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

无人机(UAV)越来越多地被用于一些商业和民用应用，如包裹递送、交通监测、精准农业、遥感、边境巡逻、灾害监测、救灾和搜救行动，为附近的地面指挥站收集数据/图像。目前基于无人机的操作的实施在很大程度上依赖于人类控制器的控制、推理、任务分配和规划，这可能会限制无人机在执行任务之前对操作场进行完全可见的任务，并且在执行任务期间无人机与地面站之间或队友无人机之间无法进行可靠和持续的通信。无人机在这种非结构化和未知的环境中可以特别有用，以提供灵活的测量或搜救行动。因此，未来无人机技术的重点是发展小型、低成本、具有更高自主水平的智能无人机。这种无人机可以促进由一支合作无人机机队执行的各种复杂任务，人为干预最少，成本更低。这项研究的目标是在最少的人为干预下，为在动态和未知环境中完全自主的多智能体系统(例如，无人机)的操作、态势感知、协调和通信开发理论和实践框架。该研究为自主多智能体系统在远程动态环境中的应用提供了新的思路。该项目包括一套完整的研究、实施和实验验证推力，以开发无人机系统中自主决策、联盟形成、协调、频谱管理和任务分配的新框架。开发的技术可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，在这些系统中，需要在有限的人工干预下进行快速搜索、监视和反应。该项目还提供了一些教育和推广活动，以整合这项研究的结果，包括课程改进、学生辅导、吸引代表不足的少数族裔和女性学生，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动手传感实验，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智力优势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.