Research on Path Planning of Autonomous Vehicle Based on Improved Ant Colony Algorithm

Addressing the challenge of path planning in autonomous vehicles, this article introduces an improved ant colony algorithm that integrates distance, safety, smoothness, and efficiency considerations. First, the algorithm adjusts the initial distribution of pheromones according to the placement of obstructions. Additionally, it incorporates a distance factor and a smoothness factor into both the heuristic function and the pheromone updating process. Furthermore, the algorithm integrates a dynamic mechanism for adjusting pheromone volatility coefficients to enhance the algorithm's optimization performance and convergence speed. This led to the determination of an extensive optimal path. The experimental findings demonstrate that the refined algorithm proficiently resolves the issues associated with path planning in intricate environments by using a raster map for simulation experiments performed in MATLAB.