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A Unified Architecture for Physical and Ergonomic Human–Robot Collaboration

Published online by Cambridge University Press:  19 June 2019

Federica Ferraguti Open the ORCID record for Federica Ferraguti [Opens in a new window] ,
Renzo Villa ,
Chiara Talignani Landi ,
Andrea Maria Zanchettin ,
Paolo Rocco  and
Cristian Secchi
Federica Ferraguti*
Affiliation:
Università di Modena e Reggio Emilia, Dipartimento di Scienze e Metodi dell’Ingegneria, via Amendola 2, 42122 - Reggio Emilia, Italy. E-mails: chiara.talignanilandi@unimore.it, cristian.secchi@unimore.it
Renzo Villa
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133 - Milano, Italy. E-mails: renzo.villa@polimi.it, andreamaria.zanchettin@polimi.it, paolo.rocco@polimi.it
Chiara Talignani Landi
Affiliation:
Università di Modena e Reggio Emilia, Dipartimento di Scienze e Metodi dell’Ingegneria, via Amendola 2, 42122 - Reggio Emilia, Italy. E-mails: chiara.talignanilandi@unimore.it, cristian.secchi@unimore.it
Andrea Maria Zanchettin
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133 - Milano, Italy. E-mails: renzo.villa@polimi.it, andreamaria.zanchettin@polimi.it, paolo.rocco@polimi.it
Paolo Rocco
Affiliation:
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza L. Da Vinci 32, 20133 - Milano, Italy. E-mails: renzo.villa@polimi.it, andreamaria.zanchettin@polimi.it, paolo.rocco@polimi.it
Cristian Secchi
Affiliation:
Università di Modena e Reggio Emilia, Dipartimento di Scienze e Metodi dell’Ingegneria, via Amendola 2, 42122 - Reggio Emilia, Italy. E-mails: chiara.talignanilandi@unimore.it, cristian.secchi@unimore.it
*
*Corresponding author. E-mail: federica.ferraguti@unimore.it
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Summary

Industrial applications that involve working on and moving a heavy load or that constrain the operator to work in uncomfortable positions can take advantage of the assistance of a robotic assistant. In this paper, we propose an architecture for an ergonomic human–robot co-manipulation of objects of various shapes and weight. The object is carried by the robot and, thanks to an ergonomic planner, is positioned in the most comfortable way for the user. Furthermore, thanks to an admittance control with payload compensation, the user can easily adjust the position of the object for working on different parts of it. The proposed architecture is experimentally validated in a robotic cell including an ABB industrial robot.

Keywords

Human–Robot interactionControl of robotic systemsMan–Machine system.
Type
Articles
Information
Robotica , Volume 38 , Issue 4 , April 2020 , pp. 669 - 683
Copyright
© Cambridge University Press 2019 

