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Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications
Complexity ( IF 2.3 ) Pub Date : 2020-06-30 , DOI: 10.1155/2020/9053715
Nan Liu 1, 2 , Liangyu Li 1 , Bing Hao 3 , Liusong Yang 3 , Tonghai Hu 3 , Tao Xue 2 , Shoujun Wang 2 , Xingmao Shao 2
Affiliation  

In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In this paper, the Semiparametric Deep Learning (SDL) method is proposed to model robot inverse dynamics. SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics (RBD) model and Nonparametric Deep Learning (NDL) model. The SDL model takes advantage of the global characteristics of classic RBD and the powerful fitting capabilities of the deep learning approach. Moreover, the parametric and nonparametric parts of the SDL model can be optimized at the same time instead of being optimized separately. The proposed method is validated using experiments, performed on a UR5 robotic platform. The results show that the performance of SDL model is better than that of RBD model and NDL model. SDL can always provide relatively accurate joint torque prediction, even when the RBD or NDL model is not accurate.

中文翻译:

智能城市和工业应用的半参数深度学习机械手逆动力学建模方法

在智慧城市和工厂中,机器人应用需要高精度和高安全性,这取决于精确的逆动力学建模。但是,物理建模方法不能包括机械手的不确定性因素,例如柔韧性,关节间隙和摩擦力。本文提出了一种半参数深度学习(SDL)方法来对机器人逆动力学进行建模。SDL是一种深度学习框架,旨在结合刚性刚体动力学(RBD)模型和非参数深度学习(NDL)模型来进行最佳推理。SDL模型利用了传统RBD的全局特性以及深度学习方法的强大拟合能力。而且,可以同时优化SDL模型的参数和非参数部分,而不必分别进行优化。通过在UR5机器人平台上执行的实验验证了所提出的方法。结果表明,SDL模型的性能优于RBD模型和NDL模型。即使RBD或NDL模型不准确,SDL始终可以提供相对准确的关节扭矩预测。
更新日期:2020-06-30
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