Mona: an Affordable Open-Source Mobile Robot for Education and Research

  • Farshad Arvin
  • Jose Espinosa
  • Benjamin Bird
  • Andrew West
  • Simon Watson
  • Barry Lennox
Open Access


Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread adoption is limited by their high cost and the complexity of user interfaces and programming tools. To overcome these issues, a new affordable, adaptable and easy-to-use robotic platform is proposed. Mona is a low-cost, open-source and open-hardware mobile robot, which has been developed to be compatible with a number of standard programming environments. The robot has been successfully used for both education and research at The University of Manchester, UK.


Mobile robot Robotics for education Open-hardware Open-source 



This work was supported by the EPSRC (Project No. EP/P01366X/1 and EP/P018505/1), Innovate UK (Project No. KTP 009811), CONACyT and the National Nuclear Laboratory.


  1. 1.
    Merdan, M., Lepuschitz, W., Koppensteiner, G., Balogh, R. (eds.): Robotics in Education : Research and Practices for Robotics STEM Education. Springer, Berlin (2017)Google Scholar
  2. 2.
    Jojoa, E.M.J., Bravo, E.C., Cortes, E.B.B.: Tool for experimenting with concepts of mobile robotics as applied to children’s education. IEEE Trans. Educ. 53(1), 88–95 (2010)CrossRefGoogle Scholar
  3. 3.
    Chaudhary, V., Agrawal, V., Sureka, P., Sureka, A.: An experience report on teaching programming and computational thinking to elementary level children using lego robotics education kit. In: IEEE Eighth International Conference on Technology for Education, pp. 38–41 (2016)Google Scholar
  4. 4.
    Scott, M.J., Counsell, S., Lauria, S., Swift, S., Tucker, A., Shepperd, M., Ghinea, G.: Enhancing practice and achievement in introductory programming with a robot olympics. IEEE Trans. Educ. 58(4), 249–254 (2015)CrossRefGoogle Scholar
  5. 5.
    Wang, D., Chen, J., Liu, L.: Discussion of robot application laboratory construction. International Journal of Education and Learning 5(1), 1–12 (2016)CrossRefGoogle Scholar
  6. 6.
    Conti, D., Nuovo, S.D., Buono, S., Nuovo, A.D.: Robots in eduction and care of children with developmental disabilities: a study on acceptance by experienced and future professionals. Int. J. Soc. Robot. 9(1), 51–62 (2016)CrossRefGoogle Scholar
  7. 7.
    Scilliano, B., Khatib, O.: Springer Handbook of Robotics. Springer, Berlin (2008)Google Scholar
  8. 8.
    Sadanand, R., Joshi, R.P., Chittawadigi, R.G., Saha, S.K.: Virtual experiments for integrated teaching and learning of robot mechanics using RoboAnalyzer. In: CAD/CAM, Robotics and Factories of the Future, pp. 59–68. Springer (2016)Google Scholar
  9. 9.
    Calvo, I., Cabanes, I., Quesada, J., Barambones, O.: A multidisciplinary pbl approach for teaching industrial informatics and robotics in engineering. IEEE Trans. Educ. 61(1), 21–28 (2018)CrossRefGoogle Scholar
  10. 10.
    Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005)Google Scholar
  11. 11.
    Felder, R.M., Soloman, B.A., et al.: Learning styles and strategies. At (2000)
  12. 12.
    Rivera, J.H.: Science-based laboratory comprehension: an examination of effective practices within traditional, online and blended learning environments. Open Learning: The Journal of Open, Distance and e-Learning 31(3), 209–218 (2016)CrossRefGoogle Scholar
  13. 13.
    Nguyen, K.A., DeMonbrun, R.M., Borrego, M.J., Prince, M.J., Husman, J., Finelli, C.J., Shekhar, P., Henderson, C., Waters, C.: The variation of nontraditional teaching methods across 17 undergraduate engineering classrooms. In: 2017 ASEE Annual Conference & Exposition (2017)Google Scholar
  14. 14.
    Spolaôr, N., Benitti, F.B.: Robotics applications grounded in learning theories on tertiary education: a systematic review. Comput. Educ. 112, 97–107 (2017)CrossRefGoogle Scholar
