Advertisement

ROS 2 for RoboCup

  • Marcus M. ScheunemannEmail author
  • Sander G. van Dijk
Conference paper
  • 188 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11531)

Abstract

There has always been much motivation for sharing code and solutions among teams in the RoboCup community. Yet the transfer of code between teams was usually complicated due to a huge variety of used frameworks and their differences in processing sensory information. The RoboCup@Home league has tackled this by transitioning to ROS as a common framework. In contrast, other leagues, such as those using humanoid robots, are reluctant to use ROS, as in those leagues real-time processing and low-computational complexity is crucial. However, ROS 2 now offers built-in support for real-time processing and promises to be suitable for embedded systems and multi-robot systems. It also offers the possibility to compose a set of nodes needed to run a robot into a single process. This, as we will show, reduces communication overhead and allows to have one single binary, which is pertinent to competitions such as the 3D-Simulation League. Although ROS 2 has not yet been announced to be production ready, we started the process to develop ROS 2 packages for using it with humanoid robots (real and simulated). This paper presents the developed modules, our contributions to ROS 2 core and RoboCup related packages, and most importantly it provides benchmarks that indicate that ROS 2 is a promising candidate for a common framework used among leagues.

Keywords

ROS 2 Robot framework Robot software Embedded system Real-time system Minimal hardware Open source Humanoid robots Autonomous robots 

References

  1. 1.
    Bestmann, M., et al.: Hamburg bit-bots and WF wolves team description for RoboCup 2019. Technical report (2019)Google Scholar
  2. 2.
    Bestmann, M., Hendrich, N., Wasserfall, F.: ROS for humanoid soccer robots. In: The 12th Workshop on Humanoid Soccer Robots at 17th IEEE-RAS International Conference on Humanoid Robots, p. 1 (2017)Google Scholar
  3. 3.
    van Dijk, S.G., Scheunemann, M.M.: Deep learning for semantic segmentation on minimal hardware. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 349–361. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-27544-0_29CrossRefGoogle Scholar
  4. 4.
    Fernandez, E., Foote, T., Woodall, W., Thomas, D.: Next-generation ROS: building on DDS. In: ROSCon Chicago, September 2014. https://roscon.ros.org/2014/wp-content/uploads/2014/07/ROSCON-2014-Next-Generation-of-ROS-on-top-of-DDS.pdf
  5. 5.
    Forero, L.L., Yáñez, J.M., del Solar, J.R.: Integration of the ROS framework in soccer robotics: the NAO case. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS, vol. 8371, pp. 664–671. Springer, Berlin Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_63CrossRefGoogle Scholar
  6. 6.
    Gutiérrez, C.S.V., Juan, L.U.S., Ugarte, I.Z., Vilches, V.M.: Towards a distributed and real-time framework for robots: evaluation of ROS 2.0 communications for real-time robotic applications. https://arxiv.org/abs/1809.02595
  7. 7.
    Houliston, T., et al.: NUClear: a loosely coupled software architecture for humanoid robot systems. Front. Robot. AI 3, 20 (2016).  https://doi.org/10.3389/frobt.2016.00020CrossRefGoogle Scholar
  8. 8.
    Maruyama, Y., Kato, S., Azumi, T.: Exploring the performance of ROS2.In: Proceedings of the 13th International Conference on Embedded Software.pp. 1–10. EMSOFT 2016, ACM, New York, NY, USA (2016).  https://doi.org/10.1145/2968478.2968502
  9. 9.
    Matamoros, M., Seib, V., Memmesheimer, R., Paulus, D.: RoboCup@Home: summarizing achievements in over eleven years of competition. In: 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, April 2018.  https://doi.org/10.1109/icarsc.2018.8374181
  10. 10.
    Object Management Group (OMG): Data Distribution Service (DDS), Version 1.4., April 2015. https://www.omg.org/spec/DDS/1.4. Accessed 20 Mar 2019
  11. 11.
    Röfer, T., Laue, T.: On B-human’s code releases in the standard platform league – software architecture and impact. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS (LNAI), vol. 8371, pp. 648–655. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_61CrossRefGoogle Scholar
  12. 12.
    ROS 2: ROS 2 Documentation, April 2019. https://index.ros.org/doc/ros2/. Accessed 18 April 2019
  13. 13.
    Scheunemann, M.M., van Dijk, S.G., Miko, R., Barry, D., Evans, G.M., Polani, D.: Bold Hearts Team Description for RoboCup 2019 (Humanoid Kid Size League). Technical report, School of Computer Science, University of Hertfordshire, College Lane, AL10 9AB, UK, December 2018. https://arxiv.org/abs/1904.10066
  14. 14.
    Schwarz, M., et al.: Humanoid TeenSize open platform NimbRo-OP. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS (LNAI), vol. 8371, pp. 568–575. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_51CrossRefGoogle Scholar
  15. 15.
    Xu, Y., Vatankhah, H.: SimSpark: an open source robot simulator developed by the RoboCup community. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS (LNAI), vol. 8371, pp. 632–639. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_59CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of HertfordshireHatfieldUK

Personalised recommendations