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Duckietown: An Innovative Way to Teach Autonomy

  • Jacopo Tani
  • Liam Paull
  • Maria T. Zuber
  • Daniela Rus
  • Jonathan How
  • John Leonard
  • Andrea Censi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 560)

Abstract

Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The “duckie” theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field.

Keywords

Duckietown Autonomous vehicles Educational robotics Active learning Constructive alignment Backwards design 

Notes

Acknowledgments

This work was funded by the National Science foundation through award IIS #1318392 and through the National Robotics Initiative award #1405259. The work was also supported by the Toyota Research Institute and the Ford Motor Company.

References

  1. 1.
    Freeman, S., Eddy, S.L., McDonough, M., Smith, M.K., Okoroafor, N., Jordt, H., Wenderoth, M.P.: Active learning increases student performance in science, engineering, and mathematics. Proc. Nat. Acad. Sci. 111(23), 8410–8415 (2014)CrossRefGoogle Scholar
  2. 2.
    Bloom, B.S.: The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ. Researcher 13(6), 4–16 (1984)CrossRefGoogle Scholar
  3. 3.
    Wieman, C.E.: Large-scale comparison of science teaching methods sends clear message. Proc. Nat. Acad. Sci. 111(23), 8319–8320 (2014)CrossRefGoogle Scholar
  4. 4.
    Halpern, D.F., Hakel, M.D.: Applying the science of learning to the university and beyond: teaching for long-term retention and transfer. Change Mag. High. Learn. 35(4), 36–41 (2003)CrossRefGoogle Scholar
  5. 5.
    Wiggins, G., McTighe, J.: What is backward design? Understanding by Design, pp. 7–19 (2011)Google Scholar
  6. 6.
    Biggs, J.: Aligning teaching for constructing learning. Higher Education Academy, pp. 1–4 (2003)Google Scholar
  7. 7.
    Monaco, M., Martin, M.: The millennial student: a new generation of learners. Athletic Training Educ. J. 2, 42–46 (2007)Google Scholar
  8. 8.
    Novotney, A.: Engaging the Millennial Learner 41(3), 60 (2010)Google Scholar
  9. 9.
    Paull, L., Tani, J., Ahn, H., Alonso-Mora, J., Carlone, L., Cap, M., Chen, Y.F., Choi, C., Dusek, J., Fang, Y., Okuyama, I.F., Hoehener, D., Liu, S.Y., Novitzky, M., Pazis, J., Rosman, G., Varricchio, V., Wang, H.C., Yershov, D., Zhao, H., Benjamin, M., Carr, C., Zuber, M., Karaman, S., Frazzoli, E., Del Vecchio, D., Rus, D., How, J., Leonard, J., Censi, A.: Duckietown: an open, inexpensive and flexible platform for autonomy education and research. In: IEEE International Conference on Robotics and Automation (ICRA) (2017), Submitted. http://duckietown.mit.edu/materials.html, Accessed 15 October 2016
  10. 10.
    Olson, E.: Apriltag: a robust and flexible visual fiducial system. In: IEEE International Conference on Robotics and Automation (ICRA), May 2011Google Scholar
  11. 11.
    Siegel, M., Breazeal, C., Norton, M.I.: Persuasive robotics: the influence of robot gender on human behavior. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers (IEEE), October 2009Google Scholar
  12. 12.
    Green, T.: Why are too few females in robotics? could it be the robots? (2015)Google Scholar
  13. 13.
    Greensted, C.: Intended learning outcomes. EFMD Glob. Focus 8(1), 20–25 (2014)Google Scholar
  14. 14.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5, Kobe (2009)Google Scholar
  15. 15.
    Sturm, P.: Pinhole camera model. In: Computer Vision, pp. 610–613. Springer (2014)Google Scholar
  16. 16.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  17. 17.
    Daum, F.: Nonlinear filters: beyond the kalman filter. IEEE Aerosp. Electron. Syst. Mag. 20(8), 57–69 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Karlsson, N., Di Bernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P., Munich, M.E.: The vSLAM algorithm for robust localization and mapping. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 24–29. IEEE (2005)Google Scholar
  19. 19.
    Masehian, E., Sedighizadeh, D.: Classic and heuristic approaches in robot motion planning-A chronological review. World Acad. Sci. Eng. Technol. 29(1), 101–106 (2007)Google Scholar
  20. 20.
    Crowley, J.: Navigation for an intelligent mobile robot. IEEE J. Robot. Autom. 1(1), 31–41 (1985)CrossRefGoogle Scholar
  21. 21.
    Hafner, M.R., Cunningham, D., Caminiti, L., Del Vecchio, D.: Cooperative collision avoidance at intersections: algorithms and experiments. IEEE Trans. Intell. Transp. Syst. 14(3), 1162–1175 (2013)CrossRefGoogle Scholar
  22. 22.
    Duperret, J.M., Hafner, M.R., Del Vecchio, D.: Formal design of a provably safe robotic roundabout system. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2006–2011. IEEE (2010)Google Scholar
  23. 23.
    Cao, Y., Yu, W., Ren, W., Chen, G.: An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans. Industr. Inf. 9(1), 427–438 (2013)CrossRefGoogle Scholar
  24. 24.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  25. 25.
    Tani, J., Mishra, S., Wen, J.T.: Motion blur-based state estimation. IEEE Trans. Control Syst. Technol. 24(3), 1012–1019 (2016)CrossRefGoogle Scholar
  26. 26.
    Tanenbaum, A.S., Van Steen, M.: Distributed Systems. Prentice-Hall, Upper Saddle River (2007)zbMATHGoogle Scholar
  27. 27.
    Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Rob. Syst. 53(3), 263–296 (2008)CrossRefGoogle Scholar
  28. 28.
    Naser, F., Dorhout, D., Proulx, S., Pendleton, S.D., Andersen, H., Schwarting, W., Paull, L., Alonso-Mora, J., Jr., M.H.A., Karaman, S., Tedrake, R., Leonard, J., Rus, D.: A parallel autonomy research platform. In: IEEE Conference on Robotics and Automation (ICRA), pp. 1–8 (2017). SubmittedGoogle Scholar
  29. 29.
    Slack: Slack (2016) Accessed 10 October 2016Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jacopo Tani
    • 1
  • Liam Paull
    • 1
  • Maria T. Zuber
    • 1
  • Daniela Rus
    • 1
  • Jonathan How
    • 1
  • John Leonard
    • 1
  • Andrea Censi
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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