Abstract:

This paper studies the problem of using multiple unmanned
air vehicles (UAVs) to search for moving targets with
sensing capabilities. When multiple UAVs (multi-UAV) search for a
number of moving targets in the mission area, the targets can intermittently
obtain the position information of the UAVs from sensing
devices, and take appropriate actions to increase the distance
between themselves and the UAVs. Aiming at this problem, an
environment model is established using the search map, and the
updating method of the search map is extended by considering the
sensing capabilities of the moving targets. A multi-UAV search path
planning optimization model based on the model predictive control
(MPC) method is constructed, and a hybrid particle swarm optimization
algorithm with a crossover operator is designed to solve
the model. Simulation results show that the proposed method can
effectively improve the cooperative search efficiency and can find
more targets per unit time compared with the coverage search
method and the random search method.