A Model-Data Driven Approach for Calibration of a 5-DOF Hybrid Machining Robot

Abstract Current research on robot calibration can be roughly classified into two categories, and both of them have certain inherent limitations. Model-based methods are difficult to model and compensate the pose errors arising from configuration-dependent geometric and non-geometric source errors, whereas the accuracy of data-driven methods depends on a large amount of measurement data. Using a 5-DOF (degrees of freedom) hybrid machining robot as an exemplar, this study presents a model data-driven approach for the calibration of robotic manipulators. An f -DOF realistic robot containing various source errors is visualized as a 6-DOF fictitious robot having error-free parameters, but erroneous actuated/virtual joint motions. The calibration process essentially involves four steps: (1) formulating the linear map relating the pose error twist to the joint motion errors, (2) parameterizing the joint motion errors using second-order polynomials in terms of nominal actuated joint variables, (3) identifying the polynomial coefficients using the weighted least squares plus principal component analysis, and (4) compensating the compensable pose errors by updating the nominal actuated joint variables. The merit of this approach is that it enables compensation of the pose errors caused by configuration-dependent geometric and non-geometric source errors using finite measurement configurations. Experimental studies on a prototype machine illustrate the effectiveness of the proposed approach.