Study On The Path Planning Method Of UAV Group For Environmental Monitoring

The combination of UAV technology and environmental monitoring technology can make environmental monitoring more flexible,reduce costs and expand the scope of monitoring.Using multi-UAV for environmental monitoring can effectively improve the monitoring efficiency and make the collected environmental parameters time-sensitive.The path planning of UAV group is the core part of the environmental monitoring system based on UAV.The path planning method for the purpose of timeliness is of great importance to the path planning of environmental monitoring UAV group.Clustering method and ant colony algorithm are adopted to conduct an in-depth study on this problem.The main indexes of the planning are the task allocation balance among the UAV groups and the timeliness of information collection.The method of UAV group path planning of environmental monitoring system is mainly divided into the task assignment of UAV group and the path planning of UAV.In the task allocation of multiple UAV,the k-means clustering algorithm is improved with the balanced UAV task allocation and the time as the main index.Firstly,the clustering algorithm is used to realize the clustering of targets.The clustering results obtained by the improved k-means clustering algorithm are more balanced and concentrated.Then the enumeration method is used to match the target cluster with the UAV to get the optimal task assignment result.Ant colony algorithm is easy to get into the local optimal solution when solving the problem of UAV multimission target point.By improving the ant colony algorithm,the ability of searching the global optimal solution is improved.The update mode of pheromone is adjusted to reduce the running time of the algorithm.By changing the state transition probability of the ant colony algorithm and adjusting the pheromone updating mode,the improved ant colony algorithm reduces the convergence time of the algorithm,and the convergence time is smaller than that of the genetic ant colony hybrid algorithm.Path planning based on improved ant colony algorithm can effectively reduce the flight time of UAV.Finally,on the basis of the above algorithm,the simulation experiment of UAV group path planning is completed by MATLAB simulation software.The experimental results show that applying the improved k-means clustering algorithm and the improved ant colony algorithm to the path planning of the UAV group for environmental monitoring can effectively improve the task allocation balance of the UAV group and reduce the flight time of the UAV.Mission allocation reached 0.94:1:1 and reduced flight time by 4.3%.