The kinematic calibration accuracy of serial manipulators is affected by the error expression ability of the selected measurement configurations and non-geometric errors such as joint disturbance, measurement noise, etc. Based on the observability of configurations, deviation of identifiable parameters, and calibration robustness, this paper proposes a multilevel evaluation criterion for measurement configuration optimization. In addition, based on the Compute Unified Device Architecture (CUDA) parallel computing technique, the most time-consuming Jacobian matrix calculation program in the algorithm is modified, and an efficient optimization algorithm for measurement configurations is established, to guarantee the feasibility of the evaluation criterion. Combined with CUDA algorithm, fast calibration is implemented with fewer measurement points and relatively higher accuracy, by means of multilevel optimization. The results illustrate the effectiveness and the universality of the proposed multilevel evaluation criterion. The criterion can be applied in calibration experiments of multi-degree of freedom (DOF) serial manipulators with complex structures.
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