mlsanityData to 14 Oct 2026

AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions

arXiv 2409.17012 ↗First posted 25 Sep 2024cs.AIcited by 0

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Abstract (arXiv)

The proliferation of debris in Low Earth Orbit (LEO) represents a significant threat to space sustainability and spacecraft safety. Active Debris Removal (ADR) has emerged as a promising approach to address this issue, utilising Orbital Transfer Vehicles (OTVs) to facilitate debris deorbiting, thereby reducing future collision risks. However, ADR missions are substantially complex, necessitating accurate planning to make the missions economically viable and technically effective. Moreover, these servicing missions require a high level of autonomous capability to plan under evolving orbital conditions and changing mission requirements. In this paper, an autonomous decision-planning model based on Deep Reinforcement Learning (DRL) is developed to train an OTV to plan optimal debris removal sequencing. It is shown that using the proposed framework, the agent can find optimal mission plans and learn to update the planning autonomously to include risk handling of debris with high collision risk.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 10 references that are in the map. First posted on arXiv 25 Sep 2024.

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1 of 10 references.

References · 1

  1. 2021Deep Reinforcement Learning based Dynamic Optimization of Bus Timetablecited by 4

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  1. 2026Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling
  2. 2026Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance
  3. 2026Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal
  4. 2024Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous
  5. 2025Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning
  6. 2026Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
  7. 2024On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making
  8. 2024Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris