mlsanityData to 14 Oct 2026

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

arXiv 2602.17685 ↗First posted 4 Feb 2026cs.LG cs.RO physics.space-phcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 27 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

6 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous25 Sep

Other references · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 236
  3. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  4. 2019A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealingcited by 3
  5. 2024Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspectioncited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal
  2. 2026Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance
  3. 2024AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions
  4. 2026Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
  5. 2025Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning
  6. 2026Phase-Gated Constrained Reinforcement Learning for End-to-End Capture and Detumbling of Non-Cooperative Targets
  7. 2024Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris
  8. 2024Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback