针对部分未知环境,提出一种基于粒子滤波的动态路径规划方法.将全局最优路径视为受机器人运动及环境影响的变化量,采用粒子滤波算法,利用机器人运动信息预测路径,并利用实时环境信息更新路径,通过在线跟踪全局最优路径获得不断更新的全局优化路径.将传统全局路径规划先规划后执行的模式改为边规划边执行的模式,既减少了等待时间,又为机器人的移动误差及部分未知环境提供了较强的适应能力.仿真及实验验证了该方法的有效性. Aiming at partially unknown environment, a dynamic path planning method based on particle filter is proposed. The global optimal path is considered as the change of the robot’s motion and environmental impact. Particle filter algorithm is used to predict the path by using the robot motion information. Information update path to continuously update the global optimization path by tracking the global optimal path.Transforming the traditional global path planning after the first planning to the one executed while planning and reducing both the waiting time and the robot movement Error and some unknown environment provide strong adaptability.The simulation and experimental results verify the effectiveness of the method.