Optimization of unmanned aerial vehicle inspection path planning based on ant colony algorithm
To solve the problems of traditional ant colony algorithm,such as limited path search direction and vision,inability to find the shortest path,and prone to deadlock,an optimized ant colony algorithm suitable for grid map environment is proposed.This method preprocesses the grid map environment,extracts the feature points of obstacles,and selects these feature points as path-finding access nodes.Then,based on the ant colony algorithm,the uneven distribution of pheromone is used to improve the construction effi-ciency of the solution,and the Tent chaotic map is used to enhance the guidance of the path search,and the pheromone volatilization coefficient is dynamically adjusted to avoid premature convergence of the algo-rithm.Finally,by comparing the simulation results of the proposed algorithm and the traditional ant colony algorithm in the complex grid map environment,the feasibility,effectiveness and superiority of the proposed algorithm are verified.
ant colony algorithmunmanned aerial vehicle inspectionpath planningdata processingstructural optimization
万方数据
维普
