An Efficient Kinematic Calibration Method for Parallel Robots With Compact Multi-Degrees-of-Freedom Joint Models

Abstract Forward kinematics-based modeling approaches are capable of constructing complete kinematic error models for parallel robots in a general way. The existing forward kinematics-based modeling methods replace multi-degrees-of-freedom (multi-DOF) joints with several 1DOF joints, allowing each limb of the parallel robot to be modeled like a serial robot. Nonetheless, this substitution complicates the kinematic model and results in additional computation. To overcome this limitation, an efficient kinematic calibration method adopting compact multi-DOF joint models is proposed. First, compact kinematic models for multi-DOF joints are established with the product of exponentials formula and adopted in the forward kinematic formulation of limbs. Error models of limbs are derived by simplifying the forward kinematic formulas' differentials, and the geometric error model for parallel robots is established by further concatenating and reformulating the limb error models. Next, the kinematic model is iteratively updated with the geometric parameter errors identified by the Levenberg–Marquardt algorithm. Error compensation is achieved through the inverse kinematics of the calibrated kinematic model. Finally, simulations and an experiment are implemented for validation. Compared with the existing forward kinematics-based modeling approaches, the error modeling procedures are simplified as the equivalent substitution of multi-DOF joints is avoided. The proposed approach also enhances the error compensation efficiency while maintaining high accuracy improvement.