标题： 航天器神经制导与控制中的最优性原理 显示英文标题

标题： Optimality Principles in Spacecraft Neural Guidance and Control

摘要： 旨在探索太阳系的航天器和无人机的设计需要充分利用机载资源，以确保任务的成功。因此，传感器和运动行为通常基于分配给每个任务的高级别、可量化且最优的原则，并利用最优控制理论中的成熟工具来实现。 计划好的行为是在地面制定并通过传输到机载系统中，其中控制器的任务是跟踪上传的引导配置文件。 我们在此提出，端到端神经网络引导与控制系统（这里称为G&CNets）可以将依据这些最优原则的行为负担转移到机载系统上。这样，传感器信息可以实时转化为最优计划，从而提高任务的自主性和鲁棒性。 我们讨论了在模拟环境中训练这种神经架构用于行星际转移、着陆以及近距离操作的主要成果，强调了神经模型成功学习最优原则的结果。 然后，我们建议无人机竞速作为测试这些架构在真实机器人平台上的理想环境，从而增加对未来太空探索任务中使用这些架构的信心。 无人机竞速与航天器任务共享有限的机载计算能力和由所寻求的最优原则诱导出的相似控制结构，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在资源受限的无人机上的成功应用展示了它们将实时最优控制推向更广泛种类机器人系统的潜力，无论是在太空中还是在地球上。 显示英文摘要

摘要： 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.