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

该奖项的研究目标是开发小型/微型无人机（UAV/MAVs）的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，捕获车辆与环境的相互作用，使命目标，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。 一个关键的概念，将发挥主要作用，在发展这些结果将是广义（策梅洛）Voronoi图，它可以捕捉动态的状态，代理在网络中（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图好得多。借助这些图，可以表征不同智能体之间和/或智能体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，本研究的结果将使小型自主航空，海洋或水下航行器能够以团队的形式朝着共同的目标进行操作，例如环境监测，分布式监视，多目标分配、协调的目标追踪等。由于这些空中平台体积小，其轨迹受到盛行风的强烈影响，并受到现有机载资源的限制。在这项研究中开发的工具也将在其他领域的网络控制系统，智能代理必须合作，以执行他们的使命，这将是不可行的任务，如果它是由一个单一的团队成员进行的应用。研究生和本科工程专业的学生以及当地的高中教师将受益于他们通过NSF参与这项研究？的REU和RET项目，并通过格鲁吉亚技术？的PURA和Dash本科研究奖学金计划。

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.