一种面向机器人机械系统的程式化动力学建模方法

iming at the problem of low efficiency caused by complex and redundant calculation of robot dynamics model, aprogrammed modeling method is proposed. Taking the Stanford Arm with six degrees of freedom as an example, the dynamicmodel based on Lagrangian equation is established by using this method. According to the core idea of "forward analysis,reverse output", the recursive process of the model is analyzed emphatically. On the basis of verifying the correctness ofthe model, the indexes such as the dimensions and running time of the Stanford Arm model based on the PMM and the conventionalLagrange equation without the use of the PMM are compared. The results show that relative to the conventionalLagrange method, the complexity of the model established by PMM is reduced by 67.6%, and the computational efficiencyis increased by 66.3%. Stanford Arm is a complete constrained system. PMM is extended to underactuated nonholonomicconstrained systems, numerical simulation and physical prototype experiment analysis are carried out by using partial feedbacklinearization control algorithm which is closely related to the model, it’s reliability and effectiveness of the programmedmodeling method are verified, which provides a modeling method with higher efficiency and better versatility for differenttypes of robots.