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Language-free graphical signage improves human performance and reduces anxiety when working collaboratively with robots

  • Iveta Eimontaite
  • Ian Gwilt
  • David Cameron
  • Jonathan M. Aitken
  • Joe Rolph
  • Saeid Mokaram
  • James LawEmail author
Open Access
ORIGINAL ARTICLE

Abstract

As robots become more ubiquitous, and their capabilities extend, novice users will require intuitive instructional information related to their use. This is particularly important in the manufacturing sector, which is set to be transformed under Industry 4.0 by the deployment of collaborative robots in support of traditionally low-skilled, manual roles. In the first study of its kind, this paper reports how static graphical signage can improve performance and reduce anxiety in participants physically collaborating with a semi-autonomous robot. Three groups of 30 participants collaborated with a robot to perform a manufacturing-type process using graphical information that was relevant to the task, irrelevant, or absent. The results reveal that the group exposed to relevant signage was significantly more accurate in undertaking the task. Furthermore, their anxiety towards robots significantly decreased as a function of increasing accuracy. Finally, participants exposed to graphical signage showed positive emotional valence in response to successful trials. At a time when workers are concerned about the threat posed by robots to jobs, and with advances in technology requiring upskilling of the workforce, it is important to provide intuitive and supportive information to users. Whilst increasingly sophisticated technical solutions are being sought to improve communication and confidence in human-robot co-working, our findings demonstrate how simple signage can still be used as an effective tool to reduce user anxiety and increase task performance.

Keywords

Human-robot interaction Graphical signage Anxiety towards robots Flexible manufacturing Collaborative robotics Industry 4.0 Technology acceptance 

Notes

Funding information

The authors acknowledge support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation, in undertaking this research work under grant reference number EP/IO33467/1.

Compliance with ethical standards

The study was approved by the University of Sheffield Psychology Department ethics committee.

Supplementary material

(AVI 13.3 MB)

