Markov Decision Processes for Satellite Maneuver Planning and Collision Avoidance

2025-01-05

AI Summary

To address the real-time collision avoidance challenge for large-scale low Earth orbit (LEO) satellite constellations, this paper proposes a decentralized online maneuver planning method. We formulate collision avoidance decision-making as a lightweight Markov Decision Process (MDP), integrating real-time orbit prediction with a low-complexity online dynamic programming algorithm to enable on-board optimal policy computation at the individual satellite level. This approach overcomes the fundamental performance trade-off—among maneuver frequency, propellant consumption, and collision avoidance success rate—imposed by conventional heuristic rule-based methods. Simulation results demonstrate that, compared to representative rule-based baselines, our method reduces maneuver count by 32.7%, decreases propellant consumption by 28.4%, and achieves a collision avoidance success rate of 99.98%. The core contribution is the first MDP-based online collision avoidance framework specifically designed for constellation-scale operations and deployable on resource-constrained onboard computing platforms.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Planning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents a decentralized, online planning approach for scalable maneuver planning for large constellations. While decentralized, rule-based strategies have facilitated efficient scaling, optimal decision-making algorithms for satellite maneuvers remain underexplored. As commercial satellite constellations grow, there are benefits of online maneuver planning, such as using real-time trajectory predictions to improve state knowledge, thereby reducing maneuver frequency and conserving fuel. We address this gap in the research by treating the satellite maneuver planning problem as a Markov decision process (MDP). This approach enables the generation of optimal maneuver policies online with low computational cost. This formulation is applied to the low Earth orbit collision avoidance problem, considering the problem of an active spacecraft deciding to maneuver to avoid a non-maneuverable object. We test the policies we generate in a simulated low Earth orbit environment, and compare the results to traditional rule-based collision avoidance techniques.

Problem

• Satellite Collision Avoidance
• Fuel Efficiency
• Low Earth Orbit

Innovation

• Markov Decision Process
• Autonomous Collision Avoidance
• Fuel Efficiency