Affiliations: [a] School of Computer Science, Baoji University of Arts and Sciences, China
| [b] Faculty of Management and Economics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| [c] Faculty of Computing, IBM CoE and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia
| [d] Faculty of Management Engineering, Huaiyin Institute of Technology, Huai’an, China
| [e] Business School, Northwest University of Political Science and Law, Xi’an, China
| [f] Institute of Research and Development, Duy Tan University, Da Nang, Vietnam | [g]
Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Malaysia
Correspondence:
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Address for correspondence: Zaher Mundher Yaseen, Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam. E-mail: [email protected].
Abstract: BACKGROUND:The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers. OBJECTIVES:In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace. RESULTS:The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk. CONCLUSION:Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
