CAREER: Towards Hierarchical and Provably Safe Control for Learning-Enabled Autonomous Systems

职业：为支持学习的自主系统实现分层且可证明安全的控制

• 批准号: 2237850
• 负责人: Xiangru Xu
• 金额: $ 60.36万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237850&HistoricalAwards=false
• 关键词: CAREER; Towards; Hierarchical; Provably; Safe; CAREER; Towards; Hierarchical; Provably; Safe

This Faculty Early Career Development Program (CAREER) grant will fund research that enhances the reliability, trustworthiness, and societal acceptance of autonomous systems that rely on machine learning-enabled technologies, thereby promoting the progress of science, and advancing the national prosperity and welfare. Autonomous robotic systems, such as self-driving cars and drones, are shaping the nation's future insofar as the transportation, logistics, and service segments of the economy are concerned. Artificial neural networks have become an indispensable component of modern autonomous systems, especially in their perception and control pipelines. However, neural networks are complex, difficult to analyze, and sensitive to input perturbations or adversarial attacks. This renders their rigorous analysis and design very challenging. Thus, despite the continued optimism and tremendous technological progress in recent years, truly autonomous systems remain elusive because of outstanding safety and reliability concerns. This project overcomes these concerns by establishing a rigorous methodological framework and efficient algorithms for the analysis, verification, motion planning, and control design of safety-critical dynamic systems with learning-enabled components. It demonstrates how this framework enables provable performance guarantees for safe and reliable operation. Through close integration of research, education, and outreach, the project aims to leverage knowledge discovery to stimulate teaching and learning, use inspired teaching to encourage excitement in research, and make newly generated knowledge accessible to the public. This is accomplished through active learning-based design of a course on safety control in robotics, by engaging students from underrepresented groups in research and organizing K-12 summer workshops with hands-on robotics activities, and by increasing public literacy, awareness, and trust in safety-related technologies for autonomous systems.This research aims to develop the foundations of a mathematically rigorous framework for the multi-rate and provably safe motion planning and control of autonomous systems with neural network components. It achieves this aim by investigating constrained zonotope- and hybrid zonotope-based algorithms for computing over-approximated reachable sets for neural feedback systems with a tunable trade-off between computational efficiency and approximation accuracy; robust quadratic program-based methods for designing provably safe, periodic event-triggered tracking controllers; second-order cone program-based trajectory planning methods for neural feedback systems with continuous-time safety guarantees; and provably safe multi-rate planning and control algorithms with an assume-guarantee contract between the planning and tracking layers. Verification and validation of the theoretical results will be performed using high-fidelity vehicle dynamics software simulations and with physical experiments on two lab-based robotic platforms.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.

这项教师早期职业发展计划（职业）赠款将资助研究，以增强依靠支持机器学习技术的自主系统的可靠性，可信赖性和社会认可，从而促进科学的进步，并促进国家繁荣和福利。自动驾驶汽车和无人机等自主机器人系统正在塑造国家的未来，而经济的运输，物流和服务部门涉及经济。人工神经网络已成为现代自治系统的必不可少的组成部分，尤其是在其感知和控制管道中。但是，神经网络很复杂，难以分析，并且对输入扰动或对抗性攻击敏感。这使他们的严格分析和设计非常具有挑战性。因此，尽管近年来一直保持乐观和巨大的技术进步，但由于出色的安全性和可靠性问题，真正的自主系统仍然难以捉摸。该项目通过建立严格的方法框架和有效的算法来克服这些问题，用于分析，验证，运动计划和控制启用学习的组件的安全关键动态系统的设计。它演示了该框架如何为安全可靠的操作提供可证明的性能保证。通过紧密整合研究，教育和外展，该项目旨在利用知识发现来刺激教学，利用灵感的教学来鼓励研究兴奋，并使新产生的知识可供公众获得。这是通过基于基于学习的机器人技术安全控制课程的积极学习的设计，通过使代表性不足的小组的学生与动手机器人活动一起在研究和组织K-12的夏季研讨会中吸引的学生来实现这一目标，从而实现这一目标，并提高公众素养，意识，对自动企业的安全相关技术，以开发与安全性的跨性别框架和经典的建筑物，以实现与安全性相关的技术。具有神经网络组件的自主系统。它通过研究基于限制的生物体和杂化界限算法来计算过度评估的神经反馈系统的可及可触及的集合来实现这一目标。强大的基于二次程序的方法，用于设计可证明的安全，周期性事件触发的跟踪控制器；具有连续时间安全保证的神经反馈系统的二阶锥体轨迹计划方法；可证明安全的多率计划和控制算法，并在规划层和跟踪层之间具有假定保证合同。验证和验证理论结果将使用高保真车辆动力学软件模拟以及在两个基于实验室的机器人平台上进行物理实验进行验证。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影响的评估标准通过评估来进行评估的。