Work

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

Year: 2026

Type: article

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.

Source: Journal of Intelligent & Robotic Systems

DOI: 10.1007/s10846-026-02425-6

Authors: Duo Zhao, Ganke Huang, Minyu Liu, Tai Ren, Qichao Tang

Institutions: Southwest Jiaotong University, Southwest Petroleum University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,090