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移动机器人智能重采样的IRBPF-SLAM算法

槐创锋 张子昊 贾雪艳

槐创锋,张子昊,贾雪艳. 移动机器人智能重采样的IRBPF-SLAM算法[J]. 机械科学与技术,2021,40(11):1681-1687 doi: 10.13433/j.cnki.1003-8728.20200354
引用本文: 槐创锋,张子昊,贾雪艳. 移动机器人智能重采样的IRBPF-SLAM算法[J]. 机械科学与技术,2021,40(11):1681-1687 doi: 10.13433/j.cnki.1003-8728.20200354
HUAI Chuangfeng, ZHANG Zihao, JIA Xueyan. IRBPF-SLAM Algorithm for Intelligent Resampling of a Mobile Robots[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(11): 1681-1687. doi: 10.13433/j.cnki.1003-8728.20200354
Citation: HUAI Chuangfeng, ZHANG Zihao, JIA Xueyan. IRBPF-SLAM Algorithm for Intelligent Resampling of a Mobile Robots[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(11): 1681-1687. doi: 10.13433/j.cnki.1003-8728.20200354

移动机器人智能重采样的IRBPF-SLAM算法

doi: 10.13433/j.cnki.1003-8728.20200354
基金项目: 高铁车体无尘干磨系统设计项目(2003618305)
详细信息
    作者简介:

    槐创锋(1981−),教授,硕士生导师,研究方向为机器人研究与应用、机械设计,hcf811225@163.com

  • 中图分类号: TH162

IRBPF-SLAM Algorithm for Intelligent Resampling of a Mobile Robots

  • 摘要: 为了提高激光SLAM技术的建图精度,本文提出了一种智能重采样的IRBPF-SLAM算法,算法采用BAT启发式自适应重采样,对小颗粒进行重采样,产生新的解决方案,随机选择最佳解决方案,更新蝙蝠数量,将粒子归一化,优化的机器人状态更新,最后进行粒子重置,其迭代时间可以根据滤波器发散的程度进行自适应调整。此外,将激光传感器改进提议分布融合到算法中,以获得更好的提议分布和建图结果。仿真结果表明,所提出的IRBPF具有更好的准确性、计算效率和滤波一致性。在大型室内空间中,将IRBPF-SALM算法融合到基于ROS为框架下,在阿克曼转向移动平台进行测试,测试结果表明了新方法比原始方法更具优势。
  • 图  1  激光传感器提议分布图

    图  2  改善粒子分布示意图

    图  3  RBPF-SLAM仿真图

    图  4  IRBPF-SLAM仿真图

    图  5  优化前后均方根对比图

    图  6  移动机器人平台

    图  7  四轮机器人的示意图

    图  8  角速度标定与线速度标定

    图  9  栅格地图的改进前后的对比

    表  1  算法参数解释

    参数数值
    i时刻蝙蝠初始响度$A_i^0$0.9
    常数$\alpha ,\gamma $0.9
    i时刻蝙蝠初始心率$r_i^0$0.1
    随机向量$\boldsymbol{\varepsilon}$0.001
    频率范围$[ {{f_{\min }},{f_{\max }}} ]$[0,1]
    迭代时间范围$[ {{N_{\min }},{N_{\max }}} ]$[3,10]
    下载: 导出CSV

    表  2  算法精度参数对比

    算法均值方差百米误差/cm粒子数
    RBPF-SLAM0.8230.5671701500
    改进的IRBPF-SLAM0.0960.07835520
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-06-04
  • 网络出版日期:  2022-03-02
  • 刊出日期:  2021-11-05

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