Path Planning Method for Live Working Robot in the Power Industry

Abstract

Given the complexity of live working environments in power distribution networks, where autonomous obstacle avoidance by robots often involves numerous path nodes and low exploration efficiency, the Bidirectional Node-Controlled Rapidly Exploring Random Tree (BNC-RRT) algorithm is proposed. This algorithm guides path search by progressively altering the sampling area and employs a node control mechanism to constrain the random tree expansion and extract effective boundary points. This approach reduces the number of ineffective nodes and collision checks during the search process, thereby enhancing path planning efficiency. Comparative simulation experiments conducted in various scenarios demonstrate that this algorithm reduces the number of path nodes and improves planning efficiency compared to classical algorithms. Finally, real-world experiments on a live working robot developed by our team show that the proposed algorithm shortens the average path length by 8.6%, and reduces the average planning and movement times by 44.7% and 28.7%, respectively, compared to classical path planning algorithms. These results indicate that the algorithm effectively improves path planning efficiency and is suitable for live working tasks in the power distribution industry.