A Deep Reinforcement Learning Approach to Train Autonomous Space Debris Remediation Spacecraft
Channel: COSPAR2021 Workshops
Date: 2021-02-02

Author: Gabriel Sutherland

Abstract: This project studied whether a deep reinforcement learning (DRL) approach could be used to train an AI-enabled spacecraft with the goal of autonomously intercepting and deorbiting space debris in near Earth orbit. The proposed satellite employs a mesh net for debris capture, allowing for a relatively low mass while retaining a high degree of kinetic resilience. The satellite is able to alter its orbit through an experimental form of electromagnetic propulsion. The net portion of the satellite, which is composed of an Electroactive Polymer and carbon-nanofiber composite, was designed to have a high degree of kinetic resilience while still maintaining flexibility, allowing for a versatile capture system for space debris. The ASTOS orbital dynamics simulator was used as a training environment for a Deep Reinforcement Learning model. The agent was trained to intercept target debris while avoiding active satellites. In the virtual environment a satellite with a 1km diameter net could neutralize multiple space debris and deorbit itself, breaking apart upon re-entry. Results indicate this system could be viable as a space-debris capture and neutralization mechanism. Currently, research is being done to determine radiation resistance of the DRL algorithm, as well as investigating repair of radiation damage to data through a neuromorphic software approach. Future studies will investigate the effectiveness of a fully autonomous DRL flight management system for effecting orbit transfers for space debris intercepts, and determining resistance to radiation damage long-term.