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References

Pedrocchi, N., Malosio, M. and Molinari Tosatti, L., “Safe Obstacle Avoidance for Industrial Robot Working Without Fences,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009) pp. 3435–3440.Google Scholar
Meziane, R., Otis, M. J.-D. and Ezzaidi, H., “Human-robot collaboration while sharing production activities in dynamic environment: SPADER system,” Robot. Comput.-Integr. Manuf. 48, 243–253 (2017).CrossRefGoogle Scholar
Hagele, M., Schaaf, W. and Helms, E., “Robot Assistants at Manual Workplaces: Effective Co-operation and Safety Aspects,” Proceedings of the International Symposium on Robotics, Washington, DC, USA (2002).Google Scholar
De Santis, A., Siciliano, B., De Luca, A. and Bicchi, A., “Atlas of physical human-robot interaction,” Mech. Mach. Theory. 43(3), 253–270 (2008).CrossRefGoogle Scholar
Kruger, J., Lien, T. K. and Verl, A., “Cooperation of human and machines in assembly lines,” CIRP Ann. Manuf. Technol. 58(2), 628–646 (2009).CrossRefGoogle Scholar
Schraft, R. D., Hagele, M. and Breckweg, A., “Man and robot without separating systems,” In: World of Automation and Metalworking, Frankfurt/M (2006).Google Scholar
Villani, L. and De Schutter, J., “Force control,” In: Springer Handbook of Robotics (Siciliano, B. and Khatib, O., eds.) (Springer, Berlin Heidelberg, 2008).Google Scholar
Bascetta, L., Ferretti, G., Magnani, G. and Rocco, P., “Walk-through programming for robotic manipulators based on admittance control,” Robotica 31(7), 1143–1153 (2013).CrossRefGoogle Scholar
Duchaine, V. and Gosselin, C., “Safe, Stable and Intuitive Control for Physical Human-robot Interaction,” Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 3383–3388.Google Scholar
Kosuge, K. and Kazamura, N., “Control of a Robot Handling an Object in Cooperation with a Human,” Proceedings of the IEEE International Workshop on Robot and Human Communication, Sendai, Japan (1997) pp. 142–147.Google Scholar
Kubus, D., Kröger, T. and Wahl, F., “Improving Force Control Performance by Computational Elimination of Non-contact Forces/Torques,” Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, USA (2008) pp. 2617–2622.Google Scholar
Talignani Landi, C., Ferraguti, F., Secchi, C. and Fantuzzi, C., “Tool Compensation in Walk-Through Programming for Admittance-Controlled Robots,” Proceedings of the Annual Conference of IEEE Industrial Electronics Society, Firenze, Italy (2016).Google Scholar
Bestick, A. M., Burden, S. A., Willits, G., Naikal, N., Sastry, S. S. and Bajcsy, R., “Personalized Kinematics for Human-robot Collaborative Manipulation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany (2015) pp. 1037–1044.Google Scholar
Hu, N., Bestick, A., Englebienne, G., Bajscy, R. and Kröse, B., “Human Intent Forecasting using Intrinsic Kinematic Constraints,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, South Korea (2016) pp. 787–793.Google Scholar
Kim, W., Lee, J., Peternel, L., Tsagarakis, N. and Ajoudani, A., “Anticipatory robot assistance for the prevention of human static joint overloading in human-robot collaboration,” IEEE Robot. Autom. Lett. 3(1), 68–75 (2018).CrossRefGoogle Scholar
Karhu, O., Kansi, P. and Kuorinka, I., “Correcting working postures in industry: A practical method for analysis,” Appl. Ergon. 8(4), 199–201 (1977).CrossRefGoogle Scholar
Lee, G. I., Lee, M. R., Clanton, T., Sutton, E., Park, A. E. and Marohn, M. R., “Comparative assessment of physical and cognitive ergonomics associated with robotic and traditional laparoscopic surgeries,” Surg. Endosc. 28(2), 456–465 (2014).CrossRefGoogle Scholar
Bauer, W., Bender, M., Rally, P., Scholtz, O. and Hämmerle, M., Lightweight Robots and Human Interaction in Assembly Systems (Springer International Publishing, Cham, 2016) pp. 113–122.Google Scholar
McAtamney, L. and Corlett, E. N., “Rula: a survey method for the investigation of work-related upper limb disorders,” Appl. Ergon. 24(2), 91–99 (1993).CrossRefGoogle Scholar
Kee, D., and Karwowski, W., “LUBA: An assessment technique for postural loading on the upper body based on joint motion discomfort and maximum holding time,” Appl. Ergon. 32, 357–366 (2001).CrossRefGoogle Scholar
De Groote, F., De Laet, T., Jonkers, I. and De Schutter, J., “Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis,” J. Biomech. 41(16), 3390–3398 (2008).CrossRefGoogle Scholar
Zanchettin, A. M. and Rocco, P., “Motion planning for robotic manipulators using robust constrained control,” Control Eng. Prac. 59, 127–136 (2017).CrossRefGoogle Scholar
Zanchettin, A. M., Ceriani, N. M., Rocco, P. and Matthias, B., “Safety in human-robot collaborative manufacturing environments: metrics and control,” IEEE Trans. Autom. Sci. Eng. 13(2), 882–893 (2016).CrossRefGoogle Scholar
Ragaglia, M., Zanchettin, A. M. and Rocco, P., “Trajectory generation algorithm for safe human-robot collaboration based on multiple depth sensor measurements,” IFAC Mechatronics 55, 267–281 (2018).CrossRefGoogle Scholar
Biagiotti, L. and Melchiorri, C., ‘Trajectory Planning for Automatic Machines and Robots (Springer, Berlin Heidelberg, 2008).Google Scholar
Kroeger, T. and Wahl, F., “Online trajectory generation: basic concepts for instantaneous reactions to unforeseen events,” IEEE Trans. Robot. 26, 94–111 (2010).CrossRefGoogle Scholar
Talignani Landi, C., Ferraguti, F., Sabattini, L., Secchi, C., Bonfè, M. and Fantuzzi, C., “Variable Admittance Control Preventing Undesired Oscillating Behaviors in Physical Human-robot Interaction,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Vancouver, Canada (2017).Google Scholar
Farsoni, S., Talignani Landi, C., Ferraguti, F., Secchi, C. and Bonfè, M., “Compensation of load dynamics for admittance controlled interactive industrial robots using a quaternion-based kalman filter,” IEEE Robot. Autom. Lett. 2(2), 672–679 (2017).CrossRefGoogle Scholar
Kubus, D., Kröger, T. and Wahl, F., “On-line Estimation of Inertial Parameters using a Recursive Total Least-Squares Approach,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008).CrossRefGoogle Scholar
Voyles, R. M. Jr, Morrow, J. D. and Khosla, P. K., “A comparison of force sensors,” Advanced manipulators laboratory, the Robotic Institute Carnegie Mellon University (1994).Google Scholar
Talignani Landi, C., Ferraguti, F., Secchi, C. and Fantuzzi, C., “Tool Compensation and Force Password Identification on Admittance-Controlled Robots in Walk-Through Programming,” 9th International Workshop on Human Friendly Robotics, Genoa, Italy (2016).Google Scholar
Schmidtler, J., Bengler, K., Dimeas, F. and Campeau-Lecours, A., “A questionnaire for the evaluation of physical assistive devices (quead),” IEEE International Conference on Systems, Man and Cybernetics (SMC), Canada (2017) pp. 876–881.Google Scholar