Design and verification of autonomous docking guidance system for modular flying vehicle

doi:10.3785/j.issn.1008-973X.2023.12.001

Journal of ZheJiang University (Engineering Science)

2023, Vol. 57

Issue (12): 2345-2355 DOI: 10.3785/j.issn.1008-973X.2023.12.001

Design and verification of autonomous docking guidance system for modular flying vehicle

Chen WANG1(

),Wei LIN2,Liang-peng HU1,Jun-ming ZHANG1

1. School of Construction Machinery, Chang’an University, Xi’an 710064, China
2. Technology Center, NORINCO Group Test and Measuring Academy, Xi’an 710043, China

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Abstract

The process architecture, software and hardware systems, core algorithms, and the validation of the autonomous docking guidance system for a modular flying vehicle were investigated. The remote, medium range, and short range multi segment fusion guidance was adopted based on the transition of guidance methods. The point density clustering algorithm and the kernel correlation filter algorithm were used to provide smooth fusion information in response to the false detections and missed detections in the actual use of YOLOv4-tiny. A correction factor method was proposed to achieve fusion correction of AprilTag measurement data in the short range guidance stage, and the pose compensation algorithm was used to solve the camera pose problem of fixed connection between the camera and the drone. The dark light image enhancement algorithm was introduced and combined with the visual guidance algorithm to meet the docking requirements in low-light environment. A simulation platform and an engineering application platform were built, and the process, the system architecture and the algorithms were verified step by step. Experimental results showed that the engineering application flight platform could safely, stably and accurately guide the landing into a conical docking mechanism with an allowable error of only 6 cm and an angle error of 5°. The results prove that the developed autonomous docking technology has good accuracy and reliability.

Key words： modular flying vehicle autonomous docking guidance monocular vision RTK-GPS multisensor data fusion

Received: 04 March 2023 Published: 27 December 2023

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Cite this article:

Chen WANG,Wei LIN,Liang-peng HU,Jun-ming ZHANG. Design and verification of autonomous docking guidance system for modular flying vehicle. Journal of ZheJiang University (Engineering Science), 2023, 57(12): 2345-2355.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.12.001 OR https://www.zjujournals.com/eng/Y2023/V57/I12/2345

分体式飞行汽车全自主对接导引系统设计与验证

研究针对分体式飞行汽车全自主对接导引系统的流程架构、软硬件系统、核心算法以及验证. 根据导引方式的过渡，采用远程、中程、近程多段融合导引. 针对YOLOv4-tiny实际使用中的误检、漏检情况，使用点密度聚类和核相关滤波算法提供光顺的融合信息. 提出修正因子方法以实现近程导引阶段AprilTag测量数据的融合修正，通过姿态补偿算法解决相机与无人机固连的相机姿态问题. 引入暗光图像增强算法，将引入算法与视觉导引算法结合，以满足低照度环境下的对接导引需求. 搭建仿真平台和工程应用平台，逐步对发展的流程、系统架构以及算法进行验证. 试验结果表明，工程应用飞行平台可以导引安全、平稳且精准的降落任务，在圆锥形对接机构中的容许误差为6 cm、角度误差为5°. 该结果证明提出的全自主对接导引技术精度良好且具有可靠性.

关键词： 分体式飞行汽车, 全自主对接导引, 单目视觉, RTK-GPS, 多传感器数据融合

Fig.1 Composition of modular flying vehicle

Fig.2 Flight module guidance landing process

Fig.3 Docking guidance hardware system of modular flying vehicle

Fig.4 Target detection point density clustering method based on sliding window

Fig.5 Comparison of image before and after enhancement

Fig.6 Multi-sensor data fusion based on Kalman filter

Fig.7 Analysis of visual identification code location error at different sampling points

Fig.8 Correspondence between engineering application platform and modular flying vehicle

Fig.9 Target correction process of error detection frame

Tab.1 Target detection results of different algorithms of medium range in two scenarios

Fig.10 Position error in horizontal direction of docking guidance under different roll angles

Fig.11 Comparison of related parameters after fusion of modified uncertainty matrix and initial uncertainty matrix

Fig.12 3D flight path after fusion of modified uncertainty matrix and initial uncertainty matrix

Tab.2 Simulation test results of multi-sensor data fusion

Fig.13 Autonomous guidance landing process in up-docking test

Fig.14 Change of relative pose deviation in up-docking test

Fig.15 Autonomous guidance landing process in lower-docking test

Fig.16 Change of relative pose deviation of lower-docking test


[1]	PAN G, ALOUINI M S Flying car transportation system: Advances, techniques, and challenges[J]. IEEE Access, 2021, 9: 24586- 24603 doi: 10.1109/ACCESS.2021.3056798

[2]	SASAKI T, HIDAKA M, TOMITA Y, et al. Proximity operation and automated docking on HTV-X: guidance, navigation, and control strategy [C]// 2022 IEEE Aerospace Conference. Big Sky: IEEE, 2022: 1-10.

[3]	VOLPE R, CIRCI C Optical-aided, autonomous and optimal space rendezvous with a non-cooperative target[J]. Acta Astronautica, 2019, 157: 528- 540 doi: 10.1016/j.actaastro.2019.01.020

[4]	MCMAHON J W, GEHLY S, AXELRAD P. Enhancing relative attitude and trajectory estimation for autonomous rendezvous using flash LIDAR [C]// AIAA/AAS Astrodynamics Specialist Conference. San Diego: [s.n.], 2014: 4359.

