A Distributed Slack Barrier Recurrent Neural Network for Multiple Redundant Manipulators Collaborative System in Obstacles Environment
针对障碍环境中分布式多机械臂系统的运动生成问题，提出一种分布式松弛障碍递归神经网络，将问题转化为时变二次规划并求解，实验验证了方法的有效性和精度。
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.