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基于海马体位置细胞的认知地图构建与导航

阮晓钢 柴洁 武悦 张晓平 黄静

阮晓钢, 柴洁, 武悦, 张晓平, 黄静. 基于海马体位置细胞的认知地图构建与导航. 自动化学报, 2021, 47(3): 666−677 doi: 10.16383/j.aas.c190288
引用本文: 阮晓钢, 柴洁, 武悦, 张晓平, 黄静. 基于海马体位置细胞的认知地图构建与导航. 自动化学报, 2021, 47(3): 666−677 doi: 10.16383/j.aas.c190288
Ruan Xiao-Gang, Chai Jie, Wu Yue, Zhang Xiao-Ping, Huang Jing. Cognitive map construction and navigation based on hippocampal place cells. Acta Automatica Sinica, 2021, 47(3): 666−677 doi: 10.16383/j.aas.c190288
Citation: Ruan Xiao-Gang, Chai Jie, Wu Yue, Zhang Xiao-Ping, Huang Jing. Cognitive map construction and navigation based on hippocampal place cells. Acta Automatica Sinica, 2021, 47(3): 666−677 doi: 10.16383/j.aas.c190288

基于海马体位置细胞的认知地图构建与导航

doi: 10.16383/j.aas.c190288
基金项目: 国家自然科学基金(61773027, 61573029, 61903006), 北京市自然科学基金(4204096)资助
详细信息
    作者简介:

    阮晓钢:北京工业大学信息学部教授. 主要研究方向为人工智能与机器人.E-mail: adrxg@bjut.edu.cn

    柴洁:北京工业大学信息学部博士研究生. 主要研究方向为认知学习和认知导航.E-mail: chaijie@emails.bjut.edu.cn

    武悦:北京工业大学信息学部硕士研究生. 主要研究方向为环境认知建模与类脑计算.E-mail: wuy50271@gmail.com

    张晓平:北方工业大学电气与控制工程学院讲师. 主要研究方向为认知机器人.E-mail: zhangxiaoping369@163.com

    黄静:北京工业大学信息学部人工智能与自动化学院副教授. 主要研究方向为认知机器人, 机器学习及工业大数据应用. 本文通信作者.E-mail: huangjing@bjut.edu.cn

Cognitive Map Construction and Navigation Based on Hippocampal Place Cells

Funds: Supported by National Natural Science Foundation of China (61773027, 61573029, 61903006) and Beijing Natural Science Foundation (4204096)
More Information
    Author Bio:

    RUAN Xiao-Gang Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers artificial intelligence and robotics

    CHAI Jie Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers cognitive learning and cognitive navigation

    WU Yue Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers environment cognition modelling and brain-inspired computing

    ZHANG Xiao-Ping Lecturer at the College of Electrical and Control Engineering, North China University of Technology. Her research interest covers cognitive robotics

    HUANG Jing Associate professor at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers cognitive robotics, machine learning, and industrial big data. Corresponding author of this paper

  • 摘要:

    针对移动机器人环境认知问题, 受老鼠海马体位置细胞在特定位置放电的启发, 构建动态增减位置细胞认知地图模型(Dynamic growing and pruning place cells-based cognitive map model, DGP-PCCMM), 使机器人在与环境交互的过程中自组织构建认知地图, 进行环境认知. 初始时刻, 认知地图由初始点处激活的位置细胞构成; 随着与环境的交互, 逐渐得到不同位置点处激活的位置细胞, 并建立其之间的连接关系, 实现认知地图的动态增长; 如果机器人在已访问区域发现新的障碍物, 利用动态缩减机制对认知地图进行更新. 此外, 提出一种位置细胞序列规划算法, 该算法以所构建的认知地图作为输入, 进行位置细胞序列规划, 实现机器人导航. 为验证模型的正确性和有效性, 对Tolman的经典老鼠绕道实验进行再现. 实验结果表明, 本文模型能使机器人在与环境交互的过程中动态构建并更新认知地图, 能初步完成对Tolman老鼠绕道实验的再现. 此外, 进行了与四叉树栅格地图、动态窗口法的对比实验和与其他认知地图模型的讨论分析. 结果表明了本文方法在所构建地图的简洁性、完整性和对动态障碍适应性方面的优势.

