Please wait a minute...
Chin J Eng Design  2022, Vol. 29 Issue (5): 634-642    DOI: 10.3785/j.issn.1006-754X.2022.00.076
Whole Machine and System Design     
UAV autonomous stringing system based on ground station assistance
Qiang CHEN1(),Shi-qiang HU1(),Ling-kun LUO1,Bing LIU2,Yuan FANG2
1.School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China
2.AVIC Aeronautical Radio Electronics Research Institute, Shanghai 200240, China
Download: HTML     PDF(2742KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The utilization of unmanned aerial vehicle (UAV) technology to realize the power line laying is gradually becoming the main trend of power industry development. Studying the UAV autonomous stringing technology can effectively improve operational efficiency, reduce construction cost and ensure security of workers. However, the problems of UAV stringing technology at this stage are mainly as follows: 1) Most UAVs rely on manual control or ground station to release waypoint control, which has a low level of intelligence, and long-term operation will bring strong load to electric workers; 2) The UAV lacks autonomous obstacle avoidance ability and has insufficient perception ability, and the obstacles such as wires and poles will cause safety hazards to it. In order to solve the above problems, fitstly, the hardware framework and the modular software framework based on ROS (robot operating system) of UAV autonomous stringing system were constructed, and on this basis, the stringing task planning algorithm and the stringing bow detection algorithm were implemented, so that the UAV had a stable autonomous stringing capability. Then, the collision model of UAV was constructed by using collision detection method, and the path planning algorithm of UAV was proposed. At the same time, the ground station-based obstacle avoidance strategy was introduced to effectively improve the safety of UAV. The experimental results showed that the software and hardware framework of the designed UAV autonomous stringing system was reasonable, and the stringing task planning algorithm could help the UAV complete the autonomous stringing task efficiently and stably. The ground station could monitor the status of the UAV in real-time, and assisted the UAV to avoid obstacles when necessary, so as to ensure the safety of UAV to the greatest extent. The designed system is safe and reliable, and can meet the actual electric stringing operation requirements.



Key wordsunmanned aerial vehicle (UAV)      autonomous stringing system      modular software framework      stringing bow detection      ground station-based obstacle avoidance     
Received: 25 March 2022      Published: 02 November 2022
CLC:  V 249.3  
Corresponding Authors: Shi-qiang HU     E-mail: cq_smile@163.com;sqhu@sjtu.edu.cn
Cite this article:

Qiang CHEN,Shi-qiang HU,Ling-kun LUO,Bing LIU,Yuan FANG. UAV autonomous stringing system based on ground station assistance. Chin J Eng Design, 2022, 29(5): 634-642.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2022.00.076     OR     https://www.zjujournals.com/gcsjxb/Y2022/V29/I5/634


基于地面站辅助的无人机自主架线系统

将无人机(unmanned aerial vehicle, UAV)技术用于实现电力线展放正逐步成为电力行业发展的主要趋势,研究无人机自主架线技术能够有效提高作业效率、降低施工成本和保障工人安全。但现阶段无人机架线技术面临的问题主要有:1)大多数无人机依赖人工操控或地面站发布航点控制,智能化水平低,长期作业会给电力工人带来较强负荷;2)无人机缺乏自主避障能力且感知能力不足,电线、电杆等障碍物会对其造成安全隐患。为解决上述问题,首先,构建了无人机自主架线系统的硬件框架和以ROS(robot operating system,机器人操作系统)为基础的模块化软件框架,并在此基础上实现了架线任务规划算法和架线弓检测算法等,使无人机具备稳定的自主架线能力。然后,利用碰撞检测方法构建了无人机碰撞模型,并提出了无人机路径规划算法,同时引入地面站辅助避障策略,以有效提高无人机的安全性。实验结果表明:所设计的无人机自主架线系统的软、硬件框架合理,架线任务规划算法能帮助无人机高效稳定地完成自主架线任务;地面站能够实时监控无人机状态,并在必要时及时辅助无人机避障,最大程度地保证了无人机的安全。所设计系统安全可靠,可满足实际电力架线作业的需求。


