Path Planning of Mobile Robot based on Improved Ant Colony Algorithm

Abstract

Aiming at the problems of traditional ant colony optimization algorithm in path planning, such as slow convergence, easy to fall into local optima, and too many inflection points, an improved ant colony algorithm is proposed. In order to reduce the redundant search, the grid map is preprocessed. By improving the composition of the heuristic function and introducing the guiding function, the mobile robot can be guided to find a better path quickly. The algorithm combines ant colony algorithm with genetic algorithm, adopts elite retention strategy, and puts forward the formulas of crossover and mutation probability. The pheromone update mechanism of the algorithm is improved, and the pheromone concentration of different paths is updated differentially, which can speed up the convergence speed of the algorithm. Finally, the optimal path is further optimized, and smooth operations are taken on it. After a large number of comparative simulation experiments, the results show that the improved algorithm is better than other comparative algorithms in complex environment.