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

确保自主性的基于优化的规划和控制：概括自主空间任务的见解

基本信息

批准号：
1931821
负责人：
Marin Kobilarov
金额：
$ 35万
依托单位：
Johns Hopkins 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 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931821&HistoricalAwards=false
关键词：
Optimization Based Planning Control Assured

项目摘要

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.

在过去的三十年中，我们目睹了前往火星的历史任务，无人驾驶太空车辆成功地登陆并探索了火星表面，以寻找前世的证据。最近，可重复使用的火箭通过向轨道运送有效载荷，然后安全地降落在地球上，从而捕捉了公众的想象力。这些太空车辆的普遍要求是，它们必须在大气进入，下降和着陆（EDL）中自主操作。此外，在实际任务期间，他们第一次被检查为完全集成的系统。这使EDL极具挑战性和风险。一项启用了这些成功的太空任务的关键技术是控制车辆在EDL期间控制车辆运动的机载软件，该软件必须在任务条件下的所有预期变化下正常工作。我们的研究旨在为航空工程工程的这些有效的点设计解决方案的启发，旨在为在具有越来越复杂的性能需求越来越复杂的挑战性环境中开发一个统一的算法框架，以针对大型基于地球的自动驾驶汽车进行运动计划和控制。申请包括自主空中，地面和水下车辆，例如搜索和救援，救灾，救灾，地形映射和监视以及有毒的溢出清理应用程序，以少数名字命名。我们的主要假设是，基于优化的运动计划和控制能够在多种多样的范围内弥补这一范围，并且可以在跨越的范围内进行自动范围，并在此方面提供了跨越的范围。车辆。我们的项目旨在通过利用NASA的旗舰任务到火星的宝贵见解和经验来建立这种基于优化的框架。这些任务在第一次尝试期间必须取得成功，任何失败都会导致灾难性结果，即没有错误的余地。因此，火星着陆被认为是典型的基准问题，因为它涵盖了一个人也将与其他（基于地球的）自动驾驶汽车面临的复杂性：在各种操作模式之间切换；有限的燃油，力量和任务时间；状态和控制约束；以及情境意识，感知，驱动，车辆动态和环境中的不确定性。我们的项目旨在为基于优化的运动计划和控制提供算法基础。它既有一个理论成分，可以产生基本的结果，可用于构建可信赖的算法，也可以使用全面的实验组件，以产生在现实世界中评估这些算法所需的经验证据，即自动及四方旋转器和水下汽车。我们的研究团队集中在空间应用中学习的这些教训，并开发基于优化的计划和控制方法，这些计划和控制方法可以无缝过渡到实践。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来评估的。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance

用于基于高效优化的碰撞避免的闭式 Minkowski 和近似

DOI：
发表时间：
2022
期刊：
Proceedings of the American Control Conference
影响因子：
0
作者：
Guthrie, James;Kobilarov, Marin;Mallada, Enrique
通讯作者：
Mallada, Enrique

Decentralized Safety for Aggressively Maneuvering Multi-Robot Interactions

用于主动操纵多机器人交互的分散安全性

DOI：
发表时间：
2022
期刊：
Proceedings of the American Control Conference
影响因子：
0
作者：
Rivera, Phillip;Kobilarov, Marin
通讯作者：
Kobilarov, Marin

Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)

DOI：
10.1109/lra.2023.3315209
发表时间：
2022-10
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
A. Polevoy;Marin Kobilarov;Joseph L. Moore
通讯作者：
A. Polevoy;Marin Kobilarov;Joseph L. Moore

Robust Policy Search for an Agile Ground Vehicle Under Perception Uncertainty

DOI：
10.1109/iros51168.2021.9636552
发表时间：
2021-09
期刊：
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
S. Sefati;Subhransu Mishra;Matthew Sheckells;Kapil D. Katyal;Jin Bai;Gregory Hager;Marin Kobilarov
通讯作者：
S. Sefati;Subhransu Mishra;Matthew Sheckells;Kapil D. Katyal;Jin Bai;Gregory Hager;Marin Kobilarov

Adaptive sampling with an autonomous underwater vehicle in static marine environments