Finding Measurement Configurations for Accurate Robot Calibration: Validation With a Cable-Driven Robot

It is well known that, by properly selecting the measurement configurations in robot calibrations, the observability index of unknown parameters can be maximized, leading to high calibration accuracy. For this purpose, many configuration-search methods were proposed. However, the established methods were mainly based on derivative-free or metaheuristic techniques, whose computational costs were high. Moreover, the robustness of observability index and convergences of configuration searches were not investigated. In this paper, by extending a recent result in matrix perturbation theory to robot kinematics, we establish the closed-form mapping from configuration perturbations to singular-value variations. Based on this mapping, an efficient configuration-search method is proposed, the robustness of the observability index under bounded configuration perturbations is analyzed, and the convergence of configuration searches is studied. The proposed methods were validated by simulations on serial and parallel robots. With roughly estimated initial parameters, self-calibration experiments on a redundant cable-driven parallel robot were performed. The effectiveness of the proposed methods is demonstrated by the experiment results.