电力设施多模态精细化机器人巡检关键技术及应用

张辉; 杜瑞; 钟杭; 曹意宏; 王耀南

doi:10.16383/j.aas.c230809

电力设施多模态精细化机器人巡检关键技术及应用

doi: 10.16383/j.aas.c230809 cstr: 32138.14.j.aas.c230809

张辉^{1, 2,},
杜瑞^{1, 2,},
钟杭^{1, 2,},
曹意宏^{1, 2,},
王耀南^{2, 3,}

1.
湖南大学机器人学院长沙 410082
2.
机器人视觉感知与控制技术国家工程研究中心长沙 410082
3.
湖南大学电气与信息工程学院长沙 410082

基金项目: 科技创新2030——“新一代人工智能”重大项目(2021ZD0114503), 国家自然科学基金重大研究计划(92148204), 国家自然科学基金(62027810), 湖南省科技创新领军人才(2022RC3063), 湖南省十大技术攻关项目(2024GK1010), 湖南省重点研发计划(2023GK2068, 2022GK2011), 国网湖南省电力有限公司科技项目(5216A522001Y, 5216A5240003), 国家电网有限公司科技项目(5700-202423229A-1-1-ZN)资助

详细信息

作者简介:
张辉：湖南大学机器人学院教授. 主要研究方向为机器视觉, 图像处理和机器人控制. E-mail: zhanghuihby@126.com

杜瑞：湖南大学机器人学院博士研究生. 2020年获湘潭大学硕士学位. 主要研究方向为机器视觉, 图像处理. 本文通信作者.E-mail: durui@hnu.edu.cn

钟杭：湖南大学机器人学院副教授. 分别于2013年、2016年和2020年获湖南大学电气与信息工程学院自动化科学学士、硕士和博士学位. 主要研究方向为航空机器人, 多机器人系统, 视觉伺服, 视觉导航和非线性控制. E-mail: zhonghang@hnu.edu.cn

曹意宏：湖南大学机器人学院博士研究生. 2021年获湖南师范大学硕士学位. 主要研究方向为计算机视觉. E-mail: caoyihong@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学电气与信息工程学院教授. 1995 年获湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-31
- 录用日期: 2024-05-30
- 网络出版日期: 2024-10-11

The Key Technology and Application of Multi-modal Fine Robot Inspection for Power Facilities

ZHANG Hui^{1, 2
,},
DU Rui^{1, 2
,},
ZHONG Hang^{1, 2
,},
CAO Yi-Hong^{1, 2
,},
WANG Yao-Nan^{2, 3
,}

1.
School of Robotics, Hunan University, Changsha 410082
2.
National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha 410082
3.
College of Electrical and Information Engineering, Hunan University, Changsha 410082

Funds: Supported by National Key Research and Development Program of China (2021ZD0114503), Major Research Plan of National Natural Science Foundation of China (92148204), National Natural Science Foundation of China (62027810), Hunan Leading Talent of Technological Innovation (2022RC3063), Top Ten Technical Research Projects of Hunan Province (2024GK1010), Key Research and Development Program of Hunan Province (2023GK2068, 2022GK2011), Science and Technology Project of State Grid Hunan Electric Power Company Limited. (5216A522001Y, 5216A5240003), and Science and Technology Project of State Grid Corporation of China (5700-202423229A-1-1-ZN)

More Information

Author Bio:
ZHANG Hui　Professor at the School of Robotics, Hunan University. His research interest covers machine vision, image processing, and robot control

DU Rui　Ph.D. candidate at the School of Robotics, Hunan University. He received his master degree from Xiangtan University in 2020. His research interest covers machine vision and image processing. Corresponding author of this paper

ZHONG Hang　Associate professor at the School of Robotics, Hunan University. He received his bachelor degree, master degree and Ph.D. degree in automation science from the College of Electrical and Information Engineering, Hunan University in 2013, 2016 and 2020, respectively. His research interest covers aerial robotics, multi-robot systems, visual servoing, visual navigation, and nonlinear control

CAO Yi-Hong　Ph.D. candidate at the School of Robotics, Hunan University. He received his master degree from Hunan Normal University in 2021. His main research interest is computer vision

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control, and image processing

摘要

摘要: 电力设施巡检对于加快电网基础设施智能化改造和智能微电网建设, 提高电力系统互补互济和智能调节能力的需求具有重要作用, 近年来, 智能巡检机器人开始在电力巡检中广泛应用. 在提高电力设施巡检效率和准确性、提升安全性、降低成本和促进电力智能化发展等方面发挥关键作用. 本文从电力巡检机器人的智能感知和导航技术出发, 重点介绍目标检测、语义分割、自主导航等共性关键技术的国内外发展现状. 然后以可见光红外双光融合、可见光图像和点云数据融合、声纹和可见光融合为例, 阐述电力场景多模态数据融合方式. 并进一步介绍电力部件精准分割和异物检测、线路点云杆塔倾斜检测、输电线路覆冰多模态检测和电力架空线路缺陷分析及台账异常检测等电力设施多模态机器人相关案例. 最后探讨电力设施多模态精细化机器人巡检关键技术的发展趋势和所面临的挑战.
- 电力设施巡检 /
- 机器人 /
- 智能感知 /
- 多模态 /
- 输电线路
Abstract: Power facilities inspection plays an important role in accelerating the intelligent transformation of power grid infrastructure and the construction of intelligent microgrid, and improving the complementary and intelligent adjustment ability of power system. In recent years, intelligent inspection robots have been widely used in power inspection. They play a key role in improving the efficiency and accuracy of power facilities inspection, improving safety, reducing costs and promoting the development of power intelligence. This paper starts from the intelligent perception and navigation technology of power inspection robots. This paper focuses on the development status of common key technologies such as target detection, semantic segmentation and autonomous navigation at home and abroad, and then takes visible light infrared dual-light fusion, visible light image and point cloud data fusion, voiceprint and visible light fusion as examples. This paper expounds the multi-modal data fusion method of power scene, and further introduces the related cases of multi-modal robots for power facilities, such as accurate segmentation and foreign body detection of power components, tilt detection of line point cloud towers, multi-modal detection of transmission line icing, defect analysis of power overhead lines and abnormal detection of ledgers, etc.. Finally, the development trend and challenges of key technologies for multi-modal fine robot inspection of power facilities are discussed.
- Power facilities inspection /
- robots /
- intelligent perception /
- multi-modal /
- transmission lines

