Path Planning Based on an Improved Ant Colony Algorithm

Abstract

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

Research on Intelligent Transportation System Based on Improved Ant Colony Algorithm

“…Use the improved ant colony algorithm to study the intelligent traffic system and plan out the optimal path. The specific implementation steps of the algorithm are as follows: (4) (1) Establish a spatial model and use the Dijkstra algorithm to generate the initial sub-optimal path. Finally complete the path planning initialization;…”

“…In the literature [3], an ant colony algorithm with real-time planning is proposed, and the author adds a local search optimization algorithm for specific problems in the search process, which can realize real-time path induction of vehicles. In the literature [4], by analyzing and transforming traffic constraints, an improved ant colony algorithm is proposed to improve the path planning efficiency of the algorithm. The literature [5] uses Dijkstra-ant colony algorithm to study the path planning of parking system, and combines Dijkstra algorithm with ant colony algorithm to effectively improve the efficiency of path search and improve the quality of search path.…”