用神经网络直接从传感器数据生成最优近地小行星操作指令
End-to-End Imitation Learning for Optimal Asteroid Proximity Operations
- 端到端神经网络直接处理原始传感器数据生成控制指令
- 混合模型预测控制引导的模仿学习使计算效率优于传统MPC
- 适合需要低延迟、低功耗的深空探测任务
深空中小行星附近的航天器控制面临诸多挑战：通信延迟要求大量使用有限的星载计算资源，同时燃料效率至关重要，以支持长时间的数据采集。此外，由于缺乏传统参考系统，状态确定困难，因此理想的制导、导航与控制（GNC）系统需兼具计算和燃料效率，并具备鲁棒的状态估计能力。本文提出一种端到端算法，利用神经网络从原始传感器数据生成近似最优的控制指令；并设计了一种由混合模型预测控制（MPC）引导的模仿学习控制器，在计算效率上优于传统MPC控制器。
原文摘要 · Abstract (English)
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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