三维表面扫描机器人误差建模与补偿方法

对于三维表面扫描机器人系统,整个系统的测量误差主要来源于如下几个环节:机器人的本体定位误差、线结构光传感器的数据采集误差和手眼矩阵带来的误差。对于以上环节的各个部分,可以看成是局部过程。不论各个环节或部分的误差为多大,最后的误差均归结于所得到的测量数据。然而,整个系统的误差模型很难通过解析方法得到。为了提高系统的测量精度,通过微粒群径向基神经网络建立系统的误差模型,网络的输入选择激光条纹图像坐标系中的坐标,输出选择为经过迭代最近点算法配准后的最近点与测量点之间的误差。利用微粒群算法优化初始所得的神经元中心和宽度,在相同网络性能的前提下,压缩了网络的规模。在测量过程中利用所建立的误差网络模型将测量误差得以补偿,通过实际的试验验证该方法提高系统测量精度的有效性。

Error Modeling and Compensation Approach for Three-dimensional Surface Scanning Robot

For the three-dimensional surface scanning robotic system,the system measuring accuracy mainly depends on the following aspects: Robot positioning accuracy,line structured light vision sensor measuring accuracy,hand to eye calibration accuracy.The three aspects mentioned above can be considered as part ones.Whatever how much error will these three aspects be,the final error will be reflected in the obtained measuring data.However,the error model of the system is difficult to be established via mathematical models.As to enhance the system measuring accuracy,the error model is constructed via particle swarm optimization-radial basis function neural network(PSO-RBFNN).The input of PSO-RBFNN is chosen as the image coordinates of laser stripes,while the output of PSO-RBFNN is chosen as the errors between measured points and closest points after iterative closest point technique.The centers and widths of RBFNN are optimized by particle swarm optimization(PSO) to find more suitable parameters and thus reducing the number of neurons in the hidden layer.Experimental results demonstrate that the system measuring accuracy is improved after the error compensation scheme via constructed error network model.