Optimization-Based Planning and Control for Assured Autonomy: Generalizing Insights From Autonomous Space Missions

在过去的三十年里，我们目睹了历史性的火星任务，无人驾驶航天器成功着陆并探索火星表面，寻找过去生命的证据。最近，可重复使用的火箭通过将有效载荷送入轨道然后安全返回地球而吸引了公众的想象力。这些航天器的一个共同要求是它们必须在进入大气层、下降和着陆（EDL）期间自主运行。此外，它们第一次作为完全集成的系统进行测试是在实际任务期间。这使得 EDL 极具挑战性和风险。使最近这些成功的太空任务成为可能的一项关键技术是在 EDL 期间控制飞行器运动的机载软件，该软件必须在任务条件的所有预期变化下正常工作。 在这些来自航空航天工程的有效点设计解决方案的推动下，我们的研究旨在开发一个统一的算法框架，用于在具有日益复杂的性能要求的挑战性环境中运行的一大类基于地球的自动驾驶车辆的运动规划和控制。应用包括自主空中、地面和水下航行器，服务于许多安全关键任务，例如搜索和救援、救灾、地形测绘和监测以及有毒泄漏清理应用等。我们的主要假设是，基于优化的运动规划和控制提供了一个有效且统一的数学框架，能够处理空间应用中遇到的自主问题，并且该框架可以推广到各种自主航行器。我们的项目旨在利用 NASA 旗舰火星任务的宝贵见解和经验来构建这个基于优化的框架。这些任务必须在第一次尝试时取得成功，任何失败都将导致灾难性的结果，即没有犯错的余地。因此，火星登陆可以被认为是一个典型的基准问题，因为它包含了其他（基于地球的）自动驾驶车辆也会面临的复杂性：在多种操作模式之间切换；有限的燃料、动力和任务时间；状态和控制约束；以及态势感知、传感、驱动、车辆动力学和环境的不确定性。我们的项目旨在为基于优化的运动规划和控制提供算法基础。它既有理论部分，可以产生可用于构建值得信赖的算法的基本结果，也有综合实验部分，可以产生在现实世界的例子（即自主四旋翼和水下航行器）上评估这些算法所需的经验证据。 我们的研究团队聚集在一起，以在太空应用中吸取的经验教训为基础，开发基于优化的规划和控制方法，这些方法可以无缝过渡到实践。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

Over the last three decades we have witnessed historic missions to Mars where unmanned space vehicles successfully landed on and explored the Martian surface in search of evidence of past life. Recently reusable rockets have captured the public's imagination by delivering payloads to orbit and then landing safely back on Earth. A common requirement for these space vehicles is that they must be operated autonomously during the atmospheric entry, descent, and landing (EDL). Furthermore, the first time they are ever tested as a fully integrated system is during the actual mission. This makes EDL extremely challenging and risky. A key technology that has enabled these recent successful space missions is the onboard software that controls the vehicle's motion during EDL, which must work properly under all expected variations in the mission conditions. Motivated by these effective point-design solutions from aerospace engineering, our research aims to develop a unified algorithmic framework for motion planning and control for a large class of Earth-based autonomous vehicles that operate in challenging environments with increasingly complex performance requirements. Applications include autonomous aerial, ground, and underwater vehicles serving many safety critical tasks in, for example, search and rescue, disaster relief, terrain mapping and monitoring, and toxic spill cleanup applications to name few.Our main hypothesis is that optimization-based motion planning and control provides an effective and unifying mathematical framework that is able to handle the autonomy problems encountered in space applications and this framework can be generalized to a large variety of autonomous vehicles. Our project aims to build this optimization-based framework by leveraging invaluable insights and experiences from NASA's flagship missions to Mars. These missions had to succeed during their first attempt and any failure would have led to catastrophic results, i.e., there was no margin for error. Hence Mars landing can be considered a prototypical benchmark problem, as it encompasses complexities that one would also face with other (Earth-based) autonomous vehicles: switching between a variety of operational modes; limited fuel, power, and mission time; state and control constraints; and uncertainties in the situational awareness, sensing, actuation, vehicle dynamics, and environment. Our project aims to provide algorithmic foundations for optimization-based motion planning and control. It has both a theoretical component to produce fundamental results that can be used to build trustworthy algorithms and a comprehensive experimental component to produce the empirical evidence necessary to evaluate these algorithms on real-world examples, i.e., autonomous quad-rotors and underwater vehicles. Our research team is assembled to build on these lessons learned in space applications and to develop optimization-based planning and control methods that can seamlessly be transitioned to practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.