UAV Path Planning Based on The Fusion Algorithm of Genetic and Improved Ant Colony

摘要

A track planning method for UAV based on the fusion algorithm(GIACO) of genetic algorithm(GA) and improved ant colony algorithm (IACO) is introduced. The optimal solution that obtained by GA initializes the pheromone matrix of ant colony to improve the convergence speed. To save from a trouble of local extremum, the state transition rule of ant colony algorithm is changed and the feasible potential number of grids is considered. The method of smoothing the path uses the gradient descent. And the step size can be adjusted for evading sudden threats. Simulation experiment analysis results show that the fusion algorithm can not only evade threat safely, but also plan the trajectory quickly, safely and effectively.