| 改进的RBFNN用于机器人三维表面 测量系统曲面重构 |
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| 引用本文： | 吴德烽,李爱国,马孜,王文标,徐慧朴. 改进的RBFNN用于机器人三维表面 测量系统曲面重构[J]. 北京理工大学学报, 2010, 0(S1): 69-72 |
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| 作者姓名： | 吴德烽 李爱国 马孜 王文标 徐慧朴 |
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| 作者单位： | 大连海事大学自动化研究中心,辽宁,大连 116026;大连海事大学自动化研究中心,辽宁,大连 116026;大连海事大学自动化研究中心,辽宁,大连 116026;大连海事大学自动化研究中心,辽宁,大连 116026;大连海事大学自动化研究中心,辽宁,大连 116026 |
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| 基金项目： | 辽宁省科技厅资助项目(2007219003) |
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| 摘 要： | 为克服三坐标测量机检测速度慢等缺点,提出机器人三维表面测量系统. 针对该系统设计了一种基于径向基神经网络(RBFNN)的简洁快速曲面重构方法. 该方法考虑到RBFNN选取的神经元函数为高斯函数,将机器人三维表面测量系统获得的点云数据投影到二维平面,然后将该二维平面平均分割,选取分割点为RBFNN神经元的中心,避免了模糊c-均值法选取中心需要迭代计算的缺点,并且重构的网络训练精度和测试精度均高于模糊c-均值法选取中心设计的网络精度. 利用该测量系统获得的实际点云数据验证了
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| 关 键 词： | 机器人 表面测量 曲面重构 径向基神经网络 |
| 收稿时间： | 2010-03-30 |
Application of Improved RBFNN for Surface Reconstruction in a Robot Based 3D Measurement System |
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| WU De-feng,LI Ai-guo,MA Zi,WANG Wen-biao and XU Hui-pu. Application of Improved RBFNN for Surface Reconstruction in a Robot Based 3D Measurement System[J]. Journal of Beijing Institute of Technology(Natural Science Edition), 2010, 0(S1): 69-72 |
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| Authors: | WU De-feng LI Ai-guo MA Zi WANG Wen-biao XU Hui-pu |
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| Affiliation: | Automation Research Center, Dalian Maritime University, Dalian, Liaoning 116026, China;Automation Research Center, Dalian Maritime University, Dalian, Liaoning 116026, China;Automation Research Center, Dalian Maritime University, Dalian, Liaoning 116026, China;Automation Research Center, Dalian Maritime University, Dalian, Liaoning 116026, China;Automation Research Center, Dalian Maritime University, Dalian, Liaoning 116026, China |
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| Abstract: | A robot based 3D measurement system is proposed to overcome the disadvantage that is time-consuming when using coordinate measuring machine for surface measurement. Considering the physical meanings of the Gaussian function in RBFNN, a simple and fast methodology is developed for surface reconstruction and applied in the robot based 3D measurement system. The point cloud data obtained by 3D measurement system is projected in a 2D space, then the 2D space is divided equally and the breakpoints are selected as centers for RBFNN. Therefore, the shortcoming that needs iterative computation when using FCM method to determine the centers is avoided. Furthermore, the training and test accuracy of constructed RBFNN is better than FCM technique. Finally, point cloud data collected by measurement system from a real object is used to validate the effectiveness of the presented surface reconstruction technique. |
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| Keywords: | robot surface measurement surface reconstruction radial basis function neural network(RBFNN) |
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