移动机器人路径规划中的仿真研究

摘要

Mobile robot path planning problem is studied. Conventional mobile robot path planning algorithms have problems such as long searching time and low efficiency. This paper puts forward a robot path planning method based on particle swarm optimization algorithm (PSO). This method firstly uses neural network to build robot's working environment and establishes new map based on transformation, then the path is represented by particle position,and path length is used as particle swarm of fitness value. Finally the particle position and velocity are updated by the cooperation between particles, and obtains an optimal path from starting point to target. Simulation experiment is carried out with MATLAB, and the experimental results show that the robot path planning method based on PSO improves the computational efficiency and reliability of path planning, and it can be applied to the real -time navigation robot.%研究移动机器人路径规划问题.针对传统移动机器人路径规划算法搜索时间长,效率低,寻优能力差等问题,提出了一种基于粒子群算法的机器人路径规划方法.该方法首先采用神经网络描述机器人工作环境,在此基础上通过坐标变换建立新地图;然后将机器人路径表示为粒子位置,并以路径长度为粒子群的适应度值;最后粒子之间的相互协作,不断更新粒子位置和速度,获得-条从起始点到目标点全局最优路径.在MATLAB平台上对该方法进行了仿真,实验结果表明,基于粒子群的机器人路径规划方法提高了路径规划的计算效率和可靠性,可应用于机器人的实时导航.