A CONTROL SCHEME FOR MEASURE OF PERFORMANCE AND EFFICIENCY OF

TACTICAL COOPERATIVE ROBOTS

Amir Shirkhodaie

Intelligent Tactical Mobility Robotic Research Laboratory

Department of Mechanical & Manufacturing Engineering

 Tennessee State University

Ashirkhodaie@TNState.edu

ABSTRACT

We have discussed technical issues regarding supervised world

perception modeling and task planning of cooperative mobile

robots performing tactical tasks in unstructured environment.

We have introduced a hierarchy Supervisory Controller for

robust cooperative task deployment of heterogeneous semiautonomous robotic vehicles. Primarily, we have described

functional and modular architecture of the Supervisory

Controller and presented our strategies for separation of

supervisory functions according to their level abstraction,

complexity, precedence, and intelligence. Furthermore, we have

discussed control schemes for measure of performance and

measure of efficiency of our cooperative robots tested under

different task situations. Results indicate the hybrid Supervisory

controller performs satisfactorily in most simulated cases.

Keywords: Cooperative Robots, Supervisory Control,

Measure of Performance, and Measure of Efficiency.

1. INTRODUCTION

Cooperative robots are critically important for solving a number

of real-world problems. Their applications span from civilian

search and rescue missions to military battlefield reconnaissance

and surveillance covert operations to deep space scientific

research explorations. In very near future, potential commercial

applications of cooperative robots are foreseen to be ubiquitous.

The cooperative robots while benefit from inherent parallelism,

their robust control is both multi-folded and challenging to

achieve and sustain reliably. Many different techniques and

approaches for mobile robots control have been developed. The

proposed control techniques in literature can be, categorically,

classified as either deliberative, reactive, or hybrid in nature.

The control schemes that tend to be more deliberative require

relative more knowledge about the world. They use this

knowledge to predict the outcome of their actions, an ability that

enables them to optimize their performance relative to their

model of the world. Deliberative reasoning about task planning

of cooperative robots requires strong assumptions about this

world model. Primary knowledge upon which the reasoning is

based on, should be consistent, reliable, and certain. In a

dynamic world, where objects may have arbitrarily moves (i.e.,

in a battlefield or a crowded hallway), it is potentially risky to

rely on information that no longer be valid. Instead, world

representational models are generally constructed at run-time

using a combination of past knowledge gained about the

environment and exteroceptive sensory data.

At the other end of the spectrum, the reactive control systems

attempt to tightly couple perception and action in order to

achieve faster robot response in dynamic and unstructured

worlds while minimizing computational overhead. With a

purely reactive control system, it is rather difficult to achieve

planned deliberated tasks consistently. This is mainly due to

variability in world uncertainty and lack of robot’s knowledge

and ability in resolving conflicts between competitive world

perceptions in a given situation. In most cases, a wrongly

selected world perception may cause the robot an unrecoverable

deadlock situation or failure.

The temporal inconsistency and stability of the environment and

the robot’s immediate sensing inadequacy for a task are typically

coupled. Difficulty in proper localization of a robot and the way

that the robot perceives its surrounding world also yield possible

situations that typically grounds erratic conflicts in decision

making process of the robot. To demonstration this notion,

consider figure 1 that illustrates spatial configuration of three

cooperative robots. Should each robot presume the other two

robots as obstacles or as its teammates approaching it

unintentionally? One answer is it would be dependent on nature

of the task and how the navigational modes of the robots are

defined. If the robots had reactive behavior, they would

probably try to find a way out of the crowd or avoid the

deadlock situation. If the robots had deliberative behavior, each

robot might refer to its chronicle memory and try to reason why

the other robots are there in the first place before it makes any

decision. Situation like this example can occur frequently

during task execution of cooperative robots within a limited

work environment. Hence, it is responsibility of an intelligent

supervisory system to deal with such situations in a manner that

causes least perplexity to plan execution of robots.

