| 樊绍胜,杨迪,邹德华,严宇.输电线路螺栓紧固带电作业机器人的视觉搜索、识别与定位方法[J].电子测量与仪器学报,2017,31(9):1514-1523 |
| 输电线路螺栓紧固带电作业机器人的视觉搜索、识别与定位方法 |
| Vision based tracing, recognition and positioning strategy for bolt tightening live working robot on power transmission line |
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| DOI：10.13382/j.jemi.2017.09.024 |
| 中文关键词: 输电线路 螺栓搜索识别 带电作业 视觉检测 |
| 英文关键词:power transmission lines bolt tracing and recognition live working visual detection |
| 基金项目:国家自然科学基金（61473049）资助项目 |
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| Author | Institution |
| Fan Shaosheng | School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China |
| Yang Di | School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China |
| Zou Dehua | Live Working Center of State Grid Hunan Electric Power Company, Changsha 410100, China |
| Yan Yu | Live Working Center of State Grid Hunan Electric Power Company, Changsha 410100, China |
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| 中文摘要: |
| 在输电线路采用机器人进行螺栓的全自动带电紧固作业是一项非常有挑战性的工作，螺栓的自动紧固首先必须解决螺栓的自动搜索、识别与定位，由于线路环境复杂，这些工作变得十分困难，为此，提出一种新的螺栓视觉搜索识别定位方法，该方法分为两部分，基于参考物的螺栓追踪，通过设定引流线为参考物，先对引流线进行定位，然后沿着引流线方向来搜索螺栓，从而简化螺栓搜索过程，降低螺栓识别难度；基于改进Hough变换的螺栓识别算法，通过对经典Hough变换的峰值选择策略进行改进来实现螺栓的精确识别，然后利用螺栓头部圆形特征来完成螺栓中心的验证，并通过HOG和SVM技术来实现目标物体的识别分类，消除外界不相关物体对目标图像的影响，进一步提高识别精度。根据该方法，设计了机器人原理样机并进行了模拟测试和现场测试，测试结果表明，该方法能够高效地实现输电线路上螺栓的搜索、识别与定位，极大程度地提高了机器人的带电作业效率。 |
| 英文摘要: |
| Autonomous bolt tightening is a challenging task for maintenance robot on power transmission lines, because bolts could hardly be traced and recognized without manual command. In this paper, a structure of bolt tightening robot and its bolt tracing, recognition and positioning strategy utilizing vision detection are proposed. The bolt tightening robot is equipped with a camera installed bolt tightening unit which is connected by an arm with three joints, the proposed bolt detecting strategy consists of two steps. First is bolt tracing in which the drainage wire is used as a reference, through the visual detection of the location and direction of the drainage wire, the bolt tracing task can be carried out by tracing along the wire, thus the tracing process is simplified and the difficulty of vision recognition in complicated environment is reduced. The second step is bolt recognizing step in which an improved Hough transform is proposed and the center of the circle shape edge is utilized to verifying the recognizing result. To make the bolt detecting more reliable, an initial classification algorithm utilizing HOG and SVM techniques is applied at the very beginning. The experimental results show that the proposed strategy can detect the bolt efficiently and pave the way for robot based automatic bolt tightening live work on lines. |
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