DocumentCode
441603
Title
Mas-Based Pursuit-Evasion Algorithm Under Unknown Environment
Author
Chen, Ying-chun ; Qi, Huan ; Liu, Xia
Author_Institution
Dept. of Control Science & Engineering, Huazhong University of Science & Technology, Wuhan 430074, China; E-MAIL: chyc@21cn.com
Volume
1
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
265
Lastpage
269
Abstract
Based on the interaction protocols of Contract Net and Subscribe-publish, a distributive pursuit-evasion algorithm is proposed for multi-agents with limited detection ranges to pursue multi-evaders in an unknown grid map with obstacles. The pursuit agents first explore the map according to the method of Artificial Potential Field. When any obstacle is detected within the neighborhood of a pursuer, it updates the map. If an evader is detected, the pursuer (initiator) sends call-for-proposals messages to other pursuers. The other pursuers use D* algorithm to calculate their steps to reach the evader and submit their proposals. The initiator picks up the proposals and evaluates, chooses the nearest ones to join the pursuit team, sends accept-proposals to the selected agents and manages the team during the process of pursuit. If the evader is within the kill radius of all pursuit team members, the task is done; otherwise, D* algorithm is applied by each pursuit member to plan a new path once the evader moves to a new position. Distributive in nature, it allows a large-scale robot fleet to perform complicated tasks in a coordinated way. The algorithm can be easily extended to 3D cases. Simulation results show that the pursuit agents can dynamically form a pursuit team to catch evaders efficiently.
Keywords
Agent; Artificial Potential Field; Cooperation; D* Algorithm; Evasion; Multi Agent System; Pursuit; Search; Biological system modeling; Contracts; Costs; Electronic mail; Iterative algorithms; Large-scale systems; Proposals; Protocols; Pursuit algorithms; Robot kinematics; Agent; Artificial Potential Field; Cooperation; D* Algorithm; Evasion; Multi Agent System; Pursuit; Search;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1526956
Filename
1526956
Link To Document