End-to-End Imitation Learning for Optimal Asteroid Proximity Operations
This addresses challenges in deep-space asteroid proximity operations for spacecraft control, though it appears incremental as it builds on existing methods like MPC.
The paper tackles the problem of controlling spacecraft near asteroids by proposing an end-to-end algorithm using neural networks to generate near-optimal control commands from raw sensor data, resulting in improvements in computational efficiency over traditional MPC controllers.
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.