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TECHNICAL PAPERS

Cooperative Avoidance Control for Multiagent Systems

[+] Author and Article Information
Dušan M. Stipanović1

Department of Industrial and Enterprise Systems Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801dusan@uiuc.edu

Peter F. Hokayem

Department of Electrical and computer Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801hal@uiuc.edu

Mark W. Spong

Department of Electrical and computer Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801mspong@uiuc.edu

Dragoslav D. Šiljak

Department of Electrical Engineering, Santa Clara University, Santa Clara, CA 95053dsiljak@scu.edu

1

Corresponding author.

J. Dyn. Sys., Meas., Control 129(5), 699-707 (Apr 27, 2007) (9 pages) doi:10.1115/1.2764510 History: Received March 23, 2006; Revised April 27, 2007

The objective of this paper is to present a methodology for designing cooperative control laws for individual agents that guarantee collision avoidance in multiagent systems. The proposed avoidance control laws are easy to design and implement, and may be directly appended to the optimal control laws of the individual agents within the cooperation framework. The avoidance control laws are computed using value functions that resemble the behavior of barrier functions in the static optimization theory. The most attractive feature of the proposed optimization scheme is the fact that the avoidance laws are active only in the bounded sensing regions of each individual agent, and they do not interfere with the agents’ individual optimal control laws outside of these regions.

Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Agents starting to resolve the conflict

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Conflict resolved

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Agents continuing toward the equilibrium

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Initial to final configuration

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Initial configuration

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Figure 8

Agents start moving toward the equilibrium

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Figure 9

Agents starting to resolve the conflict

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Figure 10

Conflict resolved

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Figure 11

Agents continuing toward the equilibrium

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Figure 12

Initial to final configuration

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Figure 13

Initial configuration

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Figure 14

Agents start moving toward the equilibrium

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Figure 15

Agents starting to resolve the conflict

Grahic Jump Location
Figure 17

Agents continuing toward the equilibrium

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Figure 18

Initial to final configuration

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Figure 16

Conflict resolved

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Figure 2

Agents start moving toward the equilibrium

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Figure 1

Initial configuration

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