IABC: An iteratively automatic machine learning boosted hand-eye calibration method for laser displacement sensor

An on-machine measuring (OMM) system with a laser displacement sensor (LDS) is designed for measuring free-form surfaces of hypersonic aircraft’s radomes. To improve the measurement accuracy of the OMM system, a novel Iteratively Automatic machine learning Boosted hand-eye Calibration (IABC) method is proposed. Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC. Firstly, a new objective function is derived, containing analytical parameters of the hand-eye relationship and LDS errors. Then, a hybrid calibration model composed of two kernels is proposed to solve the objective function. One kernel is the analytical kernel designed for solving analytical parameters. Another kernel is the automatic machine learning (AutoML) kernel designed to model LDS errors. The two kernels are connected with stepwise iterations to find the best calibration results. Compared with traditional methods, hand-eye experiments show that IABC reduces the calibration RMSE by about 50%. Verification experiments show that IABC reduces the measurement deviations by about 25%-50% and RMSEs within 40%. Even when the training data are obviously less than the test data, IABC performs well. Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.     

An information-volume-based distance measure for decision-making

D-S evidence theory, as a general framework for reasoning with uncertainty, allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure. However, the mass assignments given by unknown information sources are disordered. How to measure the difference between the mass assignments has aroused people’s interest. In this paper, inspired by the information volume, a novel distance-based measure is proposed to measure the difference between mass assignments. The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments. At the same time, it is verified that the measure not only meets the properties of distance, but also proves the superiority of the proposed Information Volume Distance (IVD) through simulation experiments. Meanwhile, in the process of information fusion, the reliability of each source could be quantified through IVD. Therefore, based on IVD, a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion. Moreover, algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.     

基于MEMS传感器的四旋翼组合测姿研究

针对四旋翼无人机机体尺寸较小、带载荷能力有限的特点,设计基于低成本MEMS惯性器件的测姿系统,以满足四旋翼无人机在系统控制方面的需求。但是低成本的MEMS惯性器件具有精度低,随机漂移大,容易受到外界环境干扰等缺点。本文充分发挥四旋翼无人机搭载的MEMS惯性器件的功能,分别利用两种方法测量载体姿态,并通过卡尔曼滤波的方法实现这两种测姿方法的数据融合。经过仿真分析,融合后组合测姿系统的测姿精度得到提高。     

加工装配误差影响下偏转射流压力伺服阀静态特性分析

电液压力伺服阀是电液压力伺服控制系统的核心控制元件，广泛应用于航空、航天、军事等领域。区别于流量伺服阀，压力伺服阀在滑阀放大器的设计上多采用带有压力控制容腔的三通阀结构，不同的滑阀结构使得现有的偏转射流流量伺服阀仿真模型难以满足压力伺服阀性能预测的需求。本文基于AMESim平台建立了偏转射流压力伺服阀的仿真模型，并通过实验对仿真模型进行了验证，验证结果表明仿真模型能够准确地描述压力伺服阀的静态特性。最后，通过仿真模型分析了加工装配误差影响下压力伺服阀输出性能差异，仿真结果可为偏转射流压力伺服阀的性能预测和结构设计提供参考。