Research On UAV Track Research Based On Improved Ant Colony Algorithm

With the progress of China's aviation industry,unmanned aerial vehicles have made great progress in the military and civilian fields and made great progress.At present,the track planning technology has become one of the research directions of UAV technology with its advantages of ensuring flight safety and air penetration.However,the track planning technology still has problems such as blind selection of paths and slow calculation speed.Therefore,it is very important to study the track planning of drones.In order to improve the convergence speed of the traditional ant colony algorithm,the track path is optimized.In this paper,the basic principles of UAV flight path planning are analyzed,and several typical track planning algorithms are studied.An improved ant colony algorithm is proposed for the problem that traditional ant colony algorithm is easy to fall into local optimum and long search time.The state transition guidance method is used to predict the heading of the target node,and the guiding factor is set according to the path length and the number of iterations,which reduces the randomness of the initial search ofthe ant,and advances the search direction toward the target point;by optimizing the ant-circumference model and the node pheromone,the path is optimized.The length is sorted,the weight is updated according to the ant pheromone contribution degree,the state transition probability is increased,and the local inhibition is trapped in the optimal solution.In the pheromone volatilization,the Gaussian distribution is used to dynamically adjust the volatilization factor,and the pheromone concentration after volatilization Limited to the range of the maximum and minimum limits.Finally,a grid-like track planning space model is established,and the influence factors of the track planning and the performance index of the fuselage,as well as the flight terrain threat index and the track comprehensive cost function are set.It is simulated and analyzed based on the improved ant colony algorithm.The simulation results show that the improved ant colony algorithm shortens the average shortest path by 3.2%and the calculation time by 4s.This method expands the search space of feasible paths and preferentially searches for the shortest path,which provides a theoretical reference for track planning technology.