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

• 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

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，我们见证了火星上的历史性任务，无人驾驶航天器成功地降落在火星表面并进行了探索，以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，然后安全降落在地球上，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大气层进入、下降和着陆(EDL)过程中自主操作。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任务期间。这使得EDL极具挑战性和风险性。使最近这些成功的太空任务得以实现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，我们的研究旨在为一大类在具有日益复杂的性能要求的苛刻环境中运行的地面自主飞行器开发一个统一的运动规划和控制算法框架。应用包括执行许多安全关键任务的自主空中、地面和水下机器人，例如搜索和救援、救灾、地形测绘和监测以及有毒物质泄漏清理应用等等。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和统一的数学框架，能够处理空间应用中遇到的自主问题，该框架可以推广到多种类型的自主机器人。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中成功，任何失败都将导致灾难性的结果，也就是说，没有出错的余地。因此，登陆火星可以被认为是一个典型的基准问题，因为它包含了人们在其他(地面)自动驾驶飞行器上也会面临的复杂性：在各种操作模式之间切换；有限的燃料、功率和任务时间；状态和控制约束；以及态势感知、感知、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。它既有一个理论部分来产生可用于构建可信算法的基本结果，也有一个全面的实验部分来产生在真实世界的例子上评估这些算法所需的经验证据，即自主四旋翼和水下机器人。我们的研究团队是为了在空间应用中吸取这些经验教训的基础上建立起来的，并开发可以无缝过渡到实践的基于优化的规划和控制方法。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

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

• 发表时间: 2022
• 期刊: Proceedings of the American Control Conference
• 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

• 发表时间: 2022
• 期刊: Proceedings of the American Control Conference
• 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

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)
• 作者: S. Sefati;Subhransu Mishra;Matthew Sheckells;Kapil D. Katyal;Jin Bai;Gregory Hager;Marin Kobilarov

Identifying Performance Regression Conditions for Testing & Evaluation of Autonomous Systems

确定测试的性能回归条件

• DOI: 10.1109/iros51168.2021.9636004
• 发表时间: 2021
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Stankiewicz, Paul;Kobilarov, Marin
• 通讯作者: Kobilarov, Marin