An overview of common conceptions of the behavior-based

approaches is given by Mataric [1]. Brooks [2] describes four

key concepts that lead to behavior-based robotic: situatedness,

embodiment, intelligence, and emergence. The design of

behavior-based systems is often referred to as a "bottom up"

process, but this offers not so much to determination of the

structure of the system as to a basis in physical sensing and
action, and incremental development of sophistication from

simple to complex. Namely, there are structured in terms of

observable activity that they produce, rather than traditional

functional decompositions. The activity producing components,

behaviors, compete for actuator resources as well as share

perceptions of the world rather than any centralized

representation.

Recently, hybrid deliberative/reactive robotic architectures have

emerged that rely more heavily on explicit world representations

and tend to combine many aspects of traditional AI symbolic

methods with situated-based reasoning. They operate based on

abstract representation of the world in light of providing faster

response, better robustness, and more tractable than deliberative

and reactive systems. Hybrid architectures permit

reconfiguration of reactive control systems based on available

world knowledge through their ability to reason over the

underlying behavioral components. However, building of such

hierarchy systems requires compromise and full utilization of

reactive and deliberative systems to maintain the desirable

system performance and efficiency.

An overview of approaches and issues in cooperative robotics

has been also reported in [3,4,5]. Parker [6] has demonstrated

multi-robot target observation using the ALLIANCE

Architecture, where action selection consists of inhibition

(through motivation behaviors). As opposed to her architecture,

Pirajanian and Mataric [7] have developed an approach to multirobot coordination in the context of cooperative target

acquisition. Their approach is based on multiple objective

behavior coordination extended to multiple cooperative robots.

They have demonstrated a mechanism for distributed command

fusion across a group of robots to pursue multiple goals in

parallel. This technique enables individual robot to select

actions that no only benefit itself but also benefit the group as a

whole. A significant amount of work in this area is being

conducted by researchers at NIST. Their hierarchical control

architecture is shown to have capability in controlling several

unmanned mobile robotic vehicles in unstructured environment

using a hierarchy platoon level control scheme where the robots

follow a designated leader while maintaining a fair distance

apart [14].

Nonetheless, intelligent control of multi-agent robots is both

complex and challenging. The complexity of the task is

contributed to a number of compounding factors including:

multi-agent task decomposition, task distribution, resource

allocation, sensory world perception modeling and data sharing,

pattern recognition and reasoning, skill learning and adaptation,

communication networking, man-machine interaction, and

others. For intelligent strategic task planning, execution, and

monitoring of cooperative robots, one should be concerned with

many of above technical challenges.

In this paper, we will present a hybrid hierarchical

deliberative/reactive robotic architecture called, Supervisor

Mobility Controller - in short "Supervisor" for controlling a team

of cooperative robots. By combining reactive and deliberative

navigational schemes, we have created a set of group

navigational techniques assisting task deployment of the robots.

The Supervisor has been tested for localization and dynamic

cooperative task planning of robots. Performance and efficiency

of the system is measured on a physical robotic system

consisting of five cooperative robots.

The proposed Supervisor control system has been developed

under FMCell software [18]. FMCell provides tools for world

perception construction and sensors modeling in 3D virtual

simulation environment. Other features of the software include:

high-level object-oriented environment with embedded robot

behavioral modeling tools, fast image processing tools, AI-based

inference engines for knowledge processing and reasoning, and

soft computing developmental tools such as neural networks,

fuzzy logics, and genetic algorithms for modeling, simulation

and validation of control strategies.

2. SUPERVISOR ARCHITECTURE

The Supervisor was originally designed for control of semiautonomous robots operating under one central control unit.

The modular software implementation of the Supervisor is

presented in Figure 2. Supervisor has a hierarchical

architecture and designed to handle hybrid reactive/deliberative

task deployment of the cooperative robots. Detailed description

of different functions of this Supervisor is behind the scope of

this paper and can be found elsewhere [8,9]. The hierarchy

architecture consists of sensing, planning and acting

components. The system allows direct interaction of the human

operator at different levels of abstract task planning, execution,

and monitoring with minimum restriction. An exclusive

language allows for mission plans of the virtual robots with

concise details. The supervisor can be used for control of both

simulated and physical cooperative robots.. The same syntax

as used for programming of physical robots is used for

programming of the simulated robots. This feature

significantly reduces development and implementation times

Figure 1. Three Semi-autonomous Cooperative Robots in a 

Tactical Formation.
and makes it submissive to both software and hardware

requirements.

