Cauchy DMP: Improving 3C industrial assembly quality with the Cauchy kernel and singular value decomposition

Author:

Liu Meng1ORCID,Zhu Wenbo1,Luo Lufeng1,Lu Qinghua1,Yeh Weichang2,Zhang Yunzhi1

Affiliation:

1. School of Mechatronic Engineering and Automation Foshan University Guangdong China

2. Department of Industrial Engineering and Engineering Management National Tsing Hua University Taiwan China

Abstract

AbstractAlthough Dynamic Movement Primitives (DMP) is an effective tool for robotic arm trajectory generalisation, the application of DMP in the 3C (Computer, Communication, Consumer Electronics) industry still faces challenges such as low precision and high‐time consumption. To address this problem, we propose a novel Cauchy DMP framework. The main improvements and advantages of Cauchy DMP, compared to the original DMP, are (1) since the Cauchy distribution has a simpler model and wider shape, using the Cauchy distribution instead of the Gaussian distribution in the original DMP reduces the complexity of the algorithm and saves time. (2) Singular Value Decomposition (SVD) can effectively model the error. To reduce the interference of the rounding and human error on the trajectory, SVD can be used to obtain the weight of each basis function. The proposed Cauchy DMP framework combines the above two points and is validated on a real UR5 robotic arm. The results show that Cauchy DMP retains the learnability of the original DMP and has the advantages of short time consumption and low error rate.

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition,Experimental and Cognitive Psychology

Reference30 articles.

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