Collision-free human-robot collaboration based on context awareness. (February 2021)

• Record Type: Journal Article
• Authors: Liu, Hongyi; Wang, Lihui
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 67(2021)
• Publication Date: 2021-02
• Subjects: Human-robot collaboration -- Collision-free system -- Context awareness -- Deep learning; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2020.101997
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: The paper introduced a collision-free human-robot collaboration system based on context awareness concept that can improve the overall efficiency of the system. An efficient transfer learning based human poses recognition module is proposed to recognise human operators' assembly poses. An overall human-robot collaboration system architecture is designed and integrated. Different modules and algorithms introduced in the paper are tested with case studies. Abstract: Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators' poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.