Abstract:

In response to the problems of traditional ant colony algorithm such as lack of initial pheromone, slow convergence speed and inability to effectively avoid obstacles, this study proposes a global path planning based on improved ant colony algorithm.The introduction of a normal distribution function improves the traditional heuristic function, greatly improving the efficiency of the algorithm and shortening the time required for convergence; adaptively adjusting the pheromone volatility coefficient to limit the pheromone range and avoid premature convergence; and smoothing the algorithm path to shorten the path length, thus realising global path planning for the robot.Simulation results show that under a 20×20 environment, the average number of iterations of the proposed algorithm is 28 generations less than that of the traditional ant colony algorithm, resulting in faster convergence, the average number of inflection points is reduced by 33.3%, making the path smoother, overcoming the lack of initial pheromone, speeding up convergence, reducing the number of inflection points, and enabling effective avoidance of obstacles in the environment, demonstrating the feasibility of the algorithm.

Key words: environmental modeling, improved ant colony algorithm, global path planning algorithm, normal distribution function, improved the heuristic function, pheromone volatility factor, limiting pheromone concentration, path smoothing