As space debris has become a cause of concern for space operations around Earth, active debris removal and satellite servicing missions have gained increasing attention. Within this framework, in specific scenarios, the chaser might be asked to operate autonomously in the vicinity of a non-cooperative, unknown target. This paper presents a sampling-based receding-horizon motion planning algorithm that selects inspection maneuvers while taking many complex constraints into account. The proposed guidance solution is compared with classical approaches and it is shown to take advantage of the characteristics of the natural dynamics of the relative motion to outperform them. In addition, the impact of different input sampling exploration strategies is explored to propose a simple and more robust approach based on subset simulation.
Guidance Strategy for Autonomous Inspection of Unknown Non-Cooperative Resident Space Objects
Maestrini, Michele;Di Lizia, Pierluigi
2022-01-01
Abstract
As space debris has become a cause of concern for space operations around Earth, active debris removal and satellite servicing missions have gained increasing attention. Within this framework, in specific scenarios, the chaser might be asked to operate autonomously in the vicinity of a non-cooperative, unknown target. This paper presents a sampling-based receding-horizon motion planning algorithm that selects inspection maneuvers while taking many complex constraints into account. The proposed guidance solution is compared with classical approaches and it is shown to take advantage of the characteristics of the natural dynamics of the relative motion to outperform them. In addition, the impact of different input sampling exploration strategies is explored to propose a simple and more robust approach based on subset simulation.File | Dimensione | Formato | |
---|---|---|---|
MAESM_OA_02-21.pdf
Open Access dal 03/12/2021
Descrizione: Paper Open Access
:
Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione
6.87 MB
Formato
Adobe PDF
|
6.87 MB | Adobe PDF | Visualizza/Apri |
MAESM02-22.pdf
Accesso riservato
Descrizione: Paper
:
Publisher’s version
Dimensione
1.57 MB
Formato
Adobe PDF
|
1.57 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.