A hybrid optical–mechanical calibration procedure for the Scalable-SPIDAR haptic device

Abstract

In this research, a simple, yet, efficient calibration procedure is presented in order to improve the accuracy of the Scalable-SPIDAR haptic device. The two-stage procedure aims to reduce discrepancies between measured and actual values. First, we propose a new semi-automatic procedure for the initialization of the haptic device. To perform this initialization with a high level of accuracy, an infrared optical tracking device was used. Furthermore, audio and haptic cues were used to guide the user during the initialization process. Second, we developed two calibration methods based on regression techniques that effectively compensate for the errors in tracked position. Both neural networks and support vector regression methods were applied to calibrate the position errors present in the haptic device readings. A comparison between these two regression methods was carried out to show the underlying algorithm and to indicate the inherent advantages and limitations for each method. Initial evaluation of the proposed procedure indicated that it is possible to improve accuracy by reducing the Scalable-SPIDAR's average absolute position error to about 6 mm within a 1 m × 1 m × 1 m workspace.