An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction

Chengxi Li, Pai Zheng, Yue Yin, Yat Ming Pang, Shengzeng Huo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires manufacturing equipment (robots, etc.) interactively assist human workers to deal with dynamic and complex production tasks. To achieve symbiotic human–robot interaction (HRI), the safety issue serves as a prerequisite foundation. Regarding the growing individualized demand of manufacturing tasks, the conventional rule-based safe HRI measures could not well address the safety requirements due to inflexibility and lacking synergy. To fill the gap, this work proposes a mutual-cognitive safe HRI approach including worker visual augmentation, robot velocity control, Digital Twin-enabled motion preview and collision detection, and Deep Reinforcement Learning-based robot collision avoidance motion planning in the Augmented Reality-assisted manner. Finally, the feasibility of the system design and the performance of the proposed approach are validated by establishing and executing the prototype HRI system in a practical scene.

Original languageEnglish
Article number102471
JournalRobotics and Computer-Integrated Manufacturing
Volume80
DOIs
Publication statusPublished - Apr 2023

Keywords

  • Augmented reality
  • Deep reinforcement learning
  • Human robot interaction
  • Manufacturing safety
  • Smart manufacturing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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