A Distributed Slack Barrier Recurrent Neural Network for Multiple Redundant Manipulators Collaborative System in Obstacles Environment

To address the motion generation problem in distributed multimanipulator system operating in obstacles environment, a distributed slack barrier recurrent neural network (DSB-RNN) is proposed in this article. First, the communication topology among the multimanipulator system is summarized using an undirected graph representation. Then, the communication constraints and positions of the multimanipulators collaborative system are formulated as equality constraints with coupling variables. Additionally, nonstrict inequality constraints for obstacle avoidance and bilateral constraints for the manipulator joints are taken into account. Based on minimum velocity norm optimization criterion, the problem of motion generation for distributed multimanipulator system in obstacles environment is transformed into a time-varying quadratic programming problem. Next, a Lagrangian function is established and summarized as the original Karush-Kuhn–Tucker (KKT) conditions. To make this special time-varying problem solvable, barrier parameters and slack parameters are designed to improve the KKT conditions. Based on the improved KKT conditions and neurodynamics formula, a distributed recurrent neural network DSB-RNN is proposed. Experimental results demonstrate the effectiveness and accuracy of the proposed DSB-RNN method, and comparisons with other methods verify its advantages in terms of applicability and precision.