Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection
Journal:
arXiv
Published Date:
Feb 26, 2025
Abstract
This paper addresses the problem of satellite inspection, where one or more
satellites (inspectors) are tasked with imaging or inspecting a resident space
object (RSO) due to potential malfunctions or anomalies. Inspection strategies
are often reduced to a discretized action space with predefined waypoints,
facilitating tractability in both classical optimization and machine learning
based approaches. However, this discretization can lead to suboptimal guidance
in certain scenarios. This study presents a comparative simulation to explore
the tradeoffs of passive versus active strategies in multi-agent missions. Key
factors considered include RSO dynamic mode, state uncertainty, unmodeled
entrance criteria, and inspector motion types. The evaluation is conducted with
a focus on fuel utilization and surface coverage. Building on a Monte-Carlo
based evaluator of passive strategies and a reinforcement learning framework
for training active inspection policies, this study investigates conditions
under which passive strategies, such as Natural Motion Circumnavigation (NMC),
may perform comparably to active strategies like Reinforcement Learning based
waypoint transfers.
