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

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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.