Literature Surveys/Background:

• Swarms: Coordination and Stability: A variety of organisms exhibit swarming behavior while foraging for food or moving to avoid predators. There are mechanisms for coordination to achieve such swarm behavior that are of interest in the study of cooperative control for UAAVs. For instance, it is of interest to determine minimum vehicular and communication characteristics to achieve "stable" (cohesive) swarm behavior. The literature overview of swarms by Yang Liu and Kevin Passino from OSU, which you can obtain by clicking here, provides an overview of existing literature on swarms, with a focus on stable swarms.

• Cooperative Robotics and Robo-Soccer: Recently, there has been a lot of interest by the AI and robotic communities in multi-agent control for robotic soccer. Several cooperative control techniques have been developed in an autonomous multi-agent system framework, and some of these techniques are relevant to the problem of cooperative control of UAVs. The literature review by Yanli Yang and Marios Polycarpou from UC, which you can obtain by click here, provides an overview of the existing literature on robotic soccer, with special emphasis on cooperative control issues.

Biomimicry of Bacterial Foraging for Distributed Optimization and Control

K. Passino

IEEE Control Systems Magazine, 2002.

Abstract: In this paper it is explained how individual and groups of bacteria forage for nutrients, and how to model this as a distributed optimization process. Also, there is a brief explanation of how social foraging strategies can be used for adaptive control and the distributed optimization and control problem studied in this project.

The topics studied in the above paper were also given as a talk: K. Passino, "Distributed Optimization and Control Using Only a Germ of Intelligence", Plenary Address, IEEE Int. Symp. on Intelligent Control / IEEE Mediterranean Conference on Automation and Control, (brief associated paper of the same title in the conference Proceedings on pp. 5-13), Patras, Greece, July 19, 2000. The slides for this talk are available in .pdf form by clicking here (11.2MB). Note, however, that I do not include the animations.

You can obtain this paper by clicking here.

Cooperative Behavior Schemes for Improving the Effectiveness of Autonomous Wide Area Search Munitions

Capt. Daniel P. Gillen, and LtCol. David R. Jacques

Chapter in the proceedings of the Cooperative Control Workshop, FL, Dec. 2000.

Abstract: The problem being addressed is how to best find and engage an unknown number of targets in unknown locations (some moving) using multiple autonomous wide area search munitions. In this research cooperative behavior is being investigated to improve the overall mission effectiveness. A computer simulation was used to emulate the behavior of autonomous wide area search munitions and measure their overall expected performance. This code was modified to incorporate the capability for cooperative engagement based on a parameterized decision rule. Using Design of Experiments (DOE) and Response Surface Methodologies (RSM), the simulation was run to achieve optimal decision rule parameters for given scenarios and to determine the sensitivities of those parameters to the precision of the Autonomous Target Recognition (ATR) algorithm, lethality and guidance precision of the warhead, and the characteristics of the battlefield.

You can obtain the .pdf file for this chapter by clicking here.

Cooperative Control for Autonomous Air Vehicles

Kevin Passino, Marios Polycarpou, David Jacques, Meir Pachter, Yang Liu, Yanli Yang, Matt Flint, and Michael Baum

Chapter in the proceedings of the Cooperative Control Workshop, FL, Dec. 2000.

Abstract: The main objective of this research is to develop and evaluate the performance of strategies for cooperative control of autonomous air vehicles that seek to gather information about a dynamic target environment, evade threats, and coordinate strikes against targets. The air vehicles are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. They are assumed to have some "physical'' limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (i) on-line learning of the environment and storing of the information in the form of a "target search map''; and (ii) utilization of the target search map and other information to compute on-line a guidance trajectory for the vehicle to follow. We study the stability of vehicular swarms to try to understand what types of communications are needed to achieve cooperative search and engagement, and characteristics that affect swarm aggregation and disintegration. Finally, we explore the utility of using biomimicry of social foraging strategies to develop coordination strategies.

You can obtain the .pdf file for this chapter by clicking here.

A Cooperative Search Framework for Distributed Agents

Marios M. Polycarpou, Yanli Yang, and Kevin M. Passino

Proc. of the IEEE Int. Symp. on Intelligent Control / IEEE Conf. on Control Applications, Mexico City, Mexico, 2001.

Abstract: This paper presents an approach for cooperative search of a team of distributed agents. We consider two or more agents, or vehicles, moving in a geographic environment, searching for targets of interest and avoiding obstacles or threats. The moving agents are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. The agents are assumed to have some "physical'' limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (i) on-line learning of the environment and storing of the information in the form of a "search map''; and (ii) utilization of the search map and other information to compute on-line a guidance trajectory for the agent to follow. The distributed learning and planning approach for cooperative search is illustrated by computer simulations.

You can obtain a .pdf file of this paper by clicking here.

Stability of a One-Dimensional Discrete-Time Asynchronous Swarm

Veysel Gazi and Kevin M. Passino

Proc. of the IEEE Int. Symp. on Intelligent Control / IEEE Conf. on Control Applications, Mexico City, Mexico, 2001.

