CAREER: Real-time Convex Optimization for High-Performance Control of Autonomous Systems

职业：自治系统高性能控制的实时凸优化

• 批准号: 1613235
• 负责人: Behcet Acikmese
• 金额: $ 48.72万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613235&HistoricalAwards=false
• 关键词: CAREER; Real; time; Convex; Optimization

This Faculty Early Career Development (CAREER) Program grant will build innovative mathematical and computational methods of decision making for control of next generation high-performance autonomous systems. A broad range of emerging technologies utilize autonomy, including self-driving vehicles, quadrotors for delivery, search and rescue robots, mobile sensors for environmental monitoring, climate control systems for warehouses and buildings, automated drug dispensing, and next-generation power networks. Large-scale deployment of autonomous systems is a critical technological leap that will fundamentally transform our lives. Autonomous systems will maximize efficiency and reliability. Furthermore, they will improve safety by reducing or removing direct human involvement in hazardous tasks. However, autonomous systems present fundamentally difficult technical challenges to go beyond the limitations of the current state of the art in automation. This project lays the mathematical and computational foundations for a transformative approach to autonomy, one that makes previously impractical calculations tractable, and provides guaranteed levels of performance and stability. To meet fundamentally new and distinctly challenging performance and reliability requirements, future autonomous systems must make best possible decisions to control their actions without a human operator in the loop. They must utilize their full performance envelopes, while simultaneously satisfying critical mission and environmental constraints. Consequently, mathematical optimization problems are ubiquitous in autonomous control. Though optimization provides a powerful formulation framework, thus far real-time optimization based control has not transitioned to common practice due to shortcomings in the current algorithmic capabilities. To enable real-time optimization based control, this CAREER grant will develop a comprehensive theory of convexification -- formulation of control problems as tractable convex optimization problems -- enabling high performance control that is currently not achievable in many applications. Specifically, it will develop: accurate formulations of autonomous control problems as convex optimizations; customized numerical optimization algorithms that exploit problem structure for extremely fast and reliable onboard computations; and rigorous verification methods to certify the performance and robustness of the resulting autonomous control algorithms. These advances and resulting insights will also benefit systematic systems engineering and design for autonomous systems, high-level autonomous mission planning, and control of autonomous multi-agent systems. Collectively the theoretical and algorithmic products will form a strong technical foundation allowing the transition of real-time optimization-based control for autonomous systems into practice.

这种教师早期职业发展（职业）计划赠款将建立创新的数学和计算方法，以控制下一代高性能自治系统。广泛的新兴技术利用自治，包括自动驾驶汽车，用于交付和救援机器人的四肢，用于环境监测的移动传感器，用于仓库和建筑物的气候控制系统，自动化药物分配以及下一代电力网络。自治系统的大规模部署是一个关键的技术飞跃，它将从根本上改变我们的生活。自主系统将最大化效率和可靠性。此外，它们将通过减少或消除人类直接参与危险任务来提高安全性。但是，自主系统在根本上面临着根本困难的技术挑战，以超越自动化中最新现状的局限性。该项目为一种变革性的自治方法奠定了数学和计算基础，该方法可以使以前不切实际的计算可进行，并提供了保证的性能和稳定性水平。为了满足从根本上新的且挑战性的绩效和可靠性要求，未来的自主系统必须做出最佳决定，以控制其行动，而无需循环中的人类操作员。他们必须利用自己的全部性能信封，同时满足关键任务和环境限制。因此，数学优化问题在自主控制中无处不在。尽管优化提供了一个强大的公式框架，但由于当前算法功能的缺点，迄今为止，基于实时优化的控制并未过渡到共同实践。为了启用基于实时优化的控制，这项职业赠款将开发出一个综合的凸化理论 - 将控制问题作为可拖动的凸优化问题制定 - 实现了当前在许多应用中无法实现的高性能控制。具体而言，它将发展：自主控制问题的准确表述作为凸优化；自定义的数值优化算法将问题结构用于极快和可靠的机载计算；以及严格的验证方法，以证明由此产生的自主控制算法的性能和鲁棒性。这些进步和产生的见解还将受益于自主系统，高级自主任务计划以及对自动多代理系统的控制的系统系统工程和设计。总体而言，理论和算法产品将构成强大的技术基础，从而使基于实时优化的自主系统的控制过渡到实践中。