Path Planning Based on Ant Colony Algorithm with Artificial Potential Field

Ant colony optimization (ACO) is a popular bionic technology optimization method commonly used for shortest path algorithms. However, this method is associated with various issues, including slow convergence rates and deep local optimal solutions. To address these problems, an improved ant colony optimization algorithm is proposed in this paper, utilizing a new heuristic function and an attractive update strategy to enhance the convergence speed and searchability of the ant colony algorithm. The integration of the artificial potential field function formula correctly guides the overall planning path of the ant colony, allowing it to bypass obstacles using the potential field force and disconnect redundant paths. Simulation experiments show that the algorithm proposed in this paper has obtained a better path and fewer iterations compared to the IAACO improved algorithm. This confirms that the algorithm is effective and reliable in path planning, particularly in complex scenarios and special environments.