A Clustering Algorithm Of Large Scale FANET And UAV Trajectory Planning

With the rapid development of UAV's automatic flight control processing system and integrated circuit technology,a large number of UAVs are deployed to build a large-scale UAV network.Because UAVs are widely used in disaster area rescue and border monitoring,largescale UAV network has gradually become a research hotspot.In order to ensure the network scalability and maintainability of large-scale UAV network,clustering method is introduced into the flying ad hoc network(FANET).In order to solve the problem of low transmission delay in large-scale UAV network,this paper proposes a cluster networking method based on delay prediction.At the same time,in order to solve the problem of the lack of base station in remote areas,UAV needs to carry out tasks for a long time,this paper uses the method of ferry UAV assistance,and designs the track of ferry UAV under the low delay requirements.The main contents of this paper are as follows:Firstly,this paper proposes a large-scale unmanned unit network method based on time delay prediction.In this paper divides the network into three layers: common nodes(CM),cluster head nodes(CH)and super cluster head nodes(Super CH).Under the delay requirement,based on the weighted distributed clustering algorithm,a network layering method suitable for this scenario is proposed.In the clustering algorithm,the transmission delay and energy are selected as the system parameters.For U2 U communication of UAVs,we base on the IEEE 802.11 p.Based on CSMA / CA mechanism,a delay prediction method is proposed,and the cluster head election algorithm is designed by this method.Besides,we use distributed clustering algorithm to build cluster structure and maintain the cluster structure.The simulation results show that the proposed clustering algorithm based on time delay prediction is lower than the classical algorithm,including minimum ID algorithm and low-power adaptive clustering algorithm(LEACH)in the overall data collection delay.This shows that the cluster based on the algorithm proposed in this paper has lower data collection delay in the whole network,which is suitable for the large-scale UAV ad hoc network environment with strict time delay requirements for field data monitoring.Then,in view of the lack of base station,in order to ensure the long-term work of UAV cluster,we take advantage of large cache of ferry UAV to collect data in the large-scale UAV network.In order to solve the problem of p delay,we designed the data collection trajectory of the ferry UAV.According to the task distribution,the area is divided,and the cache data of each area is collected by the ferry UAV.In every area,CH collects CMs' data and Super CH collects CHs' data,temporarily caching the data in Super CHs.Finally,the way of ferry UAV collects Super CHs' data and take it back to the ground.In this part,aiming at the problem of data collection of ferry UAV,an algorithm is proposed to minimize the delay of data collection,and the data collection trajectory of ferry UAV is designed.Based on the ant colony algorithm to find the optimal access sequence of the region of the ferry UAV,the paper improves the ant colony algorithm to solve the shortest data collection delay of the ferry UAV when the safe distance of data collection is set.The simulation results show that,compared with other classical algorithms,including simulated annealing algorithm and genetic algorithm,the time delay of the improved ant colony algorithm is lower than the two algorithms in the data collection delay,and in the convergence performance,the algorithm proposed in this paper is also the fastest convergence to the optimal solution,and the calculation speed is better than other algorithms.Comparing and analyzing the UAV networks of different scales,the path length of the improved ant colony algorithm in this paper is significantly lower than that before optimization.