Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
2026-08-10 • Machine Learning
Machine LearningRobotics
AI summaryⓘ
The authors developed a smart system that helps satellites avoid collisions in busy orbits around Earth, especially where many new satellites are being launched. They used a type of machine learning called reinforcement learning to train their system to make good avoidance decisions, testing it in a realistic simulator they created. Their system avoided collisions 97.5% of the time, doing much better than traditional methods. The authors also made their simulator and results publicly available for others to use and improve.
Collision avoidanceLow-Earth Orbit (LEO)Geosynchronous Equatorial Orbit (GEO)Reinforcement learningProximal Policy Optimization (PPO)Astrodynamics simulatorTwo-body dynamicsDelta-vOrbital debrisCurriculum learning
Authors
Logan Luna, Juan Ortiz Couder, Raul Alejandro Vargas-Acosta
Abstract
Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO). However, these events have been growing in frequency as orbital congestion worsens with the launch of megaconstellations. Consequently, conjunction alerts and collision risks are becoming increasingly common. Current practices, which are commonly manual or rule-based, have difficulty scaling to these worsening dynamic environments. To address this intensifying situation, we propose a reinforcement-learning policy for autonomous collision avoidance, trained via Proximal Policy Optimization (PPO) along with an open-source, high-fidelity astrodynamics simulator for training and evaluation. In 1,000 deterministic GEO episodes, our agent achieves a 97.5% collision avoidance success rate, outperforming traditional controllers such as a rule-based baseline (20.7% success) and an impulsive delta-v planner baseline (27.5% success). To achieve these results, we designed a simulator to train and evaluate our agent, using real-world and simulated debris. We simulate Newtonian two-body dynamics using Sun/Moon third-body perturbations, fuel-dependent thrust, and configurable debris fields. The agent is trained with curriculum learning and shaped rewards oriented toward encouraging survival, adequate projected miss distance, and delta-v conservation. Finally, our evaluation consisted of a fully deterministic pipeline, including shared seeds, per-episode logs, and telemetry exports. Our work is a publicly available framework at https://purl.org/sat-trajectory-avoidance