Research On UAV Track Planning Method Based On Improve Ant Colony Algorithm

With the advancement of modern communication technology,automatic control technology and chip manufacturing technology,UAV(Unmanned Aerial Vehicle)technology has developed rapidly in recent years.Compared with manned aircraft,UAVs are more likely to excel in the face of dangerous and complex missions due to their superior physical properties.Recently,with the rapid development of science and technology,the application of UAV is expanding,and their mission planning and autonomous flight capabilities are receiving more and more attention from researchers.However,the current trajectory planning technology still has problems such as blind path selection and slow algorithm calculation speed.Based on two-dimensional plane and three-dimensional space,this paper focuses on the problem of UAV’s trajectory planning,and then proposes an effective trajectory planning algorithm,and finally optimizes the trajectory.The research contents of this paper are as follows:Firstly,after considering the physical properties of the quadrotor UAV,the general flight environment and the current GPS positioning accuracy,this paper applies the raster map method to construct a two-dimensional planning plane using digital processing.In the 3D spatial environment,the trajectory is also affected by the obstacle height,so in order to improve the accuracy of the trajectory planning in the flight environment,the 3D planning space is processed by two-dimensional three-time convolutional interpolation processing on the basis of the 2D plane,which makes the built spatial model more consistent with the real environment.Secondly,the traditional ant colony algorithm is used as the basis of the trajectory planning algorithm in this paper,after expounding and analyzing the applicable scope and advantages and disadvantages of the algorithm.In this paper,two methods are proposed to improve the classical ACO in terms of the two characteristics of the ACO,namely the long time spent on finding the optimal path and the tendency to fall into the local optimum during operation.The first method is to add a bootstrap factor to the ant colony algorithm which is mainly used to enhance the purposefulness of the ant colony search activity by updating the evaporation rate of pheromone in the algorithm,to improve the convergence speed and enable the algorithm to find feasible solutions faster.The second method is to combine the Social Spider Optimization Algorithm with the traditional ant colony algorithm,because the swarm spider algorithm converges faster,while the ant colony algorithm is more advantageous in the global planning of paths.In this paper,the two algorithms are fused from the perspective of complementary advantages,so that the advantages of the fused two algorithms can be revealed and have stronger applicability in dealing with the path planning problem.Finally,in order to verify the feasibility of the improved ant colony algorithm in solving the UAV trajectory planning problem and the optimization effect of the trajectory planning,this paper uses Matlab to conduct simulation experiments on the improved fusion ant colony algorithm in two-dimensional plane and three-dimensional space respectively.After a comprehensive analysis and comparison of the experiments,the corresponding planning tracks of the traditional ant colony algorithm and the improved ant colony algorithm are comprehensively explored.It is further shown that the improved ant colony algorithm can ensure the basic maneuverability of UAV planning and is feasible and reliable in both two-dimensional plane and three-dimensional space.In addition,the corresponding planning track performance index is more effective than the traditional ant colony algorithm.Under the three-dimensional environmental conditions,the corresponding track length and algorithm operation time decrease by 23.0% and 22.2% respectively.The application of the three-time uniform B-sample method to optimize the initial trajectory of complex three-dimensional space planning,so that the trajectory is stable and smooth,with higher accuracy and stronger actual flight capability.