Achieving Consensus Among Autonomous Dynamic Agents using Control Laws that Maintain Performance as Network Size Increases

自动化和机器人技术的最新进展创造了对新“协议”的迫切需求，即允许多个自主代理团队合作并完成复杂任务的算法或控制律。不幸的是，许多用于多代理协调问题的最佳协议都存在可伸缩性问题，也就是说，虽然它们在代理数量较少或中等时表现良好，但随着网络中代理数量的增加，它们的性能会急剧下降。该项目将为一系列多智能体问题开发新的控制律，这些问题的性能即使在网络规模变得非常大时也能保持不变。将考虑许多具有广泛实际重要性的任务，包括有限资源在代理之间的最优分配，合作跟踪和估计，以及最优感知的自适应定位。有了这些新协议，大量的自主代理（如移动机器人或无人驾驶飞行器）将能够快速完成许多有用和重要的任务。为了让自动驾驶汽车和其他联网自动驾驶系统的新兴技术实现其潜在的经济和社会效益，需要这些进步。主要的技术贡献将是加速广泛使用的最近邻交互类。在多智能体控制中，通常通过局部更新来优化全局目标，局部更新将最大化局部目标与有效耦合这些目标的共识条款交织在一起。该项目将开发技术来加速这种类似共识的更新。通过各智能体的权重选择和外推的合理结合，共识更新的收敛时间将提高一个或几个数量级。这些加速进一步意味着依赖于类似共识的更新的许多多代理问题的快速收敛时间。应用的技术混合了代数图论、优化、切换动力系统和联合谱半径的最新进展。

Recent advances in automation and robotics have created a pressing need for new "protocols," that is, for algorithms or control laws that allow teams of multiple autonomous agents to cooperate and accomplish complex tasks. Unfortunately, many of the best protocols for multi-agent coordination problems suffer from scalability issues, that is, while they perform well when the number of agents is small or moderate, their performance degrades sharply as the number of agents in the network grows. This project will develop new control laws for a range of multi-agent problems whose performance is maintained even as network size becomes very large. A number of tasks with broad practical importance will be considered, including optimal distribution of limited resources among agents, cooperative tracking and estimation, and adaptive positioning for optimal sensing. With these new protocols, large groups of autonomous agents(such as mobile robots or unpiloted aerial vehicles) will be able to quickly accomplish a number of useful and important tasks. These advances are needed to allow emerging technologies for autonomous vehicles and other networked autonomous systems to realize their potential economic and societal benefits.The main technical contribution will be to speed up a widely-used class of nearest neighbor interactions. It is common to optimize a global objective in multi-agent control by means of local updates that interleave the maximization local objectives with consensus terms that effectively couple these objectives. This project will develop techniques to speed up such consensus-like updates. By a judicious combination of weight-selection and extrapolation by each agent, the convergence time of consensus updates will be improved by one or several orders of magnitude. These speedups further imply quick convergence times for a number of multi-agent problems relying on consensus-like updates. The techniques applied mix recent advances from algebraic graph theory, optimization, switched dynamical systems, and the joint spectral radius.