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在分析手眼标定问题数值特征的基础上,提出一种新的基于非最小化优化的手眼标定方法。采用张量的形式描述手眼标定方程,提出了非最小化优化条件下的代价函数,通过特征计算求解相应估计方程。扰动分析证明了该方法求解的精确性。分别采用仿真数据和真实数据进行试验。试验结果显示,该方法能够通过无初值的计算实现手眼方程的求解,避免了优化迭代产生的复杂计算,具有较高的鲁棒性、有效性和天然的运动选择特性。与其他方法相比较,新方法大大节省了运算时间,并降低了计算误差,为机器人系统的实际标定提供一个很好的选择。
Based on the analysis of numerical characteristics of hand-eye calibration problem, a new hand-eye calibration method based on non-minimization optimization is proposed. The hand-eye calibration equation is described in the form of tensor, and the cost function under the non-minimized optimization condition is proposed. The corresponding estimation equation is solved by the feature calculation. The perturbation analysis proves the accuracy of the method. Respectively, using simulation data and real data to test. The experimental results show that the proposed method can solve the hand-eye equation through the non-initial calculation and avoid the complex calculation of optimization iteration, which has high robustness, validity and natural motion selection. Compared with other methods, the new method can greatly save the computation time and reduce the calculation error, which provides a good choice for the actual calibration of the robot system.