Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Abstract

An improved ant colony algorithm is proposed for the traditional ant colony algorithm in mobile robot path planning with low convergence precision and easy to fall into local optimum. Firstly, the transition probability of the algorithm is improved, the steering cost is added, the unnecessary turning is reduced, and the heuristic performance of the heuristic funrtion is not strong enough to improve the path heuristic information. Then an adaptive parameter adjustment pseudo-randomstate transition strategy is proposed to dynamically change the parameter values to avoid prematurely falling into search stagnation and enhance the comprchensiveness of the search. At the same time, the pheromone update method is improved, the pher-omone volatilization coefficient is adjusted, and the ant is found to find the optimal path capability. Finally, through matlab and other algorithms, the simulation results show that the improved ant colony algorithm has a fast convergence speed, the path length and algorithm iteration number arc significantly reduced, and the global optimal path can be obtained, Proving the feasibility and effectiveness of the improved ant colony algorithm, which has certain application value in mobile robot path planning.