Safe, Resilient and Efficient Operation of Autonomous Aerial and Ground Vehicles

近年来，自动驾驶车辆单独或与其他自动驾驶车辆协同工作，已成为许多军事和执法任务(例如，监视、远程通信、目标捕获和跟踪，甚至武器交付)不可或缺的一部分。最近，这些自动驾驶车辆(飞行机器人或无人机)的空中版本已经进入民用部门，并已成功地用于许多应用，如火灾探测、作物除尘、航空测量、娱乐业、交通监测、基础设施检查、天气/飓风监测和商业产品交付等。无人机(UAV)市场正在爆炸式增长，这些自动驾驶飞行器往往将不得不在高度不确定的环境中运行，甚至是对抗性的。例如，对于无人驾驶的空中或海洋/水下航行器，风或海流对系统性能有很大影响。同样，自动驾驶车辆必须与周围的交通流量进行操作和互动。本研究开发的理论和方法将使这种自动驾驶车辆和系统能够更好地协调，从而提高它们的效率、可靠性和整体性能。这一研究成果将有助于延长空中和海上自动驾驶车辆的可用性和续航时间，为自动驾驶车辆在交通中的安全运行做出贡献。该项目的教育方面还包括努力让少数群体和其他代表性不足的学生参与研究。这项研究解决了在存在外源或内源干扰的情况下运行的自动车辆的微分对策和最优轨迹生成领域的一个基本问题。基于类Voronoi分解的多智能体系统协调控制分析的最新进展，以及基于水平集的数值技术，将被用来以数值有效的方式解决多智能体追逐-规避和目标分配问题。一种新的可达性集包含性质允许解决一大类具有多个智能体的追逐-逃避问题，即使在漂移场等外部扰动的影响下也是如此。这项研究的结果将使协调策略和分布式追逐-逃避协议超越通常的欧几里德度量标准，在代理之间建立邻近关系，包括时间、能量和燃料。通过利用新的分解方法来求解由水平集传播的随机公式产生的随机偏微分方程组，还将解决对随机环境的扩展。

Autonomous vehicles working alone or in coordination with other autonomous vehicles have become indispensable in recent years for many military and law enforcement missions (e.g., in surveillance, long-range communication, target acquisition and tracking, and even weapon delivery). Most recently, the aerial versions of these autonomous vehicles ('flying robots' or 'drones') have entered the civilian sector and have been used successfully in many applications such as fire detection, crop dusting, aerial surveying, entertainment industry, traffic monitoring, infrastructure inspection, weather/hurricane monitoring, and commercial product delivery, to name a few. The unmanned air vehicle (UAV) market is exploding and most often than not these autonomous vehicles will have to operate in an environment that is highly uncertain, and even adversarial. For instance, for the case of unmanned aerial or marine/underwater vehicles winds or sea currents have a great impact on system performance. Similarly, self-driving vehicles must operate and interact with the surrounding traffic flow. The theory and methodologies developed in this research will make it possible to enable better coordination of such autonomous vehicles and systems, thus increasing their efficiency, reliability, and overall performance. The results of this research will help in extending the usability and endurance of aerial and marine autonomous vehicles and contribute to the safe operation of self-driving vehicles in traffic. The educational aspects of the project also includes efforts for involving minority and other under-represented students in the research. The research tackles a fundamental problem in the area of differential games and optimal trajectory generation for autonomous vehicles operating in the presence of exogenous or endogenous disturbances. Recent advances in the analysis of coordinated control of multi-agent systems in the presence of external flow fields using Voronoi-like decompositions, along with numerical techniques based on level sets will be utilized to solve multi-agent pursuit-evasion and target assignment problems in a numerically efficient manner. A novel reachability set inclusion property allows for the solution of a large class of such pursuit-evasion problems with multiple agents, even under the influence of external disturbances such as the drift field. The results of this research will allow coordination strategies and distributed pursuit-evasion protocols that go beyond the usual Euclidean metrics to establish proximity relationships between the agents, including time, energy and fuel. Extensions to stochastic environments will also be addressed, by leveraging new decomposition methods to solve stochastic partial differential equations arising from a stochastic formulation of level set propagation.