Determination of Optimal Measurement Configurations for Robot Calibration Based on a Hybrid Optimal Method

Robot calibration is an important method to improve robot accuracy. Optimal measurement configurations for robot calibration are helpful to reducing measure points, and save time consume. In this paper, a hybrid optimal method was proposed, and the measuring configurations for a six degree-of-freedom industrial robot calibration were selected based on the proposed algorithm. First, the robot error model was constructed according to a modified complete and parametrically continuous (MCPC) kinematic model, and then the optimal objective function was obtained, which was the condition number of error identification Jacobi. Second, the validity and the procedure of the proposed algorithm were discussed in detail. The hybrid optimal algorithm realized the combination of global search and local search, and the distinct characteristics are good global ability; good convergence and time consumed less, also easy to programme. Simulation studies were conducted to examine the efficiency of the hybrid optimal algorithm, and the results show that the hybrid optimal algorithm is good at determination of optimal measurement configurations. The results also suggest that the proposed algorithm is better than the single chaos optimal algorithm.