Cooperative Tracking of Multiple Targets by a Team of Autonomous UAVs

This research is concerned with dynamically determining appropriate flight patterns for a set of autonomous UAVs in an urban environment, for persistent and accurate tracking of moving ground targets. The authors assume that there are limited communication capabilities between the UAVs, and that there exist possible line of sight constraints between the UAVs and the targets. Each UAV (i) operates its own dynamic feedback loop, in a receding horizon framework, incorporating local information on the targets (from UAV i perspective) as well as remote information on the targets (from the perspective of the ‘neighbor’ UAVs) to determine the optimal flight path of UAV i over the planning horizon. This results in a decentralized and more realistic model of the real-world situation. As the flight-plan optimization formulation is NP-hard, a new heuristic for continuous global optimization is applied to solve for the flight plan. Results show that efficient flight patterns for the UAVs can be achieved.