Repetitive motion of redundant robots planned by three kinds of recurrent neural networks and illustrated with a four-link planar manipulator's straight-line example

Authors:
Yunong Zhang;Zhiguo Tan;Ke Chen;Zhi Yang;Xuanjiao Lv
Affiliations:
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China
Venue:
Robotics and Autonomous Systems
Year:
2009

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Abstract

In this paper, a dual neural network, LVI (linear variational inequalities)-based primal-dual neural network and simplified LVI-based primal-dual neural network are presented for online repetitive motion planning (RMP) of redundant robot manipulators (with a four-link planar manipulator as an example). To do this, a drift-free criterion is exploited in the form of a quadratic performance index. In addition, the repetitive-motion-planning scheme could incorporate the joint physical limits such as joint limits and joint velocity limits simultaneously. Such a scheme is finally reformulated as a quadratic program (QP). As QP real-time solvers, the aforementioned three kinds of neural networks all have piecewise-linear dynamics and could globally exponentially converge to the optimal solution of strictly-convex quadratic-programs. Furthermore, the neural-network based RMP scheme is simulated based on a four-link planar robot manipulator. Computer-simulation results substantiate the theoretical analysis and also show the effective remedy of the joint angle drift problem of robot manipulators.