Improved Human-Robot Collaboration Through Simulation-Based Optimization
2019 (English)In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 153-158, article id 10.3233/ATDE190027Conference paper, Published paper (Refereed)
Abstract [en]
In order to pursue the dream combination of human flexibility and robot automation, human robot collaboration (HRC) is increasingly being investigated through academic research and industrial scenarios. HRC involves several challenges ranging from safety and comfort of the human to process efficiency and cost of robot operation. Achieving the right balance between these aspects is critical to implementing a safe, profitable and sustainable HRC environment. In this paper,we propose the use of simulation-based optimization (SBO) for assembly task allocation and scheduling for a HRC working cell in which an industrial robot assists a human worker. The list of product assembly operations are classified according to the capability of human worker and robot, and the sequencing constraints on them are the initial inputs of the method. The operators’ ergonomic load scores and cycletime of the assembly process are achieved by simulation. The optimized solutions are sorted to find the trade-offs between ergonomics and cycle time. We demonstratethe feasibility of the proposed approach through an industrial case study.
Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2019. Vol. 9, p. 153-158, article id 10.3233/ATDE190027
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 9
Keywords [en]
simulation based optimization, task allocation, scheduling, assembly, human robot collaboration
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
INF201 Virtual Production Development; VF-KDO; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-17792DOI: 10.3233/ATDE190027Scopus ID: 2-s2.0-85111830875ISBN: 978-1-64368-008-8 (print)ISBN: 978-1-64368-009-5 (electronic)OAI: oai:DiVA.org:his-17792DiVA, id: diva2:1361498
Conference
17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK
2019-10-162019-10-162023-02-24Bibliographically approved