摘要
随着无人汽车、移动机器人智能设备的不断发展和应用,PRM路径规划算法应用越来越广泛,PRM算法是一种随机路标图法,传统的PRM算法会舍弃处于障碍物区域内的采样点,导致采样点不足,寻到次优路径甚至不能寻到路径,在均匀撒点的基础上引入人工势场,使落在障碍物内的点移动到自由空间内,达到增加自由空间内节点数的效果,再利用A*算法进行路径搜索,设计了一种基于人工势场法与A*算法相结合的路径优化算法。仿真结果表明,改进的PRM算法提供的路径合理,采样点的利用率高,实现了路径目标的优化。
With the continuous application and development of intelligent equipment such as unmanned vehicles and mobile robots,the PRM path planning algorithm is more and more widely used.The PRM algorithm is a random roadmap method.The traditional PRM algorithm will discard the sampling points in the obstacle area,resulting in insufficient sampling points,finding a suboptimal path or even failing to find a path.This article introduced an artificial potential field on the basis of evenly spreading points,so that the points falling in the obstacle could move into the free space to increase the number of nodes in the free space.A algorithm was used to search for the path,and a path optimization algorithm based on the combination of artificial potential field method and A algorithm was designed.The simulation results show the rationality of the path provided by the improved PRM algorithm and the high utilization rate of sampling points,thereby realizing the optimization of the path target.
作者
贺颖
周盈伶
布升强
杨家富
HE Ying;ZHOU Ying-ling;BU Sheng-qiang;YANG Jia-fu(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing Jiangsu 210037,China)
出处
《林业机械与木工设备》
2021年第5期34-39,共6页
Forestry Machinery & Woodworking Equipment
基金
江苏省大学生实践创新训练计划项目(201910298054Z)
南京市科技创新项目(2016CX020459)。
关键词
无人车
路径规划
人工势场
A*算法
路径优化
unmanned vehicle
path planning
artificial potential field
A*algorithm
path optimization
