Collaborative Research: Dynamic Task-Based Coordination of Large-Scale Mobile Robotic Networks

该项目的主要目标是开发一种系统的方法来设计可伸缩的、健壮的和自适应的算法来解决大规模移动代理网络中基于任务的动态运动协调问题。智能体的运动受到微分和代数约束，例如，包括非完整动力学和碰撞避免。任务是由外部源随着时间的推移而产生的，该外部源被建模为随机或对抗性过程，独立于代理的运动而行动。该项目的目标是精确描述算法的闭环系统性能和实现复杂性。虽然所考虑的问题属于具有不确定信息的随机和组合决策系统的类别，但通过显式地解决对主体的运动、信息交换和环境的局部感知的微分和代数约束，在这些问题中增加了几何分量。该方法基于综合了系统与控制、机器人学、组合优化和分布式计算思想的工具的开发，该项目解决了协调可能大量移动代理的运动的问题，以便在地理上扩展的感兴趣区域内实现动态变化的任务。例如，代理可以代表收集关于时变空间场的信息(例如，温度分布、化学浓度等)所需的移动传感器，或者在感兴趣的区域上提供无线通信服务的移动中继器。该项目的成功完成将促进移动机器人网络的发展，其能力远远超过当前技术所提供的能力，并精确地了解网络的规模如何影响其性能及其复杂性。能够在更大的空间收集和传播信息的移动代理网络的可用性将极大地影响我们在环境监测、行星探测、搜索和救援、交通拥堵管理、监视、灾害管理和国家安全等应用领域的能力。

The main objective of this project is the development of a systematic approach to the design of scalable, robust, and adaptive algorithms for a class of dynamic task-based motion coordination problems for large-scale networks of mobile agents. The agents are subject to differential and algebraic constraints on their motion including, for example, non-holonomic dynamics and collision avoidance. Tasks are generated over time by an external source, modeled either as a stochastic or adversarial process, acting independently from the agents' motion. The project will aim at a precise characterization of the closed-loop performance and of the implementation complexity of the algorithms. While the class of problems under consideration falls within the class of stochastic and combinatorial decision systems with uncertain information, a geometric component is added to these problems by explicitly addressing differential and algebraic constraints on the motion of the agents, information exchange, and local sensing of the environment. The proposed approach is based the development of tools combining ideas from systems and control, robotics, combinatorial optimization, and distributed computing.This project addresses the problem of coordinating the motion of a possibly large number of mobile agents, in order to achieve dynamically-changing tasks over a geographically extended region of interest. For example, agents can represent mobile sensors required to collect information about a time-varying spatial field (e.g., temperature profiles, chemical concentration, etc.), or mobile relays providing wireless communication services over a region of interest. The successful completion of this project will facilitate the development of mobile robotic networks with capabilities that are well beyond those offered by current technology, with a precise understanding of how the size of the network affects its performance and its complexity. The availability of possibly large networks of mobile agents, able to collect and disseminate information over an extended volume of space will greatly impact our capabilities in application areas such as environmental monitoring, planetary exploration, search and rescue, traffic congestion management, surveillance, disaster management and national security.