BulbRobot – Inexpensive Open Hardware and Software Robot Featuring Catadioptric Vision and Virtual Sonars

  • João FerreiraEmail author
  • Filipe Coelho
  • Armando Sousa
  • Luís Paulo Reis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


This article proposes a feature-rich, open hardware, open software inexpensive robot based on a Waveshare AlphaBot 2. The proposal uses a Raspberry Pi and a chrome plated light bulb as a mirror to produce a robot with an omnidirectional vision (catadioptric) system. The system also tackles boot and network issues to allow for monitor-less programming and usage, thus further reducing usage costs. The OpenCV library is used for image processing and obstacles are identified based on their brightness and saturation in contrast to the ground. Our solution achieved acceptable framerates and near perfect object detection up to 1.5-m distances. The robot is usable for simple robotic demonstrations and educational purposes for its simplicity and flexibility.



This research was partially supported by LIACC - Artificial Intelligence and Computer Science Laboratory of the University of Porto (FCT/UID/CEC/00027/2019).


  1. 1.
    Benosman, R., Deforas, E., Devars, J.: A new catadioptric sensor for the panoramic vision of mobile robots. In: Proceedings of the IEEE Workshop on Omnidirectional Vision (Cat. No. PR00704), pp. 112–116, June 2000.
  2. 2.
    Chen, P., Dang, Y., Liang, R., Zhu, W., He, X.: Real-time object tracking on a drone with multi-inertial sensing data. IEEE Trans. Intell. Transp. Syst. 19(1), 131–139 (2018). Scholar
  3. 3.
    Cho, D., Park, J., Tai, Y., Kweon, I.: Asymmetric stereo with catadioptric lens: high quality image generation for intelligent robot. In: 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 240–242, August 2016.
  4. 4.
    Colomina, I., Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogramm. Remote. Sens. 92, 79–97 (2014). Scholar
  5. 5.
    Darma, S., Buessler, J.L., Hermann, G., Urban, J.P., Kusumoputro, B.: Visual servoing quadrotor control in autonomous target search. In: 2013 IEEE 3rd International Conference on System Engineering and Technology, pp. 319–324, August 2013.
  6. 6.
    Ergezer, H., Leblebicioglu, K.: Path planning for UAVs for maximum information collection. IEEE Trans. Aerosp. Electron. Syst. 49(1), 502–520 (2013). Scholar
  7. 7.
    Eubanks, A.M., Strader, R.G., Dunn, D.L.: A comparison of compact robotics platforms for model teaching. J. Comput. Sci. Coll 26(4), 35–40 (2011)Google Scholar
  8. 8.
    Gomez, C., Hernandez, A.C., Crespo, J., Barber, R.: Integration of multiple events in a topological autonomous navigation system. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 41–46, May 2016.
  9. 9.
    Hoshino, S., Niimura, K.: Robot vision system for real-time human detection and action recognition. In: Intelligent Autonomous Systems 15, pp. 507–519. Springer, Cham (2019)Google Scholar
  10. 10.
    Kertész, C.: Clear sky detection with deep learning and an inexpensive infrared camera for robot telescopes. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1698–1702, November 2018.
  11. 11.
    Kho, Y.H., Abdulla, A.E., Yan, J.C.Z.: A vision-based autonomous vehicle tracking robot platform. In: 2014 IEEE Symposium on Industrial Electronics Applications (ISIEA), pp. 173–176, September 2014.
  12. 12.
    Lim, H., Sinha, S.N.: Monocular localization of a moving person onboard a quadrotor MAV. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2182–2189, May 2015.
  13. 13.
    Lopes, G., Ribeiro, F., Pereira, N.: Catadioptric system optimisation for omnidirectional RoboCup MSL robots. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds.) RoboCup 2011: Robot Soccer World Cup XV, pp. 318–328. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Michael, N., Mellinger, D., Lindsey, Q., Kumar, V.: The GRASP multiple micro-UAV testbed. IEEE Robot. Autom. Mag. 17(3), 56–65 (2010). Scholar
  15. 15.
    Millard, A.G., Joyce, R., Hilder, J.A., Fleşeriu, C., Newbrook, L., Li, W., McDaid, L.J., Halliday, D.M.: The Pi-puck extension board: a Raspberry Pi interface for the e-puck robot platform. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 741–748, September 2017.
  16. 16.
    Miller, K.S., Robila, S.A.: LIDAR for Scribbler 2. In: 2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1–6, May 2017.
  17. 17.
    Rubenstein, M., Cimino, B., Nagpal, R., Werfel, J.: AERobot: an affordable one-robot-per-student system for early robotics education. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 6107–6113. IEEE (2015)Google Scholar
  18. 18.
    Weiss, R., Overcast, I.: Finding your bot-mate: criteria for evaluating robot kits for use in undergraduate computer science education. J. Comput. Sci. Coll. 24(2), 43–49 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • João Ferreira
    • 1
    Email author
  • Filipe Coelho
    • 1
  • Armando Sousa
    • 1
    • 2
  • Luís Paulo Reis
    • 3
    • 4
  1. 1.FEUP - Faculty of Engineering, UP - University of PortoPortoPortugal
  2. 2.INESC TEC - INESC Technology and SciencePortoPortugal
  3. 3.LIACC/UP - Artificial Intelligence and Computer Science Laboratory, UPPortoPortugal
  4. 4.DEI/FEUP - Informatics Engineering Department, FEUPPortoPortugal

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