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

CPS：中：协作研究：基于优化的规划和控制以确保自主：概括自主空间任务的见解

• 批准号: 1931815
• 负责人: Marco Pavone
• 金额: $ 35万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931815&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Optimization

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.

在过去的三十年里，我们目睹了历史性的火星任务，无人驾驶的太空飞行器成功地降落在火星表面并探索火星表面，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，吸引了公众的想象力。对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）期间自主操作。此外，它们第一次作为一个完全集成的系统进行测试是在实际使命期间。这使得EDL极具挑战性和风险。使这些最近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，该软件必须在使命条件的所有预期变化下正常工作。 在航空航天工程这些有效的点设计解决方案的推动下，我们的研究旨在为大量在具有日益复杂的性能要求的挑战性环境中运行的地球自主车辆开发一个统一的运动规划和控制算法框架。应用包括自主空中、地面和水下航行器，用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，和有毒物质泄漏清理应用程序仅举几例。我们的主要假设是，优化-基于运动规划和控制的系统提供了一个有效的和统一的数学框架，它能够处理在空间应用中遇到的自主性问题，框架可以推广到各种各样的自动驾驶车辆。我们的项目旨在通过利用NASA火星旗舰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成功，任何失败都会导致灾难性的结果，即，没有犯错的余地。因此，火星着陆可以被认为是一个典型的基准问题，因为它包含了其他（地球上的）自动驾驶车辆也会面临的复杂性：在各种操作模式之间切换;有限的燃料，动力和使命时间;状态和控制约束;以及态势感知，传感，驱动，车辆动力学和环境中的不确定性。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。它既有一个理论组成部分，以产生可用于构建值得信赖的算法的基本结果，也有一个全面的实验组成部分，以产生在现实世界的例子中评估这些算法所需的经验证据，即，自主四旋翼和水下航行器。 我们的研究团队将在空间应用中吸取的经验教训的基础上，开发基于优化的规划和控制方法，这些方法可以无缝地过渡到实践中。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Sequential Convex Programming For Non-Linear Stochastic Optimal Control

• DOI: 10.1051/cocv/2022060
• 发表时间: 2020-09
• 期刊: ArXiv
• 作者: Riccardo Bonalli;T. Lew;M. Pavone

Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems

使用多任务回拉束动力系统的可组合几何运动策略

• 发表时间: 2021
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Bylard, A.;Bonalli, R.;Pavone, M.
• 通讯作者: Pavone, M.

Collision-Inclusive Trajectory Optimization for Free-Flying Spacecraft

自由飞行航天器的包含碰撞的轨迹优化

• DOI: 10.2514/1.g004788
• 发表时间: 2020
• 期刊: and Dynamics
• 作者: Mote, Mark;Egerstedt, Magnus;Feron, Eric;Bylard, Andrew;Pavone, Marco
• 通讯作者: Pavone, Marco

Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization

• DOI: 10.23919/ecc51009.2020.9143595
• 发表时间: 2020-05
• 期刊: 2020 European Control Conference (ECC)
• 作者: T. Lew;Riccardo Bonalli;M. Pavone

Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control

连续时间最优控制的顺序凸规划的理论和数值性质分析

• DOI: 10.1109/tac.2022.3207865
• 发表时间: 2022
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Bonalli, Riccardo;Lew, Thomas;Pavone, Marco
• 通讯作者: Pavone, Marco