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Human–Robot Collaboration Using Visual Cues for Communication

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The 21st Century Industrial Robot: When Tools Become Collaborators

Part of the book series: Intelligent Systems, Control and Automation: Science and Engineering ((ISCA,volume 81))

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Abstract

The present chapter addresses the fundamental roles played by communication and mutual awareness in human/robot interaction and co-operation at the workplace. The chapter reviews how traditional industrial robots in the manufacturing sector have been used for repetitive and strenuous tasks for which they were segregated due to their hazardous size and strength, and so are still perceived as threatening by operators in manufacturing. This means that successful introduction of new collaborative systems where robotic technology will be working alongside and directly with human operators depends on human acceptance and engagement. The chapter discusses the important reassuring role played by communication in human–robot interaction and how involving users in the design process increases not only the efficiency of communication, but provides a reassuring effect.

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References

  1. Heyer C (2010) Human-robot interaction and future industrial robotics applications. In: 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4749–4754 [Online]. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651294. Accessed 18 Mar 2016

  2. Pawar VM, Law J, Maple C (2016) Manufacturing robotics. The next robotic industrial revolution (white paper). UK-RAS

    Google Scholar 

  3. Castellion G, Markham SK (2013) Perspective: new product failure rates: influence of argumentum ad populum and self-interest. J Prod Innov Manag 30(5):976–979. https://doi.org/10.1111/j.1540-5885.2012.01009.x

    Article  Google Scholar 

  4. Tufte ER (1993) The visual display of quantitative information, vol 2. Graphics Press, Connecticut

    Google Scholar 

  5. Lamont D, Kenyon S, Lyons G (2013) Dyslexia and mobility-related social exclusion: the role of travel information provision. J Transp Geogr 26:147–157. https://doi.org/10.1016/j.jtrangeo.2012.08.013

    Article  Google Scholar 

  6. Ben-Bassat T, Shinar D (2006) Ergonomic guidelines for traffic sign design increase sign comprehension. Human Fact J Human Fact Ergon Soc 48(1):182–195. https://doi.org/10.1518/001872006776412298

    Article  Google Scholar 

  7. Sanders TL, Wixon T, Schafer KE, Chen JYC, Hancock PA (2014) The influence of modality and transparency on trust in human-robot interaction. In: 2014 IEEE international inter-disciplinary conference on cognitive methods in situation awareness and decision support (CogSIMA), pp 156–159. https://doi.org/10.1109/CogSIMA.2014.6816556

  8. Selkowitz AR, Lakhmani SG, Larios CN, Chen JYC (2016) Agent transparency and the autonomous squad member. Proc Human Fact Ergon Soc Ann Meet 60(1):1319–1323. https://doi.org/10.1177/1541931213601305

    Article  Google Scholar 

  9. Cameron et al D (2015) Framing factors: the importance of context and the individual in understanding trust in human-robot interaction, presented at the IEEE/RSJ international conference on intelligent robots and systems [Online]. http://iros15-desrps.chrisbevan.co.uk/papers/cameron.pdf. Accessed 5 Feb 2016

  10. Hancock PA, Billings DR, Schaefer KE, Chen JYC, de Visser EJ, Parasuraman R (2011) A meta-analysis of factors affecting trust in human-robot interaction. Human Fact J Human Fact Ergon Soc 53(5):517–527. https://doi.org/10.1177/0018720811417254

    Article  Google Scholar 

  11. Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Human Fact J Human Fact Ergon Soc 46(1):50–80

    Article  MathSciNet  Google Scholar 

  12. Mathews A, Mackintosh B (1998) A cognitive model of selective processing in anxiety. Cogn Ther Res 22(6):539–560

    Article  Google Scholar 

  13. Ozer EM, Bandura A (1990) Mechanisms governing empowerment effects: a self-efficacy analysis. J Personal Soc Psychol 58(3):472

    Google Scholar 

  14. Ussher J, Kirsten L, Butow P, Sandoval M (2006) What do cancer support groups provide which other supportive relationships do not? The experience of peer support groups for people with cancer. Soc Sci Med 62(10):2565–2576. https://doi.org/10.1016/j.socscimed.2005.10.034

    Article  Google Scholar 

  15. Lautizi M, Laschinger HKS, Ravazzolo S (2009) Workplace empowerment, job satisfaction and job stress among Italian mental health nurses: an exploratory study. J Nurs Manag 17(4):446–452. https://doi.org/10.1111/j.1365-2834.2009.00984.x

    Article  Google Scholar 

  16. Pearson LC, Moomaw W (2005) The relationship between teacher autonomy and stress, work satisfaction, empowerment, and professionalism. Educ Res Q 29(1):37

