ACTA AERONAUTICAET ASTRONAUTICA SINICA >
A binocular vision⁃based autonomous aerial docking system design for UAVs
Received date: 2023-04-18
Revised date: 2023-05-22
Accepted date: 2023-06-27
Online published: 2023-06-27
Supported by
National Natural Science Foundation of China(62003266);Industry-University-Research Innovation Foundation for the Chinese Ministry of Education(2021ZYA07002)
In this paper， a binocular vision-based Unmanned Aerial Vehicle （UAV） autonomous aerial docking system is designed for the docking segment in autonomous aerial refueling mission， which requires high accuracy， efficiency and security. Firstly， a target detection module based on binocular vision and YOLOv4-Tiny network is proposed for target detection and position estimation， and data enhancement is performed on the drogue image dataset taken under various environments. Test results of the module indicate that it can perform drogue detection and positioning in multiple intricate circumstances， maintaining the depth estimation error within 2%. After that， to satisfy the control accuracy and robustness requirements of UAV controller during the docking process in the presence of strong external environment disturbance， dynamics-modelling and disturbance-observer based active anti-disturbance control law design is carried out on a quadcopter testing platform， and the designed control law has also been verified by simulation tests. Finally， flight experiments of autonomous aerial docking are conducted on the quadcopter testing platform. Experiments results show that the autonomous aerial docking system has a properly designed structure， which can maintain attitude stability under strong external wind disturbance， perform accurate drogue detection and positioning， and complete the docking mission successfully and efficiently.
Yifang FU , Xinyue HU , Yulu HUANG , Ban WANG , Jiangtao HUANG . A binocular vision⁃based autonomous aerial docking system design for UAVs[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(20) : 628884 -628884 . DOI: 10.7527/S1000-6893.2023.28884
| 1 | 陆宇平， 杨朝星， 刘洋洋. 空中加油系统的建模与控制技术综述［J］. 航空学报， 2014， 35（9）： 2375-2389. |
| LU Y P， YANG C X， LIU Y Y. A survey of modeling and control technologies for aerial refueling system［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（9）： 2375-2389 （in Chinese）. | |
| 2 | 苏子康， 徐忠楠， 李春涛， 等. 伸缩套臂式无人机空基回收建模与对接控制［J］. 航空学报， 2023， 44（1）： 326315. |
| SU Z K， XU Z N， LI C T， et al. Modeling and docking control of UAV aerial recovery in form of telescopic boom［J］. Acta Aeronautics et Astronautica Sinica， 2023， 44（1）： 326315 （in Chinese）. | |
| 3 | GAO Y， YANG S B， LIU X L. Drogue position measurement of autonomous aerial refueling based on embedded system［J］. Sensors and Actuators A： Physical， 2023， 353： 114251. |
| 4 | LEE A， DALLMANN W， NYKL S， et al. Long-range pose estimation for aerial refueling approaches using deep neural networks［J］. Journal of Aerospace Information Systems， 2020， 17（11）： 634-646. |
| 5 | 全权， 魏子博， 高俊， 等. 软管式自主空中加油对接阶段中的建模与控制综述［J］. 航空学报， 2014， 35（9）： 2390-2410. |
| QUAN Q， WEI Z B， GAO J， et al. A survey on modeling and control problems for probe and drogue autonomous aerial refueling at docking stage［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（9）： 2390-2410 （in Chinese）. | |
| 6 | WANG X F， LI J M， KONG X W， et al. Towards docking safety analysis for unmanned aerial vehicle probe-drogue autonomous aerial refueling based on docking success-probability and docking reachability［J］. Proceedings of the Institution of Mechanical Engineers， Part G： Journal of Aerospace Engineering， 2019， 233（11）： 3893-3905. |
| 7 | SHANKAR A， ELBAUM S， DETWEILER C. Dynamic path generation for multirotor aerial docking in forward flight［C］∥ 2020 59th IEEE Conference on Decision and Control （CDC）. Piscataway： IEEE Press， 2021： 1564-1571. |
| 8 | THOMAS P R， BHANDARI U， BULLOCK S， et al. Advances in air to air refuelling［J］. Progress in Aerospace Sciences， 2014， 71： 14-35. |
| 9 | ZHAO K D， SUN Y R， LI H， et al. A novel drogue pose estimation method for autonomous aerial refueling based on monocular vision sensor［J］. IEEE Sensors Journal， 2022， 22（23）： 23064-23076. |