关键词: 无人机,  自主架线系统,  模块化软件框架,  架线弓检测,  地面站辅助避障 
Fig. 1 Hardware framework of UAV autonomous stringing system based on ground station assistance
Fig.2 Software framework of UAV autonomous stringing system based on ground station assistance
Fig.3 Collision detection surround box model of UAV
Fig.4 Obstacle threat cone model
Fig.5 Obstacle avoidance strategy of UAV based on ground station assistance
Fig.6 Flow of UAV autonomous stringing
Fig.7 Physical object of stringing bow
指标空旷场地真实场地
飞行速度/(m/s)22
障碍物尺寸/cm20(半径)
最小感知距离/m2
实验次数5030
成功次数5030
未感知到障碍物次数06
地面站辅助次数16
Table 1 Experimental results of autonomous obstacle avoidance of UAV at different sites
参数hmaxhminsmaxsminvmaxvmin
取值4150255125550
Table 2 Optimal values of HSV parameters of color segmentation algorithm
场景总次数准确率/%成功率/%
室内100100100
室外草坪10010098
真实场地5010094
Table 3 Experimental results of stringing bow detection at different sites
Fig.8 UAV autonomous stringing site
指标数值
无人机飞行速度/(m/s)2.5
无人机牵引负载/kg1.9
最大风力/级5
平均作业时间/s306.7
作业次数30
直接成功次数22
地面站辅助次数8
地面站辅助成功次数8
Table 4 Complete operation experiment results of UAV autonomous stringing system
[1]   乔媛媛.输电线路架线施工方案综合评价研究[D].西安:西安建筑科技大学,2016:1-5.
QIAO Yuan-yuan. Comprehensive evaluation on the construction scheme of overhead transmission line[D]. Xi’an: Xi’an University of Architecture and Technology, 2016: 1-5.
[2]   梁兴隆.分析动力伞在高压输电线路架线施工中的应用[J].中国新通信,2013,15(20):61-62. doi:10.3969/j.issn.1673-4866.2013.20.047
LIANG Xing-long. Analysis of the application of dynamic umbrellas in the construction of high-voltage transmission lines[J]. China New Communications, 2013, 15(20): 61-62.
doi: 10.3969/j.issn.1673-4866.2013.20.047
[3]   李光辉,江全才,何海波,等.输电线路施工机械及设备[M].北京:中国电力出版社,2009:173-204.
LI Guang-hui, JIANG Quan-cai, HE Hai-bo, et al. Transmission line construction machinery and equipment [M]. Beijing: China Electric Power Press, 2009: 173-204.
[4]   杨建东.小型无人直升机在特高压线路架线中的应用探索[J].中国新技术新产品,2011(19):141-142. doi:10.3969/j.issn.1673-9957.2011.19.134
YANG Jian-dong. Application of small unmanned helicopter in the UHV line stand in line to explore the application[J]. China New Technologies and Products, 2011(19): 141-142.
doi: 10.3969/j.issn.1673-9957.2011.19.134
[5]   董永华,姚燕,隋少杰.输电线路架设中对无人机的应用实践研究[J].工程技术(文摘版),2017,33:143.
DONG Yong-hua, YAO Yan, SUI Shao-jie. Research on the application of UAV in transmission line construction[J]. Engineering Technology (Abstract Edition), 2017, 33: 143.
[6]   张涛,芦维宁,李一鹏.智能无人机综述[J].航空制造技术, 2013(12):32-35. doi:10.3969/j.issn.1671-833X.2013.12.003
ZHANG Tao, LU Wei-ning, LI Yi-peng. Intelligent UAV[J]. Aeronautical Manufacturing Technology, 2013(12): 32-35.
doi: 10.3969/j.issn.1671-833X.2013.12.003
[7]   陈麒杰,晋玉强,韩露.无人机路径规划算法研究综述[J]. 飞航导弹,2020(5):54-58.
CHEN Qi-jie, JIN Yu-qiang, HAN Lu. Research review of UAV path planning algorithms[J]. Airborne Missiles, 2020(5): 54-58.
[8]   VOLODYMYR M, KORAY K, DAVID S, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529-533.
[9]   金泽选.植保无人机地面站系统研究[D].杭州:杭州电子科技大学,2017:1-7.
