publication . Article . Other literature type . 2015

Reinforcement learning control for coordinated manipulation of multi-robots

Li, Yanan; Chen, Long; Tee, Keng Peng; Li, Qingquan;
Open Access
  • Published: 10 Jul 2015 Journal: Neurocomputing, volume 170, pages 168-175 (issn: 0925-2312, Copyright policy)
  • Publisher: Elsevier BV
  • Country: United Kingdom
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.
arXiv: Computer Science::Robotics
free text keywords: Reinforcement learning control, Robot control, Machine learning, computer.software_genre, computer, Control theory, Reinforcement learning, Robot manipulator, Robot, Trajectory, Minification, Artificial intelligence, business.industry, business, Robot learning, Mathematics
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2.2. Preliminary For the approximation of a continuous function h(Z) : Rr → R, the following Radial Function Basis (RBF) Neural Networks (NN) are used [26]:

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