Abstract: In this article we consider a discrete time one-dimensional asynchronous swarm. First, we describe the mathematical model for motions of the swarm members. Then, we analyze the stability properties of that model. The stability concept that we consider, which matches exactly with stability of equilibria in control theory, characterizes stability of a particular position (relative arrangement) of the swarm members, that we call the comfortable position (with comfortable intermember distance). Our stability analysis employs some results on contractive mappings from the parallel and distributed computation literature.

You can obtain a .pdf file of this paper by clicking here.

Stability Analysis of One-Dimensional Asynchronous Swarms

Yang Liu, Kevin M. Passino, and Marios M. Polycarpou

Paper submitted to IEEE Transactions on Automatic Control, April, 2001.

Abstract: Coordinated dynamical swarm behavior occurs when certain types of animals forage for food or try to avoid predators. Analogous behaviors can occur in engineering systems (e.g. in groups of autonomous mobile robots or air vehicles). In this paper we characterize swarm "cohesiveness'' as a stability property and provide conditions under which collision-free convergence can be achieved for an asynchronous swarm with finite-size swarm members that have proximity sensors and neighbor position sensors that only provide delayed position information. Moreover, we give conditions under which an asynchronous mobile swarm following (pushed by) an "edge-leader'' can maintain cohesion during movements even in the presence of sensing delays and asynchronism. Such stability analysis is of fundamental importance if one wants to understand the coordination mechanisms for groups of autonomous vehicles or robots where inter-member communication channels are less than perfect and collisions must be avoided.

You can obtain a .pdf file of this paper by clicking here.

Cooperative Control of Distributed Multi-Agent Systems

Marios M. Polycarpou, Yanli Yang, and Kevin M. Passino

Paper to appear in IEEE Control Systems Magazine.

Abstract: This paper presents an approach for cooperative search by a team of distributed agents. We consider two or more agents moving in a geographic environment, cooperatively searching for targets of interest and avoiding obstacles or threats. The moving agents are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. The agents are assumed to have some "physical" limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (i) on-line learning of the environment and storing of the information in the form of a "search map"; and (ii) utilization of the search map and other information to compute on-line a guidance trajectory for the agent to follow. We develop a real-time approach for on-line cooperation between agents, which is based on treating the paths of other vehicles as "soft obstacles" to be avoided. Based on artificial potential field methods we develop the concept of "rivaling force" between agents as a way of enhancing cooperation. The proposed distributed learning and planning approach is illustrated by computer simulations.

You can obtain a .pdf file of this paper by clicking here.

Stability Analysis of M-Dimensional Asynchronous Swarms with a Fixed Communication Topology

Yang Liu, Kevin M. Passino, and Marios M. Polycarpou

Paper to appear in IEEE Trans. on Automatic Control (submitted Oct. 2001) and a shorter version in the Proc. of the American Control Conference, 2002.

Abstract: Coordinated dynamical swarm behavior occurs when certain types of animals forage for food or try to avoid predators. Analogous behaviors can occur in engineering systems (e.g. in groups of autonomous mobile robots or air vehicles). In this paper, we study a model of an M-dimensional (M &Mac179; 2) asynchronous swarm with a fixed communication topology, where each member only communicate with fixed neighbors, to provide conditions under which collision-free convergence can be achieved with finite-size
swarm members that have proximity sensors and neighbor position sensors that only provide delayed position information. Moreover, we give conditions under which an M-dimensional asynchronous mobile swarm with a fixed communication topology following an “edge-leader” can maintain cohesion during movements even in the presence of sensing delays and asynchronism. In addition, the swarm movement flexibility is analyzed. Such stability analysis is of fundamental importance if one wants to understand the coordination mechanisms for groups of autonomous vehicles or robots where inter-member communication channels are less than perfect and collisions must be avoided.

You can obtain a .pdf file of this paper by clicking here.

Opportunistically Cooperative Neural Learning in Mobile Agents

Yanli Yang, Marios M. Polycarpou, Ali A. Minai

Paper to appear in the Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), Honolulu, Hawaii, May 2002.

Abstract: Searching a spatially extended environment using autonomous mobile agents is a problem that arises in many applications, e.g., search-and-rescue, search-and-destroy, intelligence gathering, surveillance, disaster response, exploration, etc. Since agents such as UAV's are often energy-limited and operate in a hostile environment, there is a premium on efficient cooperative search without superfluous communication. In this paper, we consider how a group of mobile agents, using only limited messages and incomplete information, can learn to search an environment efficiently. In particular, we consider the issue of centralized vs. decentralized intelligence and the effect of opportunistic sharing of learned information on search performance.

You can obtain a .pdf file of this paper by clicking here.

Search, Classification, and Attack Decisions for Cooperative Wide Area Search Munitions

LtCol David Jacques

Chapter in the proceedings of the Cooperative Control Workshop, FL, Dec. 2001.

You can obtain a .pdf file of this chapter by clicking here.

Cooperative Control Design for Uninhabited Air Vehicles

Marios M. Polycarpou, Kevin M. Passino, Yanli Yang, Yang Liu

Chapter in the proceedings of the Cooperative Control Workshop, FL, Dec. 2001.

You can obtain a .pdf file of this chapter by clicking here.

Last updated: 4/22/02