Research/ ORBITAL ROBOTICS
End-to-End Imitation Learning for Optimal Asteroid Proximity Operations
Patrick Quinn, George Nehma, Madhur Tiwari
Learning-based MethodsarXiv: cs.LG · Guidance, Dynamics & ControlOpenAlex keyword: spacecraft guidance, navigation and control · Perception & State EstimationOpenAlex keyword: state estimation
arXivcs.RO · cs.LGOpenAlexSpacecraft Dynamics and Control · Reinforcement Learning in Robotics · Space Satellite Systems and Control
Controlling spacecraft near asteroids in deep space comes with many challenges. The delays involved necessitate heavy usage of limited onboard computation resources while fuel efficiency remains a priority to support the long loiter times needed for gathering data. Additionally, the difficulty of state determination due to the lack of traditional reference systems requires a guidance, navigation, and control (GNC) pipeline that ideally is both computationally and fuel-efficient, and that incorporates a robust state determination system. In this paper, we propose an end-to-end algorithm utilizing neural networks to generate near-optimal control commands from raw sensor data, as well as a hybrid model predictive control (MPC) guided imitation learning controller delivering improvements in computational efficiency over a traditional MPC controller.
Comments: 7 pages, 8 figures. Submitted to the 2025 IEEE Aerospace Conference