  15. 15.
    Cielniak, G., Bellotto, N., Duckett, T.: Integrating mobile robotics and vision with undergraduate computer science. IEEE Trans. Educ. 56(1), 48–53 (2013)CrossRefGoogle Scholar
  16. 16.
    Ortiz, O.O., Franco, J.A.P., Garau, P.M.A., Martin, R.H.: Innovative mobile robot method: improving the learning of programming languages in engineering degrees. IEEE Trans. Educ. 60(2), 143–148 (2017)CrossRefGoogle Scholar
  17. 17.
    Arvin, F., Watson, S., Turgut, A.E., Espinosa, J., Krajník, T., Lennox, B.: Perpetual robot swarm: long-term autonomy of mobile robots using on-the-fly inductive charging. J. Intell. Robot. Syst. (2017).
  18. 18.
    Matthews, D., Dang, S., Christodoulou, L., Chen, H., Hawit, Y.: Embedded systems project: innovative autonomous line-following buggy design and implementation. In: Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives, pp. 1–5 (2014)Google Scholar
  19. 19.
    Surendran, A., Mija, S.J.: Sliding mode controller for robust trajectory tracking using haptic robot. In: IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems, pp. 1–6 (2016)Google Scholar
  20. 20.
    Michieletto, S., Tosello, E., Pagello, E., Menegatti, E.: Teaching humanoid robotics by means of human teleoperation through rgb-d sensors. Robot. Auton. Syst. 75, 671–678 (2016)CrossRefGoogle Scholar
  21. 21.
    Browne, A.F., Conrad, J.M.: A versatile approach for teaching autonomous robot control to multi-disciplinary undergraduate and graduate students. In: IEEE Access (2017)Google Scholar
  22. 22.
    Kofinas, N., Orfanoudakis, E., Lagoudakis, M.G.: Complete analytical forward and inverse kinematics for the nao humanoid robot. J. Intell. Robot. Syst. 77(2), 251–264 (2015)CrossRefGoogle Scholar
  23. 23.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competition, vol. 1, pp. 59–65 (2009)Google Scholar
  24. 24.
    Riedo, F., Chevalier, M., Magnenat, S., Mondada, F.: Thymio II, a robot that grows wiser with children. In: IEEE Workshop on Advanced Robotics and its Social Impacts, pp. 187–193 (2013)Google Scholar
  25. 25.
    Gyebi, E., Hanheide, M., Cielniak, G., et al.: Affordable mobile robotic platforms for teaching computer science at African Universities. In: 6th International Conference on Robotics in Education (2015)Google Scholar
  26. 26.
    Afari, E., Khine, M.S.: Robotics as an educational tool: impact of lego mindstorms. International Journal of Information and Education Technology 7(6), 437–442 (2017)CrossRefGoogle Scholar
  27. 27.
    Álvarez, A., Larrañaga, M.: Experiences incorporating lego mindstorms robots in the basic programming syllabus: lessons learned. J. Intell. Robot. Syst. 81(1), 117–129 (2016)CrossRefGoogle Scholar
  28. 28.
    Alkilabi, M.H.M., Narayan, A., Tuci, E.: Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies. Swarm Intell. 11, 185–209 (2017)CrossRefGoogle Scholar
  29. 29.
    Chovanec, M., Čechovič, L., Mandák, L.: Aeris—Robots Laboratory with Dynamic Environment. In: Robotics in Education, pp. 169–180. Springer International Publishing (2017)Google Scholar
  30. 30.
    López-Rodríguez, F.M., Cuesta, F.: Andruino-a1: low-cost educational mobile robot based on android and arduino. J. Intell. Robot. Syst. 81(1), 63–76 (2016)CrossRefGoogle Scholar
  31. 31.
    Arvin, F., Murray, J., Zhang, C., Yue, S.: Colias: an autonomous micro robot for swarm robotic applications. Int. J. Adv. Robot. Syst. 11(113), 1–10 (2014)Google Scholar
  32. 32.
    Szymanski, M., Breitling, T., Seyfried, J., Wörn, H.: Distributed shortest-path finding by a micro-robot swarm. In: International Workshop on Ant Colony Optimization and Swarm Intelligence, pp. 404–411. Springer, Berlin (2006)Google Scholar
  33. 33.
