Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks

Authors:
Shuai Li;Sanfeng Chen;Bo Liu;Yangming Li;Yongsheng Liang
Affiliations:
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA;Key Lab of Visual Media Processing and Transmission, Shenzhen Institute of Information Technology, Shenzhen, Guangdong 518029, China;Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA;Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 07309, China;Key Lab of Visual Media Processing and Transmission, Shenzhen Institute of Information Technology, Shenzhen, Guangdong 518029, China
Venue:
Neurocomputing
Year:
2012

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Abstract

This paper studies the decentralized kinematic control of multiple redundant manipulators for the cooperative task execution problem. The problem is formulated as a constrained quadratic programming problem and then a recurrent neural network with independent modules is proposed to solve the problem in a distributed manner. Each module in the neural network controls a single manipulator in real time without explicit communication with others and all the modules together collectively solve the common task. The global stability of the proposed neural network and the optimality of the neural solution are proven in theory. Application orientated simulations demonstrate the effectiveness of the proposed method.