Robot path planning based on improved ant colony optimization

Aimed at the problems of ant colony algorithm in solving robot path planning, such as slow convergence speed and tending to fall into local optimization, an improved ant colony optimization was proposed. Firstly, a trend heuristic function was established, makeing the nodes to be selected closer to the line between the starting point and the end point, which plays a certain role in avoiding the local optimization, and the Cauchy distribution function was introduced on the basis of this function, which constantly weakens the influence of the trend heuristic function, and improves the global search ability of the algorithm in the later stage. Secondly, the distance heuristic function was improved to speed up the convergence of the algorithm by integrating the distance relationship between the nodes to be selected and the starting point as well as between the nodes to be selected and the end point. Then, a pheromone volatilization factor that is dynamically adjusted according to the number of iterations was proposed, and the pheromone volatilization factor is continuously reduced until the appropriate size, which enhances the global search ability. Finally, a cubic B-spline curve was used for the path smoothing process, which smoothes the paths and shortens the length of the paths. Simulation results show that compared with the traditional algorithm, the improved algorithm reduces the convergence time by 3%, the shortest path length by 12%, and the number of convergence iterations by 76%. The improved algorithm has a shorter minimum path length, faster convergence speed and smoother paths than the traditional algorithm; it proves the effectiveness of the improved algorithm in solving the problems of slow convergence speed and easy to fall into local optimization.