UWB Indoor Positioning of Mobile Robot Based on GA-BP Neural Network

LIANG Feng; XIONG Ling

Chinese

您当前的位置：

首页 >

文章列表页 >

UWB Indoor Positioning of Mobile Robot Based on GA-BP Neural Network

更新时间：2026-09-04

- UWB Indoor Positioning of Mobile Robot Based on GA-BP Neural Network
- Vol. 36, Issue 4, Pages: 33-37(2019)
- 作者机构：
  
  1. 武汉科技大学机构机器人与智能系统研究院,湖北,武汉,430081
  2. 武汉科技大学信息科学与工程学院,湖北,武汉,430081
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
梁丰, 熊凌. 基于GA-BP神经网络的移动机器人UWB室内定位梁[J]. 微电子学与计算机, 2019, 36(4): 33-37, 42.

LIANG Feng, XIONG Ling. UWB Indoor Positioning of Mobile Robot Based on GA-BP Neural Network[J]. 2019, 36(4): 33-37.
梁丰, 熊凌. 基于GA-BP神经网络的移动机器人UWB室内定位梁[J]. 微电子学与计算机, 2019, 36(4): 33-37, 42. DOI：

LIANG Feng, XIONG Ling. UWB Indoor Positioning of Mobile Robot Based on GA-BP Neural Network[J]. 2019, 36(4): 33-37. DOI：

摘要

BP神经网络算法用于机器人超宽带(UWB)定位时

有较好的定位性能

但易陷入局部极值.为解决此问题采用遗传算法优化BP网络的随机权值及阈值.分别用BP神经网络和优化后的GA-BP神经网络对移动机器人进行定位实验

优化后的GA-BP神经网络能够克服BP神经网络易陷入局部极值的问题.在室内视距(LOS)和非视距(NLOS)环境下

优化后的方法平均定位误差分别下降了46%和24%;在同一概率条件下

LOS环境中GA-BP神经网络的定位误差比BP神经网络定位误差下降了约48%

NLOS环境中GA-BP神经网络的定位误差比BP神经网络定位误差下降了约20%.

Abstract

BP neural network has good performance in robot UWB positioning

but it is easy to get into local extreme value. To solve the problem

the weights and threshold randomly generated by BP neural network are optimized with genetic algorithm. With the BP neural network and the optimized GA-BP neural network for mobile robot localization experiment

the optimized GA-BP neural network can overcome the defects of BP neural network. In indoor line-of-sight (LOS) and non-line-of-sight (NLOS) environment

the mean positioning errors of the optimize method were reduced by 46% and 24% respectively; Under the same probability condition

the positioning error of GA-BP neural network in LOS environment is about 48% lower than that of BP neural network

and in NLOS environment is about 20%.

关键词

Keywords

references

SAHINOGLU Z. Ultra-Wideband Positioning Systems[M]. Cambridge University Press, 2008.

赵红梅.超宽带室内定位系统应用技术[M].北京: 电子工业出版社, 2017.

李尧, 汪明, 于晓谕.基于ZigBee室内定位算法[J].计算机系统应用, 2016, 25(6):180-184. DOI: 180

董永峰, 王安娜, 周艳聪, 等.一种新的基于RFID的室内移动机器人自定位方法研究[J].计算机应用研究, 2016, 33(3):749-753. DOI: 749

吴松林.超宽带信号指纹定位算法的研究[D].青岛: 中国海洋大学, 2014.

陆音, 缪辉辉.复杂室内环境下的WiFi定位技术研究[J].计算机科学, 2016, 43(11):152-154. DOI: 152

罗恒, 张昱, 邹优敏, 等.复杂环境下蓝牙定位模型优化方法研究[J].计算机测量与控制, 2017, 25(11):244-247. DOI: 244

YAO L, WU Y W A, YAO L, et al. An integrated IMU and UWB sensor based indoor positioning system[C]// International Conference on Indoor Positioning and Indoor Navigation. IEEE, 2017: 1-8.

GUO K, QIU Z, MIAO C, et al. Ultra-Wideband-Based Localization for Quadcopter Navigation[J]. Unmanned Systems, 2016, 04(1):23-34. DOI: 23

张涛, 徐亚明. UWB测距与室内定位精度研究[J].全球定位系统, 2016, 41(5):56-60. DOI: 56

杨洲, 汪云甲, 陈国良, 等.超宽带室内高精度定位技术研究[J].导航定位学报, 2014(4):31-35. DOI: 31

卞佳兴, 朱荣, 陈玄.基于改进双向测距-到达时间差定位算法的超宽带定位系统[J].计算机应用, 2017, 37(9):2496-2500. DOI: 2496

刘公绪, 史凌峰.室内导航与定位技术发展综述[J].导航定位学报, 2018, 6(2):7-14. DOI: 7

赖琴.多径环境下的TDOA定位算法研究[D].武汉: 华中科技大学, 2015.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A GABP neural network based calibration algorithm for pipelined ADC

Secure cluster routing algorithm for wireless secure communication networks based on group permutation cipher

Research on graph reconstruction method based on neural network and genetic algorithm

The Application of Bat Neural Network Algorithm in Image Denoising

Research on Maximum flow Algorithm in Rechargeable Wireless Sensor Networks

Related Author

JIN Weiran

MU Chunlin

YANG Long

WANG Shuxing

WANG Wei

HAO Yaping

WANG Wei-yi

Related Institution

College of Electronics Engineering/International Semiconductor College, Chongqing University of Posts and Telecommunications

Information Technology Construction and Management Center, Changzhou Institute of Technology

Information Center, Changzhou Vocational Institute of Industry Technology

Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University

Ordos Institute Of Technology

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