未知环境下基于改进DWA的多机器人编队控制
作者:
作者单位:

1.南京理工大学;2.南京信息工程大学

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中图分类号:

TP24

基金项目:

江苏省自然科学基金面上项目(BK20191286), 中央高校基本科研业务费专项资金资助项目(30920021139)


Multi-robot formation control in unknown environment based on improved DWA
Author:
Affiliation:

1.Nanjing University of Science and Technology;2.Nanjing University of Information Science and Technology

Fund Project:

the National Natural Science Foundation of Jiangsu Province(BK20191286), the Fundamental Research Funds for the Central Universities(30920021139)

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    摘要:

    针对多机器人系统在未知环境下难以有效避障和保持队形的问题,本文在改进了动态窗口法(DWA)的基础上,提出一种领航-跟随法和基于行为法相结合的多机器人编队控制算法.首先,通过修正速度窗口和三个现有评价函数,并添加两个新的评价函数改进了DWA算法,增加了速度的采样范围,提高了优秀轨迹的评分,并增强了机器人朝目标导航和未知环境下的全局搜索能力.其次,对周围环境和编队状态实时检测,为各机器人设计不同的行为(包括导航,避障,跟踪和等待)及其选择方式,兼顾了编队避障及队形保持.然后,基于改进DWA和社会力模型(SFM)设计行为控制策略,在未知环境下使领航者能规划适合整体编队运行的路径,跟随者能根据编队的不同状态自适应地切换跟随方式.最后,基于Matlab和V-REP进行了一系列仿真,结果表明在未知环境下,提出的改进DWA能显著提高机器人的通行效率和全局搜索能力,编队控制算法能够实现队形稳定保持,灵活避障与变换.

    Abstract:

    Multi-robot system may be difficult to avoid obstacles and maintain formation in the unknown environment. Based on the improvement of dynamic window method(DWA), a multi-robot formation control algorithm combining leader-following method and behavior-based method is proposed. First, the original DWA is improved by modifying the speed window and three existing evaluation functions and adding two new evaluation functions. As a result, the sample range of speed is increased, the score of the better trajectory is improved, and abilities of navigation to the target and global search in unknown environment are enhanced. Second, based on the formation state and surrounding environment detected in real-time, different behaviors(navigation, obstacle avoidance, tracking and waiting) and the selection method is designed to consider both the obstacle avoidance and maintenance of the formation. Then, the control method of these behaviors are designed based on the improved DWA and social force model(SFM), so that in the unknown environment the leader can plan the path suitable for the whole formation and the follower can adaptively switch the following modes according to the different states of formation. Finally, a series of simulations based on Matlab and V-REP are carried out whose results show that the improved DWA can significantly improve the traffic efficiency and global search ability of the robot, and the formation control algorithm can achieve the maintenance, obstacle avoidance and transformation of the formation.

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历史
  • 收稿日期:2020-12-27
  • 最后修改日期:2021-07-19
  • 录用日期:2021-07-20
  • 在线发布日期: 2021-08-01
  • 出版日期: