改进人工势场法的智能车路径规划
Improved Artificial Potential Field Method for Smart Vehicle Path Planning
张宇迪 1龚鹏 1胡为2
作者信息
- 1. 沈阳航空航天大学机电工程学院,沈阳 110136
- 2. 沈阳航空航天大学自动化学院,沈阳 110136
- 折叠
摘要
人工势场法常被应用于路径规划中,针对路径规划中的传统人工势场算法存在前期引力过大、目标不可达和局部极小值点等相关问题,本文提出了一种改进的人工势场算法.首先针对前期引力过大问题,建立动态增益函数,作为引力函数的动态增益系数;然后将智能车到目标点的距离引入到斥力场函数中,使得斥力的构成形式得到改变,解决传统人工势场法中目标不可达的问题;最后,针对局部极小值点问题,增加便于智能车跳出局部极小值点的指引力.分别利用仿真实验和实车试验,结果表明改进后的人工势场法无论从路径长度还是行驶时间上同传统的人工势场法进行对比都得到了优化,可以有效解决传统人工势场法中存在的问题.
Abstract
Artificial potential field method is often used in path planning.Aiming at the traditional artificial potential field algorithm in path planning,there are related problems such as excessive gravitational force in the early stage,unreachable target and local minimum value points,this paper proposes an improved artificial potential field algorithm.First of all,for the problem of excessive gravitational force in the early stage,a dynamic gain function is established as the dynamic gain coefficient of the gravitational function;Then the distance from the intelligent vehicle to the target point is introduced into the repulsive field function,so that the composition of the repulsive force is changed to solve the problem of unreachability of the target in the traditional artificial potential field method;Finally,in view of the problem of local minimum value points,the guiding force that facilitates smart cars to jump out of local minimum value points is increased.The results show that the improved artificial potential field method has been optimized compared to the traditional artificial potential field method in terms of both path length and travel time,and can effectively solve the problems above in the traditional artificial potential field method.
关键词
智能车/路径规划/人工势场法/动态增益系数Key words
smart vehicle/path planning/artificial potential field method/dynamic gain coefficien引用本文复制引用
基金项目
辽宁省"百千万人才工程"人选科技活动支持项目(2020921030)
出版年
2024
国家科技期刊平台
NSTL
万方数据