Reinforcement learning control for coordinated manipulation of multi-robots

Reinforcement learning control for coordinated manipulation of multi-robots
Title:
Reinforcement learning control for coordinated manipulation of multi-robots
Other Titles:
Neurocomputing
DOI:
10.1016/j.neucom.2015.02.091
Publication Date:
10 July 2015
Citation:
Yanan Li, Long Chen, Keng Peng Tee, Qingquan Li, Reinforcement learning control for coordinated manipulation of multi-robots, Neurocomputing, Volume 170, 25 December 2015, Pages 168-175, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2015.02.091. (http://www.sciencedirect.com/science/article/pii/S0925231215009339)
Abstract:
In this paper, coordination control is investigated for multi-robots to manipulate an object with a common desired trajectory. Both trajectory tracking and control input minimization are considered for each individual robot manipulator, such that possible disagreement between different manipulators can be handled. Reinforcement learning is employed to cope with the problem of unknown dynamics of both robots and the manipulated object. It is rigorously proven that the proposed method guarantees the coordination control of the multi-robots system under study. The validity of the proposed method is verified through simulation studies.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
Description:
ISSN:
0925-2312
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