    Google Scholar 

  17. Thorvald P, Lindblom J (2014) Initial development of a cognitive load assessment tool. In: The 5th AHFE international conference on applied human factors and ergonomics, 19–23 July 2014, Krakow, Poland, pp 223–232 [Online]. http://books.google.com/books?hl=en&lr=&id=6oVYBAAAQBAJ&oi=fnd&pg=PA223&dq=%22This+has+resulted+in+identification+and+classification+of+factors+suitable+for+assessment+of%22+%22assessment+of+a+task+performed+at+a+workstation.+Future+development+of+the+tool+will+include+validation%22+&ots=yEESFsIux-&sig=fwinalYk8a3b_GNvqxiyy2DJUx0. Accessed 25 Feb 2016

  18. Bahar G, Masliah M, Wolff R, Park P (2007) Desktop reference for crash reduction factors

    Google Scholar 

  19. Laughery KR (2006) Safety communications: warnings. Appl Ergon 37(4):467–478. https://doi.org/10.1016/j.apergo.2006.04.020

    Article  Google Scholar 

  20. Chen R, Wang X, Hou L (2010) Augmented reality for collaborative assembly design in manufacturing sector. In: Virtual technologies for business and industrial applications: innovative and synergistic approaches: innovative and synergistic approaches, p 105

    Google Scholar 

  21. Gwilt I, et al (2018) Cobotics: developing a visual language for human-robotic collaborations

    Google Scholar 

  22. Ibarguren A, Eimontaite I, Outón JL, Fletcher S (2020) Dual arm co-manipulation architecture with enhanced human-robot communication for large part manipulation. Sensors (Basel) 20(21). https://doi.org/10.3390/s20216151

  23. Tang C-H, Wu W-T, Lin C-Y (2009) Using virtual reality to determine how emergency signs facilitate way-finding. Appl Ergon 40(4):722–730. https://doi.org/10.1016/j.apergo.2008.06.009

    Article  Google Scholar 

  24. Vilar E, Rebelo F, Noriega P (2014) Indoor human wayfinding performance using vertical and horizontal signage in virtual reality: indoor human wayfinding and virtual reality. Human Fact Ergon Manufact Service Indus 24(6):601–615. https://doi.org/10.1002/hfm.20503

    Article  Google Scholar 

  25. Nomura T, Kanda T, Suzuki T, Kato K (2008) Prediction of human behavior in human-robot interaction using psychological scales for anxiety and negative attitudes toward robots. IEEE Trans Rob 24(2):442–451. https://doi.org/10.1109/TRO.2007.914004

    Article  Google Scholar 

  26. Eimontaite I et al (2016) Assessing graphical robot aids for interactive co-working. In: Schlick C, Trzcieliński S (eds) Advances in ergonomics of manufacturing: managing the enterprise of the future, vol 490. Springer International Publishing, Cham, pp 229–239

    Chapter  Google Scholar 

  27. Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Advances in psychology, vol. 52. Elsevier, pp 139–183

    Google Scholar 

  28. Compeau DR, Higgins CA (1995) Computer self-efficacy: development of a measure and initial test. MIS Quarterly 189–211

    Google Scholar 

  29. Stafford et al RQ (2010) Improved robot attitudes and emotions at a retirement home after meeting a robot. In: RO-MAN, 2010 IEEE, pp 82–87 [Online]. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5598679. Accessed 19 Feb 2016

  30. Nomura T, Shintani T, Fujii K, Hokabe K (2007) Experimental investigation of relationships between anxiety, negative attitudes, and allowable distance of robots. In: Proceedings of the 2nd IASTED international conference on human computer interaction. ACTA Press, Chamonix, France, pp 13–18 [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.517.6166&rep=rep1&type=pdf. Accessed 19 Feb 2016

  31. Zuckerman M (1979) Attribution of success and failure revisited, or: the motivational bias is alive and well in attribution theory. J Pers 47(2):245–287

    Article  Google Scholar 

  32. Prati G, Pietrantoni L, Cicognani E (2010) Self-efficacy moderates the relationship between stress appraisal and quality of life among rescue workers. Anxiety Stress Coping 23(4):463–470. https://doi.org/10.1080/10615800903431699

    Article  Google Scholar 

  33. Sim H-S, Moon W-H (2015) Relationships between self-efficacy, stress, depression and adjustment of college students. Indian J Sci Technol 8(35). https://doi.org/10.17485/ijst/2015/v8i35/86802

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Acknowledgements

This work is supported by the projects A-GRAfIC (funded by EPSRC Centre for Innovative Manufacturing in Intelligent Automation under grant agreement EP/IO33467/1) and SHERLOCK (funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820689). The researchers would like to thank all project partners and participants for their support enabling this work to be completed.

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Correspondence to Iveta Eimontaite .

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Eimontaite, I. (2022). Human–Robot Collaboration Using Visual Cues for Communication. In: Aldinhas Ferreira, M.I., Fletcher, S.R. (eds) The 21st Century Industrial Robot: When Tools Become Collaborators. Intelligent Systems, Control and Automation: Science and Engineering, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-78513-0_5

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