Path Planning Based On Improved Ant Colony Algorithm

With the development and progress of science and technology,the requirements for the performance of intelligent algorithms in the field of path planning are constantly improving,so how to improve the performance of intelligent algorithms and apply it to the field of path planning is particularly important.As an intelligent algorithm widely used in the field of path planning,ant colony algorithm is of great significance to study based on improved ant colony algorithm and its application in path planning.This paper mainly focuses on how to improve the ant colony algorithm and its path planning application.Firstly,according to the research status of ant colony algorithm and its typical path planning application experiments,it is found that ant colony algorithm has three typical problems in the process of optimization: local optimization,low search efficiency and stagnation of search.In view of the above problems,combined with typical improvement strategies of ant colony algorithm,this paper proposes an improved scheme of the algorithm,which will be applied to the application and implementation of two-dimensional and three-dimensional path planning problems.Specific content as follows:Aiming at the problem that ant colony algorithm is easy to fall into local optimum in the process of path planning,this paper proposes the idea of angle division to construct heuristic function to avoid the interference of falling into local shortest distance in the process of path planning.Specifically,in the application of two-dimensional path planning,heuristic functions are constructed by introducing the dynamic division of included angles between the current node and the global endpoint,and between nodes to be selected,so that node selection tends to the global optimal direction.Specifically,in the application of threedimensional path planning,the idea of angle division is taken as an angle factor,a distance factor and a feasibility factor to construct diversified heuristic information,thus effectively improving the trend of the target points of path planning.In view of the low search efficiency of ant colony algorithm in path planning,this paper proposes the principle of initial pheromone selective allocation to solve the problem of slow search caused by lack of pheromone in the early stage.Specifically,in the application of two-dimensional path planning,this paper uses Dijkstra algorithm to roughly plan a suboptimal path,which is used as the initial path for ant colony algorithm to allocate initial information,in order to overcome the phenomenon of slow search caused by insufficient pheromone in the early stage.Specifically,in the application of threedimensional path planning,we introduce a search strategy that combines hierarchical strategy and blocking strategy to reduce the blindness of early search.Aiming at the problem of search stagnation in the path planning process of ant colony algorithm,this paper proposes pheromone adaptive update rules to overcome the shortage of search stagnation caused by blind search and unable to converge.Specifically,in the application of two-dimensional and threedimensional path planning,the positive feedback effect of pheromone in the search process is gradually weakened with the increase of iteration times by limiting the pheromone threshold range and introducing dynamically adjusted pheromone volatilization coefficient,so as to reduce the randomness in the later search period,accelerate the convergence speed and quickly output the optimal solution.