PSO优化算法在平滑路径规划中的应用

Application of PSO Optimization Algorithm in Smooth Path Planning

粒子群(PSO)算法是路径规划领域最常用的算法之一,但也有着缺乏对速度的动态调节、容易出现局部最优等局限性。针对这类问题,提出一种基于种群进化状态的自适应粒子群算法(ES-PSO)。该方案对种群进化能力进行更适当的评估,从而有效提高寻优能力,并针对移动机器人路径选择应用场景,提出将ES-PSO算法与贝塞尔曲线相结合的方法使路径更加平滑。仿真结果表明,该方法相比几种传统的粒子群算法,在收敛精度及寻优能力上均有3%~8%的提高,能够有效、快速地寻找到机器人的平滑最优路径。

The particle swarm optimization(PSO)algorithm is one of the most commonly used algorithms in the field of path planning,but it also has limitations such as the lack of dynamic adjustment of speed and the prone to local optimization.This paper proposes an adaptive particle swarm optimization algorithm(ES-PSO)based on the evolution state of the population for such problems.The pro⁃gram evaluates the evolution ability of the population more appropriately,thereby effectively improving the ability to find the best.Aim⁃ing at the application scenario of mobile robot path selection,the ES-PSO algorithm combined with Bezier curve method is proposed to make the path smooth.The simulation results show that compared with several traditional particle swarm algorithms,this method has a 3%~8%improvement in convergence accuracy and optimization ability,which can be effective and quickly find the smooth optimal path to the robot.