Summary of Revisiting Space Mission Planning: a Reinforcement Learning-guided Approach For Multi-debris Rendezvous, by Agni Bandyopadhyay and Guenther Waxenegger-wilfing
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous
by Agni Bandyopadhyay, Guenther Waxenegger-Wilfing
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 The paper introduces a novel application of a masked Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning to determine the most efficient sequence of space debris visitation, using the Lambert solver as per Izzo’s adaptation for individual rendezvous. The goal is to optimize the sequence to minimize total time for rendezvous in the entire mission. A neural network policy is developed and trained on simulated space missions with varying debris fields. After training, the neural network calculates approximately optimal paths using Izzo’s adaptation of Lambert maneuvers. Performance is evaluated against standard heuristics in mission planning. The reinforcement learning approach demonstrates a significant improvement in planning efficiency by optimizing the sequence for debris rendezvous, reducing total mission time by an average of approximately 10.96% and 13.66% compared to Genetic and Greedy algorithms, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a special kind of machine learning called deep reinforcement learning to help plan the best way to clean up space debris. Right now, it’s like trying different paths to get to each piece of debris, but this new approach can find the fastest route overall! It’s trained on pretend missions with different amounts of debris and then tries out its ideas. The results show that this method is much better at planning than usual ways of doing things, saving around 10-14% of time in total. |
Keywords
* Artificial intelligence * Machine learning * Neural network * Optimization * Reinforcement learning
