Autonomous live working robot navigation with real‐time detection and motion planning system on distribution line

Abstract

In this study, an autonomous robot navigation system is designed for live working on distribution line. The developed system features a real-time detection and motion planning system, incorporating a manipulator capable of grasping power components. In order to accurately identify targets, the authors propose an object detection method based on the Larger Scale ‘You Only Look Once’ Version 4 (LS-YOLOv4) algorithm for detecting the insulators and drop fuses. The LS-YOLOv4 extracts features of power components by Convolutional Neural Network (CNN), and then performs feature fusion. Then the authors develop a motion planning method based on the Node Control Optimal Rapidly Exploring Random Trees (NC-RRT*), which can drive the robot to realise the autonomous robot motion planning and obstacle avoidance. On the grasping function, the authors present a reliable Lightweight-based Convolutional Neural Network (L-CNN) grasping point detection method. Finally, the authors evaluate fully autonomous robotic system in both simulated and real-world experiments. The experimental results demonstrate that the proposed system can effectively identify the target and complete the grasping task in an efficient way. Notably, the proposed motion planning method can take into account both planning efficiency and accuracy to manipulation tasks.