Advertisement

Visualizing trajectories for industrial robots from sampling-based path planning on mobile devices

  • Jan Guhl
  • Axel Vick
  • Vojtech Vonasek
  • Jörg Krüger
Conference paper

Zusammenfassung

Production lines are nowadays transforming into flexible modular and interconnected cells to react to rapidly changing product demands. The arrangement of the workspace inside the modular cells will vary according to the actual product being developed. Tasks like motion planning will not be possible to precompute. Instead, it has to be solved on demand. Planning the trajectories for the industrial robots with respect to changing obstacles and other varying environment parameters is hard to solve with classical path planning approaches. A possible solution is to employ sampling-based planning techniques. In this paper we present a distributed sampling-based path planner and an augmented reality visualization approach for verification of trajectories. Combining the technologies ensures a confirmed continuation of the production process under new conditions. Using parallel and distributed path planning speeds up the planning phase significantly and comparing different mobile devices for augmented reality representation of planned trajectories reveals a clear advantage for hands-free HoloLens. The results are demonstrated in several experiments in laboratory scale.

Schlüsselwörter

Irrdustrial Robots Path Planning Augmented Reality 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. [Dyu2015]
    A. A. Dyumin, L. A. Puzikov, M. M. Rovnyagin, G. A. Urvanov and I. V. Chugunkov, “Cloud computing architectures for mobile robotics”, in Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), 2015 IEEE NW Russia, St. Petersburg, pp. 65--70, 2015Google Scholar
  2. [Guhl2017]
    J. Guhl, S.T. Nguyen and J. Krüger, “Concept and architecture for programming industrial robots using augmented reality with mobile devices like microsoft HoloLens” 2017 22nd IEEE International Conference on Ernerging Technologies and Factory Automation (ETFA), Limassol, Cyprus, 2017, pp. 1-4Google Scholar
  3. [Hu2012]
    G. Hu, W. Tay and Y. Wen, “Cloud robotics: architecture, challenges and applications”, in IEEE Network, vol. 26, no. 3, pp. 21--28, 2012Google Scholar
  4. [Larn2014]
    M. L. Lam and K. Y. Lam, “Path planning as a service PPaaS: Cloud-based robotic path planning”, in Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1839-1844, Bali, 2014Google Scholar
  5. [Lamb2013]
    J. Lambrecht, M. K.leinsorge, M. Rosenstrauch, J. Krüger. (2013). Spatial Programming for Irrdustrial Robots through Task Demonstration. International Journal of Advanced Robotic Systems. 10. 1.  https://doi.org/10.5772/55640.
  6. [LaVa1998]
    S. M. LaValle. “Rapidly-exploring random trees: A new tool for path planning”, 1998. Technical report 98-11.Google Scholar
  7. [LaVa2006]
    S. M. LaValle. “Planning Algorithms”. Cambridge University Press, Cambridge, U.K., 2006.Google Scholar
  8. [Lloyd1997]
    J. E. Lloyd, Jeffrey S. Beis, Dinesh K. Pai and David G. Lowe, “Model-based telerobotics with vision”, 1997 IEEE Proceedings oflnternational Conference on Robotics and Automation (ICRA), Albuquerque, pp. 1297-1304Google Scholar
  9. [Mich2017]
    S. Michas, E. Matsas, G. C. Vosniakos, “Interactive programming of industrial robots for edge tracing using a virtual reality gaming environment”. In International Journal of Mechatronics and Manufacturing Systems (2017) Vol. 10, No. 3, pp. 237-259.Google Scholar
  10. [Moha2015]
    G. Mohanarajah, D. Hunziker, R. D’Andrea and M. Waibel, “Rapyuta: A Cloud Robotics Platform”, in IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 481--493, 2015Google Scholar
  11. [Perz2001]
    D. Perzanowski, A. C. Schnitz, W. Adams, E. Marsh and M. Bugajska, “Building a multimodal human-robot interface”, in IEEE Intelligent Systems, vol. 16, no. 1, pp. 16-21 , Jan-Feb 2001.Google Scholar
  12. [Shko2008]
    A. Shkolnik and R. Tedrake. “High-dimensional underactuated motion planning via task space control”. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3762-3768, 2008.Google Scholar
  13. [Shko2009]
    A. Shkolnik and R. Tedrake. “Path planning in 1000+ dimensions using a task-space Voronoi bias”. In IEEE international conference on Robotics and Automation (ICRA), pages 2892-2898, 2009.Google Scholar
  14. [Vick2015]
    A. Vick, V. Vonasek, R. Penicka and J. Krüger, “Robot control as a service - Towards cloud-based motion planning and control for industrial robots”, in Proceedings of 10th IEEE International Workshop on Robot Motion and Control (RoMoCo), pp. 33--39, 2015Google Scholar
  15. [Yao2005]
    Z. Yao and K. Gupta. “Path planning with general end-effector constraints: Using task space to guide configuration space search”. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1875-1880, 2005.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  • Jan Guhl
    • 1
  • Axel Vick
    • 2
  • Vojtech Vonasek
    • 3
  • Jörg Krüger
    • 2
  1. 1.Department of lndustrial Automation TechnologyTechnische Universität BerlinBerlinDeutschland
  2. 2.Fraunhofer Institute for Production Systems and Design Technology (IPK) BerlinBerlinDeutschland
  3. 3.Faculty of electrical engineeringCzech Technical University PraguePragueCzech Republic

Personalised recommendations