computational robotics

from tasks to motions

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

  1. maximize the area covered by sensors mounted on each UAV;
  2. provide persistent surveillance;
  3. maintain high sensor data quality, and
  4. 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. The efficiency and scalability of PARCov is demonstrated in simulation using different risk models and an increasing number of UAVs to conduct risk-sensitive surveillance. Evidence of successful physical deployment is provided by experiments with AscTec Pelican quadcopters.

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.

Related Publications

  • Wallar A, Plaku E, and Sofge D (2014): “A Planner for Autonomous Risk-Sensitive Coverage (PARCov) by a Team of Unmanned Aerial Vehicles.” IEEE Symposium on Swarm Intelligence, pp. 283–289  [publisher]  [preprint]