Decentralized optimal control of cooperating networked multi-agent systems

多智能体系统涵盖了广泛的应用，从连接的自动驾驶车辆和新兴的汽车互联网，其中空间域可能是数百英里，时间范围超过数小时，以米长和分钟时间尺度运行的微型飞行器，以及纳米空间微秒时间分辨率的纳米操纵。该项目旨在解决网络多代理系统中的五个关键挑战：（1）可扩展性，这是由所设计的联网系统的日益增长的大规模性质所必需的，（2）个人级别的自治，这是确保弹性和安全系统所需的，（3）安全和有效的通信，在代理具有有限能量资源的无线设置中特别关键，（4）避免由系统交互的复杂性质引起的并且可能产生差的性能的局部最优，以及（5）利用实时数据，利用数据丰富环境的现代现实。虽然该项目的核心是集中在一个理论的方法，在不同的长度和所需的时间尺度延伸，它还包括使用机器人平台进行实验验证，这些平台将提供一个平台，向广泛的受众展示和交流结果。（静态和动态）框架，其中包括研究人员和从业人员面临的绝大多数有趣的问题。在这一框架内，我们将开展三项具体任务：（1）开发网络化多智能体系统中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式解决方案的性能的情况下进行分散化，并开发显式分散控制算法，即使在需要一些性能降级的情况下，以及（3）通过使用提升函数来逃避这些局部最优值，来解决优化中的多个局部最优值的挑战。这些任务的智力价值在于三个概念基石：（1）用事件驱动的方法代替传统的时间驱动的范例，允许其复杂性随着这样的事件的数量而不是网络的状态维度而增长的算法。考虑到一种能够处理现实世界系统的日益增加的复杂性的方法，其中基于优雅但通常不充分的经典模型的传统方法失败了，以及（3）在分布式优化中逃避局部最优，其中用于逃避这些局部解的新颖机制的使用克服了基于梯度的方法固有的限制。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，包括子问题，如覆盖控制，共识，持续监控和最佳编队控制，以及从连接的自动化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中推进最先进的技术。此外，还将开展教育和推广方面的具体任务，包括在PI的实验室中接待正在上升的高中毕业生进行夏季研究实习，通过移动的机器人演示向初中和高中学生展示成果，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.