Markov Decision Processes for Satellite Maneuver Planning and Collision Avoidance

TL;DR

This paper introduces a Markov decision process framework for online satellite maneuver planning to enhance collision avoidance, demonstrating improved decision-making efficiency over traditional rule-based methods in simulated low Earth orbit environments.

Contribution

It is the first to formulate satellite maneuver planning as an MDP for online optimal decision-making, enabling scalable and low-cost collision avoidance strategies.

Findings

MDP-based policies outperform rule-based methods in simulations

Reduced maneuver frequency and fuel consumption

Effective collision avoidance in low Earth orbit scenarios

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.