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FREE: Flexible and Safe Interactive Human-Robot Environment for Small Batch Exacting Applications

  • Dario AntonelliEmail author
  • Sergey Astanin
  • Gabriella Caporaletti
  • Francesco Donati
Conference paper
  • 848 Downloads
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 94)

Abstract

The FREE experiment addresses the field of small-batch production where handwork is still the main manufacturing option since automation is more expensive and lacks the prescribed flexibility. FREE aims at addressing this situation by introducing a flexible and safe interactive human-robot environment, achievable through a combination of standard commercial robot equipments with the state of the art safety and control technologies. The core idea is to add to the system a further control loop operating at a level hierarchically superior with respect to the standard robot controller. Such a control loop, defined “Superior Hierarchical Control”, is the interface between the robot and the human operator through a variety of sensors providing contact-less human position detection for safety and human work recording for task learning.

Keywords

Training by demonstration automated welding human-robot interaction 

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References

  1. 1.
    An executive summary of the strategic research agenda for robotics in Europe. Coordination Action for Robotics in Europe (CARE), FP6 IST-045058 (June 2008)Google Scholar
  2. 2.
    Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5), 469–483 (2009)CrossRefGoogle Scholar
  3. 3.
    Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Handbook of Robotics Chapter 59: Robot Programming by Demonstration (2007)Google Scholar
  4. 4.
    Kuniyoshi, Y., Inaba, M., Inoue, H.: Learning by watching: Extracting reusable task knowledge from visual observation of human performance. IEEE Transactions on Robotics and Automation 10(6), 799–822 (1994)CrossRefGoogle Scholar
  5. 5.
    Fenghua, G., Caiming, Z.: Curves reconstruction from ordered point cloud data. In: 2010 International Conference on Environmental Science and Information Application Technology (ESIAT), vol. 2, pp. 84–87. IEEE (July 2010)Google Scholar
  6. 6.
    Billard, A., Epars, Y., Calinon, S., Schaal, S., Cheng, G.: Discovering optimal imitation strategies. Robotics and Autonomous Systems 47(2), 69–77 (2004)CrossRefGoogle Scholar
  7. 7.
    Atkeson, C.G., Schaal, S.: Robot learning from demonstration. In: Machine Learning-International Workshop then Conference, pp. 12–20. Morgan Kaufmann Publishers, Inc. (July 1997)Google Scholar
  8. 8.
    Gasparetto, A., Lanzutti, A., Vidoni, R., Zanotto, V.: Experimental validation and comparative analysis of optimal time-jerk algorithms for trajectory planning. Robotics and Computer-Integrated Manufacturing 28(2), 164–181 (2012)CrossRefGoogle Scholar
  9. 9.
    Corfiati, M., Antonelli, D.: A Semi-Automated Welding Station Exploiting Human-Robot Interaction. In: AMST 2011, Mali Losinj, HR (2011)Google Scholar
  10. 10.
    Antonelli, D., Astanin, S., Galetto, M., Mastrogiacomo, L.: Training by Demonstration a Welding Robot. In: ICME 2012, Ischia, Italy (2012)Google Scholar
  11. 11.
    ARFLEX (Adaptive Robots for Flexible Manufacturing Systems), FP6 EU project EC-NMP2-CT-2005-016680, http://www.arflexproject.eu
  12. 12.
    Caporaletti, G.: The ARFLEX project: Adaptive robots for flexible manufacturing systems. Intelligent Manufacturing Systems 8(1), 253–258 (2007)Google Scholar
  13. 13.
  14. 14.
    Koskinen, J., Heikkila, T., Pulkkinen, T.: Monitoring of co-operative assembly tasks: functional, safety and quality aspects. In: IEEE International Symposium on Assembly and Manufacturing, ISAM 2009, pp. 310–315. IEEE (November 2009)Google Scholar
  15. 15.
    Zaeh, M., Roesel, W.: Safety aspects in a human-robot interaction scenario: a human worker is co-operating with an industrial robot. In: Kim, J.-H., et al. (eds.) Progress in Robotics. CCIS, vol. 44, pp. 53–62. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Ude, A.: Trajectory generation from noisy positions of object features for teaching robot paths. Robotics and Autonomous Systems 11(2), 113–127 (1993)CrossRefGoogle Scholar
  17. 17.
    Dierckx, P.: Curve and surface fitting with splines. Oxford University Press (1995)Google Scholar
  18. 18.
    Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing 25(5), 578–596 (2007)CrossRefGoogle Scholar
  19. 19.
    Horn, B.K.: Closed-form solution of absolute orientation using unit quaternions. JOSA A 4(4), 629–642 (1987)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Shoemake, K.: Euler angle conversion. Graphics Gems IV, 222–229 (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dario Antonelli
    • 1
    Email author
  • Sergey Astanin
    • 1
  • Gabriella Caporaletti
    • 2
  • Francesco Donati
    • 2
  1. 1.Department of Production Systems and EconomicsPolitecnico di TorinoTorinoItaly
  2. 2.EICAS AutomazioneTorinoItaly

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