基于$pm3sigma$正态概率区间分族遗传蚁群算法的移动机器人路径规划
作者:
作者单位:

1. 哈尔滨工程大学 机电工程学院,哈尔滨 150001;2. 哈尔滨工程大学 船舶工程学院,哈尔滨 150001

作者简介:

通讯作者:

E-mail: zhuhaitao@hrbeu.edu.cn.

中图分类号:

TP242.6

基金项目:

国家自然科学基金项目(51709063).


Path planning of mobile robot based on pm$3sigma$ normal probability interval population division using genetic ant-colony algorithm
Author:
Affiliation:

1. College of Mechanical and Electrical Engineering,Harbin Engineering University,Harbin 150001,China; $ $;2. College of Shipbuilding Engineering, Harbin Engineering University,Harbin 150001,China

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    摘要:

    针对移动机器人路径规划问题,提出一种基于正态概率区间分族的家族遗传蚁群融合算法.首先提出初始种群优化及删除算子解决传统遗传蚁群融合算法中遗传阶段随机生成的初始种群质量低的问题;然后引入适应度值正态概率区间种群分族机制及家族混合交叉算子,解决传统遗传蚁群融合算法中易出现未成熟收敛的问题;最后引入混合变异策略以提高随机变异后生成的路径质量.将全局路径规划算法与局部路径规划算法-动态窗口算法相结合形成完整移动机器人运动规划.基于Matlab仿真平台与机器人操作系统平台进行实验分析,结果验证了所提出正态化概率分族遗传蚁群融合算法求解移动机器人路径规划问题的有效性.

    Abstract:

    With a focus on the issue of path planning for mobile robots, a genetic ant-colony fusion algorithm is proposed based on pm$3\sigma$ normal probability interval. Given the low quality of the initial population randomly generated by the traditional genetic ant-colony fusion algorithm, an initial population optimization and deletion operator is proposed. Because of the premature convergence of the traditional genetic ant-colony fusion algorithm, a population division mechanism with the fitness value of $\pm3\sigma$ normal probability interval, as well as a family hybrid crossover operator, is proposed. To improve the quality of the generated path after a random mutation, a hybrid mutation strategy is proposed. A global path-planning algorithm and a local path-planning algorithm(the dynamic window method are combined to form a complete mobile robot motion plan. The experimental analysis using the Matlab simulation platform and the robot operating system (ROS) verifies the effectiveness of the proposed algorithm in paper to solve the path-planning problem of mobile robots.

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包汉,祝海涛,刘迪.基于$pm3sigma$正态概率区间分族遗传蚁群算法的移动机器人路径规划[J].控制与决策,2021,36(12):2861-2870

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  • 在线发布日期: 2021-11-18
  • 出版日期: 2021-12-20