Research on Robot Joint Error Based on Neural Network

Jincai Huang, Jianfei Liao, Bingchu Chen, Yongxiang Li, Youbing Wu

Abstract


Since the beginning of the 21st century, robots have played an important role in both daily life and industrial production. This paper aims to evaluate the types of robot joint errors. First, the types of robot joint errors are described, and the two characteristic indexes, Δô€Œ and Δô€œ´, are identified. Next, robot joint errors are divided into adjustable ones and nonadjustable ones. A robot joint error type model is established using PB neural network model. The areas of adjustable joint errors and nonadjustable joint errors are defined. Finally, reasonable suggestions are given for different types of robot joint errors.

Keywords


joint error of robot adjustable and nonadjustable; PB neural network


DOI
10.12783/dtetr/emme2016/9803

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