基于视觉方法的输电线断股检测与机器人行为规划

doi:10.13973/j.cnki.robot.2015.0204

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引用本文:
宋屹峰, 王洪光, 李贞辉, 姜勇. 基于视觉方法的输电线断股检测与机器人行为规划[J]. 机器人, 2015, 37(2): 204-211,223.DOI: 10.13973/j.cnki.robot.2015.0204. SONG Yifeng, WANG Hongguang, LI Zhenhui, JIANG Yong. Vision Based Transmission Line Broken Strand Detection and Robot Behaviour Planning. ROBOT, 2015, 37(2): 204-211,223. DOI: 10.13973/j.cnki.robot.2015.0204.

摘要

输电线维护机器人用于代替人工完成危险作业，准确的故障检测与合理的行为规划对于作业效果至关重要．针对以上需求，采用视觉方法，提出了一种基于图像特征分类的输电线断股检测方法．该方法提取边缘梯度向量作为图像特征，采用支持向量机方法进行分类运算完成线路断股检测．在断股检测的基础上，利用断股检测信息与机器人传感器测得的信息构建机器人状态向量．根据当前状态向量，结合机器人断股补修作业流程，提出了面向捋线与压接复杂作业的机器人断股补修作业行为规划方法．利用实验室模拟线路开展实验，验证了提出的输电线断股检测及行为规划方法的有效性．

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关键词 ：电力机器人, 断股检测, 行为规划, 支持向量机

Abstract：

Power line maintenance robots are used to replace workers due to the dangerous maintenance operation, and the robot maintenance effect is much related with accurate fault detection and rational behavior planning. With those requirements in mind, a visual method is presented to detect the broken strand fault based on the classification of an image feature. In the visual detection method, image edge gradient histogram is firstly extracted as the image feature, and broken strand detection can be accomplished by the classification of the image feature with support vector machine (SVM) method. On this basis, several robot state vectors are established by combining the broken strand detection result and the information of robot sensors. Based on the current state vector and robotic broken strand repair process, a behavior planning method for broken strand repair is proposed toward complex operations of broken strand return and clamps installation. Experiments are carried out in the laboratory, and results demonstrate the effectiveness of the proposed broken strand detection method and the behavior planning method.

Key words： power line robot broken strand detection behavior planning SVM (support vector machine)

收稿日期: 2014-04-15 录用日期: 2014-09-09

TP242

基金资助:

国家自然科学基金资助项目（61179049）；辽宁省自然科学基金资助项目（2013020054）．

通讯作者: 宋屹峰，songyifeng@sia.cn E-mail: songyifeng@sia.cn

作者简介: 宋屹峰（1984-），男，博士研究生．研究领域：机器人机构学，机器人建模与控制．
王洪光（1965-），男，博士，研究员．研究领域：机器人机构学，特种机器人、机电一体化技术．
李贞辉（1984-），男，博士研究生．研究领域：机器人建模与控制．

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2015.0204 或 https://robot.sia.cn/CN/Y2015/V37/I2/204

[1] Katrasnik J, Pernus F, Likar B. A survey of mobile robots for distribution power line inspection[J]. IEEE Transactions on Power Delivery, 2010, 25(1): 485-493.

[2] Toussaint K, Pouliot N, Montambault S. Transmission line maintenance robots capable of crossing obstacles: State of the art review and challenges ahead[J]. Journal of Field Robotics, 2009, 26(5): 477-499.

[3] Jiang X, Xia Y, Hu J, et al. An S-transform and support vector machine (SVM)-based online method for diagnosing broken strands in transmission lines[J]. Energies, 2011, 4(9): 1278- 1300.

[4] Haag T, Beadle B M, Sprenger H, et al. Wave-based defect detection and interwire friction modeling for overhead transmission lines[J]. Archive of Applied Mechanics, 2009, 79(6/7): 517-528.

[5] Shoureshi R A, Lim S W, Dolev E, et al. Electro-magneticacoustic transducers for automatic monitoring and health assessment of transmission lines[J]. Journal of Dynamic Systems, Measurement, and Control, 2004, 126(7): 303-308.

[6] Hatsukade Y, Miyazaki A, Matsuura H, et al. Study of inspection of wire breakage in aluminum transmission line using SQUID[C]// International Conference on Advanced Nondestructive Evaluation. Kidlington, UK: Elsevier Science, 2009: 170-173.

[7] Fu S, Li W, Zhang Y, et al. Structure-constrained obstacles recognition for power transmission line inspection robot[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2006: 3363-3368.

[8] Li W H, Tajbakhsh A, Rathbone C, et al. Image processing to automate condition assessment of overhead line components[C]//International Conference on Applied Robotics for the Power Industry. Piscataway, USA: IEEE, 2010: 1-6.

[9] Yao G, Liu Y, Dong F, et al. An obstacle detection approach of transmission lines based on contour view synthesis[C]//International Conference on Automation and Logistics. Piscataway, USA: IEEE, 2010: 19-24.

[10] 谭磊,王耀南,沈春生,等.输电线路除冰机器人障碍视觉检测识别算法[J].仪器仪表学报,2011,32(11):2564- 2571. Tan L, Wang Y N, Shen C S, et al. Vision based obstacle detection and recognition algorithm for transmission line deicing robot[J]. Chinese Journal of Scientific Instrument, 2011, 32(11): 2564-2571.

[11] 唐健隆,梁自泽,蔡丽,等.基于状态机的巡线机器人控制系统设计[J].微计算机信息,2008,24(2):247-249. Tang J L, Liang Z Z, Cai L, et al. Design of controlling system for power transmission line inspection robot based on finite state machine[J]. Microcomputer Information, 2008, 24(2): 247-249.

[12] 郭伟斌,王洪光,姜勇,等.一种输电线路巡检机器人越障规划方法[J].机器人,2012,34(4):505-512. Guo W, Wang H G, Jiang Y, et al. Obstacle navigation planning for a power transmission line inspection robot[J]. Robot, 2012, 34(4): 505-512.

[13] 李恩,梁自泽,谭民.基于规则库的巡线机器人自主越障动作规划[J].机器人,2005,27(5):400-405. Li E, Liang Z Z, Tan M. Rule base based motion planning method for inspection robot to cross obstacles autonomously[J]. Robot, 2005, 27(5): 400-405.

[14] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge, UK: Cambridge University Press, 2000.

[15] Platt J C, Cristianini N, Shawe-Taylor J. Large margin DAGs for multiclass classification[C]//Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 1999: 547-553.