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Peg-in-hole assembly in live-line maintenance based on generative mapping and searching network
Robotics and Autonomous Systems ( IF 4.3 ) Pub Date : 2021-06-09 , DOI: 10.1016/j.robot.2021.103797
Wei Wu , Hui Zhou , Yu Guo , Yifei Wu , Jian Guo

Replacement of lightning arrester is one of the common tasks in live-line maintenance, and peg-in-hole assembly is a very difficult operation for a robot, because there are visual inaccuracy and force model uncertainty in the process of assembly. This paper presents a new implementation approach fusing signals of vision detection and fuzzy force to realize the high efficiency peg-in-hole assembly by a manipulator autonomously. YOLOv3 is applied as the visual detection network for rough alignment. In the phase of precise hole-searching, we establish a two-dimensional hole-searching model by fusing signal of vision detection and fuzzy force as the condition of state transitions, and propose a new semi-supervised learning network to optimize the hole-searching routine. The performance of the approach is verified by experiments in the simulation environment and the laboratory environment.



中文翻译:

基于生成映射和搜索网络的带电维修中的桩孔装配

更换避雷器是带电维护中的常见任务之一,孔洞装配对于机器人来说是一项非常困难的操作,因为装配过程中存在视觉不准确和力模型不确定性。本文提出了一种新的实现方法,融合视觉检测和模糊力的信号,以实现机械手自主高效地钉孔组装。YOLOv3 被用作粗对齐的视觉检测网络。在精确寻孔阶段,我们通过融合视觉检测信号和模糊力作为状态转换条件建立二维寻孔模型,并提出一种新的半监督学习网络来优化寻孔常规。

更新日期:2021-06-18
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