# Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2310.18966 · HTML: https://mlsanity.com/paper/2310.18966/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 29 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Towards Automated Satellite Conjunction Management with Bayesian Deep Learning](https://mlsanity.com/paper/2012.12450/) (2020)
- [Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers](https://mlsanity.com/paper/1902.02095/) (2019)
- [Analytical framework for space debris collision avoidance maneuver design](https://mlsanity.com/paper/2012.12651/) (2020)

## Built on by (most cited first, 5 of 5)

- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning](https://mlsanity.com/paper/2510.08812/) (2025)
- [An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras](https://mlsanity.com/paper/2506.16436/) (2025)
- [Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance](https://mlsanity.com/paper/2608.09628/) (2026)
- [Chance-Constrained Belief-Space Maneuver Planning for Autonomous Collision Avoidance Under Uncertainty](https://mlsanity.com/paper/2609.13428/) (2026)

## Abstract (arXiv)

The space environment around the Earth is becoming increasingly populated by both active spacecraft and space debris. To avoid potential collision events, significant improvements in Space Situational Awareness (SSA) activities and Collision Avoidance (CA) technologies are allowing the tracking and maneuvering of spacecraft with increasing accuracy and reliability. However, these procedures still largely involve a high level of human intervention to make the necessary decisions. For an increasingly complex space environment, this decision-making strategy is not likely to be sustainable. Therefore, it is important to successfully introduce higher levels of automation for key Space Traffic Management (STM) processes to ensure the level of reliability needed for navigating a large number of spacecraft. These processes range from collision risk detection to the identification of the appropriate action to take and the execution of avoidance maneuvers. This work proposes an implementation of autonomous CA decision-making capabilities on spacecraft based on Reinforcement Learning (RL) techniques. A novel methodology based on a Partially Observable Markov Decision Process (POMDP) framework is developed to train the Artificial Intelligence (AI) system on board the spacecraft, considering epistemic and aleatory uncertainties. The proposed framework considers imperfect monitoring information about the status of the debris in orbit and allows the AI system to effectively learn stochastic policies to perform accurate Collision Avoidance Maneuvers (CAMs). The objective is to successfully delegate the decision-making process for autonomously implementing a CAM to the spacecraft without human intervention. This approach would allow for a faster response in the decision-making process and for highly decentralized operations.