Advertisement

Reference Frame Identification and Distributed Control Strategies in Human-Robot Collaboration

  • Alberto BorboniEmail author
  • Giuseppe Carbone
  • Nicola Pellegrini
Conference paper
  • 54 Downloads
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 84)

Abstract

The reference frame definition is a significant postulate for the robot motion planning process. Reference frame selection can be classified as allocentric, egocentric and route-centric. The identification of a reference frame affects the complexity of measuring and implementing the controlling variables. The work reports this topic by referring specifically at the human robot collaboration in a workstation. Accordingly, this work proposes a methodology for the evaluation of the reference frame in a human robot communication aimed at enabling, effective and safe distributed control strategies.

Keywords

Reference frame Human-Robot interaction Distributed control 

Notes

Acknowledgements

The paper presents results from the research activities of the project ID 37_215, MySMIS code 103415 “Innovative approaches regarding the rehabilitation and assistive robotics for healthy ageing” co-financed by the European Regional Development Fund through the Competitiveness Operational Programme 2014-2020, Priority Axis 1, Action 1.1.4, through the financing contract 20/01.09.2016, between the Technical University of Cluj-Napoca and ANCSI as Intermediary Organism in the name and for the Ministry of European Funds.

References

  1. 1.
    Dugar, V., Choudhury, S., Scherer, S.: A kiTE in the wind: smooth trajectory optimization in a moving reference frame. In: Proceedings of the IEEE International Conference on Robotics and Automation, Article no. 7989017, pp. 109–116 (2017)Google Scholar
  2. 2.
    Tan, J., Xi, N., Goradia, A., Sheng, W.: Coordination of multi-robot and human systems in a perceptive reference frame. Int. J. Veh. Auton. Syst. 2(3–4), 201–216 (2004)CrossRefGoogle Scholar
  3. 3.
    Tarn, T.-J., Tan, J., Xi, N.: A perceptive reference frame for cooperative and reconfigurable multi-robot systems. IFAC Proc. Volumes (IFAC) 16, 553–558 (2005)CrossRefGoogle Scholar
  4. 4.
    Nada, A.A., Hussein, B.A., Megahed, S.M., Shabana, A.A.: Floating frame of reference and absolute nodal coordinate formulations in the large deformation analysis of robotic manipulators: a comparative experimental numerical study. In: DETC 2009, vol. 4, pp. 889–900 (2010)Google Scholar
  5. 5.
    Garcia-Vallejo, D., Sugiyama, H., Shabana, A.A.: Finite element analysis of the geometric stiffening effect. Part 1: A correction in the floating frame of reference formulation. J. Multi-body Dyn. 219(2), 187–202 (2005)Google Scholar
  6. 6.
    Garcia-Vallejo, D., Sugiyama, H., Shabana, A.A.: Finite element analysis of the geometric stiffening effect. Part 2: Non-linear elasticity. Proc. Inst. Mech. Eng. Part K: J. Multibody Dyn. 219(2), 203–211 (2005)Google Scholar
  7. 7.
    Wickens, C.D., Keller, J.W., Small, R.L.: Left. no, right! development of the frame of reference transformation tool (FORT). In: Proceedings of the Human Factors and Ergonomics Society, vol. 2, pp. 1022–1026 (2010)Google Scholar
  8. 8.
    Oess, T., Krichmar, J.L., Röhrbein, F.: A computational model for spatial navigation based on reference frames in the hippocampus, retrosplenial cortex, and posterior parietal cortex. Front. Neurorobot. 11, 4 (2017)CrossRefGoogle Scholar
  9. 9.
    Wolbers, T., Wiener, J.M.: Challenges for identifying the neural mechanisms that supports patial navigation: the impact of spatial scale. Front. Hum. Neurosci. 8, 571 (2014)CrossRefGoogle Scholar
  10. 10.
    Stoltmann, K., Fuchs, S., Krifka, M.: The influence of animacy and spatial relation complexity on the choice of frame of reference in German. In: Lecture Notes in Computer Science. LNAI, vol. 11034, pp. 119–133 (2018)Google Scholar
  11. 11.
    Kalla, P., Koona, R., Ravindranath, P., Sudhakar, I.: Coordinate reference frame technique for robotic planar path planning. Materials 5(9), 19073–19079 (2018)Google Scholar
  12. 12.
    Brown, A., Uneri, A., De Silva, T., Manbachi, A., Siewerdsen, J.H.: Design and validation of an open-source library of dynamic reference frames for research and education in optical tracking. In: Progress in Biomedical Optics and Imaging - SPIE, vol. 10576, p. 105760M (2018)Google Scholar
  13. 13.
    Mäkinen, P., Dmitrochenko, O., Mattila, J.: Floating frame of reference formulation for a flexible manipulator with hydraulic actuation - modelling and experimental validation. In: BATH/ASME 2018 Symposium on Fluid Power and Motion Control, FPMC 2018 (2018)Google Scholar
  14. 14.
    James, K.B.: System for controlling artificial knee joint action in an above knee prosthesis. U.S. Patent US5571205 A, 5 Novemeber 1996Google Scholar
  15. 15.
    Pathak, A.: System and method for stabilizing unintentional muscle movements. U.S. Patent 20140052275 A1, 20 February 2014Google Scholar
  16. 16.
    Abiyev, R., Ibrahim, D., Erin, B.: Navigation of mobile robots in the presence of obstacles. Adv. Eng. Softw. 41, 1179–1186 (2010)CrossRefGoogle Scholar
  17. 17.
    Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile Robot, Massachusetts Institute of Technology Press, Cambridge (2011)Google Scholar
  18. 18.
    Al-Taharwa, I., Sheta, A., Al-Weshah, M.: A mobile robot path planning using genetic algorithm in static environment. J. Comput. Sci. 4(4), 341–344 (2008)CrossRefGoogle Scholar
  19. 19.
    García De Jalón, J., Unda, J., Avello, A.: Natural coordinates for the computer analysis of multibody systems. Comput. Methods Appl. Mech. 56, 309–327 (1986)CrossRefGoogle Scholar
  20. 20.
    Chirikjian, G.S., Zhou, S.: Metrics on motion and deformation of solid models. J. Mech. Des. 120(2), 252–261 (1998)CrossRefGoogle Scholar
  21. 21.
    Mazzotti, C., Sancisi, N., Parenti-Castelli, V.: A measure of the distance between two rigid-body poses based on the use of platonic solids. In: Robot Design, Dynamics and Control, ROMANSY21 2016. CISM, vol 569. Springer, Heidelberg (2016)Google Scholar
  22. 22.
    Cordero, C.A., Carbone, G., Ceccarelli, M., Echávarri, J., Muñoz, J.L.: Experimental tests in human–robot collision evaluation and characterization of a new safety index for robot operation. Mech. Mach. Theory 80, 184–199 (2014)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alberto Borboni
    • 1
    Email author
  • Giuseppe Carbone
    • 2
  • Nicola Pellegrini
    • 1
  1. 1.Department of Mechanical and Industrial EngineeringUniversità degli Studi di BresciaBresciaItaly
  2. 2.Department of Mechanical, Energy and Management EngineeringUniversità della CalabriaRendeItaly

Personalised recommendations