eduMorse: An Open-Source Framework for Mobile Robotics Education

  • Daniele De MartiniEmail author
  • Andrea Bonandin
  • Tullio Facchinetti
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 630)


The increasing spreading of robotics applications requires the formation of more and more experts with knowledge in core aspects of robotics systems. This paper introduces eduMorse, a novel framework for the education in the scope of mobile robotics. The framework addresses the accurate simulation of single- and multi-robot systems, with special focus on the possibility to implement path planning, navigation and control strategies, to handle sensors and actuators, and the communication among robots, thus allowing for the simulation of multi-robot coordination strategies. eduMorse leverages open-source tools to build a modular client-server framework for the simulation of mobile robots, with the aim of a simple setup of the simulation as a primary goal. The paper describes the components of eduMorse and its architecture. An example of application is also presented to show the effectiveness of the robotics simulation and the usage workflow of the system.


Robotics Education Simulation Multi-robot systems Path planning Navigation Coordination Free/Open source software 


  1. 1.
    Almeida, L., Fonseca, P., Azevedo, J.L.: The micro-rato contest: a popular approach to improve self-study in electronics and computer science. In: IEEE International Conference on Systems, Man and Cybernetics, October 2000Google Scholar
  2. 2.
    Amy Eguchi, L.A.: RoboCupJunior: promoting stem education with robotics competition. In: 4th International Conference on Robotics in Education (RiE) (2013)Google Scholar
  3. 3.
    Azevedo, J., Oliveira, M., Pacheco, P., Reis, L.P.: A Cooperative CiberMouse@RTSS08 Team, pp. 251–262. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    Birk, A.: The true spirit of RoboCup [Education]. IEEE Robot. Autom. Mag. 17, 108–108 (2010)CrossRefGoogle Scholar
  5. 5.
    Boeing, A., Bräunl, T.: Evaluation of real-time physics simulation systems. In: Proceedings of the 5th International Conference on Computer Graphics and Interactive Techniques in Australia and Southeast Asia, pp. 281–288. ACM, New York (2007)Google Scholar
  6. 6.
    Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, Boston (2005)zbMATHGoogle Scholar
  7. 7.
    Echeverria, G., Lassabe, N., Degroote, A., Lemaignan, S.: Modular open robots simulation engine: MORSE. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 46–51, May 2011Google Scholar
  8. 8.
    Echeverria, G., Lemaignan, S., Degroote, A., Lacroix, S., Karg, M., Koch, P., Lesire, C., Stinckwich, S.: Simulating complex robotic scenarios with MORSE. In: Proceedings of the 3rd International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SiMPAR). pp. 197–208. Springer (2012)Google Scholar
  9. 9.
    Felinto, D., Pan, M.: Game Development with Blender. Cengage Learning PTR (2013)Google Scholar
  10. 10.
    Gerkey, B.P., Vaughan, R.T., Howard, A.: The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings of the 11th International Conference on Advanced Robotics, pp. 317–323 (2003)Google Scholar
  11. 11.
    Gonzalez, R., Mahulea, C., Kloetzer, M.: A matlab-based interactive simulator for mobile robotics. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp. 310–315, August 2015Google Scholar
  12. 12.
    Hartness, K.: Robocode: using games to teach artificial intelligence. J. Comput. Sci. Coll. 19(4), 287–291 (2004)Google Scholar
  13. 13.
    Klassner, F., Anderson, S.D.: Lego mindstorms: not just for k-12 anymore. IEEE Robot. Autom. Mag. 10(2), 12–18 (2003)CrossRefGoogle Scholar
  14. 14.
    Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: International Conference on Intelligent Robots and Systems, Sendai, Japan, pp. 2149–2154, September 2004Google Scholar
  15. 15.
    Lentin, J.: Learning Robotics Using Python. PACKT (2015)Google Scholar
  16. 16.
    Michel, O.: Cyberbotics Ltd Webots TM: professional mobile robot simulation. Int. J. Adv. Robot. Syst. 1, 39–42 (2004)CrossRefGoogle Scholar
  17. 17.
    Ortiz, O.O., Franco, J.P., Garau, P.M.A., Martn, R.H.: Innovative mobile robot method: improving the learning of programming languages in engineering degrees. IEEE Trans. Educ. PP(99), 1–6 (2016)Google Scholar
  18. 18.
    Pickem, D., Wang, L., Glotfelter, P., Diaz-Mercado, Y., Mote, M., Ames, A.D., Feron, E., Egerstedt, M.: Safe, remote-access swarm robotics research on the Robotarium. ACM Computing Research Repository (CoRR) abs/1604.00640 (2016)Google Scholar
  19. 19.
    Simões, D., Brás, R., Lau, N., Pereira, A.: A Coordinated Team of Agents to Solve Mazes, pp. 381–392. Springer (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Daniele De Martini
    • 1
    Email author
  • Andrea Bonandin
    • 1
  • Tullio Facchinetti
    • 1
  1. 1.Department of Electrical, Computer and Biomedical EngineeringUniversità degli Studi di PaviaPaviaItaly

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