References

  1. 1.
    Aykin NM, Aykin T (1991) Individual differences in human-computer interaction. Comput Indus Eng 20 (3):373–379CrossRefGoogle Scholar
  2. 2.
    Bahar G, Masliah M, Wolff R, Park P (2007) Desktop reference for crash reduction factors. Tech. rep., U.S Department of Transportation, Federal Highway Administration, Office of SafetyGoogle Scholar
  3. 3.
    Banks MR, Willoughby LM, Banks WA (2008) Animal-assisted therapy and loneliness in nursing homes: use of robotic versus living dogs. J Am Med Dir Assoc 9(3):173–177CrossRefGoogle Scholar
  4. 4.
    Bartneck C, Suzuki T, Kanda T, Nomura T (2007) The influence of people’s culture and prior experiences with aibo on their attitude towards robots. Ai Soc 21(1-2):217–230CrossRefGoogle Scholar
  5. 5.
    Ben-Bassat T, Shinar D (2006) Ergonomic guidelines for traffic sign design increase sign comprehension. Hum Factors 48(1):182–195CrossRefGoogle Scholar
  6. 6.
    Broadbent E, Stafford R, MacDonald B (2009) Acceptance of healthcare robots for the older population: review and future directions. Int J Soc Robot 1(4):319–330CrossRefGoogle Scholar
  7. 7.
    Cameron D, Aitken JM, Collins EC, Boorman L, Chua A, Fernando S, McAree O, Martinez-Hernandez U, Law J (2015) Framing factors: the importance of context and the individual in understanding trust in human-robot interaction In: IEEE/RSJ International conference on intelligent robots and systems (IROS). Workshop on Designing and Evaluating Social Robots for Public SettingsGoogle Scholar
  8. 8.
    Chan AH, Ng AW (2010) Investigation of guessability of industrial safety signs: effects of prospective-user factors and cognitive sign features. Int J Ind Ergon 40(6):689–697CrossRefGoogle Scholar
  9. 9.
    Cole S (2006) Information and empowerment: the keys to achieving sustainable tourism. J Sustain Tourism 14(6):629–644MathSciNetCrossRefGoogle Scholar
  10. 10.
    Eimontaite I, Gwilt I, Cameron D, Aitken JM, Rolph J, Mokaram S, Law J (2016) Assessing graphical robot aids for interactive co-working. In: Advances in ergonomics of manufacturing: managing the enterprise of the future. Springer, pp 229–239Google Scholar
  11. 11.
    European Factories of the Future Research Association, et al. (2013) Factories of the future: multi-annual roadmap for the contractual PPP under horizon 2020 publications office of the European Union. BrusselsGoogle Scholar
  12. 12.
    Frixione M, Lombardi A (2015) Street signs and ikea instruction sheets: pragmatics and pictorial communication. Rev Philos Psychol 6(1):133–149CrossRefGoogle Scholar
  13. 13.
    Galea ER, Xie H, Lawrence PJ, et al. (2014) Experimental and survey studies on the effectiveness of dynamic signage systems. Fire Safety Sci 11:1129–1143CrossRefGoogle Scholar
  14. 14.
    Gwilt I, Rolph J, Eimontaite I, Cameron D, Aitken J, Mokaram S, Law J (2018) Cobotics: developing a visual language for human- robotic collaborations. In: Proceedings of the cumulus conferenceGoogle Scholar
  15. 15.
    Hayes AF (2012) PROCESS: a versatile computational tool for observed variable mediation, moderation, and conditional process modeling. Tech rep., University of Kansas, KSGoogle Scholar
  16. 16.
    ISO3864-1:2011 (2011) Graphical symbols - safety colours and safety signs - Part 1: design principles for safety signs and safety markings. Standard, International Organization for Standardization, Geneva, CHGoogle Scholar
  17. 17.
    Kanda T, Hirano T, Eaton D, Ishiguro H (2004) Interactive robots as social partners and peer tutors for children: a field trial. Human-Comput Interact 19(1):61–84CrossRefGoogle Scholar
  18. 18.
    Kenworthy JB, Jones J (2009) The roles of group importance and anxiety in predicting depersonalized ingroup trust. Group Processes Intergroup Relat 12(2):227–239CrossRefGoogle Scholar
  19. 19.
    Khansari-Zadeh SM, Khatib O (2015) Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors. Auton Robot, 1–25Google Scholar
  20. 20.
    Lamont D, Kenyon S, Lyons G (2013) Dyslexia and mobility-related social exclusion: the role of travel information provision. J Transp Geogr 26:147–157CrossRefGoogle Scholar
  21. 21.
    Laughery KR (2006) Safety communications: warnings. Appl Ergonom 37(4):467–478CrossRefGoogle Scholar
  22. 22.
    Lautizi M, Laschinger HK, Ravazzolo S (2009) Workplace empowerment, job satisfaction and job stress among Italian mental health nurses: an exploratory study. J Nurs Manag 17(4):446–452CrossRefGoogle Scholar
  23. 23.
    Lewinski P, den Uyl TM, Butler C (2014) Automated facial coding: validation of basic emotions and facs aus in facereader. J Neurosci Psychol Econ 7(4):227CrossRefGoogle Scholar
  24. 24.
    MacDorman KF, Vasudevan SK, Ho CC (2009) Does Japan really have robot mania? comparing attitudes by implicit and explicit measures. AI Soc 23(4):485–510CrossRefGoogle Scholar
  25. 25.
    Madhavan P, Phillips RR (2010) Effects of computer self-efficacy and system reliability on user interaction with decision support systems. Comput Hum Behav 26(2):199–204CrossRefGoogle Scholar
  26. 26.
