A Distributed Varying-Parameter Recurrent Neural Network for Solving the Motion Generation Problem of a Multimanipulator Collaborative System

To address the real-time motion generation problem of a multimanipulator collaborative system, a novel distributed varying-parameter recurrent neural network (DVP-RNN) is proposed in this article. First, an undirected graph is used to simplify the communication topology of the multimanipulator collaborative system. Then, the communication and coupled constraints of the multimanipulator collaborative system are expressed as equality constraints with coupled variables. The physical limits (i.e., angular limits and angular velocity limits) of the multimanipulator collaborative system are expressed as inequality constraints. Based on the minimum velocity norm optimization criterion, a quadratic programming problem with coupled variables and constraints is employed to formulate the motion generation problem of a multimanipulator collaborative system. Finally, a DVP-RNN is designed to solve the quadratic programming problem with coupled variables and constraints to obtain the joint trajectory of the multimanipulator collaborative system. Simulations show that the proposed DVP-RNN can effectively solve the motion generation problem of a multimanipulator collaborative system. Comparisons confirm the superiority of the DVP-RNN in terms of applicability and precision.