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Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling

arXiv:2602.17685v1 Announce Type: new Abstract: This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling logic. We benchmark three distinct planning algorithms Greedy heuristic, Monte Carlo Tree Search (MCTS), and deep reinforcement learning (RL) using Masked Proximal Policy Optimization (PPO) within a realistic orbital simulation environment featuring randomized debris fields, keep out zones, and delta V constraints. Experimental results over 100 test scenarios demonstrate that Masked PPO achieves superior mission efficiency and computational performance, visiting up to twice as many debris as Greedy and significantly outperforming MCTS in runtime. These findings underscore the promise of modern RL methods for scalable, safe, and resource efficient space mission planning, paving the way for f

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Agni Bandyopadhyay, Gunther Waxenegger-Wilfing · · 1 min read · 4 views
#cs.LG #cs.RO #physics.space-ph

arXiv:2602.17685v1 Announce Type: new Abstract: This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling logic. We benchmark three distinct planning algorithms Greedy heuristic, Monte Carlo Tree Search (MCTS), and deep reinforcement learning (RL) using Masked Proximal Policy Optimization (PPO) within a realistic orbital simulation environment featuring randomized debris fields, keep out zones, and delta V constraints. Experimental results over 100 test scenarios demonstrate that Masked PPO achieves superior mission efficiency and computational performance, visiting up to twice as many debris as Greedy and significantly outperforming MCTS in runtime. These findings underscore the promise of modern RL methods for scalable, safe, and resource efficient space mission planning, paving the way for future advancements in ADR autonomy.

Executive Summary

This article presents a novel approach to multi-debris mission planning in Low Earth Orbit (LEO) using a deep reinforcement learning (RL) framework. The authors introduce a unified co-elliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling logic. They benchmark three planning algorithms: Greedy heuristic, Monte Carlo Tree Search (MCTS), and Masked Proximal Policy Optimization (PPO). The results show that Masked PPO outperforms the other two algorithms in terms of mission efficiency and computational performance, visiting up to twice as many debris as the Greedy heuristic and significantly outperforming MCTS in runtime. This study highlights the potential of modern RL methods for scalable, safe, and resource-efficient space mission planning, paving the way for future advancements in active debris removal (ADR) autonomy.

Key Points

  • ▸ Introduction of a unified co-elliptic maneuver framework for multi-debris mission planning in LEO
  • ▸ Benchmarking of three planning algorithms: Greedy heuristic, MCTS, and Masked PPO
  • ▸ Demonstration of Masked PPO's superior performance in terms of mission efficiency and computational performance

Merits

Strength in RL framework

The authors' use of a deep RL framework, specifically Masked PPO, demonstrates the potential for scalable, safe, and resource-efficient space mission planning.

Realistic orbital simulation environment

The use of a realistic orbital simulation environment featuring randomized debris fields, keep out zones, and delta V constraints adds to the study's credibility and generalizability.

Clear presentation of results

The authors present their results in a clear and concise manner, making it easy for readers to understand the implications of their findings.

Demerits

Limited scope of study

The study only benchmarks three planning algorithms and focuses on a specific scenario, which may limit the generalizability of the results.

Assumptions about orbital environment

The study assumes a specific orbital environment, which may not reflect real-world conditions, and does not account for potential uncertainties and complexities.

Lack of discussion on safety and risk

The study focuses on efficiency and performance, but does not discuss potential safety and risk implications of deploying autonomous ADR systems in LEO.

Expert Commentary

The study presents a significant contribution to the field of space mission planning, particularly in the context of active debris removal (ADR) autonomy. The use of a deep reinforcement learning framework and a realistic orbital simulation environment demonstrates the potential for scalable, safe, and resource-efficient space mission planning. However, the study's limited scope and assumptions about the orbital environment may limit its generalizability. Furthermore, the lack of discussion on safety and risk implications of deploying autonomous ADR systems in LEO is a concern. Nevertheless, the study's findings have significant implications for the development of autonomous ADR systems and the potential for RL methods in space applications.

Recommendations

  • ✓ Future studies should investigate the use of other planning algorithms and scenarios to broaden the scope of the study and increase generalizability.
  • ✓ The authors should discuss potential safety and risk implications of deploying autonomous ADR systems in LEO and explore ways to mitigate these risks.

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