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Knowledge Sharing and Evolution of Industrial Cloud Robotics

[+] Author Affiliations
Lixue Jin, Wenjun Xu, Zhihao Liu, Junwei Yan, Zude Zhou

Wuhan University of Technology, Wuhan, China

Duc Truong Pham

University of Birmingham, Birmingham, UK

Paper No. MSEC2018-6538, pp. V001T05A020; 9 pages
doi:10.1115/MSEC2018-6538
From:
  • ASME 2018 13th International Manufacturing Science and Engineering Conference
  • Volume 1: Additive Manufacturing; Bio and Sustainable Manufacturing
  • College Station, Texas, USA, June 18–22, 2018
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5135-7
  • Copyright © 2018 by ASME

abstract

Industrial Cloud Robotics (ICR), with the characteristics of resource sharing, lower cost and convenient access, etc., can realize the knowledge interaction and coordination among cloud Robotics (CR) through the knowledge sharing mechanism. However, the current researches mainly focus on the knowledge sharing of service-oriented robots and the knowledge updating of a single robot. The interaction and collaboration among robots in a cloud environment still have challenges, such as the improper updating of knowledge, the inconvenience of online data processing and the inflexibility of sharing mechanism. In addition, the industrial robot (IR) also lacks a well-developed knowledge management framework in order to facilitate the knowledge evolution of industrial robots. In this paper, a knowledge evolution mechanism of ICR based on the approach of knowledge acquisition - interactive sharing - iterative updating is established, and a novel architecture of ICR knowledge sharing is also developed. Moreover, the semantic knowledge in the robot system can encapsulate knowledge of manufacturing tasks, robot model and scheme decision into the cloud manufacturing process. As new manufacturing tasks arrived, the robot platform downloads task-oriented knowledge models from the cloud service platform, and then selects the optimal service composition and updates the cloud knowledge by simulation iterations. Finally, the feasibility and effectiveness of the proposed architecture and approaches are demonstrated through the case studies.

Copyright © 2018 by ASME
Topics: Robotics

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