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.

中文翻译：

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带电实时检测和运动规划系统的配电线路自主带电作业机器人导航

在这项研究中，自主机器人导航系统是为带电作业配电线路而设计的。开发的系统具有实时检测和运动规划系统，结合了能够抓取动力元件的机械手。为了准确识别目标，作者提出了一种基于 Larger Scale 'You Only Look Once' Version 4 (LS-YOLOv4) 算法的目标检测方法，用于检测绝缘体和熔断器。LS-YOLOv4通过卷积神经网络（CNN）提取功率分量的特征，然后进行特征融合。然后作者开发了一种基于节点控制最优快速探索随机树（NC-RRT*）的运动规划方法，可以驱动机器人实现自主机器人运动规划和避障。在抓取功能上，作者提出了一种可靠的基于轻量级的卷积神经网络 (L-CNN) 抓取点检测方法。最后，作者在模拟和真实世界的实验中评估了完全自主的机器人系统。实验结果表明，所提出的系统能够有效地识别目标并高效地完成抓取任务。值得注意的是，所提出的运动规划方法可以兼顾规划效率和操作任务的准确性。