徐明辉2
1.安徽工程大学高端装备先进感知与智能控制教育部重点实验室 芜湖 241000
2.安徽工程大学电气工程学院 芜湖 241000
基金项目:国家自然科学基金(61903002)，安徽省自然科学基金(1808085QF215)，安徽省重点研究与开发计划项目(1804b06020375)，芜湖市科技计划项目(重点研发，2020yf59)
详细信息
作者简介:陈孟元：男，1984年生，博士，副教授，硕士生导师，研究方向为移动机器人SLAM、目标跟踪及路径规划
徐明辉：男，1995年生，硕士生，研究方向为移动机器人SLAM
通讯作者:陈孟元 mychen@ahpu.edu.cn
中图分类号:TP242.6计量
文章访问数:303
HTML全文浏览量:164
PDF下载量:42
被引次数:0
出版历程
收稿日期:2020-01-07
修回日期:2021-02-21
网络出版日期:2021-03-03
刊出日期:2021-04-20
Research on Mobile Robot Bionic Location Algorithm Based on Growing Self-Organizing Map
Mengyuan CHEN1, 2,,,Minghui XU2
1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Anhui Polytechnic University, Wuhu 241000, China
2. School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Funds:The National Natural Science Foundation of China (61903002), The Natural Science Foundation of Anhui Province (1808085QF215), The Key Research and Development Project of Anhui Province (1804b06020375), The Science and Technology Planning Project of Wuhu，Anhui Province (Key Research and Development, 2020yf59)
摘要
摘要:为提高移动机器人在同步定位和地图构建(SLAM)中的定位精度，该文提出一种基于自组织可增长映射 (GSOM)的仿生定位算法。该方法将位置细胞的激活特性和神经网络输出层神经元建立响应连接，通过GSOM神经网络构建空间的拓扑地图，利用感知距离信息实现位置细胞的激活响应从而估计机器人位置，以此还原机器人的运行路径。实验结果表明细胞间隔R对定位精度有较大影响，选取合适的细胞间隔能有效地减少神经网络的学习时间，提高定位精度，该文算法平均误差在0.153 m以内，定位精度达到90.243%，均优于原有算法。经验证该文算法建立的模型能够实现机器人的空间位置表征，提高了机器人在实验场景下的定位精度，表现出良好的位置估计性能。
关键词:移动机器人/
自组织可增长映射/
位置细胞/
位置表征/
定位精度
Abstract:In order to improve the positioning accuracy of mobile robots in Simultaneous Localization And Mapping (SLAM), a bionic localization algorithm based on Growing Self-Organizing Map(GSOM) neural network is proposed. The method connects the activation characteristics of the place cells with the neural network output layer neurons to establish a response, and constructs a spatial topology map through the GSOM neural network, and uses the perceived distance information to realize the activation response of the place cells to estimate the position of the robot. The running path of the robot is restored in this way. The experimental results show that the cell spacing R has a great influence on the positioning accuracy. Choosing the appropriate cell spacing can effectively reduce the learning time of the neural network and improve the positioning accuracy. The average error of the algorithm is less than 0.153 m, and the positioning accuracy is 90.243%, which is better than the original algorithm. It is verified that the model established by the algorithm can realize the spatial position representation of the robot, improves the positioning accuracy of the object under the experimental scene, and shows good position estimation performance.
Key words:Mobile robot/
Growing Self-Organizing Map(GSOM)/
Place cell/
Positional representation/
Positioning accuracy
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=3fa7a82e-1975-4d60-ad98-e4a4f98c452b