There are many good reasons for working with simulated

robots: “learning often requires experimenting with behaviors

that may occasionally produce unacceptable results,” [10].

Making mistakes on real physical robots can be sometimes

quite costly and dangerous. Nolfi et. Al. [17] suggests that

simulated robots be used in developing control systems for real

robots when certain special conditions are taken into account.

However, one should not expect control systems that are

evolved in a simulated environment to behave exactly the same

in a real environment. Environment has many degrees of

uncertainties, in particular in case of mobile robots where

skidding and slippage are inevitable. To reduce this ambiguous

problem, one can literally add certain degree of white noise to

parameters having anticipated uncertainties. For instance, in

dead-reckoning computation of robots, one may add random

noise to offset encoders and compass readings of the robots and

mimic irregularity in mobility behavior of the simulated robots

modeled as if they are operating in unstructured environment.

In our approach, we consider simple unknown world with

geometrically identifiable obstacles. Construction of the world

perception is achieved at three levels. Initially, fragmented

sensory data gathered by robots are filtered, analyzed, and

streamlined into a more geometrical representation, i.e., lines,

and curves. Next, robots are localized through using a

localization technique [15]. Figure 3 illustrates gradual world

perception construction of a robot navigating a simulated

indoor environment. Secondly, localization of robots within the

environment is performed to localize the robot via a target

tracking technique [16]. The refined world perception

information along with estimated global positions of the located

robots are registered in a lookup table for further processing.

From the world perception information, we extract world

geometrical features. A connectivity check is performed to

identify neighboring line segments that closely represent a

certain environment feature. The line segments belonging to a

detected environment feature are marked accordingly along

with a degree of certainty in another lookup table. To infer a

robot's task plan and the world model, we use the latter lookup

[16].

For deliberative task planning, cooperative robot team members

must be planned based on a certain governing behavioral

scheme. Some approaches in literature considered evolution of

such governing behaviors as a way of demonstrating the

cooperation among robots in an emergence way [10]. Some

others attempted to motivate robots into cooperation [6]. While

others have proposed consenting agents with mechanism for

negotiation among cooperative agents [11]. For practical

purposes, algorithmic techniques have shown good stability and

performance [12,13]. We have developed different cooperative

robots behavior-based algorithms for different applications [19-

21].

Supervisor supports different multi-agent robot platform, (see

Figure 4) and a number of cooperative navigational deployment

strategies. Each robot by itself can be assigned variety of

distinct behavior-based navigational schemes while engaging in

a cooperative task. Each subsumption-based navigational

behavior controls interaction of a robot with the environment

and arrives at common decisions regarding turning and steering

requirements of the robot for a given situation.

Supervisor handles task planning of cooperative robots using a

high-level object-oriented language that will be discussed

shortly. Supervisor recognizes two groups of task instructions.

The first group of task instructions deals with mobility

requirements of robotic vehicles and the other group deals with

sensory requirements of cooperative robots (i.e., an instruction

that activate the robot to perform an environment sensing

operation).

Supervisor handles the Deliberative tasks with the highest

priority while paying coarse attention to detailed of mobility of

robots. In coordinated task planning of cooperative robots,

coherence task planning of cooperative robots is very critical.

Commands

Dispatcher

Fuzzy Logic 

Mobility Controller

Obstacle

Obstacle

Obstacle Physical

Test Bed

Commands

Scheduler & 

Synchronizer

Motors/Actuators

Commands

Generation

Emergency

Detector

Sensory Data

Acquisition & 

Processing

Sensory Data

Analysis & Fusion 

World Perception

Model Construction 

World Perception

Recognition &

Learning 

Context-based

World Model 

Depository

Simulation Model

Calibration and

Synchronization

Parallel / Serial

Tasks Plans

Sequencing

Motion/Action 

Plans

Generation 

Robotic Vehicles

Mission Planner 

A Priori Knowledge

Multi-Agents

Coherence 

Scheme Selection

Single-Agents 

Navigational

Behaviors

Reactive 

Perceptive 

Deliberative

Mobility Control

Kinematics and

Dynamics Models

Emergency Control

Single- Agents

Mobility Schema

Composition

Physical/Virtual

World

Calibration

Command Control

Exception

Handler

Adaptive

Man-Machine

Interface

Virtual

Environment

Commands

Dispatcher

Fuzzy Logic 

Mobility Controller

Obstacle

Obstacle

Obstacle Physical

Test Bed

Commands

Scheduler & 

Synchronizer

Motors/Actuators

Commands

Generation

Emergency

Detector

Sensory Data

Acquisition & 

Processing

Sensory Data

Analysis & Fusion 

World Perception

Model Construction 

World Perception

Recognition &

Learning 

Context-based

World Model 

Depository

Simulation Model

Calibration and

Synchronization

Parallel / Serial

Tasks Plans

Sequencing

Motion/Action 

Plans

Generation 

Robotic Vehicles

Mission Planner 

A Priori Knowledge

Multi-Agents

Coherence 

Scheme Selection

Single-Agents 

Navigational

Behaviors

Reactive 

Perceptive 

Deliberative

Mobility Control

Kinematics and

Dynamics Models

Emergency Control

Single- Agents

Mobility Schema

Composition

Physical/Virtual

World

Calibration

Command Control

Exception

Handler

Adaptive

Man-Machine

Interface

Virtual

Environment

Figure 2. Modular Architecture of Supervisor Mobility Controller

Figure 3. (a) A Simulated Robot World, (b) Reflection of

Robot’s Range Sensors Data as the Robot Navigates its Path, (c)

Constructed Robot World Model Based on Sensory Data

Fusion.

robot

(a) (b)

(c)
Equally important, is the interaction between deliberative and

reactive behavioral arbitration of cooperative robots. At the

highest level of task planning, the mission plan can be designed

using structured syntax and semaphore directives. Refer to

Figure 5, for an example of cooperative robot task

programming scheme. Supervisor has a parser that interprets

abstract textual task commands as shown in the example. A

double linked list buffers all task instruction with a time stamp.

Def Task1

Channel: 1,2,3 // Assign Channel 1,2,3 For Comm

Vel: 5,5,5 // Assign Velocity 5 in/s to Robots 1,2,& 3

Acc: 3,3,3 // Assign Accelecration in/s2 to Robots 1,2,3

Loc: (10,30),(40,30),(60,-20) // Assign 3 Vectors for the

 // the robots to follow.

NavBeh: 1,2,1 // Set Navigation Behaviors of Robot 1, 2,

 //& 3 to Navigational Id # 1,2, 1 Respectively.

MC: 1 //Turn on Continuous Processing Mode

Go: 1,1,1 // Execution queued motion commands

 // for Robots 1, 2, and 3.

Wait t >4000 // Wait until 4000 ms time is elapsed.

CapImg: 1,,1 //Have Robots 1 and 3 capture images

 // of their direction.

LMScan: ,1, //Have Robot 2 Scan Using Laser

 //Measurement Range Finder.

Go: 1,1,1 //Now, have all 3 robots to perform their sensing.

WaitAck: 1,1,1 //Wait Until All Robots Acknowledge

 //Completion of Their Specific Tasks.

MC: 0 //Turn off Continuous Processing Mode

EndDef //Terminate Task Block.

Figure 5. An Example of Task Programming of Cooperative

Robots .

