Knowledge Sharing enabled Multi-Robot Collaboration for Preventive Maintenance in Mixed Model Assembly

Baotong Chen, Yu Zhang, Xuhui Xia, Miguel Martinez-Garcia, Gbanaibolou Jombo

Research output: Contribution to journalArticlepeer-review


Intelligent equipment and flexible production lines are at the cores of smart manufacturing. Meanwhile, Internet of Things and Artificial Intelligence have provided new solutions for the intelligent equipment management and maintenance in mixed model assembly (MMA). This article focuses on knowledge-driven techniques, and it proposes a knowledge sharing-enabled multirobot collaboration (KS-enabled MRC) strategy for preventive maintenance of robots in MMA. First, 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 are 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. The studied MRC can potentially avoid severe equipment degradation, thus acting as a preventive maintenance paradigm of complex equipment. Furthermore, it is applicable across different platforms and exhibits high deployment efficiency without intense programming requirements.

Original languageEnglish
Pages (from-to)8098-8107
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number11
Early online date15 Mar 2022
Publication statusPublished - 30 Nov 2022


  • Knowledge sharing (KS)
  • multirobot collaboration (MRC)
  • ontology
  • preventive maintenance


Dive into the research topics of 'Knowledge Sharing enabled Multi-Robot Collaboration for Preventive Maintenance in Mixed Model Assembly'. Together they form a unique fingerprint.

Cite this