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基于改进高斯混合模型的机器人运动状态估计

葛泉波 王贺彬 杨秦敏 张兴国 刘华平

葛泉波, 王贺彬, 杨秦敏, 张兴国, 刘华平. 基于改进高斯混合模型的机器人运动状态估计. 自动化学报, 2021, 48(x): 1−12 doi: 10.16383/j.aas.c200660
引用本文: 葛泉波, 王贺彬, 杨秦敏, 张兴国, 刘华平. 基于改进高斯混合模型的机器人运动状态估计. 自动化学报, 2021, 48(x): 1−12 doi: 10.16383/j.aas.c200660
Ge Quan-Bo, Wang He-Bin, Yang Qin-Min, Zhang Xing-Guo, Liu Hua-Ping. Estimation of robot motion state based on improved gaussian mixture model. Acta Automatica Sinica, 2021, 48(x): 1−12 doi: 10.16383/j.aas.c200660
Citation: Ge Quan-Bo, Wang He-Bin, Yang Qin-Min, Zhang Xing-Guo, Liu Hua-Ping. Estimation of robot motion state based on improved gaussian mixture model. Acta Automatica Sinica, 2021, 48(x): 1−12 doi: 10.16383/j.aas.c200660

基于改进高斯混合模型的机器人运动状态估计

doi: 10.16383/j.aas.c200660
基金项目: 国家自然科学基金(61773147, 62033010)
详细信息
    作者简介:

    葛泉波:浙江大学博士后, 博士生指导教师, 南京信息工程大学教授. 主要研究领域包括工程信息融合理论与方法、无人系统协同优化、人机混合系统智能评估和智能电网大数据分析等

    王贺彬:主要研究方向为多智能体控制、非线性非高斯状态估计

    杨秦敏:浙江大学控制科学与工程学院教授, 博士生导师. 现担任IEEE TNNLS、IEEE TSMC: Systems、TIMC等多本国内外期刊编委/客座编委. 他的研究兴趣包括工业大数据、智慧能源系统和信息驱动的控制与优化

    张兴国:中国飞行试验研究院高级工程师. 目前主要的研究方向是: 飞行器试飞测试技术、智能化测试技术等

    刘华平:清华大学计算机科学与技术系副教授, 博士生导师, 主要研究方向为机器人感知、学习与控制、多模态信息融合. 担任IEEE Trans. on Cybernetics、IEEE Trans. on Automation Science and Engineering、IEEE Trans. on Industrial Informatics的编委

Estimation of Robot Motion State Based on Improved Gaussian Mixture Model

Funds: Supported by National Natural Science Foundation of China (61773147, 62033010)
More Information
    Author Bio:

    GE Quan-Bo is a Professor and a Ph.D. supervisor in Nanjing University of Information Science and Technology. His research interests include engineering information fusion theory, coordinated optimization for autonomous systems, intelligent evaluation for human-machine hybrid system, and big data in smart grid

    WANG He-Bin graduated from at Hangzhou Dianzi University as Master. His research interest covers multi-agent control and nonlinear non-gaussian state estimation

    YANG Qin-Min is now with the State Key Laboratory of Industrial Control Technology, the College of Control Science and Engineering, Zhejiang University, China, where he is currently a professor. His research interests include industrial big data, smart energy systems, information driven control and optimization

    ZHANG Xing-Guo His main research interests include: aircraft flight test technology, intelligent test technology, and etc

    LIU Hua-Ping is an associate professor in Tsinghua University. His research fields include robotic perception, learning and control. He serves as an associate editor for IEEE Trans. on Cybernetics, IEEE Trans. on Industrial Informatics and so on

  • 摘要: 针对复杂环境下机器人运动状态估计的精度改善问题, 提出一种面向非线性非高斯系统的改进高斯和容积Kalman滤波估计方法. 首先, 引入加权信息量概念来改进EM算法目标函数惩罚项, 使得在优化过程中能考虑更全面的参数信息, 以达到减少EM算法的迭代次数和提高收敛速度的目的. 此外, 以基于Mahalanobis距离和KL距离的高斯项合并方法为基础, 提出一种能有效联合两类高斯项合并方式的融合模式. 先单独使用Mahalanobis距离和KL距离进行高斯混合项合并, 再对获得的高斯混合项进行加权融合处理, 以改善高斯和滤波中多高斯项的合并性能和保真度. 最后, 应用非线性非高斯系统的高斯和容积Kalman滤波框架实现对复杂环境下机器人的运动状态估计. 理论分析与仿真结果表明, 本文提出的方法能实现对机器人运动更好的状态估计精度, 并具有更强的鲁棒性能, 同时两种不同的高斯项合并融合模式具有相当的估计性能.
    1)  收稿日期 2020-08-17 录用日期 2021-05-12 Manuscript received August 17, 2020; accepted May 12, 2021 国家自然科学基金 (61773147, 62033010) Supported by National Natural Science Foundation of China (61773147, 62033010) 本文责任编委 邓方 Recommended by Associate Editor DENG Fang 1. 南京信息工程大学自动化学院 南京 210044 2. 淳安县千岛湖科学研究院 杭州 311799 3. 浙江大学控制科学与工程学院 杭州 310027 4. 中国飞行试验研究院 西安 710089 5. 清华大学计算机科学与技术系 北京 100190 1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China 2. Qiandao Lake Institute of Science, Chun'an, Hangzhou Zhejiang 311799,
    2)  China 3. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou Zhejiang 310027, China 4. Chinese Flight Test Establishment, Xi’an 710089, China 5. Department ofComputer Science and Technology, Tsinghua University, Beijing100190
  • 图  1  高斯和容积卡尔曼滤波算法流程

    Fig.  1  GSCKF algorithm process

    图  2  EM算法迭代流程

    Fig.  2  EM algorithm process

    图  3  改进鲁棒EM算法迭代流程

    Fig.  3  Improved robust EM algorithm process

    图  4  改进高斯和容积卡尔曼滤波

    Fig.  4  Improved GSCKF

    图  5  改进鲁棒EM算法迭代过程

    Fig.  5  Improved robust EM algorithm iterative process

    图  6  鲁棒EM算法迭代过程

    Fig.  6  Robust EM algorithm iterative process

    图  7  三种算法对于机器人状态估计

    Fig.  7  Three algorithms for robot state estimation

    图  8  三种算法的RMSE

    Fig.  8  RMSE of three algorithms

    图  9  三种算法的方位角误差

    Fig.  9  The azimuth error of the three algorithms

    表  1  改进前后鲁棒EM算法对比

    Table  1  Comparison of robust EM algorithms before and after improvement

    算法迭代次数/次马氏距离
    文献[21]算法1430.0073
    本文改进算法500.0012
    下载: 导出CSV

    表  2  三种算法RMSE及运行时间

    Table  2  RMSE and running time of algorithm

    算法RMSE/m运行时间/s
    Salmond0.07051.16
    $B( \cdot ) $0.171.06
    GSCKF-CC0.05761.20
    下载: 导出CSV
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  • 收稿日期:  2020-08-17
  • 录用日期:  2021-05-12
  • 网络出版日期:  2021-11-21

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