AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions
This addresses the challenge of economically viable and technically effective debris removal for space sustainability, though it appears incremental as it applies existing DRL methods to a specific domain.
The paper tackled the problem of planning Active Debris Removal missions in Low Earth Orbit by developing a Deep Reinforcement Learning model for autonomous scheduling, resulting in the agent finding optimal mission plans and learning to update plans to handle high-risk debris.
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.