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Proceedings Paper

A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems
Author(s): Xu Zhong; Yu Zhou
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Paper 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.

Paper Details

Date Published: 22 May 2014
PDF: 15 pages
Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 91190A (22 May 2014); doi: 10.1117/12.2053991
Show Author Affiliations
Xu Zhong, Stony Brook Univ. (United States)
Yu Zhou, State Univ. of New York Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9119:
Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII
Misty Blowers; Jonathan Williams, Editor(s)

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