Reserch On Uav Path Planning Algorithm For Data Acquisition

With the continuous development of the global Internet of Things(IoT)and the increasing number of IoT connections,traditional IoT sensor data collection methods have disadvantages such as high cost and small communication range.Unmanned aerial vehicle(UAV),with its advantages of low cost and high mobility,can effectively solve the problem of largescale sensor data acquisition of the Internet of Things.However,the limited energy becomes the main factor that restricts UAV to complete acquisition task.Therefore,this thesis studies the UAV flight planning to reduce the energy consumption,establishes the mathematical model between UAV and sensor,and proposes optimization algorithm to improve the data acquisition efficiency of UAV.The main contents and innovations of this thesis are listed as follows:A path planning algorithm based on Hilbert curve and genetic algorithm is proposed.A system model of communication between UAV and sensor nodes is built.Large-scale sensor nodes are randomly distributed in the monitoring area,and UAV collects sensor data directly above the node.The objective is to reduce the total distance of UAV by optimizing the UAV flight path,and optimize the time to obtain the UAV flight path to ensure the timeliness of information.Simulation results show that compared with simulated annealing particle swarm optimization algorithm and opposite and chaos searching genetic algorithm,this algorithm can reduce the total distance of UAV flight path and greatly reduce the time to obtain UAV flight path.Several new schemes based on UAV multi-node information transmission and node clustering are proposed.A monitoring area model was built in which the distribution of sensor nodes followed the Poisson cluster process(PCP),and the UAV stagnated in the air to collect the data of sensor nodes.In order to ensure efficient acquisition and reliable transmission of sensor data,when the cluster center coordinates are known and unknown,a path node optimization scheme based on node fusion and a clustering scheme based on improved iterative self-organizing data analysis method are proposed respectively,which can make the UAV complete the acquisition task and reduce the UAV flight energy consumption.Simulation results verify the effectiveness of the proposed scheme.