A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems

Xu Zhong; Yu Zhou

doi:10.1117/12.2053991

22 May 2014 A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems

Xu Zhong, Yu Zhou

Author Affiliations +

Proceedings Volume 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII; 91190A (2014) https://doi.org/10.1117/12.2053991
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

This paper presents a decentralized multi-robot motion control strategy to facilitate a multi-robot system, comprised of collaborative mobile robots coordinated through wireless communications, to form and maintain desired wireless communication coverage in a realistic environment with unstable wireless signaling condition. A fuzzy neural network controller is proposed for each robot to maintain the wireless link quality with its neighbors. The controller is trained through reinforcement learning to establish the relationship between the wireless link quality and robot motion decision, via consecutive interactions between the controller and environment. The tuned fuzzy neural network controller is applied to a multi-robot deployment process to form and maintain desired wireless communication coverage. The effectiveness of the proposed control scheme is verified through simulations under different wireless signal propagation conditions.

Citation Download Citation

Xu Zhong and Yu Zhou "A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems", Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 91190A (22 May 2014); https://doi.org/10.1117/12.2053991

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