HTML全文

图 1 近年全国总用电量趋势(单位: 亿千瓦时)

Fig. 1 Trend of national total electricity consumption in recent years (unit: 100 million kW·h)

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图 2 电力设施多模态机器人巡检案例

Fig. 2 Multi-modal robot inspection cases of power facilities

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图 3 智能巡检机器人感知关键技术

Fig. 3 Key technologies of intelligent inspection robot perception

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图 4 典型2D目标检测网络

Fig. 4 Typical 2D object detection networks

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图 5 3D点云检测和分割方法

Fig. 5 3D point cloud detection and segmentation methods

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图 6 电力设施红外和可见光融合图像

Fig. 6 Infrared and visible light fusion images of power facilities

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图 7 点云和可见光融合流程

Fig. 7 Point cloud and visible light fusion process

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图 8 中国南方电网巡检机器人

Fig. 8 China Southern Power Grid inspection robot

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图 9 声学图像和光学图像融合流程

Fig. 9 Acoustic image and optical image fusion process

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图 10 电力设施精准目标检测框架

Fig. 10 Accurate object detection framework for power facilities

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图 11 基于可见光和点云的杆塔倾斜检测流程图

Fig. 11 Flow chart of tower tilt detection based on visible light and point cloud

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图 12 带电除冰机器人

Fig. 12 Electric de-icing robot

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图 13 电力架空线路缺陷检测及三维台账拓扑数字孪生体

Fig. 13 Defect detection of power overhead lines and three-dimensional ledger topology digital twins

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表 1 电力设施机器人巡检智能感知关键技术

Table 1 Key technologies for intelligent perception in robotic inspection of power facilities

场景	技术	电力检测任务	代表方法
2D	目标检测	常用于可见光模态, 输出电力设备缺陷的位置和类型信息	Faster R-CNN^[25]、YOLO系列^[28]、SSD^[29]、RetinaNet^[30]等
2D	语义分割	常用于红外模态, 输出电力设备缺陷的热故障区域	U-Net^[36]、SegNet^[37]、DeepLab系列^[40−41]等
3D	目标检测	常用于激光点云, 检测电力导线三维位置	PointNet系列^{[48−49, 54]}、SESS^[50]等
3D	语义分割	常用于激光点云, 输出杆塔点区域	PointNet系列^{[48−49, 54]}、DGCNN^[57]等

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表 2 2D语义分割技术分类

Table 2 Classification of 2D semantic segmentation techniques

方法	分类类型	方法类型	原理或用途
传统方法	原理	基于阈值的方法^[33]	根据灰度值大小不同, 设定阈值完成分割
		基于边缘检测的方法^[34]	对图像边缘线条进行检测
		基于图割的方法^[35]	利用图形结构最小割分割图像
深度学习方法	用途	多尺度信息融合^[40−41]	增加感受野, 提高分割结果精度
		无监督语义分割^[44−45]	减少大量人工标注成本
		实时语义分割^[46]	节省计算资源, 加快推理时间

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表 3 可见光红外双光融合方法分类

Table 3 Classification of visible light infrared dual-light fusion methods

方法	优点	缺点
基于多尺度变换的方法	多层次子图像保留了更多图像细节信息	基于预先设定的基函数进行图像融合, 易忽略源图像部分重要特征
基于稀疏表示的方法	超完备字典蕴涵丰富的基原子, 有利于图像更好的表达和提取	难以应对复杂图像融合
基于神经网络的方法	避免了传统算法手动设计复杂的分解级别和融合规则, 并有效保留源图像信息	对计算资源需求较大, 暂未大量应用

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表 4 电力设施多模态精细化巡检应用

Table 4 Application of multi-modal fine inspection in power facilities

典型案例	融合模态	方法原理
电力部件精准分割	可见光+红外	结合可见光的高分辨率和红外图像的温度特性, 实现高精度部件分割
输电线路异物检测	可见光+点云	结合可见光的高分辨率和点云的形态特征, 充分识别线路异物
线路杆塔倾斜检测	可见光+点云	结合可见光的颜色信息和点云的位置信息, 准确分割杆塔, 实现倾斜度检测
线路覆冰多模态检测	可见光+点云	结合可见光的颜色信息和点云的位置信息定位电力线, 通过坐标计算覆冰厚度
台账异常检测	可见光+点云	通过可见光实现台账目标识别, 利用点云构建数字孪生网络以实现台账校准

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