基于遗传算法的静态环境全局路径规划

Genetic algorithm based global path planning in a static environment.

静态环境中移动机器人全局路径规划一直是路径规划中的一个重要问题.作者提出了基于遗传算法的静态环境下机器人全局路径规划方法.该方法首先提出机器人工作空间中环境信息的神经网络模型,并利用该模型建立机器人免碰撞路径与神经网络输出的关系,然后将需规划的路径的二维编码简化成一维编码,并把免碰撞要求和最短路径要求融合成一个适应度函数.通过对算法进行实验仿真表明,提出的全局路径规划方法是正确和有效的.

Mobile robot global path planning in a static environment has been an important problem all along. The paper proposes a method of global path planning based on genetic algorithm. The neural network model of environmental information in the workspace for robot is constructed. The relationship between a collision-free path and the output of the model is established based on this model and the two-dimensional coding for the via-points of path is converted to one-dimensional one. Then the fitness of the collision-free path and that of a shortest distance are fused to a fitness function. The simulation results show that the proposed method is correct and effective.