Stereo vision-based Kinematic calibration method for the Stewart platforms

Abstract

Accuracy is the most important index for the industrial applications of the Stewart platform, which can be guaranteed by the kinematic calibration method to improve the motion orbit performance of this platform. In order to improve the effectiveness of the least squares algorithm and the identified accuracy of the platform’s geometric parameter errors, an applicab-le dimensionless error model based on the structural characteristics of the Stewart platform is investigated. Moreover, a novel stereo vision-based measurement method is proposed, which can measure the 6-degree-of-freedom (DOF) pose of the moving platform. On this basis, an identification simulation is schemed to validate the efficiency of the dimensionless error model, and the kinematic calibration experiment is carried out on a prototype. The experimental results demonstrate that the position error is decreased to 0.261 mm with an improved accuracy of 89.720%, the orientation error is decreased to 0.051° with an improved accuracy of 90.351%.