Summary of On-orbit Servicing For Spacecraft Collision Avoidance with Autonomous Decision Making, by Susmitha Patnala et al.
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making
by Susmitha Patnala, Adam Abdin
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI-based implementation of autonomous On-Orbit Servicing (OOS) mission aims to assist with spacecraft collision avoidance maneuvers (CAMs). The proposed servicer is trained using Reinforcement Learning (RL) to autonomously detect potential collisions between target satellites 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. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a system that helps keep satellites safe from collisions in space using artificial intelligence. The AI learns to spot potential dangers, move towards satellites in trouble, and make the best plan to avoid crashes. So far, it’s been able to work with just one servicer spacecraft and one satellite, but there are still many challenges to overcome before this system can be used for real. |
Keywords
» Artificial intelligence » Reinforcement learning
