mlsanityData to 14 Oct 2026

Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection

arXiv 2502.19556 ↗First posted 26 Feb 2025cs.RO cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 29 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

3 of 29 references.

References · 3

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2023Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illuminationcited by 8
  3. 2023Autonomous Local Catalog Maintenance of Close Proximity Satellite Systems on Closed Natural Motion Trajectoriescited by 1

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
  2. 2024Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles
  3. 2026Simulation Based Reward Function Validation for Multi-Agent On Orbit Inspection ↗
  4. 2024Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection
  5. 2025Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems
  6. 2025Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbed
  7. 2024Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection
  8. 2025Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study