基于混沌粒子群优化算法的AGV路径规划研究

目的 优化物流AGV路径最优问题。方法 提出一种改进的混沌粒子群优化算法，采用基于Bézier曲线的路径规划模型，通过调整Bézier曲线的控制点数量，显著改善AGV轨迹路线的长度和平滑度。结果 采用混沌粒子群滤波算法（CPSO）最优化处理Bézier曲线的控制点数，引入适应度函数，评估是否满足终止标准，如果达到最大迭代次数或者在给定迭代次数时未修改最优解则终止CPSO算法，最后利用选取的控制点计算出更短、更平滑的轨迹路线，提高了算法的寻优能力。结论 采用CPSO算法初始化Bézier曲线可以获得更加平滑的最短路径。

AGV Path Planning Based on Chaos Particle Swarm Optimization Algorithm

The work aims to optimize the AGV routing problem of logistics. An improved chaos particle swarm optimization algorithm was proposed. The route planning model based on Bézier curve was adopted to significantly improve the length and smoothness of the AGV trajectory by adjusting the number of control points of the Bézier curve. Chaos particle swarm optimization (CPSO) algorithm was applied to optimize the control points of Bézier curves. The fitness function was introduced to assess whether the termination criteria were met. If the maximum iteration number was achieved or the optimal solution was not modified when the number of iterations was given, the CPSO algorithm was terminated. Finally, the selected control points were used to calculate the shortest and smoothest trajectory, which improved the optimization capacity of the algorithm. Bézier curve initialized by CPSO algorithm can obtain the smoother shortest path.