Improved Ant Colony Optimization Algorithm for Intelligent Vehicle Path Planning

摘要

In order to overcome the shortcomings of original Ant Colony Optimization (ACO) algorithm, such as slow convergence speed and easily falling into the local optimum during solving path planning problem, an improved ACO algorithm is proposed by improving its heuristic function, pheromone allocation mechanism and path selection strategy. Simulation results show that the improved ACO algorithm is effective. In this paper, the improved ACO algorithm is applied to the intelligent vehicles path planning. In order to meet the requirements of the intelligent vehicle actual trajectory, the B-spline curve is used to smooth the path generated by the improved ACO algorithm. The path following simulations in CarSim software show that the actual vehicle trajectory can conform with the target path, and the vehicle can keep the handling stability.