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, utilizing consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred onboard where controllers have the task of tracking the uploaded guidance profile. Here we argue that end-to-end neural guidance and control architectures (here called G&CNets) allow transferring onboard the burden of acting upon these optimality principles. In this way, the sensor information is transformed in real time into optimal plans thus increasing the mission autonomy and robustness. We discuss the main results obtained in training such neural architectures in simulation for interplanetary transfers, landings and close proximity operations, highlighting the successful learning of optimality principles by the neural model. We then suggest drone racing as an ideal gym environment to test these architectures on real robotic platforms, thus increasing confidence in their utilization on future space exploration missions. Drone racing shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought, but it also entails different levels of uncertainties and unmodelled effects. Furthermore, the success of G&CNets on extremely resource-restricted drones illustrates their potential to bring real-time optimal control within reach of a wider variety of robotic systems, both in space and on Earth.

翻译：旨在探索太阳系的航天器和无人机设计用于在机载资源智能利用对任务成败至关重要的条件下运行。因此，感觉运动动作通常源自为每项任务分配的高层次、可量化的最优性原则，并利用最优控制理论中的成熟工具。规划的动作在地面上推导，并传输至机载，由控制器跟踪上传的制导剖面。本文论证端到端神经制导与控制架构（此处称为G&CNets）可允许将基于这些最优性原则进行动作执行的负担转移至机载。通过这种方式，传感器信息被实时转化为最优规划，从而增强任务自主性与鲁棒性。我们讨论了在星际转移、着陆及近距离操作模拟中训练此类神经架构所获得的主要成果，强调了神经模型成功学习最优性原则的能力。随后，我们提出无人机竞速作为理想的“健身房”环境，用于在真实机器人平台上测试这些架构，从而增强对其在未来空间探索任务中应用的信心。无人机竞速与航天器任务在机载计算能力有限及由所寻求的最优性原则导出的相似控制结构方面具有共性，但同时也涉及不同程度的不确定性和未建模效应。此外，G&CNets在资源极端受限的无人机上的成功彰显了其将实时最优控制推广至更广泛的机器人系统（无论太空还是地球）的潜力。