FINAL VERSION.pdf (1.09 MB)
Download file

Knowledge sharing enabled multirobot collaboration for preventive maintenance in mixed model assembly

Download (1.09 MB)
journal contribution
posted on 20.09.2022, 11:20 authored by Baotong Chen, Eve ZhangEve Zhang, Xuhui Xia, Miguel Martinez-GarciaMiguel Martinez-Garcia, Gbanaibolou Jombo
This paper focuses on knowledge driven techniques, and it proposes a Knowledge Sharing-enabled Multi- Robot Collaboration (KS-enabled MRC) strategy for preventive maintenance of robots in Mixed Model Assembly (MMA). Firstly, a formal semantic environment for MMA is constructed by way of ontology-enabled semantic modeling. Then, task-related action primitives and ontology-based robot skill bases are established according to robot capability and task environment. Finally, the Wu-Palmer similarity metric and first-order logic are leveraged to match and reason new tasks according to the semantic rules, and a knowledge sharing and update mechanism is developed for this application. Experimental results demonstrate that the proposed KS-enabled MRC can reduce unscheduled downtime and assist in achieving a load balance for robots in MMA. It can potentially avoid severe equipment degradation, thus acting as a preventive maintenance paradigm. Furthermore, it is applicable across different platforms and exhibits high deployment efficiency without intense programming requirements.

Funding

Key Research and Development Program of Hubei Province (Grant Number: 2020BAA024)

Research on Dynamic Combination Mechanism of Remanufacturing Services for Generalized Growth of Waste Machinery Products

National Natural Science Foundation of China

Find out more...

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

18

Issue

11

Pages

8098 - 8107

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

09/03/2022

Publication date

2022-03-15

Copyright date

2022

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Dr Eve Zhang. Deposit date: 5 September 2022