Research on Fusion Algorithm for Path Planning based on Improved A* and Ant Colony

Aiming at the problems of slow convergence speed and easy to fall into local optimal solutions in the application of ant colony algorithm for path planning, a path planning fusion algorithm based on improved A* and ant colony is proposed. Firstly, the A* algorithm is improved by introducing a dynamic weighting factor Q to improve the search speed of the algorithm; then, the improved A* algorithm is utilized for path preplanning, and the regional pheromone concentration of the ant colony algorithm is redistributed to reduce the convergence time of the ant colony and to improve the path search efficiency; secondly, a new weight function is added and the transfer probability of the ant colony is improved to reduce the blindness of the transfer and the number of iterations; and finally. Three times B-splines are introduced to optimize the smoothness of the path. Analyzed by MATLAB simulation in the map set in the paper, the fusion algorithm reduces the length of the optimal path by 5.96%, the number of iterations by 34.42%, and the average time consumed by 4.47% compared with the traditional ant colony algorithm.