Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human–robot collaborative (HRC) sanding task.
Background
Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-time corrections.
Methods
Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested.
Results
The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total.
Conclusions
In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort.
Applications
The results can influence implementation of future HRC systems in manufacturing environments.
AjoudaniA.AlbrechtP.AlbrechtM.CherubiniA.Del FrancoS.FraisseP.FritzscheL.GarabiniM.RanavoloA.RosenP. H.SartoriM.TsagarakisN.VanderborghtB.WischniewskiS. (2020). Smart collaborative systems for enabling flexible and ergonomic work practices. IEEE Robotics and Automation Magazine, 27(2). https://doi.org/10.1109/MRA.2020.2985344
2.
ArmstrongT.FineL. J.RadwinR. G.SilversteinB. S. (1987). Ergonomics and the effects of vibration in hand-intensive work. Scandinavian Journal of Work, Environment & Health, 13(4), 286–289. https://doi.org/10.5271/sjweh.2049
3.
BasmajianJ.De LucaC. (1985). Muscles alive: Their functions revealed by electromyography (5th ed.). Williams & Wilkins.
4.
BernardB. (1997). Musculoskeletal disorders and workplace factors: A critical review of epidemiologic evidence for work-related disorders of the neck, upper extremity, and low back (pp. 97–141). U.S. Department of Health and Human Services (NIOSH).
5.
BhattacharjeeT.GordonE.ScaliseR.CabreraM.CaspiA.CakmakM.SrinivasaS. (2020). Is more autonomy always better? Exploring preferences of users with mobility impairments in robot-assisted feeding. In ACM/IEEE International Conference on Human-Robot Interaction. IEEE.
6.
BogueR. (2009). Finishing robots: A review of technologies and applications. Industrial Robot: International Journal, 36(1), 6–12. https://doi.org/10.1108/01439910910924611
7.
BrauerM.CurtinJ. J. (2018). Linear mixed-effects models and the analysis of nonindependent data: A unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychological Methods, 23(3), 389–411. https://doi.org/10.1037/met0000159
8.
ChenY.LuoY.YangC.YerebakanM. O.HaoS.GrimaldiN.LiS.HayesR.HuB. (2022). Human mobile robot interaction in the retail environment. Scientific Data, 9(1), 673. https://doi.org/10.1038/s41597-022-01802-8
9.
ChristierninL. G. (2017). How to describe interaction with a collaborative robot. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 93–94). IEEE. https://doi.org/10.1145/3029798.3038325
10.
ColimA.FariaC.BragaA. C.SousaN.RochaL.CarneiroP.CostaN.ArezesP. (2020). Towards an ergonomic assessment framework for industrial assembly workstations—A case study. Applied Sciences. 10(9), 3048. https://doi.org/10.3390/app10093048
11.
DesmondP. A.HancockP. A. (2000). Active and passive fatigue states. In Stress, workload, and fatigue. CRC Press.
12.
DevineC.GarbiniJ.DevasiaS. (2020). Spatially-constant material removal control under variable-speed robotic sanding. In ASME 2020 15th International Manufacturing Science and Engineering Conference. ASME. https://doi.org/10.1115/MSEC2020-8340
13.
FreriksB.HermensH. (2007). European recommendations for surface electromyography, results of the SENIAM project (CD-Rom). Roessingh Research and Development.
14.
Girbés-JuanV.SchettinoV.DemirisY.TorneroJ. (2021). Haptic and visual feedback assistance for dual-arm robot teleoperation in surface conditioning tasks. IEEE Transactions on Haptics, 14(1), 44–56. https://doi.org/10.1109/TOH.2020.3004388
15.
GonzálezC.SolanesJ. E.MuñozA.GraciaL.Girbés-JuanV.TorneroJ. (2021). Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback. Journal of Manufacturing Systems, 59, 283–298. https://doi.org/10.1016/j.jmsy.2021.02.013
16.
Guerra-ZubiagaD. A.Nasajpour-EsfahaniN.SiddiquiB.KampermanK. (2021). Implementing a novel framework to create tacit knowledge models to support human-robot interactions. In ASME 2020 International Mechanical Engineering Congress and Exposition. ASME. https://doi.org/10.1115/IMECE2020-23803
17.
HagenowM.SenftE.OrrN.RadwinR.GleicherM.MutluB.LoseyD. P.ZinnM. (2023). Coordinated multi-robot shared autonomy based on scheduling and demonstrations. IEEE Robotics and Automation Letters, 8(12), 8335–8342. https://doi.org/10.1109/LRA.2023.3327625
18.
HagenowM.SenftE.RadwinR.GleicherM.MutluB.ZinnM. (2021a). Corrective shared autonomy for addressing task variability. IEEE Robotics and Automation Letters, 6(2), 3720–3727. https://doi.org/10.1109/lra.2021.3064500
19.
HagenowM.SenftE.RadwinR.GleicherM.MutluB.ZinnM. (2021b). Informing real-time corrections in corrective shared autonomy through expert demonstrations. IEEE Robotics and Automation Letters, 6(4), 6442–6449. https://doi.org/10.1109/LRA.2021.3094480
20.
HagenowM.SenftE.RadwinR.GleicherM.ZinnM.MutluB. (2024). A System for human-robot teaming through end-user programming and shared autonomy. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (pp. 231–239). IEEE. https://doi.org/10.1145/3610977.3634965
21.
