To address the multi-objective unmanned aerial vehicle(UAV) path planning problem in a wireless sensor network(WSN)

a multi-objective unmanned aerial vehicle path planning strategy based on particle swarm optimization is proposed.First

a grid-based sensor field and an information collection model are introduced to define the unmanned aerial vehicle coverage area and achieve line-of-sight communication between unmanned aerial vehicle and sensors in the target area.Second

a multi-purpose fitness function is proposed

reflecting the sensor density of each type of sensor in the unmanned aerial vehicle coverage area

as well as the changing situation of the value of sensed information over time.Finally

a biologically inspired algorithm based on particle swarm optimization is proposed to effectively derive the optimal positions of the unmanned aerial vehicle.The experimental results show that the proposed method can maximize the utility of the objective function and achieve rapid convergence and significant advantages in finding the optimal solution compared with other methods.