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.
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Notes
A member of the research team enabled the robot’s safety switches whist sat out of view from the participant behind a screen. The researcher monitored participants interacting with the robot via CCTV, and could stop the robot at any time
References
Aykin NM, Aykin T (1991) Individual differences in human-computer interaction. Comput Indus Eng 20 (3):373–379
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 Safety
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):173–177
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):217–230
Ben-Bassat T, Shinar D (2006) Ergonomic guidelines for traffic sign design increase sign comprehension. Hum Factors 48(1):182–195
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):319–330
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 Settings
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):689–697
Cole S (2006) Information and empowerment: the keys to achieving sustainable tourism. J Sustain Tourism 14(6):629–644
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 229–239
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. Brussels
Frixione M, Lombardi A (2015) Street signs and ikea instruction sheets: pragmatics and pictorial communication. Rev Philos Psychol 6(1):133–149
Galea ER, Xie H, Lawrence PJ, et al. (2014) Experimental and survey studies on the effectiveness of dynamic signage systems. Fire Safety Sci 11:1129–1143
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 conference
Hayes AF (2012) PROCESS: a versatile computational tool for observed variable mediation, moderation, and conditional process modeling. Tech rep., University of Kansas, KS
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, CH
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):61–84
Kenworthy JB, Jones J (2009) The roles of group importance and anxiety in predicting depersonalized ingroup trust. Group Processes Intergroup Relat 12(2):227–239
Khansari-Zadeh SM, Khatib O (2015) Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors. Auton Robot, 1–25
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
Laughery KR (2006) Safety communications: warnings. Appl Ergonom 37(4):467–478
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):446–452
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):227
MacDorman KF, Vasudevan SK, Ho CC (2009) Does Japan really have robot mania? comparing attitudes by implicit and explicit measures. AI Soc 23(4):485–510
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):199–204
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 1–6
Metta G, Fitzpatrick P, Natale L (2006) Yarp: yet another robot platform. Int J Adv Robot Syst 3(1):8
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):631–642
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):15,859–15,864
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):93–101
Muir BM (1987) Trust between humans and machines, and the design of decision aids. Int J Man-Mach Stud 27(5–6):527–539
Nicholson N, Soane E, Fenton-O’Creevy M, Willman P (2005) Personality and domain-specific risk taking. J Risk Res 8(2):157–176
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 372–377
Nomura T, Suzuki T, Kanda T, Kato K (2006b) Measurement of negative attitudes toward robots. Interact Stud 7(3):437–454
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 13–18
Ozer EM, Bandura A (1990) Mechanisms governing empowerment effects: a self-efficacy analysis. J Person Soc Psychol 58(3):472
Pawar VM, Law J, Maple C (2016) Manufacturing robotics - the next robotic industrial revolution. Tech. rep., UK Robotics and Autonomous Systems Network
Pearson LC, Moomaw W (2005) The relationship between teacher autonomy and stress, work satisfaction, empowerment, and professionalism. Educ Res Quart 29(1):37
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 5
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 Commission
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 82–87
Tang CH, Wu WT, Lin CY (2009) Using virtual reality to determine how emergency signs facilitate way-finding. Appl Ergonom 40(4):722–730
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 223–232
Torkzadeh G, Koufteros X, Pflughoeft K (2003) Confirmatory analysis of computer self-efficacy. Struct Equ Model 10(2):263–275
Tufte E, Graves-Morris P (1983) The visual display of quantitative information. Graphics Press, Connecticut
Urry HL, Gross JJ (2010) Emotion regulation in older age. Curr Dir Psychol Sci 19(6):352–357
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
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– 615
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)
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 1152–1157
Wilcox R, Nikolaidis S, Shah J (2012) Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing. Robot Sci Syst VIII:441–448
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):41–46
Funding
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.
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Appendices
Appendix A: robot control
The practical human-robot collaborative process used in this experiment was carried out using a KUKA LBR iiwa 7 R800 operating in both hand-guided and autonomous modes. For the purposes of safety, the robot was operated in ‘T1’ mode.Footnote 1 This resulted in a maximum Cartesian velocity at the end effector of 250mm/s.
The position of the robot in Cartesian space can defined by the tuple, Position = {X,Y,Z,A,B,C}, where {X,Y,Z} represents the displacement around the X,Y, and Z axes respectively, and {A,B,C} represents the rotation about the X,Y and Z axes. A series of X-Y tube locations, {Tubes}, were set up on the table at a height of (Zlower). An operating height (Zraised) was defined that would allow the telescopic picking tool mounted on the robot to move above freely above the tubes in the X-Y plane with a clearance of around 1cm. A home position, (Positionhome), was set between the operator and tube locations, at a height of (Zraised).
In hand-guided mode, triggered by applying a 0.2-N force to the wrist of the robot, the operator was able to move the end effector in the X-Y plane but prevented from movement rotationally about each axis and in the Z-plane by restricting the robot’s compliance settings. The compliance settings for hand-guided (ComplianceManual) and autonomous (ComplianceAuto) modes are shown in Table 7.
1.1 Control algorithm
The robot control algorithm comprises repeated loops of hand-guided operation followed by autonomous-mode operation. These begin with the robot in the home position, waiting for the operator to enable hand-guiding mode by applying the necessary >0.2-N force.
Once in hand-guiding mode, the operator is free to move the end effector within the X-Y plane above the tube locations. Once the operator releases the robot (i.e. no X-Y forces are applied), the robot switches to autonomous mode and moves to the nearest tube. If the user applies a force, a 3-s timer is started. If an X-Y force is applied, the timer is reset so that the the robot remains in hand-guided mode until the forces are removed again and the timer expires. If no X-Y forces are applied, the robot switches to autonomous mode.
Once in autonomous mode, the robot makes a refining move (in the X-Y plane) to the nearest known tube location. Once in position, the robot then makes two vertical movements: firstly to Z = Zlower, which places the magnetic probe in contact with potential objects for picking, then back to Z = Zraised, which retrieves picked objects from the tubes. Finally, the robot moves back to the home position for the operator to retrieve any picked objects, and waits for initiation of the next hand-guided sequence. All autonomous end effector motions are linear in X-Y or Z directions, with joint accelerations governed by the KUKA control software.
Pseudo-code for the process described above is given in Algorithm 1.
Appendix B: questionnaires
Questionnaires used in the study:
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Eimontaite, I., Gwilt, I., Cameron, D. et al. Language-free graphical signage improves human performance and reduces anxiety when working collaboratively with robots. Int J Adv Manuf Technol 100, 55–73 (2019). https://doi.org/10.1007/s00170-018-2625-2
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DOI: https://doi.org/10.1007/s00170-018-2625-2