Cascaded deep reinforcement learning-based multi-revolution low-thrust spacecraft orbit-transfer

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Issue Date
2023-08-10
Embargo End Date
Authors
Zaidi, Syed Muhammad Talha
Chadalavada, Pardha Sai
Ullah, Hayat
Munir, Arslan
Dutta, Atri
Advisor
Citation

S. M. T. Zaidi, P. S. Chadalavada, H. Ullah, A. Munir and A. Dutta, "Cascaded Deep Reinforcement Learning-Based Multi-Revolution Low-Thrust Spacecraft Orbit-Transfer," in IEEE Access, vol. 11, pp. 82894-82911, 2023, doi: 10.1109/ACCESS.2023.3301726

Abstract

Transferring an all-electric spacecraft from a launch injection orbit to the geosynchronous equatorial orbit (GEO) using a low thrust propulsion system presents a significant challenge due to the long transfer time typically spanning several months. To address the challenge of determining such long time-scale orbit-raising maneuvers to GEO, this paper presents a novel technique to compute transfers starting from geostationary transfer orbit (GTO) and super-GTO. The transfer is complex, involving multiple eclipses and revolutions. To tackle this challenge, we introduce a cascaded deep reinforcement learning (DRL) model to guide a low-thrust spacecraft towards the desired orbit by determining an appropriate thrust direction at each state. To ensure mission requirements, a gradient-aided reward function incorporating the orbital elements, guides the DRL agent to obtain the optimal flight time. The obtained results demonstrate that our proposed approach yields optimal or near-optimal time-efficient spacecraft orbit-raising. DRL implementation is important for spacecraft autonomy; in this context, we demonstrate that our DRL-based trajectory planning provides significantly better transfer time as compared to state-of-the-art approaches that allow for automated trajectory computation. Author

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Open Access article licensed under a Creative Commons License (CC BY-NC-ND 4.0)
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