HartS. (2006). NASA-Task Load Index (NASA-TLX); 20 years later. Proceedings of the Human Factors and Ergonomics Society - Annual Meeting, 50(9), 904–908. https://doi.org/10.1177/154193120605000909
22.
HopkoS.KhuranaR.MehtaR.PagillaP. R. (2021). Effect of cognitive fatigue, operator sex, and robot assistance on task performance metrics, workload, and situation awareness in human-robot collaboration. IEEE Robotics and Automation Letters, 6(2), 3049–3056. https://doi.org/10.1109/lra.2021.3062787
23.
HopkoS.WangJ.MehtaR. (2022). Human factors considerations and metrics in shared space human-robot collaboration: A systematic review. Frontiers in Robotics and AI, 9, Article 799522. https://doi.org/10.3389/frobt.2022.799522
24.
IrwinE.StreileinK. (2015). Use of field-based motion capture to augment observational data in ergonomic assessment of aircraft maintenance. Procedia Manufacturing, 3, 4501–4508. https://doi.org/10.1016/j.promfg.2015.07.464
25.
JohnsonT. L.FletcherS. R.BakerW.CharlesR. L. (2019). How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection. Applied Ergonomics, 74, 1–9. https://doi.org/10.1016/j.apergo.2018.07.016
26.
KabirA.ShembekarA.MalhanR.AggarwalR.LangsfeldJ.ShahB.SatyandraG. (2018). Robotic finishing of interior regions of geometrically complex parts. In Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference, Volume 3: Manufacturing Equipment and Systems. ASME.
27.
KaltE.MonfaredR.JacksonM. (2016). Towards an automated polishing system - capturing manual polishing operations. International Journal of Renewable Energy Technology, 05(07), 182–192. https://doi.org/10.15623/ijret.2016.0507030
28.
KaufeldM.NickelP. (2019). Level of robot autonomy and information aids in human-robot interaction affect human mental workload – An investigation in virtual reality. In DuffyV. G. (Ed.), Digital human modeling and applications in health, safety, ergonomics and risk management. human body and motion (11581, pp. 278–291). Springer International Publishing. https://doi.org/10.1007/978-3-030-22216-1_21
29.
LeeG. I.LeeM. R.GreenI.AllafM.MarohnM. R. (2017). Surgeons’ physical discomfort and symptoms during robotic surgery: A comprehensive ergonomic survey study. Surgical Endoscopy, 31(4), 1697–1706. https://doi.org/10.1007/s00464-016-5160-y
30.
LiuL.SchoenA.HenrichsC.LiJ.MutluB.ZhangY.RadwinR. (2024). Human robot collaboration for enhancing work activities. Human Factors, 66(1), 158–179. https://doi.org/10.1177/00187208221077722
31.
LoyA.HofmannH. (2014). HLMdiag: A suite of diagnostics for hierarchical linear models in R. Journal of Statistical Software, 56(5), 1–28. https://doi.org/10.18637/jss.v056.i05
32.
LundA. (2001). Measuring usability with the USE questionnaire. Usability and User Experience Newsletter of the STC Usability, 8(2), 3–6.
MATLAB (2021) MATLAB version 9.11.0 R(2021b)(9.11.0 R(2021b)). (2021). [Computer software]. The MathWorks Inc.
35.
MunzerT.MollardY.LopesM. (2017). Impact of robot initiative on human-robot collaboration. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 217–218). IEEE. https://doi.org/10.1145/3029798.3038373
36.
MusgraveE.MeadT.TomaB. (2019). Sanding and buffing in the aviation manufacturing industry: A case study. Baltimore, MD, USA: Proceedings of the 8th Annual World Conference of the Society for Industrial and Systems Engineering.
37.
PearceM.MutluB.ShahJ.RadwinR. (2018). Optimizing makespan and ergonomics in integrating collaborative robots into manufacutring processes. IEEE Transactions on Automation Science and Engineering, 15(4), 1–13. https://doi.org/10.1109/TASE.2018.2789820
38.
Perez-VidalC.GraciaL.Sanchez-CaballeroS.SolanesE.SacconA.TorneroJ. (2019). Design of a polishing tool for collaborative robotics using minimum viable product approach. International Journal of Computer Integrated Manufacturing, 32(9), 848–857. https://doi.org/10.1080/0951192x.2019.1637026
39.
PeternelL.TsagarakisN.AjoudaniA. (2017). A human–robot co-manipulation approach based on human sensorimotor information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 811–822. https://doi.org/10.1109/TNSRE.2017.2694553
40.
RitcheyP.PeresC.MehtaR. (2014). User interface biomechanics: Does the interaction design of the user interface impact ergonomic risk factors?Proceedings of the Human Factors and Ergonomics Society - Annual Meeting, 58(1), 2003–2007. https://doi.org/10.1177/1541931214581418
41.
SannemanL.FourieC.ShahJ. (2020). The state of industrial robotics: Emerging Technologies, challenges, and key research directions. Now Foundations and Trends.
42.
SarterN. B.WoodsD. D.BillingsC. E. (1997). Automation surprises.
43.
ThollC.BickmannP.WechslerK.FroböseI.GriebenC. (2022). Musculoskeletal disorders in video gamers—a systematic review. BMC Musculoskeletal Disorders, 23(1), 678. https://doi.org/10.1186/s12891-022-05614-0
VasconezJ. P.KantorG. A.Auat CheeinF. A. (2019). Human–robot interaction in agriculture: A survey and current challenges. Biosystems Engineering, 179(2), 35–48. https://doi.org/10.1016/j.biosystemseng.2018.12.005
