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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 wordsmodular flying vehicle      autonomous docking guidance      monocular vision      RTK-GPS      multisensor data fusion     
Received: 04 March 2023      Published: 27 December 2023
CLC:  V 27  
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.

URL:

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
算法 场景1 场景2
FNR/% FPR/% ACC/% v/(帧·s?1 FNR/% FPR/% ACC/% v/(帧·s?1
Camshift 98.6 0 1.4 263 92.3 0 7.7 290
KCF 13.5 0 86.5 86 68.4 0 31.6 78
YOLOv4-tiny 27.2 29.2 43.6 129 44.2 14.9 40.9 132
本研究 0 8.3 91.7 74 0 6.7 93.3 65
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
不确定度矩阵类型 ${\overline X_{\rm{e}}}/{\rm{cm}} $ ${\overline Z_{\rm{e}}}/{\rm{cm}} $ ${\overline \theta _{\rm{e}}} $/(°)
初始 9.5667 15.9694 2.2724
修正 6.8554 11.3931 2.2724
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
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