An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction. (April 2023)

• Record Type: Journal Article
• Authors: Li, Chengxi; Zheng, Pai; Yin, Yue; Pang, Yat Ming; Huo, Shengzeng
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 80(2023)
• Publication Date: 2023-04
• Subjects: Smart manufacturing -- Human robot interaction -- Augmented reality -- Deep reinforcement learning -- Manufacturing safety; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102471
• Languages: English
• ISSNs: 0736-5845

Abstract

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. Highlights: Introduced an AR-assisted system architecture for mutual-cognitive safe HRI. Implemented distance-based robot velocity control and area-based workers' visual aids functions. Developed the robot DT-enabled motion preview for workers to enhance safe cognition and for robots to detect collision. Proposed a curriculum learning-based DRL motion planning policy for collision avoidance.