Autonomous relative navigation around uncooperative spacecraft based on a single camera

被引:61
作者
Pesce, Vincenzo [1 ]
Opromolla, Roberto [2 ]
Sarno, Salvatore [3 ]
Lavagna, Michele [1 ]
Grassi, Michele [2 ]
机构
[1] Politecn Milan, Dept Aerosp Sci & Technol, Via Masa 34, Milan, Italy
[2] Univ Naples Federico II, Dept Ind Engn DII, Piazzale Tecchio 80, Naples, Italy
[3] Univ Campania L Vanvitelli, Dept Engn, Via Roma 29, Aversa, Italy
关键词
ATTITUDE ESTIMATION; POSE; DESIGN; ALGORITHM; ACCURATE; SYSTEM; MOTION;
D O I
10.1016/j.ast.2018.11.042
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The interest of the space community toward missions like On-Orbit Servicing of functional satellite to extend their operative life, or Active Debris Removal to reduce the risk of collision among artificial objects in the most crowded orbital belts, is significantly increasing for both economical and safety aspects. These activities present significant technical challenges and, thus, can be enabled only by increasing the level of autonomy and robustness of space systems in terms of guidance, navigation and control functionalities. Clearly this goal requires the design and development of ad-hoc technologies and algorithms. In this framework, this paper presents an original architecture for relative navigation based on a single passive camera able to fully reconstruct the relative state between a chaser spacecraft and a noncooperative, known target. The proposed architecture is loosely coupled, meaning that pose determination and full relative state estimation are entrusted to separate, but rigidly interconnected processing blocks. Innovative aspects are relevant to both the pose determination algorithms and the filtering scheme. Preliminary performance assessment is carried out by means of numerical simulations considering multiple realistic target/chaser relative dynamics and target geometries. Results allow demonstrating robustness against measurement error sources caused possibly by image processing as well as fast rotational dynamics. (C) 2018 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:1070 / 1080
页数:11
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