东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (3): 426-430.DOI: -

• 论著 • 上一篇    下一篇

挖掘机器人铲斗不变矩及改进BP网络识别方法

王福斌;刘杰;陈至坤;王静波;   

  1. 东北大学机械工程与自动化学院;河北联合大学电气工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50775029);;

Bucket target recognition method of excavator robot based on invariant moments and improved BP neural network

Wang, Fu-Bin (1); Liu, Jie (1); Chen, Zhi-Kun (2); Wang, Jing-Bo (2)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (2) Electrical Engineering College, Hebei United University, Tangshan 063009, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Wang, F.-B.
  • About author:-
  • Supported by:
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摘要： 在利用视觉信息跟踪、识别挖掘机器人铲斗目标时,实时采集的铲斗图像存在旋转、平移、缩放等情况.为提高对铲斗目标的识别能力,提出了基于不变矩和神经网络相结合的铲斗目标识别方法.提取铲斗图像对于平移、旋转、缩放具有不变性能的7个不变矩特征向量,归一化后作为改进BP神经网络的训练样本及测试样本.应用训练后的神经网络对铲斗目标进行识别,仿真表明该方法具有较好的识别能力.

关键词: 挖掘机器人, 不变矩, 神经网络, 图像, 识别

Abstract: When tracking and identifying the bucket target of excavator robot by using visual information, there exist rotation, translation and zoom situations for the bucket images collected in real time. To improve the identifying ability of the bucket target, a recognition method of bucket target was proposed based on invariant moments and BP neural network. The method extacted seven moment characteristic quantities of bucket image with invariant performance against translation, rotation and zoom. They can act as the training and testing samples for improved BP neural network after being normalized. Using the trained neural network to identify bucket target, the simulation result shows that this method has high recognition ability.

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