    Soares, J.M., Navarro, I., Martinoli, A.: The Khepera IV mobile robot: performance evaluation, sensory data and software toolbox. In: Robot 2015: Second Iberian Robotics Conference, pp. 767–781. Springer International Publishing, Cham (2016)Google Scholar
  34. 34.
    Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., Mondada, F.: The marxbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4187–4193 (2010)Google Scholar
  35. 35.
    Yu, J., Han, S.D., Tang, W.N., Rus, D.: A portable, 3d-printing enabled multi-vehicle platform for robotics research and education. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1475–1480 (2017)Google Scholar
  36. 36.
    McLurkin, J., Rykowski, J., John, M., Kaseman, Q., Lynch, A.J.: Using multi-robot systems for engineering education: teaching and outreach with large numbers of an advanced, low-cost robot. IEEE Trans. Educ. 56(1), 24–33 (2013)CrossRefGoogle Scholar
  37. 37.
    Arvin, F., Turgut, A.E., Krajník, T., Yue, S.: Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adapt. Behav. 24(2), 102–118 (2016)CrossRefGoogle Scholar
  38. 38.
    Arvin, F., Bekravi, M.: Encoderless position estimation and error correction techniques for miniature mobile robots. Turk. J. Electr. Eng. Comput. Sci. 21, 1631–1645 (2013)CrossRefGoogle Scholar
  39. 39.
    Arvin, F., Murray, J.C., Shi, L., Zhang, C., Yue, S.: Development of an autonomous micro robot for swarm robotics. In: IEEE International Conference on Mechatronics and Automation, pp. 635–640 (2014)Google Scholar
  40. 40.
    Arvin, F., Samsudin, K., Ramli, A.R.: Development of a miniature robot for swarm robotic application. International Journal of Computer and Electrical Engineering 1, 436–442 (2009)CrossRefGoogle Scholar
  41. 41.
    Benet, G., Blanes, F., Simó, J.E., Pérez, P.: Using infrared sensors for distance measurement in mobile robots. Robot. Auton. Syst. 40(4), 255–266 (2002)CrossRefGoogle Scholar
  42. 42.
    Hu, C., Arvin, F., Xiong, C., Yue, S.: A bio-inspired embedded vision system for autonomous micro-robots: the LGMD case. IEEE Transactions on Cognitive and Developmental Systems 9(3), 241–254 (2016)CrossRefGoogle Scholar
  43. 43.
    Arvin, F., Samsudin, K., Ramli, A.R.: Development of IR-based short-range communication techniques for swarm robot applications. Advances in Electrical and Computer Engineering 10(4), 61–68 (2010)CrossRefGoogle Scholar
  44. 44.
    Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., Monasterio-Huelin, F.: An open localization and local communication embodied sensor. Sensors 8(11), 7545–7563 (2008)CrossRefGoogle Scholar
  45. 45.
    West, A., Arvin, F., Martin, H., Watson, S., Lennox, B.: ROS integration for miniature mobile robots. In: Towards Autonomous Robotic Systems (TAROS) (2018)Google Scholar
  46. 46.
    Banzi, M., Shiloh, M.: Getting Started with Arduino: the Open Source Electronics Prototyping Platform. Maker Media, Inc, San Francisco (2014)Google Scholar
  47. 47.
    Vaughan, R.: Massively multi-robot simulation in stage. Swarm Intell. 2(2), 189–208 (2008)CrossRefGoogle Scholar
  48. 48.
    Ramroop, S., Arvin, F., Watson, S., Carrasco-Gomez, J., Lennox, B.: A bio-inspired aggregation with robot swarm using real and simulated mobile robots. In: Towards Autonomous Robotic Systems (TAROS) (2018)Google Scholar
  49. 49.
    Schmickl, T., Thenius, R., Moeslinger, C., Radspieler, G., Kernbach, S., Szymanski, M., Crailsheim, K.: Get in touch: cooperative decision making based on robot-to-robot collisions. Auton. Agent. Multi-Agent Syst. 18(1), 133–155 (2009)CrossRefGoogle Scholar
  50. 50.
    Arvin, F., Turgut, A.E., Bazyari, F., Arikan, K.B., Bellotto, N., Yue, S.: Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adapt. Behav. 22(3), 189–206 (2014)CrossRefGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK

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