rd., Unit 1PT, Patuxent River, MD 20670-1547 1

Program Executive Officer, Combat Support Systems, ATTN: AF PEO CB, 1090 Air Force Pentagon, Washington, 

DC 20330-1090 1

Program Executive Officer, Joint Program Office for Biological Defense, 5201 Leesburg Pike, Suite 1200, 

Skyline #3, Falls Church, VA 22041-3203 1

Program Manager, Comanche Program Office, Bldg. 5681, Redstone Arsenal, AL 35898 1 

Program Manager for Chemical DeMilitarization, ATTN: SFAE-CD-Z, Aberdeen Proving Ground, MD 

21010-5401 1

Superintendent, U.S. Army Military Academy, West Point, NY 10996 1

NAVY

Secretary of the Navy, Pentagon, Room 4E686, Washington, DC 20350 1

Under Secretary of the Navy, Pentagon, Room 4E714, Washington, DC 20350 1

Assistant Secretary of the Navy (Research, Development and Acquisition), Pentagon, Room 4E732, Washington, 

DC 20350 1

Chief of Naval Operations, Pentagon, Room 4E674, Washington, DC 20350 1

Vice Chief of Naval Operations, Pentagon, Room 4E636, Washington, DC 20350 1

Commandant, U.S. Marine Corps, Pentagon, Room 4E714, Washington, DC 20380 1

Naval Research Advisory Committee, 800 N. Quincy Street, Arlington, VA 22217-5660 1

President, Naval War College, Code 00, 686 Cushing Rd., Newport, RI 02841-1207 1

AIR FORCE

Secretary of the Air Force, Pentagon, Room 4E871, Washington, DC 20330 1

Under Secretary of the Air Force, Pentagon, Room 4E886, Washington, DC 20330 1

Assistant Secretary of the Air Force (Acquisition), ATTN: SAF/AQ, Pentagon, Room 4E964, Washington, DC 

20330 1

Chief of Staff, United States Air Force, Pentagon, Room 4E924, Washington, DC 20330 1

Vice Chief of Staff, United States Air Force, Pentagon, Room 4E936, Washington, DC 20330 1

Air Force Scientific Advisory Board, Pentagon, Room 5D982, Washington, DC 20330 1

President, Air War College, 325 Chennault Circle, Maxwell Air Force Base, AL 36112-6427 1

OSD

Secretary of Defense, Pentagon, Room 3E880, Washington, DC 20301 1

Deputy Secretary of Defense, Pentagon, Room 3E944, Washington, DC 20301 1

Under Secretary of Defense for Acquisition and Technology, Pentagon, Room 3E933, Washington, DC 20301 1

Under Secretary of Defense (Personnel and Readiness), Pentagon, Room 3E764, Washington, DC 20301 1

Under Secretary of Defense for Policy, Pentagon, Room 4E808, Washington, DC 20301 1

Under Secretary of Defense (Comptroller/Chief Financial Officer), Pentagon, Room 3E822, Washington, DC 20301 1

Assistant Secretary of Defense (Command, Control, Communications and Intelligence), Pentagon, Room 3E172, 

Washington, DC 20301 1

Assistant Secretary of Defense for Economic Security, Pentagon, Room 3E808, Washington, DC 20301 1

Deputy Under Secretary of Defense for Advanced Technology, Pentagon, Room 3E1045, Washington, DC 20301 1

Deputy Under Secretary of Defense for Acquisition Reform, Pentagon, Room 3E1034, Washington, DC 20301 1

Deputy Under Secretary of Defense for Environmental Security, Pentagon, Room 3E792, Washington, DC 20301 1

Principal Deputy Under Secretary of Defense for Acquisition and Technology, Pentagon, Room 3E1006, 

Washington, DC 20301 1

Chairman, Joint Chiefs of Staff, Pentagon, Room 2E872, Washington, DC 20318-9999 1

Vice Chairman, Joint Chiefs of Staff, Pentagon, Room 2E860, Washington, DC 20318-9999 1

Director, Operational Test and Evaluation, Pentagon, Room 3E318, Washington, DC 20301-1700 1
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Director, Defense Research and Engineering, Pentagon, Room 3E1014, Washington, DC 20301-3030 1

Director, Defense Advanced Research Projects Agency, 3701 N. Fairfax Dr., Arlington, VA 22203-1714 1

Director, Ballistic Missile Defense Organization, Pentagon, Room 1E1081, Washington, DC 20301-7100 1

Director, Defense Information Systems Agency, 701 S. Courthouse Rd., Arlington, VA 22204-2199 1

Director, Defense Intelligence Agency, Pentagon, Room 3E258, Washington, DC 20301-7400 1

Director, Defense Intelligence Agency Missile and Space Intelligence Center, Building 4505, Redstone Arsenal, AL 

35898-5500 1

Director, Defense Logistics Agency, 8725 John J. Kingman Rd., Suite 2533, Ft. Belvoir, VA 22060-6221 1

Director, National Imagery and Mapping Agency, 4600 Sangamore Road, Bethesda, MD 20816-5003 1

Director, Defense Threat Reduction Agency, 6801 Telegraph Rd., Alexandria, VA 22310-3398 1

Director, Defense Threat Reduction Agency, 45045 Aviation Dr., Dulles, VA 20166-7517 1

Director, Defense Security Assistance Agency, 1111 Jefferson Davis Highway, Suite 303, Arlington, VA 22202 1

Director, National Security Agency, 9800 Savage Rd., Ft. Meade, MD 20755 1

Director, On-Site Inspection Agency, 201 W. Service Rd., Dulles International Airport, P.O. Box 17498, 

Washington, DC 20041-0498 1

Defense Science Board, Pentagon, Room 3D865, Washington, DC 20301 1

Commandant, Defense Systems Management College, 9820 Belvoir Rd., Suite G-38, Ft. Belvoir, VA 22060-5565 1

President, National Defense University, 300 5th Avenue, Ft. McNair, Washington, DC 20319-5066 1

Commandant, Armed Forces Staff College, 7800 Hampton Blvd., Norfolk, VA 23511-1702 1

Commandant, Industrial College of the Armed Forces, 408 4th Ave., Bldg. 59, Ft. McNair, Washington, DC 

20319-5062 1

Commandant, National War College, Washington, DC 20319-5066 1

National Security Space Architect, 2461 Eisenhower Avenue., Suite 164, Alexandria, VA 22331-0900 1

OTHER

Defense Technical Information Center, ATTN: DTIC-OCP, 8725 John J. Kingman Rd., Suite 0944, Ft. Belvoir, 

VA 22060-6218 1

Director, Central Intelligence Agency, Washington, DC 20505 1

National Research Council, Division of Military Science and Technology, Harris Bldg Rm. 258,

2101 Constitution Avenue NW, Washington DC 20418 1

Director, Institute for Defense Analyses, ATTN: TISO, 1801 N. Beauregard St., Alexandria, VA 22311-1772 1

Library of Congress, Exchange and Gift Division, Federal Document Section, Federal Advisory Committee Desk, 

Washington, DC 20540 1