mlsanityData to 14 Oct 2026

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

arXiv 2409.17125 ↗First posted 25 Sep 2024cs.AIcited by 0

Not on the map. Why?

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.

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

3 of 20 references.

References · 3

  1. 2018Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Spacecited by 4
  2. 2020Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learningcited by 4
  3. 2019Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuverscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance
  2. 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
  3. 2026Transformer-Based Reinforcement Learning for Autonomous Orbital Collision Avoidance in Partially Observable Environments
  4. 2025OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning
  5. 2026Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
  6. 2025A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance
  7. 2024Neural-based Control for CubeSat Docking Maneuvers
  8. 2024AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions