The Rαβγ categorisation framework for dexterous robotic manufacturing processes

Abstract

Dexterous robotics systems integrate advanced manipulation and perception capabilities, facilitating advanced process automation. However, the inherent complexities of such processes may induce vulnerabilities, impeding overall performance. A parsimonious categorisation of dexterous robotic processes can disambiguate process attributes and highlight intricacies. Existing robotic categorisations focusing on a single attribute (e.g. manipulator structure) lack a processes view. Existing production process categorisations lack valuation of robotic characteristics. The current work suggests Rαβγ, a holistic categorisation of dexterous robotic processes. Rαβγ integrates robotic concepts within the classical [α|β|γ] production process categorisation and is similarly divided into tiers: Workcell, Task, and Objective. Each tier is defined by qualitative descriptors and quantitative characteristics. The intricacies of each characteristic were quantified by an analytic hierarchical process (AHP), semi-structured interviews with robotic experts were conducted for validation, and utility is demonstrated by three case studies. The AHP results are consistent and interpretable. The interviewees determined that Rαβγ is valuable and comprehensive. The case studies demonstrate the categorisation’s ability to highlight major process attributes. The analysis asserts that Rαβγ can be valuable during different product life cycle phases, e.g. designing, commissioning, etc. Rαβγ uniquely integrates the manufacturing and robotic domains, offering a holistic mechanism for highlighting characteristics of dexterous robotic processes.

KEYWORDS:

• Robotics
• manufacturing
• dexterous robotic process
• analytic hierarchical process (AHP)
• categorisation

Acknowledgments

The authors thank the robotic and manufacturing experts that participated in the study for their insightful comments and their contribution to the improvement and validation the categorisation framework. The authors thank Ms Inez Mureinik for her assistance in editing the manuscript. Both authors conceptualised the project and the methodology. R.S. performed the data collection and analysis. Both authors discussed the results and contributed to the final manuscript. Both authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

Approved by The Ben-Gurion University Human Subjects Research Committee (approval number 2243-1).

Notes

1 Mobile Manipulation Hackathon IROS 2018. Retrieved October 24, 2022, from https://www.youtube.com/watch?v=mt7JGXHb8jQ.

2 WRS2018 SDU Robotics System Introduction Video. Retrieved October 24, 2022, from https://www.youtube.com/watch?v=qo08SCGUFnw.

3 Robotics 2020 Multi-Annual Roadmap. Retrieved January 30, 2022, from https://www.eu-robotics.net/sparc/upload/about/files/H2020-Robotics-Multi-Annual-Roadmap-ICT-2016.pdf.

Additional information

Funding

This work was supported by Ministry of Culture and Sport: [Grant Number 67436].

Notes on contributors

Ran Shneor

Ran Shneor is a Ph.D. student in the Department of Industrial Engineering & Management at the Ben-Gurion University of the Negev, Israel. He investigates automatic robotic assembly planning of industrial products containing deformable objects. He served in various leadership and engineering roles in the Israeli Air Force. He holds M.Sc. and B.Sc. in Industrial Engineering and Management, both from the Ben-Gurion University of the Negev.

Sigal Berman

Sigal Berman is an associate professor in the Department of Industrial Engineering & Management at Ben-Gurion University of the Negev, Beer-Sheva. She received a B.Sc. in Electrical and Computer Engineering, from the Technion, an M.Sc. in Electrical and Computer Engineering, and a Ph.D. in Industrial Engineering, both from Ben-Gurion University. Sigal leads the Intelligent Systems Engineering Laboratory (ISEL) where her research focuses on the analysis and engineering of intelligent systems capable of dexterous motion. She develops data-driven models for the synthesis of robotic motion and the analysis of human motion.