CAREER: Toward Autonomous Decision Making and Coordination in Intelligent Unmanned Aerial Vehicles' Operation in Dynamic Uncertain Remote Areas

无人驾驶飞行器（UAV）已经越来越多地用于若干商业和民用应用中，诸如包裹递送、交通监测、精准农业、遥感、边境巡逻、危险监测、救灾以及搜索和救援行动，以收集数据/图像用于附近的地面指挥站。基于UAV的操作的当前实现严重依赖于来自人类控制器的控制、推断、任务分配和规划，这可能限制无人机在任务中的操作，其中在使命之前操作场对于人类控制器是不可完全观察的，并且在使命期间在UAV与地面站之间或在队友UAV之间不可用可靠且连续的通信。无人机在这种非结构化和未知的环境中特别有用，可以提供敏捷的测量或搜索和救援行动。因此，未来无人机技术的重点是发展小型、低成本、智能化的无人机，并具有更高的自主性。 这种无人机可以促进由一个合作的无人机机群执行的广泛的复杂任务，最小的人为干预和较低的成本。本研究的目标是为完全自主的多智能体系统（例如，无人机）在动态和未知的环境中，以最少的人为干预。这项研究可以促进一组新的应用程序的自治多智能体系统在远程和动态环境。该项目涉及一套综合的研究，实施和实验验证的推力，以开发新的框架，自主决策，联盟的形成，协调，频谱管理和无人机系统的任务分配。开发的技术可以用于其他多智能体认知系统，如机器人系统和自动驾驶车辆，其中需要快速搜索，监视和反应，有限的人为干预。该项目还提供了一些教育和推广活动，将这项研究的结果整合到课程改进，学生导师，吸引代表性不足的少数民族和女学生，通过与弗拉格斯塔夫科学节的合作，为中小学生开发基于无人机的实际感知实验，并向社区推广，以提高公众对无人机系统新应用的认识。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Unmanned aerial vehicles (UAVs) have been increasingly utilized in several commercial and civil applications such as package delivery, traffic monitoring, precision agriculture, remote sensing, border patrol, hazard monitoring, disaster relief, and search and rescue operations to collect data/imagery for a ground command station nearby. Current implementations of UAV-based operations heavily rely on control, inference, task allocation, and planning from a human controller that can limit the operation of drones in missions where the operation field is not fully observable to the human controller prior to the mission and reliable and continuous communication is not available between the UAVs and the ground station or among the teammate UAVs during the mission. The UAVs can be particularly useful in such unstructured and unknown environments to provide agile surveying or search-and-rescue operations. Therefore, the future of UAV technology focuses on the development of small, low-cost, and smart drones with a higher level of autonomy. Such drones can facilitate a wide range of sophisticated missions performed by a fleet of cooperative UAVs with minimum human intervention and lower cost. The objective of this research is to develop theoretical and practical frameworks for operation, situational awareness, coordination, and communication of a network of fully autonomous multi-agent systems (e.g., UAVs) in dynamic and unknown environments with minimum human interventions. This research can facilitate a new set of applications for autonomous multi-agent systems in remote and dynamic environments. This project involves an integrated set of research, implementation, and experimental validation thrusts to develop novel frameworks for autonomous decision making, coalition formation, coordination, spectrum management, and task allocation in UAV systems. The developed techniques can be utilized in other multi-agent cognitive systems such as robotic systems, and autonomous driving vehicles where quick search, surveillance, and reactions are required with limited human interventions.This project also offers a number of educational and outreach activities to integrate the results of this research in curriculum enhancement, student mentorship, engaging underrepresented minority and female students, developing hands-on UAV-based sensing experiments for elementary and middle school students and outreach to the community to enhance public awareness about new applications of UAV systems through collaboration with Flagstaff Festival of Science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.