基于改进蚁群和DWA算法的机器人动态路径规划
作者:
作者单位:

智能系统与智能装备教育部工程中心,燕山大学工业计算机控制工程河北省重点实验室

作者简介:

通讯作者:

中图分类号:

TP242

基金项目:

国家重点研发计划项目,国家自然科学基金项目(面上项目,重点项目,重大项目),河北省青年基金


Robot dynamic path planning based on improved ant colony and DWA algorithm
Author:
Affiliation:

Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment,Key Lab of Industrial Computer Control Engineering of Hebei Province

Fund Project:

National Key Research and Development Program of China,The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan),Hebei Youth Fund

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    摘要:

    路径规划技术是移动机器人研究领域中的一个重要分支,使机器人在多障碍物环境中安全快速的找到一条相对最优路径.针对全局路径规划时蚁群算法盲目性搜索、易陷入局部最优和收敛速度慢等问题以及局部路径规划时DWA算法难以有效躲避动态障碍物等问题,提出了一种改进蚁群算法和DWA算法的融合算法.首先,通过加入GRRT-Connect算法构造初始路径来不等分配初始信息素,解决陷阱地图中早期规划时因盲目搜索而陷入局部最优问题.其次,增加蚁群接力搜索方法解决蚂蚁禁忌表自死锁问题,再利用切片取优方法优化最优路径选择机制得到全局最优路径.然后,以最优路径关键点为子目标点运行DWA算法,并提出自适应调节速度方法进行最优行驶.最后,提出预计算方法躲避动态障碍物达到局部规划效果.仿真结果表明,与现有文献结果相比,融合算法最优路径长度缩短了10.28%,收敛速度加快了6.55%,验证了该算法的有效性和优越性.

    Abstract:

    Path planning technology is an important branch in the field of mobile robot research, which enables the robot to find a relatively optimal path safely and quickly in the multi obstacle environment. Aiming at the problems of blind search of ant colony algorithm, easy to fall into local optimization and slow convergence speed in global path planning, and DWA algorithm is difficult to effectively avoid dynamic obstacles in local path planning. A fusion algorithm of improved ant colony algorithm and DWA algorithm is proposed. Firstly, the initial path is constructed by adding the GRRT-Connect algorithm to unequal allocation of initial pheromones, so as to solve the local optimization problem caused by blind search in early planning in trap map. Secondly, the ant colony relay search method is added to solve the self deadlock problem of the ant tabu list. Thirdly, the slice optimization method is used to optimize the optimal path selection mechanism to obtain the global optimal path. Then, the DWA algorithm is run with the key points of the optimal path as the sub-target points, and an adaptive speed adjustment method is proposed for optimal driving. Finally, a pre-calculation method is proposed to avoid dynamic obstacles and achieve the effect of local planning. The simulation results show that compared with the results in the existing literature, The optimal path length of the fusion algorithm is shortened by 10.28% and the convergence speed is accelerated by 6.55%.

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历史
  • 收稿日期:2021-10-20
  • 最后修改日期:2021-12-13
  • 录用日期:2021-12-30
  • 在线发布日期: 2022-02-01
  • 出版日期: