The existing kinematic synthesis of serial robots generally focuses on configurations with specified degrees-of-freedom (DOFs). This paper proposes a new generation method of configurations based on iteration concepts, defines the concept of a configuration tree, and establishes the tree-evolution kinematic synthesis framework. The configuration tree includes configurations with different DOFs, and the ‘tree’ reflects the configuration relationship. The objectives of this study are to research the performance distribution characteristics of the ‘tree’, develop the tree-evolution strategies, and propose a serial robot configuration model with hyperparameters. The study observed that many typical performances have a monotonic distribution on the ‘tree’. Based on the estimation concept, the tree-evolution strategies search for the required configurations with high efficiency. In the kinematic synthesis task of an eight-DOF robot, the efficiency of configuration classification is increased by at least 30%, and the efficiency of configuration optimisation is increased by 65%. Moreover, the calculation efficiency is improved further as the DOF increases. The example shows that the new method improves the calculation efficiency of the process of kinematic synthesis compared with that for the traditional method.

现有的串行机器人运动学综合通常集中在具有指定自由度（DOF）的配置上。本文提出了一种基于迭代概念的新一代配置方法，定义了配置树的概念，并建立了树演化运动学综合框架。配置树包括具有不同DOF的配置，并且“树”反映了配置关​​系。本研究的目的是研究“树”的性能分布特征，制定树演化策略，并提出具有超参数的串行机器人配置模型。研究发现，许多典型的表演在“树”上具有单调分布。根据估算概念，树演化策略可以高效地搜索所需的配置。在八自由度机器人的运动学综合任务中，配置分类的效率提高了至少30％，配置优化的效率提高了65％。而且，随着DOF的增加，计算效率进一步提高。算例表明，与传统方法相比，新方法提高了运动学综合过程的计算效率。