A planner for autonomous risk-sensitive coverage (PARCov) by a team of unmanned aerial vehicles

This paper proposes a path-planning approach to enable a team of unmanned aerial vehicles (UAVs) to efficiently conduct surveillance of sensitive areas. The proposed approach, termed PARCov (Planner for Autonomous Risk-sensitive Coverage), seeks to maximize the area covered by the sensors mounted on each UAV while maintaining high sensor data quality and minimizing detection risk. PARCov leverages from swarm intelligence the idea of using simple interactions among UAVs to promote an emergent behavior that achieves the desired objectives. PARCov uses a dynamic grid to keep track of the parts of the space that have been surveyed and the times that they were last surveyed. This information is then used to move the UAVs toward areas that have not been covered in a long time. Moreover, a nonlinear optimization formulation is used to determine the altitude at which each UAV flies. The efficiency and scalability of PARCov is demonstrated in simulation using complex environments and an increasing number of UAVs to conduct risk-sensitive surveillance.