Reactive Motion Planning for Unmanned Aerial Surveillance of Risk-Sensitive Areas

Abstract

This paper proposes 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: i) maximize the area covered by sensors mounted on each UAV; ii) provide persistent surveillance; iii) maintain high sensor data quality; and iv) reduce detection risk. To achieve the stated objectives, PARCov combines into a cost function the detection risk with an uncertainty measure designed to keep track of the regions that have been surveyed and the times they were last surveyed. PARCov reduces the uncertainty and detection risk by moving each quadcopter toward a low-cost region in its vicinity. By reducing the uncertainty, PARCov is able to increase the coverage and provide persistent surveillance. Moreover, a nonlinear optimization formulation is used to determine the optimal altitude for flying each quadcopter in order to maximize the sensor data quality while minimizing risk.