Path Planning of Mobile Robot Based on Adaptive Ant Colony Optimization

Abstract

In order to solve the problems of slow convergence speed and poor global search ability in mobile robot path planning, an adaptive ant colony optimization algorithm (AACO) is proposed in this paper. First, in the early stage of the ant colony search, adaptive initial pheromone distribution is used to reduce the blindness of ant colony algorithm. Using adaptive pheromone factor and adaptive evaporation factor to improve the role of pheromone in different periods of convergence of ant colony algorithm. Improve the update mechanism of pheromones and use pheromone preferential limited update to reduce the redundancy of pheromones. A novel adaptive pheromone reconstruction mechanism is proposed to improve the global search capability of the ant colony algorithm. Finally, through two random environment experiments, the proposed algorithm has better path planning ability than some similar algorithms and classical ant colony algorithms.