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

TL;DR

This paper presents a deep reinforcement learning approach for multi-debris removal in Low Earth Orbit, demonstrating superior efficiency and speed compared to traditional algorithms through extensive simulation testing.

Contribution

It introduces a novel coelliptic maneuver framework combined with deep RL for optimized debris removal planning in LEO.

Findings

Masked PPO outperforms Greedy and MCTS in debris removal efficiency.

Deep RL achieves up to twice the debris visits of heuristic methods.

The approach offers scalable and resource-efficient space mission planning.

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.