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清华大学学报(自然科学版)  2018, Vol. 58 Issue (7): 609-613    DOI: 10.16511/j.cnki.qhdxxb.2018.25.031
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于雷达信息的室内移动机器人的方位估计
刘华平1,3, 郑向梅2,3, 孙富春1,3
1. 清华大学 计算机科学与技术系, 北京 100084;
2. 北方工程设计研究院有限公司, 石家庄 050011;
3. 清华大学 智能技术与系统国家重点实验室, 北京 100084
Orientation estimate of indoor mobile robot using laser scans
LIU Huaping1,3, ZHENG Xiangmei2,3, SUN Fuchun1,3
1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2. Norendar International LTD, Shijiazhuang 050011, China;
3. State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China
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摘要 移动机器人的室内定位是机器人领域中的一个热点问题,移动机器人的定位包括位置与方位两方面。为对移动机器人方位进行有效的估计,该文提出了基于支持向量机(support vector machine,SVM)的机器人方位回归模型,选定激光雷达信息作为模型的输入量、机器人的方位作为输出量;并与基于极限学习机(extreme learning machine,ELM)的机器人方位回归模型进行对比。实验结果表明:基于极限学习机回归模型的均方误差为0.320 rad,训练时间为0.936 s;基于支持向量回归模型的均方误差为0.113 rad,训练时间为9 273 s。该回归模型可为机器人方位估计提供一定的应用价值。
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刘华平
郑向梅
孙富春
关键词 支持向量回归方向估计移动机器人    
Abstract:The positioning of mobile robots indoors is very important with the controller needed to know both the location and the orientation. This paper presents a robot orientation regression model based on a support vector machine (SVM) to estimate the robot orientation. A laser radar signal is used as the model input with the orientation as the output. Tests show that the mean square error using an extreme learning machine model is 0.320 rad with a training time of 0.936 s while the mean square error based on the current support vector regression model is 0.113 rad with a training time of 9 273 s. Thus, the regression models can provide accurate robot position estimates.
Key wordssupport vector regression    orientation estimation    mobile robot
收稿日期: 2017-12-31      出版日期: 2018-07-15
基金资助:国家“八六三”高技术项目(2015AA042306)
引用本文:   
刘华平, 郑向梅, 孙富春. 基于雷达信息的室内移动机器人的方位估计[J]. 清华大学学报(自然科学版), 2018, 58(7): 609-613.
LIU Huaping, ZHENG Xiangmei, SUN Fuchun. Orientation estimate of indoor mobile robot using laser scans. Journal of Tsinghua University(Science and Technology), 2018, 58(7): 609-613.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.25.031  或          http://jst.tsinghuajournals.com/CN/Y2018/V58/I7/609
  图1 支持向量回归结构
  图2 SVR实现机器人室内方位估计步骤
  图3 极限学习机神经网络的模型
  图4 Pioneer3GDX机器人 [17]
  图5 DR 数据集中一些房间的信息 [17]
  表1 DR数据集在 ELMR 和SVR 模型的实验结果
  图6 参数C 对SVR 的影响
  图7 SVR模型的拟合及估计结果
  图8 参数C、 N 对 ELMR能的影响
  图9 ELMR 模型的拟合及估计结果
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