Abstract: In order to meet the requirement of automatic navigation of robot arm in intelligent maintenance of railway overhead contact system,a research method is proposed to comprehensively solve the problem of path planning and obstacle avoidance. This method transforms the dual objective into a single constrained optimization problem. On this basis,the standard Rapidly exploring Random Tree (RRT) algorithm is improved,the map complexity assessment strategies and Gaussian mixed distribution sampling strategies are introduced to constrain the generation direction of random sampling points. By adding the angle constraint strategy and the variable step size mechanism near the obstacle,the generation direction of the random tree is always growing towards the target point,so that the asymptotically optimal path is planned.In addition,a Recurrent Neural Network based on the Beetle Olfactory Detection (RNNBOD) algorithm is designed to configure the optimal joint angle to drive the redundant manipulator end effector to move along the planned reference path,thereby reducing its computing cost. The simulation results show that the proposed method not only improves the search efficiency,node utilization and path quality of the standard RRT algorithm,but also successfully solves the problem of tracking and controlling the redundant manipulator during operation.

Key words: maintenance of overhead contact line, path planning, obstacle avoidance, Recurrent Neural Network (RNN) algorithm, tracking control