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

TL;DR

This paper presents a deep reinforcement learning framework for autonomous, risk-aware scheduling of active debris removal missions in space, improving planning efficiency and safety in complex orbital environments.

Contribution

It introduces a novel DRL-based decision planning model for autonomous debris removal, incorporating risk management for collision-prone debris.

Findings

The model successfully finds optimal debris removal sequences.

The system autonomously updates plans considering collision risks.

Demonstrates improved safety and efficiency in debris removal missions.

Abstract

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.