For synchronization purposes, all motion commands and

sensory data acquisition operations are buffered in a temporary

transit queue until a trigger statement is executed. Execution

of buffered commands is performed in the order that the

commands have been received. In the example below, for

instance, some preparatory commands set up communication

channel, and preset velocity and acceleration of robot. The

command Loc assigns three positional vectors to the robots to

follow. The parameters of the Loc function define position and

heading angle requirement of corresponding robot in inch and

degree respectively. Next, proper navigational behaviors are

assigned to individual cooperative robots. Instruction Go

causes execution all buffered task plans to begin with the

predefined task requirements, i.e., robots should presume a

velocity 5 in/sec and an acceleration of 3”/sec2. With

continuous processing mode on, execution of commands

proceeds right after executing Go statement without any delay.

Next, the task command processing is delayed for 4 seconds

before robots 1 and 3 are assigned to capture image along their

heading direction, while the robot 2 is assigned to scan its

heading for detection of obstacle. The last Go statement causes

transmission of proper sensory data acquisition instruction to

robots.

3. CONTROL SCHEMES FOR TASK PLAN

TESTING OF COOPERATIVE ROBOTS

In practice, a mission plan may comprise of many sub-task

plans – requiring cooperative to do many tasks either in

synchronization or independent but in harmony. Each sub-task

plan may consist of many symbolic notions of activities.

Figure 6 shows one such sub-task plan where four robots are

employed to rapidly create a consolidated world perception of

their unknown environment. In this scenario, each robot's

behavior is set to be reactive. Without explicitly defining

individual navigational task of each robot, we applied a simple

deliberative learning strategy. In this technique robots are

rewarded more for exploring unvisited area of the world. The

Supervisor performs two operations in this scenario - gathering

range data from individual robots and fusing the range data to

Figure 6. (a) A Simulated Scenario of Cooperative Robots

World. (b) World Perception Model Based on Range Sensor Data

Fusion of Cooperative Robots (c) World Perception of each of

four individual Robots.

Adaptive 

Fuzzy-Logic

Reflexive

Virtual

Potential-Field

UniformGuarded

Non-Uniform

Guarded

Adaptive 

Neural-Net-Based

Rule-Based

Laser-based

Gate-Ways

Following

Landmarks

Following

Search

 Targets

Group 

Marching

Group 

Formation

Terrain

Mapping

Targets

Tracking

Follow-The

Leader

NeighborReferenced

Tactical

Maneuvers

Terrain

Following

Turn

Cooperative Navigational

Deployment Strategies

Actuation

Subsumption-based

Navigational Behaviors

Multi-Agents

Assignment

Adaptive

Schemes

Steer

Priority

Figure 4. Cooperative Behaviors and Behavior-based Navigational 

Alternative of Multi-Agent Robots Supported by the Supervisor 

Mobility Controller. 

Adaptive 

Fuzzy-Logic

Reflexive

Virtual

Potential-Field

UniformGuarded

Non-Uniform

Guarded

Adaptive 

Neural-Net-Based

Rule-Based

Laser-based

Gate-Ways

Following

Landmarks

Following

Search

 Targets

Group 

Marching

Group 

Formation

Terrain

Mapping

Targets

Tracking

Follow-The

Leader

NeighborReferenced

Tactical

Maneuvers

Terrain

Following

Turn

Cooperative Navigational

Deployment Strategies

Actuation

Subsumption-based

Navigational Behaviors

Multi-Agents

Assignment

Adaptive

Schemes

Steer

Priority

Figure 4. Cooperative Behaviors and Behavior-based Navigational 

Alternative of Multi-Agent Robots Supported by the Supervisor 

Mobility Controller. 
construct a world perception model as shown in the upper right

hand corner of the figure 6. As the sonar range data become

available, the world model is progressively constructed. The

world model is partitioned into matrix of cells. Each cell will

have a certain potential depending upon total number of sonar

data point that it contains. The Supervisor creates a gradient

field based on potential of each cell. The robots are then

command to explore world environment with low gradient

slope. To encourage the robots to explore the entire area, they

are rewarded more for exploring the areas that they have not

visited before. By adjusting the rewarding weights, the

navigational behavioral of the robot are tuned. To prevent the

robots from over exhaustion in their search, a time-based

terminating condition is imposed that is if the new world

discovery slows than behind a threshold over a fixed period of

time, then the navigational search should stop. The algorithm

was tested on a team of five cooperative robots. One robot

from the group becomes as anchor and monitors operations of

its other team members using its on-board surveillance camera.

