Surveillance of Risk-Sensitive Areas by a Team of Unmanned Aerial Vehicles
(collaboration with the US Naval Research Laboratory)
This work develops a reactive motion-planning approach for persistent surveillance of risk-sensitive areas by a team of unmanned aerial vehicles (UAVs). The planner, termed PARCov (Planner for Autonomous Risk-sensitive Coverage), seeks to
- maximize the area covered by sensors mounted on each UAV;
- provide persistent surveillance;
- maintain high sensor data quality, and
- reduce detection risk.
This work is motivated by the viability of UAVs to enhance automation in environmental monitoring, search-and-rescue missions, package delivery, and many other applications. As UAVs become an economically-feasible option for deployment, it becomes important to enhance their autonomy so as to increase productivity. We develop an approach that uses simple interactions among UAVs to promote maximizing the area coverage while maintaining high sensor data quality and reducing the detection risk. The approach provides scalability, making it easy for UAVs to leave and join the mission as needed. Experimental results in simulation and with real quadcopters provide promising results. In future research, we would like to test and enhance the approach so that it can be used in various applications extending beyond laboratory testings.