Environment-Agent Interaction in Autonomous Networked Teams with Applications to Minimum-Time Coordinated Control of Multi-Agent Systems

自治网络团队中的环境-智能体交互及其在多智能体系统最短时间协调控制中的应用

• 批准号: 1160780
• 负责人: Panagiotis Tsiotras
• 金额: $ 24万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1160780&HistoricalAwards=false
• 关键词: Environment; Agent; Interaction; Autonomous; Networked

The research objective of this award is to develop motion coordination and path-planning algorithms for small/micro unmanned aerial vehicles (UAVs/MAVs) using appropriate, state-dependent performance metrics that capture the interactions of the vehicle with the environment, the mission objectives, as well as the system-theoretic attributes of the network. The environment includes both the ambient conditions (e.g., winds) as well as the actual, or perceived, adversarial behavior of any opponents operating in the vicinity of the vehicle. A key concept that will play a major role in developing these results will be the generalized (Zermelo) Voronoi diagrams which can capture the dynamics of the state of the agents in the network (e.g., estimated-time-of-arrival, path curvature constraints) much better than traditional Voronoi diagrams that are based solely on Euclidean distance. With the help of these diagrams, the proximity relations between different agents and/or between the agents and a set of targets will be characterized, and decentralized and distributed control strategies will be developed.If successful, the results of this research will enable small-scale autonomous aerial, marine or underwater vehicles to operate in teams towards a common objective such as environmental monitoring, distributed surveillance, multiple target allocation, coordinated target pursuit, etc. in the presence of locally strong winds or currents and with limited on-board power and computational resources. Owing to their small size, the trajectories of these aerial platforms are strongly influenced by the prevailing winds, as well as the limitations imposed by the available on-board resources. The tools to be developed in this research will also find application in other areas of networked controlled systems, where intelligent agents must cooperate to perform their mission, a task that would be infeasible if it were to be undertaken by a single member of the team. Graduate and undergraduate engineering students as well as local high school teachers will benefit from their involvement in this research through NSF?s REU and RET projects and through Georgia Tech?s PURA and Dash undergraduate research fellowship programs.

该奖项的研究目标是开发小型/微型无人机 (UAV/MAV) 的运动协调和路径规划算法，使用适当的、与状态相关的性能指标来捕获车辆与环境、任务目标的相互作用，以及网络的系统理论属性。环境包括周围条件（例如风）以及在车辆附近操作的任何对手的实际或感知的对抗行为。 在开发这些结果中将发挥重要作用的一个关键概念是广义（Zermelo）Voronoi 图，它可以捕获网络中代理状态的动态（例如，估计到达时间、路径曲率约束） ）比仅基于欧几里得距离的传统 Voronoi 图好得多。借助这些图，将描述不同代理之间和/或代理与一组目标之间的邻近关系，并开发分散和分布式控制策略。如果成功，这项研究的结果将使得小型-扩展自主空中、海上或水下航行器，在局部强风或强流且机载功率有限的情况下，以团队形式运行，以实现共同目标，例如环境监测、分布式监视、多目标分配、协调目标追踪等和计算资源。由于尺寸较小，这些空中平台的轨迹受到盛行风以及可用机载资源的限制的强烈影响。这项研究中开发的工具还将在网络控制系统的其他领域得到应用，在这些领域中，智能代理必须合作才能执行其任务，如果由团队中的单个成员来完成这项任务将是不可行的。工程专业的研究生和本科生以及当地高中教师将通过 NSF 的 REU 和 RET 项目以及佐治亚理工学院的 PURA 和 Dash 本科生研究奖学金计划从参与这项研究中受益。