Localization of cooperative searching robots is performed using

an image processing technique that localizes the cooperative

robots in the image frame of the surveillance robot. The

physical robotic test bed is shown in Figure 7. Coordinates of

the robots in the image frame are next mapped to the world

coordinate with the center of the camera at the origin [22]. A

total of twenty tests were conducted to assess performance of

the cooperative robotic team in detecting a total of 10 cans of

cokes randomly located on the floor within an area of 20'x20'.

Figure 8 shows the results of experimentation. On average over

86.5 percent of time, all 10 targets were located with 99 percent

confidence level. Figure 9 indicates the total time efficiency of

the system in detecting all targets. While time efficiency of

the system can serve as a basis for evaluation of intelligence of

the robots, but some small adjustment in the control behaviors

of the robots can have significant effect on overall efficiency of

the system. Perhaps for reducing wondering time delay of the

robots.

4. CONCLUSION

Cooperative robots have many practical applications. In this

paper, we have discussed architecture of a Supervisory

Mobility Controller with capability to facilitate deliberative/

reactive task deployment of cooperative robots. Some of the

issues regarding robot's intelligence requirement at robot

platform level and at cooperative robots level were discussed.

To have fully functional cooperative robots, many research

issues need to be addressed and taken into consideration. At

present time, there is no single established standard or in testing

procedures of the cooperative robots' intelligence. Measure of

performance and efficiency of intelligent robotic system are

(a)

(b)

Figure 7. (a) Physical Cooperative Robot Test Bed.

Localization of Cooperative Robot Using Visual

Servoing Technique.

0

10

20

30

40

50

60

70

80

90

100

13579

1 11 31 51 71 9

Figure 8. Performance Measure oCooperative Robots in

Detecting Random Targets.

Trials

% Success

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 1 2 13 14 1 5 16 1 7 1 8 19 2 0

Trials

Time (min)

Figure 9. Time Efficiency of Cooperative Robots in

Detecting Random Targets.
very subjective and conditional. Minute adjustments in control

parameters of a system can have significant influence over

performance and efficiency of the system. Furthermore,

incompatibility and heterogeneity among the robots makes it

quite difficult to relate performance and efficiency of one

system to another - even from one robot to another in the same

platform and ranking.

5. ACKNOWLEDGEMENT

This work was partly funded by the US Army TACOM under

DAAE07-01-C-L-065 and Penn State Applied Research

Laboratory Under Prime Grant Number DAAD19-01-1-0504

from the US Army Research Office. The support from both

funding agencies is acknowledged.

6. REFERENCES

[1] Mataric, M., "Behavior-based Control: Examples From

Navigation, Learning, and Group Behavior," J. of

Experimental and Theoretical AI, 9, 1997.

[2] Brooks, R., "Intelligence Without Reason," Computers

and Thoughts - IJCAI 91, 1991.

[3] Arkin, R. C., Behavior-Based Robotics. The MIT Press,

Cambridge, MA, 1998.

[4] Cao, Y. U., Fukunaga, A. S., and Kahng, A. B.,

“Cooperative Mobile Robotics: Antenedents and

Directions, Autonomous Robots, 4(1): 7-27, 1997.

[5] Mataric, M.J., “Issues and Approaches in the Design of

Coolective Autonomous Agents. Robotics and

Autonomous Systems,” 16 (2-4), pp. 321-331, 1995.

[6] Parker, L. E., “Cooperative Robotics for Multi-target

Observation,” Intelligent Automation and Soft

Computing, 5(1): 5-19, 1999.

[7] Pirjanian, P., and Mataric, M. J., “Multi-robot Target

Acquisition Using Multiple Objective Behavior

Coordination,” in Proc. Of the IEEE International Conf.

on Robotics & Automation, San Francisco, 2000.