JIN Ze-xuan. Study on ground station system of plant protection unmanned aerial vehicle[D]. Hangzhou: Hangzhou Dianzi University, 2017: 1-7.
[10]   罗旻,周萌.无人机全自动展放引线系统的开发与应用[J]. 实验室研究与探索,2019,38(8):52-56. doi:10.3969/j.issn.1006-7167.2019.08.014
LUO Min, ZHOU Meng. Development and application of UAV full-automatic guiding rope system[J]. Research and Exploration in Laboratory, 2019, 38(8): 52-56.
doi: 10.3969/j.issn.1006-7167.2019.08.014
[11]   吴佳青,魏栋,陈尤,等.基于RTK与视觉辅助定位的自主无人机架线系统[J].电气自动化,2021,43(2):27-29. doi:10.3969/j.issn.1000-3886.2021.02.011
WU Jia-qing, WEI Dong, CHEN You, et al. Autonomous unmanned aerial vehicle-assisted stringing system based on RTK and vision-assisted positioning[J]. Electrical Automation, 2021, 43(2): 27-29.
doi: 10.3969/j.issn.1000-3886.2021.02.011
[12]   肖婷婷.视觉里程计/IMU辅助GPS融合定位算法研究[D].上海:华东师范大学,2019:47-52.
XIAO Ting-ting. Research on visual odometry/IMU assisted GPS fusion location algorithm [D]. Shanghai: East China Normal University, 2019: 47-52.
[13]   白冲冲,程文雅,郭洪佚.浅析开源ROS机器人操作系统[J].科学与信息化,2019(36):2.
BAI Chong-chong, CHENG Wen-ya, GUO Hong-yi. Analysis of open source ROS robot operating system[J]. Science and Information Technology, 2019(36): 2.
[14]   马登武,叶文,李瑛.基于包围盒的碰撞检测算法综述[J]. 系统仿真学报,2006,18(4):1058-1061,1064. doi:10.3969/j.issn.1004-731X.2006.04.063
MA Deng-wu, YE Wen, LI Ying. Survey of box-based algorithms for collision detection[J]. Journal of System Simulation, 2006, 18(4): 1058-1061, 1064.
doi: 10.3969/j.issn.1004-731X.2006.04.063
[15]   李建波,潘振宽,孙志军.基于包围盒与空间分解的碰撞检测算法[J].计算机科学,2005,32(6):155-157. doi:10.3969/j.issn.1002-137X.2005.06.045
LI Jian-bo, PAN Zhen-kuan, SUN Zhi-jun. The collision detection algorithm based on combination of bounding volumes and space division[J]. Computer Science, 2005, 32(6): 155-157.
doi: 10.3969/j.issn.1002-137X.2005.06.045
[16]   刘健鑫,崔汉国,张晶,等.包围盒碰撞检测算法的优化[J].计算机工程与应用,2008,44(18):51-53. doi:10.3778/j.issn.1002-8331.2008.18.016
LIU Jian-xin, CUI Han-guo, ZHANG Jing, et al. Optimization of box-based algorithms for collision detection[J]. Computer Engineering and Applications, 2008, 44(18): 51-53.
doi: 10.3778/j.issn.1002-8331.2008.18.016
[17]   CHAKRAVARTHY A, GHOSE D. Obstacle avoidance in a dynamic environment: a collision cone approach[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 1998, 28(5): 562-574.
[18]   WATANABE Y, CALISE A, JOHNSON E. Vision-based obstacle avoidance for UAVs[C]//AIAA Guidance, Navigation and Control Conference and Exhibit, Hilton Head, South Carolina, Aug. 20-23, 2007.
[19]   李峰.基于立体相机和GPS的实时移动机器人混合导航研究[J].机械设计与研究,2020,36(5):18-23.
LI Feng. Real-time mobile robot navigation based on stereo vision and low cost GPS[J]. Machine Design & Research, 2020, 36(5): 18-23.
[20]   BARFOOT T D. State estimation for robotics[M]. Cambridge: Cambridge University Press, 2017: 88-101.
[21]   邢姗姗,赵文龙.基于YOLO系列算法的复杂场景下无人机目标检测研究综述[J].计算机应用研究,2020,37(S2):28-30.
XING Shan-shan, ZHAO Wen-long. A review of research on UAV target detection in complex scenes based on YOLO series algorithms[J]. Computer Application Research, 2020, 37(S2): 28-30.
No related articles found!