Aimed to improve the stiffness identification precision of 7-degree-of-freedom (Dof) collaborative robots (Cobots), an optimal configuration selection method for elastostatic calibration of robots is researched by the influencing factor separation method. Different from previous studies, this method can deal with the influence of redundant Dof on measurement configuration selection of redundant robotic manipulators. The independent influence of each joint on the inverse condition number which is selected as the evaluation criterion is analyzed through the orthogonal design experiment and the analysis of variance, and the optimal measuring configurations of robots for stiffness identification can be selected from joint space. Based on a 7-Dof Cobot SHIR5-III, static compliance simulations are performed to identify joint stiffness of the robot. Compared to identification results by using the configurations having large, medium and small inverse condition number, the effectiveness of this optimal configuration selection method is verified and the identification accuracy can be essentially improved with configurations having a large inverse condition number.

中文翻译：

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用于7-Dof协作机器人刚度识别的最佳配置选择

为了提高7自由度（Dof）协作机器人（Cobots）的刚度识别精度，采用影响因子分离法研究了一种用于机器人弹力标定的最优配置选择方法。与以前的研究不同，该方法可以处理冗余自由度对冗余机械手测量配置选择的影响。通过正交设计实验和方差分析，分析了各关节对作为评价标准的逆条件数的独立影响，并从关节空间中选择了用于刚度识别的机器人最佳测量配置。基于7-Dof Cobot SHIR5-III，执行静态柔量仿真以识别机器人的关节刚度。