权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Decentralized optimal control of cooperating networked multi-agent systems

协作网络多智能体系统的分散最优控制

基本信息

批准号：
1931600
负责人：
Sean Andersson
金额：
$ 39.99万
依托单位：
Trustees of Boston 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：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931600&HistoricalAwards=false
关键词：
Decentralized optimal control cooperating networked

项目摘要

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.

多智能体系统涵盖了广泛的应用，从互联的自动驾驶汽车和新兴的车联网(其空间域可能是数百英里，时间跨度在几天内)，到在米长和分钟时间尺度上操作的微型飞行器，再到纳米级空间微秒时间分辨率的纳米操纵。该项目致力于解决联网多代理系统中的五个关键挑战：(1)可扩展性，这是由于正在设计的联网系统的日益大规模的性质所必需的；(2)个体级别的自主性，确保弹性和安全的系统所必需的；(3)安全和高效的通信，在代理具有有限能量资源的无线环境中尤其重要；(4)避免由于系统交互的复杂性质而产生的局部最优，并且可能产生较差的性能；以及(5)利用实时数据，利用数据丰富的环境的现代现实。虽然该项目的核心是一种理论方法，该方法跨越了所需的不同长度和时间尺度，但它也包括使用机器人平台的实验验证，该平台将提供一个向广大受众展示和交流结果的平台。拟议项目的范围通过一个通用优化(静态和动态)框架来捕获，该框架涵盖了研究人员和实践者面临的绝大多数有趣的问题。在这个框架内，我们将追求三个具体的任务：(1)开发网络多智能体系统中动态优化问题的在线解决方案；(2)确定何时在不牺牲集中式解决方案的性能的情况下分散是可能的，并开发显式的分散控制算法，即使在需要一些性能降级的情况下也是如此；以及(3)通过使用Boost函数来避免那些局部最优，从而解决优化中存在多个局部极小的挑战。这些任务的智力优势在于三个概念基石：(1)用事件驱动的方法取代传统的时间驱动的范式，允许算法的复杂性随着此类事件的数量而不是网络的状态维度而增长，(2)使用数据驱动的方法进行优化，允许使用一种方法来处理现实世界系统日益增长的复杂性，其中基于优雅但往往不充分的经典模型的传统方法无法实现，以及(3)在分布式优化中避免局部最优，其中使用新颖的机制来避免这些局部解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框架的基础上，该框架非常广泛，包括覆盖控制、共识、持续监测和最优编队控制等子问题，以及从联网的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，还将继续开展教育和外展方面的具体任务，包括在PIS的实验室接待正在崛起的高中生进行暑期研究实习，通过移动机器人演示向初中生和高中生展示结果，以及让本科生参与研究。该奖项反映了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。