Experimental Study on Operator's Psychological Safety Field Model Considering Cobot Motion

doi:10.3901/JME.2022.18.265

Abstract

Abstract: The motion mode of collaborative robots(cobots) is essential for ensuring workers' efficiency, improving trust in cobots, and reducing psychological stress. Since the existing researches do not form mathematical models for the relevant laws, it increases workers' psychological stress caused by the traditional cobot motion, which cannot realize safe, efficient, and natural human-robot collaboration(HRC). Based on existing research, the subjects' skin conductivity response(SRC) was collected using Electrodermal activity(EDA) equipment, and the laws of psychological stress generated when the robot approached the subjects at different speeds, minimum distances, and directions were investigated. It was found that the human body produced gradually increasing EDA signals as the speed increased and the distance decreased, while there was no significant change in EDA signals with the change of approach direction when the subject could observe the cobot motion. The empirical equation for the psychological stress of cobot motion on the subject was obtained by curve fitting the mean values of the EDA signals of the speed and minimum distance single-factor tests, and the operator psychological safety field model was established accordingly. The proposed model can provide theoretical support for cobots' safe, efficient, and natural motion planning.

Key words: HRC, cobot motion, psychological stress, psychological safety field

CLC Number:

TG156

LIU Bo, WANG Wen, FU Weiping, WANG Yi, PENG Lixia, LI Rui. Experimental Study on Operator's Psychological Safety Field Model Considering Cobot Motion[J]. Journal of Mechanical Engineering, 2022, 58(18): 265-278.

