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

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Zaidi, Syed Muhammad Talha
Chadalavada, Pardha Sai
Ullah, Hayat
Munir, Arslan
Dutta, Atri

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


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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