部分未知环境中移动机器人动态路径规划方法

Dynamic path planning of mobile robot in partly unknown environment

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.

A dynamic path planning is proposed for partly unknown environment.With the globally optimal path treated as a dynamically changed state,the dynamic path planning is able to be executed online by tracking the globally optimal path using particle filter.The tracking including a prediction from the motion of the robot and a updating from the up to date environmental information.So that dynamic path planning abandons the ＂following after planning＂ strategy which is generally adopted by global path planning approaches and adopts a ＂following while planning＂ strategy instead.Simulations and experiments show that,compared with the classical global path planning approaches,the proposed method improves the efficiency by reducing the time in waiting for planning result and provides a global adaptability to both the motion error of robot and the partly unknown environment.