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

arXiv: https://arxiv.org/abs/2409.17012 · HTML: https://mlsanity.com/paper/2409.17012/

## 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.

## Builds on (most cited first, 1 of 1)

- [Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable](https://mlsanity.com/paper/2107.07066/) (2021)

## 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.