A Hybrid Hand–Eye Calibration Method for Multilink Cable-Driven Hyper-Redundant Manipulators

Abstract

Multilink cable-driven hyper-redundant manipulators (MCDHMs) have a slender body and flexible movement characteristics, which are very suitable for complex unstructured environments. Since MCDHMs have multiple coupling between active cables, linkage cables, joints, and the end-effector, the mathematical modeling of the segmented linkage MCDHM becomes more complicated, and the uncertainty of its model makes the motion accuracy of the end-effector become low. Traditional calibration methods that rely solely on eye-in-hand or eye-to-hand calibration method to calibrate the MCDHM model will produce relatively large errors. This article proposes a hybrid hand–eye calibration method, which eliminates the errors introduced by inaccurate kinematics models through the fusion modeling of eye-in-hand and eye-to-hand. First, for unstructured application scenarios, the hybrid calibration framework under the condition of large model errors is introduced, and then the multiple kinematics model of the MCDHM is derived. Then, two different types of hybrid hand–eye calibration equations are derived and solved by the recursive iteration method. Furthermore, according to the hand–eye calibration result, the MCDHM kinematic parameters are calibrated by the particle swarm optimization (PSO) method. Finally, in order to demonstrate the feasibility and superiority of the hybrid calibration method, experiments are carried out on the real MCDHM to further verify the proposed method. The experimental results show that the proposed hybrid calibration method has higher calibration accuracy.