References

[1] GERVASI R，MASTROGIACOMO L，FRANCESCHINI F. A conceptual framework to evaluate human-robot collaboration[J]. The International Journal of Advanced Manufacturing Technology，2020，108:841-865.
[2] MUKHERJEE D，GUPTA K，CHANG L H，et al. A survey of robot learning strategies for human-robot collaboration in industrial settings[J]. Robotics and Computer-Integrated Manufacturing，2022，73:102231.
[3] CASTRO A，SILVA F，SANTOS V. Trends of Human-robot collaboration in industry contexts: Handover，learning，and metrics[J]. Sensors，2021，21:4113.
[4] KUMAR S，SAVUR C，SAHIN F. Survey of human–robot collaboration in industrial settings:Awareness，intelligence，and compliance[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2020，51(1):280-297.
[5] EL ZAATARI S，MAREI M，LI W，et al. Cobot programming for collaborative industrial tasks:An overview[J]. Robotics and Autonomous Systems，2019， 116:162-180.
[6] FRUGGIERO F，LAMBIASE A，PANAGOU S，et al. Cognitive human modeling in collaborative robotics[J]. Procedia Manufacturing，2020，51:584-591.
[7] BEJARANO R，FERRER B R，MOHAMMED W M，et al. Implementing a Human-robot collaborative assembly workstation[C]//2019 IEEE 17th International Conference on Industrial Informatics (INDIN).IEEE，2019，1: 557-564.
[8] KIM E，KIRSCHNER R，YAMADA Y，et al. Estimating probability of human hand intrusion for speed and separation monitoring using interference theory[J]. Robotics and Computer-Integrated Manufacturing，2020，61:101819.
[9] ZACHARAKI A，KOSTAVELIS I，GASTERATOS A，et al. Safety bounds in human robot interaction:A survey[J]. Safety Science，2020，127:104667.
[10] POLLAK A，PALIGA M，PULOPULOS M M，et al. Stress in manual and autonomous modes of collaboration with a cobot[J]. Computers in Human Behavior，2020，112:106469.
[11] SAVUR C，KUMAR S，SAHIN F. A framework for monitoring human physiological response during human robot collaborative task[C]//2019 IEEE International Conference on Systems，July 10，2018，Bari， Italy Man and Cybernetics (SMC)，2019:385-390.
[12] SHAH J A，FONG T，LASOTA P A. A survey of methods for safe human-robot interaction[J]. Foundations and Trends in Robotics，2017，5(3):261-349.
[13] RUBAGOTTI M，TUSSEYEVA I，BALTABAYEVA S，et al. Perceived safety in physical human robot interaction:A survey[J]. Robotics and Autonomous Systems，2022，151:104047.
[14] ARAI T，KATO R，FUJITA M. Assessment of operator stress induced by robot collaboration in assembly[J]. CIRP Annals，2010，59(1):5-8.
[15] KULIC D，CROFT E. Anxiety detection during human-robot interaction[C]//2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. August，02-06,2005，Edmonton，AB，Canada，IEEE，2005:616-621.
[16] KULIC D，CROFT E A Affective state estimation for human–robot interaction[J]. IEEE Transactions on Robotics，2007(23):991-1000.
[17] ZOGHBI S，CROFT E，KULIĆ D，et al. Evaluation of affective state estimations using an on-line reporting device during human-robot interactions[C]//2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. October，11-15，2009，St. Louis，Missouri，USA，IEEE，2009:3742-3749.
[18] KULIĆ D，CROFT E. Physiological and subjective responses to articulated robot motion[J]. Robotica，2007，25:13-27.
[19] HöCHERL J，WREDE B，SCHLEGL T. Motion analysis of human-human and human-robot cooperation during industrial assembly tasks[C]//Proceedings of the 5th International Conference on Human Agent Interaction. October，17-20，2017，Bielefeld，Germany，2017:425-429.
[20] MESSERI C，MASOTTI G，ZANCHETTIN A M，et al. Human-robot collaboration:Optimizing stress and productivity based on game theory[J]. IEEE Robotics and Automation Letters，2021，6:8061-8068.
[21] PALETTA L，PSZEIDA M，NAUSCHNEGG B，et al. Stress measurement in multi-tasking decision processes using executive functions analysis[C]//International Conference on Applied Human Factors and Ergonomics. Springer，2019:344-356.
[22] KATO R，FUJITA M，ARAI T. Development of advanced cellular manufacturing system with human-robot collaboration[C]//19th International Symposium in Robot and Human Interactive Communication. September，13-15，2010，New Delhi，India，IEEE，2010:355-360.
[23] STARK J，MOTA R R，SHARLIN E. Personal space intrusion in human-robot collaboration[C]//Companion of the 2018 ACM/IEEE international conference on human-robot interaction，March 5-8，2018，Chicago，IL，USA，2018:245-246.
[24] DUFOUR K，OCAMPO-JIMENEZ J，SULEIMAN W. Visual–spatial attention as a comfort measure in human–robot collaborative tasks[J]. Robotics and Autonomous Systems，2020，133:103626.
[25] AJAVENKATANARAYANAN A，NAMBIAPPAN H R，KYRARINI M，et al. Towards a real-time cognitive load assessment system for industrial human-robot cooperation[C]//2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). August，31，2020-September，4，2020，Naples，Italy，IEEE，2020:698-705.
[26] SAVUR C，KUMAR S，ARORA S，et al. HRC-SoS:Human robot collaboration experimentation platform as system of systems[C]//2019 14th Annual Conference System of Systems Engineering (SoSE). May 19 - 22，2019，Anchorage，AK，USA，IEEE，2019:206-211.
[27] BUERKLE A，EATON W，LOHSE N，et al. EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration[J]. Robotics and Computer-Integrated Manufacturing，2021，70:102137.
[28] WANG J，WU J，LI Y. The driving safety field based on driver–vehicle–road interactions[J]. IEEE Transactions on Intelligent Transportation Systems，2015，16(4):2203-2214.
[29] WANG J，WU J，ZHENG X，et al. Driving safety field theory modeling and its application in pre-collision warning system[J]. Transportation Research Part C:Emerging Technologies，2016，72:306-324.
[30] 陶鹏飞. 基于心理场理论的驾驶行为建模[D]. 长春:吉林大学，2012. TAO Pengfei. Modeling of driving behavior based on the psychology field theory[D]. Changchun:Jilin University，2012
[31] EDWARD T H. The hidden dimension[M]. NewYork:Anchor，1988.
[32] MARGARET M B. Emotion and motivation[M]. Cambridge:Cambridge University Press，2000.
[33] LIU C，RANI P，SARKAR N. An empirical study of machine learning techniques for affect recognition in human-robot interaction[C]//2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. August，2-6，2005，Edmonton，AB，Canada，IEEE，2005:2662-2667.
[34] SCHEIRER J，FERNANDEZ R，KLEIN J，et al. Frustrating the user on purpose:A step toward building an affective computer[J]. Interacting with computers，2002，14:93-118.