DocumentCode :
65589
Title :
Multirobot Cooperative Learning for Predator Avoidance
Author :
Hung Manh La ; Lim, Robert ; Weihua Sheng
Author_Institution :
Center for Adv. Infrastruct. & Transp., Rutgers Univ., Piscataway, NJ, USA
Volume :
23
Issue :
1
fYear :
2015
fDate :
Jan. 2015
Firstpage :
52
Lastpage :
63
Abstract :
Multirobot collaboration has great potentials in tasks, such as reconnaissance and surveillance. In this paper, we propose a multirobot system that integrates reinforcement learning and flocking control to allow robots to learn collaboratively to avoid predator/enemy. Our system can conduct concurrent learning in a distributed fashion as well as generate efficient combination of high-level behaviors (discrete states and actions) and low-level behaviors (continuous states and actions) for multirobot cooperation. In addition, the combination of reinforcement learning and flocking control enables multirobot networks to learn how to avoid predators while maintaining network topology and connectivity. The convergence and scalability of the proposed system are investigated. Simulations and experiments are performed to demonstrate the effectiveness of the proposed system.
Keywords :
collision avoidance; continuous systems; convergence; discrete systems; intelligent robots; learning (artificial intelligence); multi-robot systems; network theory (graphs); topology; concurrent learning; convergence; flocking control; multirobot collaboration; multirobot cooperative learning; network topology; predator avoidance; reconnaissance; reinforcement learning; surveillance; Aerospace electronics; Collision avoidance; Learning (artificial intelligence); Network topology; Robot kinematics; Robot sensing systems; Flocking control; hybrid system; multirobot systems; reinforcement learning; reinforcement learning.;
fLanguage :
English
Journal_Title :
Control Systems Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6536
Type :
jour
DOI :
10.1109/TCST.2014.2312392
Filename :
6783781
Link To Document :
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