This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale.

中文翻译：

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航天器神经制导与控制中的最优原理


本综述讨论了在训练用于引导和控制星际转移、行星着陆和近距离操作的端到端神经架构方面获得的主要结果，强调了底层神经模型对最优性原理的成功学习。旨在探索太阳系的航天器和无人机的设计目的是在巧妙使用机载资源对任务成败至关重要的条件下运行。因此，感觉运动动作通常是利用最优控制理论中的综合工具，从分配给每项任务的高级、可量化、最优性原则中得出的。计划的行动在地面产生并传输到机上，控制人员的任务是跟踪上传的引导剖面。在这里，我们回顾了基于端到端网络（称为制导和控制网络（G＆CNet））使用的最新趋势，它允许航天器脱离这种架构并接受最佳动作的机载计算。通过这种方式，传感器信息可以实时转化为最优计划，从而提高任务自主性和鲁棒性。然后，我们将无人机竞赛作为理想的健身房环境进行分析，以在真实的机器人平台上测试这些架构，从而增强对其在未来太空探索任务中使用的信心。无人机竞赛不仅与航天器任务共享有限的机载计算能力和由寻求的最优性原理得出的类似控制结构，而且还需要不同程度的不确定性和未建模的效应以及非常不同的动态时间尺度。