Path Planning for Robotic Arm in Catenary Maintenance: An Improved RRT Algorithm Based on Obstacle Node

Abstract This paper presents an obstacle-node-based Rapidly-exploring Random Tree (OB-RRT) algorithm for robotic arm path planning in constrained maintenance environments. The proposed method incorporates obstacle-node information derived from collision samples to guide tree expansion and improve exploration efficiency. The performance of OB-RRT is evaluated through simulations in 2D and 3D environments, with comparisons to RRT, GB-RRT, RRT-Connect, RRT*, and Informed-RRT*. The results indicate that, while optimization-based planners achieve better path optimality, they generally require higher computational cost. In contrast, OB-RRT provides a favorable trade-off between planning efficiency and path quality. Furthermore, the proposed method is validated on a 6-DoF robotic arm in a catenary maintenance scenario using a digital twin framework. The planned trajectories are successfully executed on a real robotic system, demonstrating feasibility for practical applications.