计算机科学 ›› 2015, Vol. 42 ›› Issue (11): 170-173.doi: 10.11896/j.issn.1002-137X.2015.11.035

• 网络与通信 • 上一篇    下一篇

改进的室内移动机器人模糊位置指纹定位研究

毛勤,曾碧,叶林锋   

  1. 广东工业大学计算机学院 广州510006,广东工业大学计算机学院 广州510006,广东工业大学计算机学院 广州510006
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61173046),广东省自然科学基金(S2012040007326)资助

Research on Improved Indoor Mobile Robot Fuzzy Position Fingerprint Localization

MAO Qin, ZENG Bi and YE Lin-feng   

  • Online:2018-11-14 Published:2018-11-14

摘要: 在深入研究现有的室内机器人定位算法的基础上,提出了改进的室内机器人模糊位置指纹定位方法(IRF)。该方法首先利用样本节点之间的空间相关性,采用重心拉格朗日插值算法形成位置指纹库,然后将传统的求解高次坐标问题转换成空间隶属度问题,利用模糊匹配算法计算未知节点与指纹库中已知节点的贴近度,通过贴近度加权定位未知节点。利用卡尔曼滤波算法进行误差修正。实验结果表明,IRF定位方法与传统的定位算法相比,在降低误差、提高稳定性和效率方面具有更高的性能。

关键词: 接收信号强度值,位置指纹,重心拉格朗日插值算法,模糊贴近度,卡尔曼滤波算法

Abstract: On the basis of further research on existing indoor robot localization algorithm,this paper introduced an improved indoor robot fuzzy position fingerprint localization method,named IRF.This method firstly uses the spatial correlation of sample nodes to establish the fingerprint database by focus Lagrange interpolation algorithm.Then it transforms the problem of solving high order coordinates into the problem of space membership degree.The fuzzy neartude of unknown nodes and sample nodes can be calculated,which can determine the coordinates of unknown points.Kalman filtering algorithm is used to fix the error of the method.The result of experiment proves that IRF has higher performance in reducing the error,improving stability and efficiency than other traditional algorithms.

Key words: RSSI,Fingerprint,Focus Lagrange interpolation algorithm,Fuzzy neartude,Kalman filtering algorithm

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