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基于多孔特征约束的舱段对接姿态识别方法

Cabin Docking Attitude Recognition Method Based on Porous Feature Constraint
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摘要 为实现航天器舱段自动对接中多装配孔与舱段轴线同时匹配协调,提出基于多装配孔特征约束的大型舱段对接姿态辨识方法。首先通过双目视觉测量系统采集舱段对接端面上的装配孔图像,经去噪、边缘检测、弧段匹配、拟合椭圆和去重等一系列图像处理后再经基于Topsis椭圆评估去伪,获得装配孔的二维信息。然后,建立双目椭圆锥面模型,解算装配孔的姿态方向;再以多装配孔姿态匹配最优为目标,基于投影寻踪原理建立以投影向量模长最大为目标函数的最优数学模型,利用遗传算法对模型进行求解,得到舱段对接的最优姿态向量。最后,通过与理论姿态进行比较来验证该方法的有效性,并在实验台对该方法的精度进行测试。试验结果表明,舱段实际偏转姿态角和解算的姿态角相对误差为1.92%,满足舱段姿态测量的需求。 To ensure the simultaneous matching and coordination of multiple assembly holes and module axis in the automatic docking of spacecraft segments,cabin docking attitude recognition method based on the feature constraint of holes is proposed.Firstly,the assembly hole images on the docking end face of the cabin section are collected using a binocular vision measurement system.After a series of image processing such as denoising,edge detection,arc matching,fitting ellipses,and deduplication,the assembly hole is evaluated based on Topsis ellipses to obtain two-dimensional information.Then,a binocular elliptical cone model is established to solve the attitude direction of holes.Afterwards taking the optimal attitude matching of holes as the goal,the optimal mathematical model of the overall attitude estimation of the cabin was established to maximize projection vector modulus-length sum,which was made of the common projection vector in the attitude direction of these holes.The model was solved by genetic algorithm(GA)method,and the optimal attitude direction of large cabin was obtained.The simulation case showed that the effectiveness of the proposed method was verified by comparing with the ideal attitude and the accuracy of the method was tested on the experimental bench.The relative error of the actual deflection attitude angle of the module and the calculated attitude angle is 1.92%,which meets the requirements of the attitude estimation of large cabin.
作者 谭宏泽 李志杰 季薇 于博 王怀明 TAN Hongze;LI Zhijie;JI Wei;YU Bo;WANG Huaiming(North China Institute of Aerospace Engineering,Langfang 065000,China;Yanching Institute of Technology,Langfang 065201,China)
出处 《航空制造技术》 CSCD 北大核心 2023年第22期78-86,共9页 Aeronautical Manufacturing Technology
基金 河北省自然科学基金(E2021409029)。
关键词 大型舱段 椭圆检测 姿态拟合 投影寻踪 遗传算法(GA) Large-scale cabin Ellipse detection Attitude solution Projection pursuit Genetic algorithm(GA)
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