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一种具有发育机制的感知行动认知模型

张晓平 阮晓钢 王力 李志军 闫佳庆 毕松

张晓平, 阮晓钢, 王力, 李志军, 闫佳庆, 毕松.一种具有发育机制的感知行动认知模型.自动化学报, 2021, 47(2): 391-403 doi: 10.16383/j.aas.c180750
引用本文: 张晓平, 阮晓钢, 王力, 李志军, 闫佳庆, 毕松.一种具有发育机制的感知行动认知模型.自动化学报, 2021, 47(2): 391-403 doi: 10.16383/j.aas.c180750
Zhang Xiao-Ping, Ruan Xiao-Gang, Wang Li, Li Zhi-Jun, Yan Jia-Qing, Bi Song. A kind of sensorimotor cognitive model with developmental mechanism. Acta Automatica Sinica, 2021, 47(2): 391-403 doi: 10.16383/j.aas.c180750
Citation: Zhang Xiao-Ping, Ruan Xiao-Gang, Wang Li, Li Zhi-Jun, Yan Jia-Qing, Bi Song. A kind of sensorimotor cognitive model with developmental mechanism. Acta Automatica Sinica, 2021, 47(2): 391-403 doi: 10.16383/j.aas.c180750

一种具有发育机制的感知行动认知模型

doi: 10.16383/j.aas.c180750
基金项目: 

北京市自然科学基金 4204096

北京市自然科学基金 4184086

国家自然科学基金 61903006

国家自然科学基金 61174116

北京市教委项目 KM201610009001

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

    王力  北方工业大学电气与控制工程学院教授.主要研究方向为智能交通.E-mail: Li.wang@ncut.edu.cn

    李志军  北方工业大学电气与控制工程学院副教授.主要研究方向为智能机器人与智能学习系统.E-mail: lzj78@ncut.edu.cn

    闫佳庆  北方工业大学电气与控制工程学院副教授.主要研究方向为神经信息学. E-mail: yjq@ncut.edu.cn

    毕松  北方工业大学电气与控制工程学院副教授.主要研究方向为智能机器人. E-mail: bisongo@163.com

    通讯作者:

    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.本文通信作者.E-mail: zhangxiaoping369@163.com

  • 本文责任编委 张俊

A Kind of Sensorimotor Cognitive Model With Developmental Mechanism

Funds: 

Beijing Natural Science Foundation 4204096

Beijing Natural Science Foundation 4184086

National Natural Science Foundation of China 61903006

National Natural Science Foundation of China 61174116

Beijing Education Commission Project KM201610009001

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

    WANG Li    Professor at the School of Electrical and Control Engineering, North China University of Technology. His main research interest is intelligent transportation

    LI Zhi-Jun    Associate professor at the School of Electrical and Control Engineering, North China University of Technology. His research interest covers intelligent robot and intelligent learning system

    YAN Jia-Qing    Associate professor at the School of Electrical and Control Engineering, North China University of Technology. His main research interest is neuroinformatics

    BI Song    Associate professor at the School of Electrical and Control Engineering, North China University of Technology. His main research interest is intelligent robot

    Corresponding author: ZHANG Xiao-Ping    Lecturer at the School of Electrical and Control Engineering, North China University of Technology. Her research interest covers cognitive robotics. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Jun
  • 摘要: 针对结构固定认知模型中存在的学习浪费与计算浪费问题, 在具有内发动机机制的感知行动认知模型基础上, 根据操作条件反射学习特性, 借鉴潜在动作原理, 建立起一种具有发育机制的感知行动认知模型D-SSCM (Development-sensorimotor cognitive model), 具体为一个14元组, 包含离散学习时间集、内部可感知离散状态集、可输出动作集、有效输出动作空间集、潜在动作关系集、可输出动作空间探索率集及发育算法等.针对模型发育过程, 分别设计了模型结构扩展式发育方法和算法以及缩减式发育方法和算法, 定义了模型的发育式学习过程.使用两轮机器人自平衡任务对设计的学习模型进行验证, 实验结果表明, 发育机制下的感知行动认知模型D-SSCM具有更快的学习速度及更稳定的学习效果.
    Recommended by Associate Editor ZHANG Jun
    1)  本文责任编委 张俊
  • 图  1  D-SSCM结构图

    Fig.  1  Structure of D-SSCM

    图  2  D-SSCM扩展发育原理图

    Fig.  2  D-SSCM extended development diagram

    图  3  D-SSCM缩减发育原理图

    Fig.  3  D-SSCM reduced development diagram

    图  4  D-SSCM学习流程图

    Fig.  4  Learning flowchart of D-SSCM

    图  5  两轮机器人倾斜角度

    Fig.  5  Angle of two-wheeled robot

    图  6  两轮机器人倾斜角速度

    Fig.  6  Angular velocity of two-wheeled robot

    图  7  两轮机器人轮子转矩

    Fig.  7  Wheel's torque of two-wheeled robot

    图  8  D-SSCM发育过程实验结果图

    Fig.  8  Experiment results figure of D-SSCM's development process

    图  9  第1轮学习结果

    Fig.  9  Learning results of the 1st round

    图  10  第2轮学习结果

    Fig.  10  Learning results of the 2nd round

    图  11  第3轮学习结果

    Fig.  11  Learning results of the 3rd round

    图  12  10轮学习中的$n_M$及$n_{M_\text{s}}$数

    Fig.  12  $n_M$ and $n_{M_\text{s}}$ in 10 learning rounds

    表  1  D-SSCM状态划分

    Table  1  D-SSCM state division

    $\varphi\, (^{\circ})$ $\dot{\varphi}\, (^{\circ}/s)$
    $(-\infty, -17.5)$ $(-\infty, -100)$
    $[-17.5, -12.5)$ $[-100, -50)$
    $[-12.5, -7.5)$ $[-50, -20)$
    $[-7.5, -2.5)$ $[-20, -5)$
    $[-2.5, -0.5)$ $[-5, -2)$
    $[-0.5, 0)$ $[-2, 0)$
    $[0, 0.5)$ $[0, 2)$
    $[0.5, 2.5)$ $[2, 5)$
    $[2.5, 7.5)$ $[5, 20)$
    $[7.5, 12.5)$ $[20, 50)$
    $[12.5, 17.5)$ $[50, 100)$
    $[17.5, +\infty)$ $[100, +\infty)$
    下载: 导出CSV

    表  2  10轮学习中的$n_M$及$n_{M_{\rm s}}$数

    Table  2  $n_M$ and $n_{M_{\rm s}}$ in 10 learning rounds

    学习轮数 1 2 3 4 5 6 7 8 9 10
    $M$空间感知行动映射探索次数 588 589 590 592 592 598 609 609 610 610
    $M_{\rm s}$空间有效感知行动映射数 169 170 171 172 171 173 173 173 173 173
    下载: 导出CSV
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  • 收稿日期:  2018-11-11
  • 录用日期:  2019-06-02
  • 刊出日期:  2021-02-26

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