  • 图  1  DGP-PCCMM的“感知—响应”框架

    Fig.  1  The “sense-response” structure of DGP-PCCMM

    图  2  各概念及其之间相互关系

    Fig.  2  Concepts and their associations

    图  3  动态增长认知地图构建流程图

    Fig.  3  The flow chart of dynamic growing cognitive map

    图  4  机器人导航框图

    Fig.  4  The diagram of robot navigation

    图  5  位置细胞序列规划算法

    Fig.  5  The sequence planning algorithm of place cells

    图  6  Tolman老鼠绕道实验迷宫环境

    Fig.  6  Maze environment of Tolman detour task

    图  7  轮式圆形机器人俯视图

    Fig.  7  Top view of wheeled circular robot

    图  8  Tolman迷宫仿真环境

    Fig.  8  The simulation environment of Tolman maze

    图  9  认知地图构建过程($ n_m $= 1 000)

    Fig.  9  The formation process of cognitive map ($ n_m $= 1 000)

    图  10  认知地图构建过程($ n_m $= 2 000)

    Fig.  10  The formation process of cognitive map ($ n_m $= 2 000)

    图  11  位置细胞个数随学习次数变化情况

    Fig.  11  The number of place cells changing with the number of learning times

    图  12  A和门B都打开情况下的导航

    Fig.  12  Navigation with door A and door B open

    图  14  A打开门B关闭情况下的导航

    Fig.  14  Navigation with door A open and door B closed

    图  13  A关闭门B打开情况下的导航

    Fig.  13  Navigation with door A closed and door B open

    图  15  Hairpin迷宫

    Fig.  15  Hairpin maze

    图  16  四叉树栅格地图

    Fig.  16  Occupancy grids based on quadtree

    图  17  本文方法构建的认知地图

    Fig.  17  Cognitive maps based on methods of this paper

    图  18  动态窗口法和本文方法导航结果对比图

    Fig.  18  Comparation of navigation results between dynamic window approach and DGP-PCCMM

    图  19  Erdem认知地图与本文认知地图对比

    Fig.  19  Comparison between different cognitive maps

    表  1  DGP-PCCMM初始参数设置

    Table  1  Initial simulation parameters for DGP-PCCMM

    参数 参数 参数
    $ t $ 0 $ n_2 $ 3.2 $ T_{\rm{RP}} $ 3
    $ N $ 0 $ n_{m} $ 1 000 $ n_{\rm{init}} $ 1
    $ V_{\rm{GT}} $ 4.5 $ \sigma_0 $ 0.01 $ r $ 0.025 m
    $ n_1 $ 1.8 $ \alpha_0 $ 0.01 $ d_{\rm{step}} $ 0.05 m
    下载: 导出CSV

    表  2  本文与四叉树栅格对比

    Table  2  Comparison between occupancy grids based on quadtree and DGP-PCCMM

    性能指标 占用栅格或位置
    细胞个数
    有无仿生性 对动态变化
    的适应性
    四叉树栅格 > 256 (Hairpin);
    > 196 (Tolman)
    较弱
    本文认知地图 65 (Hairpin);
    90 (Tolman)
    较好
    下载: 导出CSV

    表  3  本文与动态窗口法对比

    Table  3  Comparison between dynamic window approach and our method

    性能指标环境 运行时间 (s) 导航路线长度 (cm)
    T1 T2 T3 T1 T2 T3
    动态窗口法 141.4 188.6 717.9 121.3 171.2 418.6
    本文认知地图 24.5 35.7 60.2 122.6 178.6 301.2
    下载: 导出CSV
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  • 收稿日期:  2019-04-08
  • 录用日期:  2020-03-16
  • 网络出版日期:  2021-04-02
  • 刊出日期:  2021-04-02

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