EAGER: Ensemble Design of Resource-Aware Control Strategies for Multi-Agent Robotic Systems

该项目，从物理和化学的综合设计的可扩展的，网络资源感知，分布式控制策略的多智能体机器人系统的平均场方法，旨在开发宏观模型，保留底层的多智能体机器人系统的显着特点，并使用这些模型的分布式控制策略的设计。对于复杂的网络物理系统，这有望提供一种新的设计方法，可能适用于一大类系统，因此，将导致在整个社会使用的基础知识。这个高风险，高回报的项目整合了物理，化学，控制理论和机器人技术的思想，为多智能体机器人系统的协调策略的设计，验证和改进开发新的理论基础。的智力价值在于对网络物理系统的设计，验证和改进的整体方法。平均场方法提供了底层分布式系统的系统级抽象，同时保留了各种代理级交互的显着特征。这些模型的相互作用的工程系统合奏的推广提供了新的方法，用于设计分布式控制器，是敏感的网络资源的变化，其性能可以预测和调整，以实现所需的短期和长期的性能specifications.Broader影响：这个项目的更广泛的影响是双重的。首先，平均场方法考虑了网络资源的使用和管理，为设计可扩展的分散控制和协调策略提供了一个集成的策略。其次，不同于生物启发的方法，平均场方法使分布式协调策略的设计，其性能可以系统地预测和调整，以满足详细的性能规格。这有可能统一各种现有的多代理协调方法。研究成果将通过技术会议和期刊上的出版物传播，并纳入PI？现有的本科生和研究生课程以及K-12外展工作旨在增加STEM领域的女性参与。

This project, generalizing mean-field approaches from physics and chemistry for integrated design of scalable, network resource aware, distributed control strategies for multi-agent robotic systems, aims to develop macroscopic models that retain salient features of the underlying multi-agent robotic system and use these models in the design of distributed control strategies. For complex cyber physical systems, this promises to provide a novel design methodology that is potentially applicable to a large class of systems and, therefore, will result in foundational knowledge of use to the community at large. This high-risk, high-reward project integrates ideas from physics, chemistry, control theory, and robotics to develop new theoretical foundations for the design, validation, and improvement of coordination strategies for multi-agent robotic systems.The project?s intellectual merit lies in the ensemble approach towards the design, validation, and improvement of cyber physical systems. Mean-field methods provide a system-level abstraction of the underlying distributed system while retaining the salient features of the various agent-level interactions. The generalization of these models to ensembles of interacting engineered systems provides new methods for designing distributed controllers that are sensitive to changing network resources and whose performance can be predicted and adjusted to achieve both the desired short-term and long-term performance specifications.Broader Impacts: The broader impacts of this project are twofold. First, the mean-field approach takes into account network resource usage and management, providing an integrated strategy for designing scalable decentralized control and coordination strategies. Second, different from biologically-inspired approaches, the mean-field approach enables the design of distributed coordination strategies whose performance can be systematically predicted and tuned to meet detailed performance specifications. This has the potential to unify various existing multi-agent coordination approaches. The research outcomes will be disseminated through publications in technical conferences and journals and incorporated into the PI?s existing undergraduate and graduate curriculum and K-12 outreach efforts targeted at increasing female participation in STEM fields.