SUMMARY: Based on product of exponentials (POE) formula, three explicit error models are given in this paper for kinematic calibration of serial robot through measuring its end-effector positions. To obtain these error models, the tool frame should be chosen as reference frame at first, and then each position–error-related segment in the error models using pose measurement should be selected. And during kinematic parameter identification, all the errors in joint twists are identifiable, and the initial transformation errors and the joint zero-position errors can be identified conditionally. Namely, the initial transformation errors are identifiable if they do not contain orientation errors. And the joint zero-position errors are identifiable when a robot only consists of prismatic joints and the coordinates of its joint twists are linearly independent.The effectiveness of this calibration method has been validated by simulations and experiments. The results show that: (1) the identification algorithms are robust and practical. (2) The method of position measurement is superior to that of pose measurement.

This paper proposes an ultrasonic measurement system based on least squares support vector machines (LS-SVM) for inline measurement of particle concentrations in multicomponent suspensions. Firstly, the ultrasonic signals are analyzed and processed, and the optimal feature subset that contributes to the best model performance is selected based on the importance of features. Secondly, the LS-SVM model is tuned, trained and tested with different feature subsets to obtain the optimal model. In addition, a comparison is made between the partial least square (PLS) model and the LS-SVM model. Finally, the optimal LS-SVM model with the optimal feature subset is applied to inline measurement of particle concentrations in the mixing process. The results show that the proposed method is reliable and accurate for inline measuring the particle concentrations in multicomponent suspensions and the measurement accuracy is sufficiently high for industrial application. Furthermore, the proposed method is applicable to the modeling of the nonlinear system dynamically and provides a feasible way to monitor industrial processes.

The frequency modulated continuous wave (FMCW) radar sensors have attracted much attention in short-range applications. However, when detecting a stationary target, an FMCW radar sensor obtains the time-varying beat signals due to its nonlinearity, which degrades the imaging quality. For this reason, a correction method for the nonlinearity of FMCW radar sensors is proposed based on the multisynchrosqueezing transform (MSST) in this paper. First, a reference beat signal is processed by the short-time Fourier transform (STFT) to generate the blurry time-frequency representation. Second, an iterative reassignment procedure is employed to concentrate the blurry time-frequency representation by the MSST method. Then, the concentrated time-frequency representation is used to estimate the nonlinear coefficients by ridge detection and polynomial fitting. Finally, the nonlinear beat signals are compensated using the nonlinear coefficients after time resampling. The effectiveness of the proposed method is validated by simulations and experiments. The results show that the proposed method can obtain more concentrated time-frequency representation and more accurate estimation of nonlinear coefficients, resulting in higher quality of range profiles and synthetic aperture radar (SAR) images. In addition, the proposed method has better noise robustness for the strongly time-varying and multicomponent beat signals.