Multirobot Cooperative Learning for Predator Avoidance

• Published in: IEEE transactions on control systems technology Vol. 23; no. 1; pp. 52 - 63
• Main Authors: Hung Manh La, Lim, Ronny, Weihua Sheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace electronics; Collision avoidance; Flocking control; hybrid system; Learning (artificial intelligence); multirobot systems; Network topology; reinforcement learning; Robot kinematics; Robot sensing systems; Flocking control; hybrid system; multirobot systems; reinforcement learning
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2014.2312392

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.