Artificial potential field guided ant colony algorithm for mobile robot path planning

An enhanced ant colony algorithm based on potential field guidance is proposed to address the problems of slow convergence, local optimization, and many turning points in the traditional ant colony algorithm planning in a two-dimensional grid environment. Artificial potential field directs ants to approach the target point and avoid obstacles during the search process, thereby improving global convergence. Adaptive volatility rate improves pheromone renewal rules and dynamically adjusts convergence speed. Pheromone increments combine path length, global optimal path, and worst path to improve global search capability. Finally, the generated path is optimized to become smoother and shorter. Simulation results demonstrate that the improved algorithm is better than the comparison algorithm in terms of path length, number of iterations, and number of turns.