End-to-End Imitation Learning for Optimal Asteroid Proximity Operations

• URL: http://arxiv.org/abs/2502.01034v1
• Date: Mon, 03 Feb 2025 04:09:20 GMT
• Title: End-to-End Imitation Learning for Optimal Asteroid Proximity Operations
• Authors: Patrick Quinn, George Nehma, Madhur Tiwari,
• Abstract summary: We propose an end-to-end algorithm utilizing neural networks to generate near-optimal control commands from raw sensor data.<n>A hybrid model predictive control (MPC) guided imitation learning controller delivers improvements in computational efficiency over a traditional MPC controller.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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