Abstract
Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters.
Highlights
The space community has recently paid a growing attention to the sustainability of space activities threatened by the presence of space debris, especially in the most crowded orbital regions, such as Low Earth Orbit and Geostationary Earth Orbit [1], [2]
It is worth mentioning that the relative navigation task in the context of Active Debris Removal (ADR) and On-Orbit Servicing (OOS) missions are expected to be entrusted to redundant sensor configurations including both Light Detection and Ranging system (LIDAR) and cameras, like in the case of the recent RemoveDebris mission [11] and of the Restore-L mission foreseen for launch in 2023 [12]
LITERATURE SURVEY Most research efforts regarding the challenges of relative navigation for a chaser maneuvering in close-proximity of VOLUME 7, 2019 an uncooperative spacecraft have been dedicated to the pose determination task, which only addresses the estimation of relative position and attitude parameters [9], [14]
Summary
The space community has recently paid a growing attention to the sustainability of space activities threatened by the presence of space debris, especially in the most crowded orbital regions, such as Low Earth Orbit and Geostationary Earth Orbit [1], [2]. Despite the challenges related to their higher hardware complexity, and required power and mass allocation, LIDARs can provide direct 3D measurements about the scene in the form of 3D point clouds, unlike passive monocular cameras, at farther operative range than passive stereovision system [10] They are less sensitive than passive sensors to the high-variability of illumination conditions typical of the space environment [10]. It is worth mentioning that the relative navigation task in the context of ADR and OOS missions are expected to be entrusted to redundant sensor configurations including both LIDARs and cameras, like in the case of the recent RemoveDebris mission [11] and of the Restore-L mission foreseen for launch in 2023 [12] In this framework, this paper proposes an innovative relative navigation architecture conceived to estimate with high accuracy the target-chaser relative position, velocity, attitude and angular velocity by processing raw LIDAR measurements.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
