Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm

With the development of artificial intelligence technology, automatic guided vehicle (AGV) path planning is widely used in many fields. Aiming at the problems of low convergence efficiency and easy to fall into local optimization of the traditional ant colony algorithm, this paper proposes an AGV path-planning method based on the artificial potential field-ant colony algorithm. The performance of the algorithm is improved by incorporating the artificial potential field attraction to construct the potential field heuristic function, dynamically adjusting the pheromone volatility coefficient, introducing multiple parameters to dynamically adjust the pheromone increment, and optimizing the path by using the pruning method and other improvement measures. The simulation experiments in 20 × 20 and 30 × 30 grid environments show that the improved algorithm already has significant advantages over the traditional algorithm and other improved ACO algorithms in terms of path length, convergence speed and the number of path inflection points, verifying its high efficiency and stability, and providing a better solution for AGV path planning.