[8] Shirkhodaie, A., Goetz R., Patkar, A., Mansour M.

Aderogba, S., and Avalareddy N., “Behavior-Based

Tactical Navigational And Mobility Control of Tandem

Ground Robotic Vehicles”, in the Proc. of SPIE

Unmanned Ground Vehicle Tech. II, Orlando, FL, 2000.

[9] Shirkhodaie, A., Rangavajhala, S., "Supervised Tactical

Mobility Behavior Modeling And Control of Multi-Agent

Robotic Vehicles,” in the Proceedings of SPIE,

Unmanned Ground Vehicle Technology II, April 24-25,

2001, Orlando, FL.

[10] Liu, J., and Wu, J., Multi-Agent Robotic Systems, The

CRS Press International Series on Computational

Intelligence, 2001.

[11] Rosenschein, J. S., "Consenting Agents: Negotiation

Mechanisms for Multi-Agent Systems," in Proc. of Int.

Joint Conf. On AI, pp. 792-799, 1993.

[12] Miyata, N. Ota, J., and Aiyama Y., Asama, H.., and Arai,

T., "Cooperative Transport in Unknown Environment -

Application of Real-time Task Assignment," Proc. of the

2000 IEEE Int. Conf. On Robotics and Automation, pp.

3176-3182, 2000.

[13] Mansour, M., and Shirkhodaie, A., “Navigational

Behaviors Fusion for Multi-Agent Intelligent Tandem

Mobile Robots,” in the 2000 Japan-USA Symposium on

Flexible Automation International Conference on

Manufacturing Systems” Innovation for the 21st Century,

Ann Arbor, MI, July 23-26, 2000.

[14] Lacaze, A., "Hierarchical Architecture of Coordinating

Ground Vehicles in Unstructured Environments," in

Proc. of 2001 PERMIS Workshop on Measuring

Performance and Intelligence of Systems, September 4,

2001.

[15] Hajjawi, M., and Shirkhodaie, A., "Comparative Visual

Team Working and Target Tracking of Mobile Robots",

in the 34th IEEE Southeastern Symposium on System

Theory, March 17, 19, 2002.

[16] Rangavajhala, S., and Shirkhodaie, A., and "VisualServoing and Navigation of Mobile Robots For Target

Tracking Applications With Laser Sensory Data

Fusion," in the Proceedings of the IEEE SSST 33rd

Southeastern Symposium on System Theory, March

18-20, 2001.

[17] Nolfi, S., Evolutionary Robotics: Exploiting the Full

Power of Self-Organization, Connection Science, pp.

167-183, 1998.

[18] Shirkhodaie A., "FMCell: User Manual - Vol I and II,"

Tennessee State University, Department of Mechanical

and Manufacturing Engineering, April 2002.

[19] Adergoba, S., and Shirkhodaie, A., “Navigation and

Visual-Terrain Tracking of Multiple Mobile Robots

With Vision and Sonar Using Neural Network

Techniques,” in the IMCC’2000 International

Manufacturing Conference in China,“ August 16-17,

2000, China.

[20] Avalareddy, N., and Shirkhodaie, A., “Communicationbased Tactical Task Planning of Cooperative Tandem

Mobile Robot Vehicles Under an Intelligent

Supervisory Mobility Controller,” in the 2000 JapanUSA Symposium on Flexible Automation International

Conference on Manufacturing Systems”, Ann Arbor,

MI, July 23-26, 2000.

[21] Avalareddy, N., Shirkhodaie, A., “Cooperative Task

Planning, Execution, and Monitoring of Mobile Robots

Under a Supervisory Mobility Controller”, in the IEEE

Southeastcon 2000 Conference, Nashville, TN, April 7-

9, 2000.

[22] Hajjawi, M.,"Visual Servo Tracking and Localization of

Cooperative Robots," M.S. Thesis, Mechanical

Engineering, Tennessee State University, August 2002.