Decentralized optimal control of cooperating networked multi-agent systems

多智能体系统涵盖了广泛的应用，从连接的自动驾驶汽车和新兴的汽车互联网，其中空间域可能是数百英里，时间范围可能是数小时或数天，到运行在米长和分钟时间尺度上的微型飞行器，以及具有纳米空间微秒时间分辨率的纳米操作。该项目旨在解决网络多智能体系统中的五个关键挑战：(1)可扩展性，由于正在设计的网络系统的大规模性质的增加，这是必要的；(2)个人层面的自主性，需要确保一个有弹性和安全的系统；(3)安全高效的通信，特别是在代理有限能源的无线环境中至关重要；(4)避免因系统交互的复杂性而产生的局部最优，这可能会产生较差的性能；(5)利用实时数据。利用现代现实的数据丰富的环境。虽然项目的核心集中在理论方法上，扩展了所需的不同长度和时间尺度，但它还包括使用机器人平台的实验验证，这将提供一个平台，向广大受众展示和交流结果。拟议项目的范围是通过一个通用的优化（静态和动态）框架来捕获的，该框架包含了研究人员和实践者面临的绝大多数有趣的问题。在此框架下，我们将开展三项具体工作：(1)开发网络多智能体系统动态优化问题的在线解决方案；(2)确定何时可以在不牺牲集中解决方案性能的情况下实现去中心化，即使在需要某些性能降低的情况下也可以开发明确的分散控制算法；(3)通过使用增强函数来逃避局部最优，解决优化中多个局部最小值的挑战。这些任务的智力价值在于三个概念基础：(1)用事件驱动的方法取代传统的时间驱动范式，允许算法的复杂性随着事件的数量而不是网络的状态维数而增长；(2)使用数据驱动的方法进行优化，允许一种方法可以处理现实世界系统日益增加的复杂性，而传统方法基于优雅但往往不充分的经典模型失败；(3)在分布式优化中逃避局部最优；使用新机制来逃避这些局部解决方案，克服了基于梯度的方法固有的局限性。该项目建立在网络多智能体系统的框架之上，该框架非常广泛，包括覆盖控制、共识、持续监控和最优编队控制等子问题，以及从联网自动车辆到纳米操纵的应用领域。因此，我们的研究将在依赖网络系统的所有领域推进最先进的技术。此外，还将推进教育和宣传的具体任务，包括让即将升学的高三学生在pi的实验室进行暑期研究实习、通过移动机器人向初高中学生展示研究成果、让本科生参与研究等。该奖项反映了美国国家科学基金会的法定使命，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。

Multi-agent systems encompass a broad spectrum of applications, ranging from connected autonomous vehicles and the emerging internet of cars, where the spatial domain may be hundreds of miles with time horizons over hours of days, to micro-air vehicles which operate over meter length and minute time scales, and down to nano-manipulation with nanometer spatial microsecond time resolution. This project seeks to address five key challenges in networked multi-agent systems: (1) scalability, necessitated by the increasing large-scale nature of the networked systems being designed, (2) autonomy at the individual level, required to ensure a resilient and secure system, (3) communication that is secure and efficient, particularly crucial in wireless settings where the agents have limited energy resources, (4) avoiding local optima that arise from the complex nature of the system interactions and which may yield poor performance, and (5) exploiting real-time data, taking advantage of the modern reality of data-rich environments. While the core of the project is centered on a theoretical approach that extends over the diverse length and time scales needed, it also includes experimental validation using robotic platforms that will provide a platform to showcase and communicate results to a broad audience.The scope of the proposed project is captured through a general optimization (both static and dynamic) framework which encompasses the vast majority of interesting problems faced by researchers and practitioners. Within this framework, we will pursue three specific tasks: (1) Develop on-line solutions for dynamic optimization problems in networked multi-agent systems, (2) Determine when decentralization without sacrificing the performance of a centralized solution is possible and develop explicit decentralized control algorithms even in cases where some performance degradation is needed, and (3) Address the challenge of multiple local minima in the optimization through the use of boosting functions to escape those local optima. The intellectual merit of these tasks lies in three conceptual cornerstones: (1) Replacing the traditional time- driven paradigm with an event-driven approach, allowing for algorithms whose complexity grows with the number of such events, not the state dimensionality of the network, (2) Using a data-driven approach to optimization, allowing for an approach which can handle the increasing complexity of real-world systems where traditional approaches based on elegant but often inadequate classical models fail, and (3) Escaping local optima in distributed optimization, where the use of novel mechanisms for escaping those local solutions overcomes the limitations inherent to gradient-based approaches. The project is built upon a framework for networked multi-agent systems that is extremely broad, encompassing sub-problems such as coverage control, consensus, persistent monitoring, and optimal formation control, and application domains from connected automated vehicles down to nano-manipulation. As such, our research will advance the state-of-the-art in all domains that rely on networked systems. In addition, specific tasks on education and outreach will be pursued, including hosting rising high school seniors in the labs of the PIs for a summer research internship, showcasing the results to middle and high-school students through demonstrations with mobile robots, and engaging undergraduate students in research.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.