Path planning optimization using the bidirectional ant colony algorithm

This paper proposes a bidirectional ant colony algorithm, with angle parameters for robot path searching, to overcome the challenges of insufficient search accuracy and slow convergence speed of traditional ant colony algorithms. It first optimizes the starting position of the ant colony using the ant colony algorithm, selecting the appropriate node from a series of starting points in the map based on the ant number, increasing the diversity of solutions, and simultaneously obtaining the global optimum solution. At the same time, the pheromone update rules are improved, and pheromone rewards are given to the optimal path found by the current iteration so that it can guide the ants′ path-finding process in the next iteration. Finally, in order to improve the speed of convergence of the algorithm, an angle parameter is proposed and added to the transition probability of the ants so that the ants can preferentially select the node with a smaller angular difference from the target node when selecting the next node, thereby increasing the probability of obtaining the optimal solution and speeding up the convergence in the later stages of the algorithm. The results of several simulation experiments show that the path-searching ability and the iterative convergence of the algorithm proposed in this paper are significantly improved. © 2023 Editorial Board of Journal of Harbin Engineering. All rights reserved.