System Research on the Efficient Identification of Failures or Defects in Collaborative Robots Using Digital Twin Technology

As the coexistence of humans and robots has increased since the start of the Fourth Industrial Revolution, this study proposes a platform for processing and analyzing data to efficiently address the causes and prevention of robot collisions at industrial sites. The platform facilitates easy and convenient data processing and analysis, allowing users to quickly identify the causes of collisions between collaborative robots as well as control equipment failures or defects. Utilizing data, the platform also enables the remote operation of equipment in virtual spaces, making it effective for communication and remote technical support in the manufacturing industry.