Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

IEEE Transactions on Neural Networks and Learning Systems
Zhijun ZhangYuanqing Li

Abstract

We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

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Citations

Jul 11, 2018·IEEE Transactions on Neural Networks and Learning Systems· Shuai Li Xin Luo
May 15, 2020·Journal of Neuroengineering and Rehabilitation·Giuseppe AvertaMatteo Bianchi
Nov 24, 2018·Frontiers in Neurorobotics·Zhijun ZhangWeisen Fan
Nov 22, 2018·Frontiers in Neurorobotics·Tatsuya TeramaeErhan Oztop

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