Collision Avoidance and Return Manoeuvre Optimisation for Low-Thrust Satellites Using Reinforcement Learning

Alexandru Solomon, Ciprian Paduraru

2025

Abstract

Collision avoidance is an essential aspect of day-to-day satellite operations, enabling operators to carry out their missions safely despite the rapidly growing amount of space debris. This paper presents the capabilities of reinforcement learning (RL) approaches to train an agent capable of collision avoidance manoeuvres for low-thrust satellites in low-Earth orbit. The collision avoidance process performed by the agent consists of optimizing a collision avoidance manoeuvre as well as the return manoeuvre to the original orbit. The focus is on satellites with low thrust propulsion systems, since the optimization process of a manoeuvre performed by such a system is more complex than for an impulsive system and therefore more interesting to be solved by RL methods. The training process is performed in a simulated environment of space conditions for a generic satellite in LEO subjected to a collision from different directions and with different velocities. This paper presents the results of agents trained with RL in training scenarios as well as in previously unknown situations using different methods such as DQN, REINFORCE, and PPO.

Paper Citation

in Harvard Style

Solomon A. and Paduraru C. (2025). Collision Avoidance and Return Manoeuvre Optimisation for Low-Thrust Satellites Using Reinforcement Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1009-1016. DOI: 10.5220/0013249000003890

in Bibtex Style

@conference{icaart25,
author={Alexandru Solomon and Ciprian Paduraru},
title={Collision Avoidance and Return Manoeuvre Optimisation for Low-Thrust Satellites Using Reinforcement Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1009-1016},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013249000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Collision Avoidance and Return Manoeuvre Optimisation for Low-Thrust Satellites Using Reinforcement Learning
SN - 978-989-758-737-5
AU - Solomon A.
AU - Paduraru C.
PY - 2025
SP - 1009
EP - 1016
DO - 10.5220/0013249000003890
PB - SciTePress