| 10 | SHI B W， LIU Z， ZHANG G J. Vision sensor for measuring aerial refueling drogue using robust method［J］. IEEE Sensors Journal， 2021， 21（24）： 28037-28049. |
| 11 | CHOI A J， YANG H H， HAN J H. Study on robust aerial docking mechanism with deep learning based drogue detection and docking［J］. Mechanical Systems and Signal Processing， 2021， 154： 107579. |
| 12 | REDMON J， DIVVALA S， GIRSHICK R， et al. You only look once： unified， real-time object detection［C］∥ 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2016： 779-788. |
| 13 | WANG B， YU X， MU L X， et al. Disturbance observer-based adaptive fault-tolerant control for a quadrotor helicopter subject to parametric uncertainties and external disturbances［J］. Mechanical Systems and Signal Processing， 2019， 120： 727-743. |
| 14 | WANG L A， JIANG X Q， WANG D， et al. Research on aerial autonomous docking and landing technology of dual multi-rotor UAV［J］. Sensors， 2022， 22（23）： 9066. |
| 15 | LI S， YANG J， CHEN W H， et al. Disturbance observer-based control： methods and applications［M］. Boca Raton： CRC press， 2014： 5-6. |
| 16 | OHISHI K， NAKAO M， OHNISHI K， et al. Microprocessor-controlled DC motor for load-insensitive position servo system［J］. IEEE Transactions on Industrial Electronics， 1987， IE-34（1）： 44-49. |
| 17 | YU B， KIM S， SUK J. Robust control based on ADRC and DOBC for small-scale helicopter［J］. IFAC-PapersOnLine， 2019， 52（12）： 140-145. |
| 18 | CHENG Y， JIANG L， LI T， et al. Robust tracking control for a quadrotor UAV via DOBC approach［C］∥ 2018 Chinese Control and Decision Conference （CCDC）. Piscataway： IEEE Press， 2018： 559-563. |
| 19 | WANG B， HUANG P， ZHANG W. A robust fault-tolerant control for quadrotor helicopters against sensor faults and external disturbances［J］. Complexity， 2021， 2021： 1-13. |
| 20 | MA Y B， ZHAO R J， LIU E H， et al. A novel autonomous aerial refueling drogue detection and pose estimation method based on monocular vision［J］. Measurement， 2019， 136： 132-142. |
| 21 | DENG Y M， XIAN N， DUAN H B. A binocular vision-based measuring system for UAVs autonomous aerial refueling［C］∥ 2016 12th IEEE International Conference on Control and Automation （ICCA）. Piscataway： IEEE Press， 2016： 221-226. |
| 22 | DUAN H B， LI H， LUO Q N， et al. A binocular vision-based UAVs autonomous aerial refueling platform［J］. Science China Information Sciences， 2016， 59（5）： 053201. |
| 23 | 刘爱超， 佘浩平， 杨钦宁， 等. 无人机空中对接中的视觉导航方法［J］. 导航定位与授时， 2019， 6（1）： 28-34. |
| LIU A C， SHE H P， YANG Q N， et al. Visual navigation method in drone aerial docking［J］. Navigation Positioning and Timing， 2019， 6（1）： 28-34 （in Chinese）. | |
| 24 | WU L， SUN Y R， ZHAO K D， et al. A novel vision-based PRPL multistage image processing algorithm for autonomous aerial refueling［J］. Wireless Communications and Mobile Computing， 2021， 2021： 2778857. |
| 25 | 汪刚志， 王新华， 陈冠宇， 等. 基于视觉的无人机空中加油目标识别技术研究［J］. 电子测量技术， 2020， 43（13）： 89-94. |
| WANG G Z， WANG X H， CHEN G Y， et al. Research on target recognition technology for aerial refueling of UAV based on vision［J］. Electronic Measurement Technology， 2020， 43（13）： 89-94 （in Chinese）. | |
| 26 | 张易明， 艾剑良. 基于双目视觉的空中加油锥套定位与对接控制［J］. 系统工程与电子技术， 2021， 43（10）： 2940-2953. |
| ZHANG Y M， AI J L. Positioning of aerial refueling drogue and docking control based on binocular vision［J］. Systems Engineering and Electronics， 2021， 43（10）： 2940-2953 （in Chinese）. | |
| 27 | SHARMA V， MIR R N. A comprehensive and systematic look up into deep learning based object detection techniques： a review［J］. Computer Science Review， 2020， 38： 100301. |
| 28 | HNEWA M， RADHA H. Object detection under rainy conditions for autonomous vehicles： a review of state-of-the-art and emerging techniques［J］. IEEE Signal Processing Magazine， 2021， 38（1）： 53-67. |
| 29 | BOCHKOVSKIY A， WANG C Y， LIAO H Y M. YOLOv4： optimal speed and accuracy of object detection［DB/OL］. arXiv preprint： 2004.10934， 2020. |
| 30 | HU X L， LIU Y， ZHAO Z X， et al. Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network［J］. Computers and Electronics in Agriculture， 2021， 185： 106135. |
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