Perturbed Manipulability Optimization in a Distributed Network of Redundant Robots
Long Jin, Jiazheng Zhang, Xin Luo, Mei Liu, Shuai Li, Lin Xiao, Zihao Yang
Abstract
For avoiding a singularity arising in the cooperative control of multiple redundant robot manipulators, an efficient way is to maximize the manipulability. In this article, by making progress along this direction, a distributed manipulability optimization scheme is proposed to maximize the manipulability of redundant robot manipulators in a distributed network with limited communication. With manipulability optimization incorporated in the proposed scheme, all the involved manipulators can be regulated to track their optimal configurations dynamically, in addition to the collaboration among them to complete the specified tasks. To do this, the distributed scheme is transformed into a dynamic quadratic programming (QP) problem by considering the time dependence of the parameters. Then, a generalized recurrent neural network (GRNN) is constructed and proposed to deal with the QP problem online with perturbations considered. Theoretical analysis is conducted, which confirms that the proposed GRNN is able to globally converge to the optimal solution to the dynamic QP problem in the presence of noises and perturbations. Finally, simulation results based on a distributed network of redundant robots are conducted and presented to verify the superior performance of the proposed distributed manipulability optimization scheme.