基于±3σ正态概率区间分族遗传蚁群算法的移动机器人路径规划研究
作者:
作者单位:

哈尔滨工程大学

作者简介:

通讯作者:

中图分类号:

中图分类号:TP242.6

基金项目:

国家自然科学基金(51709063)


Path Planning of a Mobile Robot Based on ±3σ Normal Probability Interval Population Division Using the Genetic Ant-Colony Algorithm
Author:
Affiliation:

Harbin Engineering University

Fund Project:

National Natural Science Foundation of China (51709063)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    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问题。最后引入混合变异策略,提高随机变异后生成路径质量。将全局路径规划算法与局部路径规划算法-动态窗口(dynamic window approach,DWA)算法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(Robot Operating System,ROS)进行实验分析,验证了该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 that is based on ± 3σ 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 ± 3σ 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 (Dynamic Window Method (DWA)) are combined to form a complete mobile robot motion plan. The experimental analysis using MATLAB simulation platform and the robot operating system (ROS) verifies the effectiveness of the algorithm proposed in this paper to solve the path-planning problem of mobile robots.□

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2020-06-12
  • 最后修改日期:2020-10-19
  • 录用日期:2020-11-03
  • 在线发布日期:
  • 出版日期: