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
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.
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.
Fig.3Docking guidance hardware system of modular flying vehicle
Fig.4Target detection point density clustering method based on sliding window
Fig.5Comparison of image before and after enhancement
Fig.6Multi-sensor data fusion based on Kalman filter
Fig.7Analysis of visual identification code location error at different sampling points
Fig.8Correspondence between engineering application platform and modular flying vehicle
Fig.9Target 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.1Target detection results of different algorithms of medium range in two scenarios
Fig.10Position error in horizontal direction of docking guidance under different roll angles
Fig.11Comparison of related parameters after fusion of modified uncertainty matrix and initial uncertainty matrix
Fig.123D 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.2Simulation test results of multi-sensor data fusion
Fig.13Autonomous guidance landing process in up-docking test
Fig.14Change of relative pose deviation in up-docking test
Fig.15Autonomous guidance landing process in lower-docking test
Fig.16Change of relative pose deviation of lower-docking test
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