[5]	SORNSIN B A, SHORT B W, BOURBEAU T N, et al. Global shutter solid state flash lidar for spacecraft navigation and docking applications [C]// Laser Radar Technology and Applications XXIV. [S.l.]: SPIE, 2019.

[6]	SHI K, LIU C, BIGGS J D, et al Observer-based control for spacecraft electromagnetic docking[J]. Aerospace Science and Technology, 2020, 99: 105759 doi: 10.1016/j.ast.2020.105759

[7]	YAZDANI A M, SAMMUT K, YAKIMENKO O, et al A survey of underwater docking guidance systems[J]. Robotics and Autonomous Systems, 2020, 124: 103382 doi: 10.1016/j.robot.2019.103382

[8]	WANG T, ZHAO Q, YANG C Visual navigation and docking for a planar type AUV docking and charging system[J]. Ocean Engineering, 2021, 224: 108744 doi: 10.1016/j.oceaneng.2021.108744

[9]	VANDAVASI B N J, ARUNACHALAM U, NARAYANASWAMY V, et al Concept and testing of an electromagnetic homing guidance system for autonomous underwater vehicles[J]. Applied Ocean Research, 2018, 73: 149- 159 doi: 10.1016/j.apor.2018.02.003

[10]	PALOMERAS N, VALLICROSA G, MALLIOS A, et al AUV homing and docking for remote operations[J]. Ocean Engineering, 2018, 154: 106- 120 doi: 10.1016/j.oceaneng.2018.01.114

[11]	MATIAS B, OLIVEIRA H, ALMEIDA J, et al. High-accuracy low-cost RTK-GPS for an unmanned surface vehicle [C]// Oceans 2015-Genova. Genova: IEEE, 2015.

[12]	HENKEL P, SPERL A. Real-time kinematic positioning for unmanned air vehicles [C]// 2016 IEEE Aerospace Conference. Big Sky: IEEE, 2016: 1-7

[13]	KANG Y, PARK B J, CHO A, et al A precision landing test on motion platform and shipboard of a tilt-rotor UAV based on RTK-GNSS[J]. International Journal of Aeronautical and Space Sciences, 2018, 19 (4): 994- 1005 doi: 10.1007/s42405-018-0081-8

[14]	CUI T, GUO C, LIU Y, et al. Precise landing control of UAV based on binocular visual SLAM [C]// 2021 4th International Conference on Intelligent Autonomous Systems. Wuhan: IEEE, 2021: 312-317.

[15]	CHEN C, CHEN S, HU G, et al An auto-landing strategy based on pan-tilt based visual servoing for unmanned aerial vehicle in GNSS-denied environments[J]. Aerospace Science and Technology, 2021, 116: 106891 doi: 10.1016/j.ast.2021.106891

[16]	席志鹏, 楼卓, 李晓霞, 等集中式光伏电站巡检无人机视觉定位与导航[J]. 浙江大学学报: 工学版, 2019, 53 (5): 880- 888 XI Zhi-peng, LOU Zhuo, LI Xiao-xia, et al Vision-based localization and navigation for UAV inspection in photovoltaic farms[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (5): 880- 888

[17]	苏贇, 王挺, 姚辰, 等基于合作目标的无人机目标跟踪方法[J]. 机器人, 2019, 41 (4): 425- 432 SU Yun, WANG Ting, YAO Chen, et al A target tracking method of UAV based on cooperative target[J]. Robot, 2019, 41 (4): 425- 432 doi: 10.13973/j.cnki.robot.180479

[18]	张咪, 赵勇, 布树辉, 等基于阶层标识的无人机自主精准降落系统[J]. 航空学报, 2018, 39 (10): 322150 ZHANG Mi, ZHAO Yong, BU Shu-hui, et al Mutil-level marker based autonomous landing system for UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39 (10): 322150 doi: 10.7527/S1000-6893.2018.22150

[19]	ARORA S, JAIN S, SCHERER S, et al. Infrastructure-free shipdeck tracking for autonomous landing [C]// 2013 IEEE International Conference on Robotics and Automation. Karlsruhe: IEEE, 2013: 323-330.

[20]	ARIANTE G, PAPA U, PONTE S, et al. UAS for positioning and field mapping using LIDAR and IMU sensors data: Kalman filtering and integration [C]// 2019 IEEE 5th International Workshop on Metrology for AeroSpace. Turin: IEEE, 2019: 522-527.

[21]	XU Y, SHMALIY Y S, AHN C K, et al Tightly coupled integration of INS and UWB using fixed-lag extended UFIR smoothing for quadrotor localization[J]. IEEE Internet of Things Journal, 2020, 8 (3): 1716- 1727

[22]	CHENG C, LI X, XIE L, et al Autonomous dynamic docking of UAV based on UWB-vision in GPS-denied environment[J]. Journal of the Franklin Institute, 2022, 359 (7): 2788- 2809 doi: 10.1016/j.jfranklin.2022.03.005

[23]	MARCON P, JANOUSEK J, KADLEC R. Vision-based and differential global positioning system to ensure precise autonomous landing of UAVs [C]// 2018 Progress in Electromagnetics Research Symposium. Wuhan: IEEE, 2018: 542-546.

[24]	KROGIUS M, HAGGENMILLER A, OLSON E. Flexible layouts for fiducial tags [C]// 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems. [S.l.]: IEEE, 2019: 1898-1903.

[25]	KIM J, WOO S, KIM K. Lidar-guided autonomous landing of an aerial vehicle on a ground vehicle [C]// 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence. Jeju: IEEE, 2017: 228-231.

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