Abstract:

The traditional ant colony algorithm is characterized by a slow convergence speed, numerous turning points, and a tendency to fall into local minima. These characteristics make the algorithm less effective for path planning research in mobile robotics. Therefore, this paper proposes an improved ant colony algorithm and applies it to global path planning for robots. The A* algorithm is used to quickly plan a path and increase the initial pheromone of that path, so that the improved algorithm is guided by the global path during the local search, preventing excessive ants from entering dead ends, and reducing the randomness in early search stages. A smoothness function and a dual heuristic function are introduced into the transition probability calculation, enhancing the safety and search efficiency of the robot's movement and improving the algorithm's adaptability to different environments. A strategy for automatic pheromone evaporation factor update and improved pheromone update rules are established to avoid getting trapped in local optima, strengthen the ants' search capabilities and enhance the quality of the planned paths. A 2D grid simulation environment is built using Matlab, and the results show that compared to other improved algorithms, the improved ant colonyalgorithm can plan higher quality and smoother paths in various environments.

Key words: ant colony algorithm, mobile robot, path planning, pheromone, heuristic function