Takeout Service Automation With Trained Robots in the Pandemic-Transformed Catering Business

Ting-Yu Lin*, Kun-Ru Wu, You-Shuo Chen, Wei-Hau Huang, Yi-Tuo Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Under strict social-distancing directives during the COVID-19 pandemic, one of the most impacted businesses is probably the catering industry, which has been forced to earn their revenue mainly from delivery and takeout services. However, traditional takeouts require patrons to wait in line, order and pick up their meals, incurring unnecessary human contact and service inefficiency. We observe these drawbacks and propose a contactless meal order and takeout service (Mots) automated system realized by AI-assisted smart robots to address the issue. In our Mots system, we develop a bump-free schedule based on the Welsh-Powell coloring algorithm for grouping robots into several non-colliding moving batches. Simulation results show that our Mots solution can effectively improve takeout efficiency and promote service accuracy, boosting business profits up to 95.4% under simulated cases for various cafeteria scales and shop popularity differences, compared to the traditional takeout method. Our experiments suggest that Mots is also capable of accommodating a sudden surge of arriving patrons within a short period of time. Furthermore, we have implemented a proof-of-concept prototype to demonstrate our Mots automated operations.

Original languageEnglish
Pages (from-to)903-910
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2021


  • Robots
  • Collision avoidance
  • Robot sensing systems
  • Business
  • Schedules
  • Pandemics
  • Automation
  • AI-enabled robotics
  • service robotics
  • embedded systems for robotic and automation
  • automation technologies for smart cities
  • collision avoidance


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