Research On UAV Trajectory Planning And Deployment Based On Bionic Optimization Algorithm

In the current context of the interconnection of all things,the related applications of VANET and UAV are becoming more and more widespread,especially in some urban communication scenarios(such as emergency communications).Combined with VANET,UAV can provide good communication services for vehicles and users who cannot access the base station signals.Two major applications of UAV in urban emergency scenarios are designed and studied in this paper.First of all,in the urban emergency scene,in order to better understand the ground disaster situation,use UAV aerial photography of each area that needs to be monitored,and then transmit the captured picture information to the driving vehicle.During this process,the flight path of the UAV will have a certain impact on the information collection and transmission performance.In order to study the optimal UAV trajectory planning,this paper establishes a model of information transmission during the cooperative movement of UAV and vehicle.For this model,the traditional particle swarm algorithm can be used to solve the UAV flight speed.However,the order in which the UAV traverses the collection points is a discrete problem,so the particles must be discretized to make the particle search space change from a continuous space to a discrete space,thereby solving the flight from the UAV to the next collection point destination.To this end,it is necessary to combine the original particle evolution mode with the discretized particle evolution mode.At the same time,considering that the particle swarm algorithm is prone to premature convergence during the calculation iteration process,the hybrid and mutation ideas in the genetic algorithm are introduced to further improve the algorithm,and finally a new hybrid algorithm is obtained-genetic hybrid particle swarm optimization algorithm.The final simulation experiment proves that the proposed algorithm can achieve a faster convergence speed on the basis of ensuring the quality of information transmission than the unimproved algorithm.In the event of major disasters such as earthquakes,infrastructure such as base stations and roadside units are destroyed.At this time,deployment of UAVs as aerial base station is used to quickly restore communications between vehicles after disasters and control of key areas.Considering the limitation of the number of flying UAVs,it is impossible to achieve full coverage of all road sections.Therefore,UAVs should focus on covering hotspots with dense vehicles and frequent communication.In view of the large amount of literature that has done related research on hotspot area division methods,the focus of this study is to achieve a limited number of maximize coverage of as many important hotspot areas as possible.Considering that the cuckoo search algorithm has fewer parameters and a stronger global search capability,this paper uses this algorithm for UAV deployment.However,the traditional cuckoo search algorithm has a strong randomness in the process of updating the nest,and often unreasonable nest positions appear.In this regard,this paper proposes an improved cuckoo search algorithm to solve the optimal deployment position of the UAV.The final simulation experiment results show that the improved cuckoo search algorithm proposed in this paper has a faster convergence rate than the algorithm before the improvement,and the result obtained by the algorithm is close to the target optimal solution.