# On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making

arXiv: https://arxiv.org/abs/2409.17125 · HTML: https://mlsanity.com/paper/2409.17125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space](https://mlsanity.com/paper/1803.09203/) (2018)
- [Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning](https://mlsanity.com/paper/2008.03215/) (2020)
- [Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers](https://mlsanity.com/paper/1902.02095/) (2019)

## Abstract (arXiv)

This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM. The RL model integrates collision risk estimates, satellite specifications, and debris data to generate an optimal maneuver matrix for OOS rendezvous and collision prevention. We employ the Cross-Entropy algorithm to find optimal decision policies efficiently. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. However, merging spacecraft rendezvous and optimal CAM presents significant complexities. We discuss design challenges and critical parameters for the successful implementation of the framework presented through a case study.