Abstract
Coupling errors are major threats to the accuracy of 3-axis force sensors. Design of decoupling algorithms is a challenging topic due to the uncertainty of coupling errors. The conventional nonlinear decoupling algorithms by a standard Neural Network (NN) are sometimes unstable due to overfitting. In order to avoid overfitting and minimize the negative effect of random noises and gross errors in calibration data, we propose a novel nonlinear static decoupling algorithm based on the establishment of a coupling error model. Instead of regarding the whole system as a black box in conventional algorithm, the coupling error model is designed by the principle of coupling errors, in which the nonlinear relationships between forces and coupling errors in each dimension are calculated separately. Six separate Support Vector Regressions (SVRs) are employed for their ability to perform adaptive, nonlinear data fitting. The decoupling performance of the proposed algorithm is compared with the conventional method by utilizing obtained data from the static calibration experiment of a 3-axis force sensor. Experimental results show that the proposed decoupling algorithm gives more robust performance with high efficiency and decoupling accuracy, and can thus be potentially applied to the decoupling application of 3-axis force sensors.
Highlights
Force sensing is crucial for on-line perception and feedback in interactions between intelligent robotic manipulators and environments
Cross validation is used to estimate the performance of decoupling by a standard Multiple Input Multiple Output (MIMO) radial basis function (RBF) Neural Network (NN)
Summarizing, the calculation results of the decoupling methods demonstrate that our decoupling method based on our coupling error model and -Support Vector Regressions (SVRs) has high reliability and fast running speed when no gross errors exist in the calibration data
Summary
Force sensing is crucial for on-line perception and feedback in interactions between intelligent robotic manipulators and environments. The accuracy of multi-axis force sensors has a great impact on force-perception based tasks with high precision requirements. The common static decoupling algorithm calculates the pseudo-inverse matrix of calibration data based on the Least Square Method (LSM) [9,10,11] This algorithm is based on the assumption that relationships between input forces and output voltages in all dimensions are linear. Instead of referring to the whole sensor system as a black box using one standard NN [14,15], the proposed decoupling algorithm is designed using the principle of coupling errors, in which the relationships between each input and output are mapped separately in the proposed coupling error model to make the algorithm more reliable. The proposed coupling error model consists of six SVRs and three linear fitting functions, which is more conformable to calibration data structure.
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