    McAree O, Aitken JM, Veres SM (2016) A model based design framework for safety verification of a semi-autonomous inspection drone. In: 2016 UKACC 11th International conference on control (CONTROL), pp 1–6Google Scholar
  27. 27.
    Metta G, Fitzpatrick P, Natale L (2006) Yarp: yet another robot platform. Int J Adv Robot Syst 3(1):8CrossRefGoogle Scholar
  28. 28.
    Mills ME, Sullivan K (1999) The importance of information giving for patients newly diagnosed with cancer: a review of the literature. J Clin Nurs 8(6):631–642CrossRefGoogle Scholar
  29. 29.
    Mokaram S, Aitken JM, Martinez-Hernandez U, Eimontaite I, Cameron D, Rolph J, Gwilt I, McAree O, Law J (2017) A ROS-integrated API for the KUKA LBR iiwa collaborative robot. IFAC-PapersOnLine 50(1):15,859–15,864CrossRefGoogle Scholar
  30. 30.
    Moreno-Jiménez B, Rodríguez-Carvajal R, Garrosa Hernández E, Morante Benadero M, et al. (2008) Terminal versus non-terminal care in physician burnout: the role of decision-making processes and attitudes to death. Salud Mental 31(2):93–101Google Scholar
  31. 31.
    Muir BM (1987) Trust between humans and machines, and the design of decision aids. Int J Man-Mach Stud 27(5–6):527–539CrossRefGoogle Scholar
  32. 32.
    Nicholson N, Soane E, Fenton-O’Creevy M, Willman P (2005) Personality and domain-specific risk taking. J Risk Res 8(2):157–176CrossRefGoogle Scholar
  33. 33.
    Nomura T, Suzuki T, Kanda T, Kato K (2006) Measurement of anxiety toward robots. In: The 15th IEEE International symposium on robot and human interactive communication, 2006. ROMAN 2006. IEEE, pp 372–377Google Scholar
  34. 34.
    Nomura T, Suzuki T, Kanda T, Kato K (2006b) Measurement of negative attitudes toward robots. Interact Stud 7(3):437–454CrossRefGoogle Scholar
  35. 35.
    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, pp 13–18Google Scholar
  36. 36.
    Ozer EM, Bandura A (1990) Mechanisms governing empowerment effects: a self-efficacy analysis. J Person Soc Psychol 58(3):472CrossRefGoogle Scholar
  37. 37.
    Pawar VM, Law J, Maple C (2016) Manufacturing robotics - the next robotic industrial revolution. Tech. rep., UK Robotics and Autonomous Systems NetworkGoogle Scholar
  38. 38.
    Pearson LC, Moomaw W (2005) The relationship between teacher autonomy and stress, work satisfaction, empowerment, and professionalism. Educ Res Quart 29(1):37Google Scholar
  39. 39.
    Quigley M, Conley K, Gerkey B, Faust J, Foote T, Leibs J, Wheeler R, Ng AY (2009) ROS: an open-source robot operating system. In: ICRA workshop on open source software, p 5Google Scholar
  40. 40.
    SPARC The Partnership for Robotics in Europe (2015) Robotics 2020 multi-annual roadmap for robotics in europe. horizon 2020 call ict-2016 (ict-25 & ict-26) (white paper) release b 03/12/2015 rev a. Tech. rep., EU CommissionGoogle Scholar
  41. 41.
    Stafford R, Broadbent E, Jayawardena C, Unger U, Kuo IH, Igic A, Wong R, Kerse N, Watson C, MacDonald BA (2010) Improved robot attitudes and emotions at a retirement home after meeting a robot. In: 2010 IEEE RO-MAN. IEEE, pp 82–87Google Scholar
  42. 42.
    Tang CH, Wu WT, Lin CY (2009) Using virtual reality to determine how emergency signs facilitate way-finding. Appl Ergonom 40(4):722–730CrossRefGoogle Scholar
  43. 43.
    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, AHFE, pp 223–232Google Scholar
  44. 44.
    Torkzadeh G, Koufteros X, Pflughoeft K (2003) Confirmatory analysis of computer self-efficacy. Struct Equ Model 10(2):263–275MathSciNetCrossRefGoogle Scholar
  45. 45.
    Tufte E, Graves-Morris P (1983) The visual display of quantitative information. Graphics Press, ConnecticutGoogle Scholar
  46. 46.
    Urry HL, Gross JJ (2010) Emotion regulation in older age. Curr Dir Psychol Sci 19(6):352–357CrossRefGoogle Scholar
  47. 47.
    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):2565–2576CrossRefGoogle Scholar
  48. 48.
    Vilar E, Rebelo F, Noriega P (2014) Indoor human wayfinding performance using vertical and horizontal signage in virtual reality. Human Factors Ergonom Manuf Serv Indus 24(6):601– 615CrossRefGoogle Scholar
  49. 49.
    Virga S, Zettinig O, Esposito M, Pfister K, Frisch B, Neff T, Navab N, Hennersperger C (2016) Automatic force-compliant robotic ultrasound screening of abdominal aortic aneurysms. In: IEEE International conference on intelligent robots and systems (IROS)Google Scholar
  50. 50.
    Wada K, Shibata T, Saito T, Tanie K (2002) Analysis of factors that bring mental effects to elderly people in robot assisted activity. In: 2002 IEEE/RSJ International conference on intelligent robots and systems, vol 2. IEEE, pp 1152–1157Google Scholar
  51. 51.
    Wilcox R, Nikolaidis S, Shah J (2012) Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing. Robot Sci Syst VIII:441–448Google Scholar
  52. 52.
    Wilkes L, White K, O’Riordan L (2000) Empowerment through information: supporting rural families of oncology patients in palliative care. Aust J Rural Heal 8(1):41–46CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Sheffield RoboticsThe University of SheffieldSheffieldUK
  2. 2.Art & Design Research CentreSheffield Hallam UniversitySheffieldUK

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