基于改进粒子群算法的机器人路径规划方法

Path planning for mobile robot based on improved particle swarm optimization

提出一种基于粒子群算法的机器人路径规划方法。将路径规划看作一个带约束的优化问题,约束条件为路径不能经过障碍物,优化目标为整个路径的长度最短。机器人工作空间中的障碍物描述为多边型,对障碍物的顶点进行编号。利用粒子群算法进行路径规划,每一个粒子定义为一个由零或障碍物顶点编号组成的集合,在粒子的迭代过程中考虑约束条件,惯性权重随迭代次数动态改变,使算法既有全局搜索能力也有较强的局部搜索能力。仿真结果表明该方法的正确性和有效性。

A new global path planning approach based on particle swarm optimization （PSO） for a mobile robot is presented. Consider path planning as an optimization problem with constraints. The constraints are the path can not pass by the obstacles and the optimization target is the distance of the path is shortest. The obstacles in the robot＇s environment are described as polygons and the vertexes of obstacles are numbered from 1 to n. The particle swarm optimization is used to get a global optimized path. Simulation results prove the effectiveness and practicability of this approach.