Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection

2025-01-03

AIAA SCITECH 2025 Forum

AI Summary

This paper addresses autonomous on-orbit inspection of resident space objects (RSOs) by multi-satellite formations, focusing on the trade-off between fuel consumption and surface coverage under passive (natural relative motion) and active (reinforcement learning–driven maneuvering) strategies. We propose a unified framework integrating high-fidelity orbital dynamics modeling, uncertainty propagation analysis, and Monte Carlo evaluation to systematically quantify, for the first time, the effectiveness boundaries of passive inspection under state estimation uncertainty and dynamical model errors. Our analysis reveals that, in typical low-uncertainty, near-circular orbit scenarios, uncontrolled natural motion camouflage (NMC) achieves over 40% fuel savings while maintaining surface coverage above 92%, matching the performance of active control. These results demonstrate the feasibility and superiority of passive inspection under specific operational conditions, establishing a novel paradigm for lightweight, long-duration space surveillance missions.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• satellite inspection strategies
• active vs passive inspection
• fuel utilization and coverage optimization

Innovation

• Monte-Carlo based evaluator
• Reinforcement Learning framework
• Natural Motion Circumnavigation