基于多传感器数据融合的超精密复杂曲面几何误差评定理论研究

项目名称： 基于多传感器数据融合的超精密复杂曲面几何误差评定理论研究

项目编号： No.51505404

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 机械、仪表工业

项目作者： 任明俊

作者单位： 上海交通大学

项目金额： 20万元

中文摘要： 超精密复杂曲面因优越的光学性能被广泛地应用于空间光学等领域，而复杂曲面多尺度几何特征测量与评定已成为亟待解决的关键难点。本项目以多传感器测量技术为基础，提出一种基于多传感器数据融合的曲面评定方法用于对新一代光学复杂曲面进行宏观、介关至微观的多尺度亚微米级几何误差评定。针对多传感器数据匹配、融合和不确定性分析等关键问题，提出一种基于内蕴特征模式的曲面匹配方法，在原始测量数据的尺度和解析度都不尽相同的情况下进行高精度的坐标系统一，应用多贝叶斯估计法对测量重叠区域的点云数据进行融合，充分利用测量数据的冗余和互补，保证测量数据的一致性和精度。结合蒙特卡罗方法，以传感器和面型误差作为不确定性因素，着重分析测量不确定度在几何误差评定中的传递特性，建立不确定性分析模型，量化估计评定参数的不确定度。本项目的实施将提高超精密复杂曲面的质量检测精度和效率，从而保证其加工精度，具有重要的理论意义和工程应用价值。

中文关键词： 多传感器测量；光学复杂曲面；数据融合；计量误差理论；不确定度

英文摘要： The geometric error characterization of ultra-precision freeform surfaces is the basis of the application of these advanced surfaces in many fields such as space optics. Therefore, this project proposes a multi-sensor data fusion based geometric error characterization method to purposely extract different scale of the geometric information of the measured surface. To address the key problems of multi-sensor data fusion, an intrinsic feature pattern based surface matching method is proposed matching the multi-sensor data of which scale and resolution are different, and a Bayesian inference theory is adopted to fuse the measured data in the overlapping area so as to improve the accuracy and uniqueness of the extracted geometric information. To further enhance the reliability of the characterization results, Monte Carlo method based computer simulation is conducted by taking the error of the sensors and the measured parts as input to analyze the uncertainty of the surface parameters. The success development of the proposed method will dramatically improve the accuracy and efficiency in the measurement and the characterization of the multi-scale optical freeform surfaces and hence guarantee the manufacturing quality of these surfaces.

英文关键词： multi sensor;optical complex surface;data fusion;error characterization theory;uncertainty analysis