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

该合同的研究目标是为小型/微型无人机（uav /MAVs）开发运动协调和路径规划算法，使用适当的、状态相关的性能指标，捕捉飞行器与环境、任务目标以及网络的系统理论属性之间的相互作用。环境包括环境条件（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。在发展这些结果中发挥重要作用的一个关键概念将是广义（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），比仅基于欧几里德距离的传统Voronoi图要好得多。在这些图的帮助下，不同智能体之间和/或智能体与一组目标之间的接近关系将被表征，并将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小型自主空中、海上或水下航行器能够在局部强风或洋流存在且机载功率和计算资源有限的情况下，以团队方式朝着共同目标（如环境监测、分布式监视、多目标分配、协调目标追踪等）进行操作。由于这些空中平台体积小，其飞行轨迹受到盛行风的强烈影响，同时也受到机载可用资源的限制。在这项研究中开发的工具也将在网络控制系统的其他领域中找到应用，在这些领域中，智能代理必须合作执行他们的任务，如果由团队中的单个成员承担，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的高中教师将从他们通过NSF参与的这项研究中受益。REU和RET项目，通过佐治亚理工学院？美国的PURA和Dash大学本科生研究奖学金项目。

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.