Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection

TL;DR

This paper compares passive and active satellite inspection strategies through simulations, analyzing their efficiency in terms of fuel use and coverage under various conditions, and explores when passive methods can match active ones.

Contribution

It introduces a simulation framework to evaluate passive and active satellite inspection strategies, highlighting scenarios where passive methods perform comparably to active reinforcement learning approaches.

Findings

Passive strategies can match active methods in certain conditions.

Active reinforcement learning policies improve coverage and efficiency.

Tradeoffs depend on RSO dynamics and state uncertainty.

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.