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Rapid Prototyping Framework for Visual Control of Autonomous Micro Aerial Vehicles

  • Ignacio Mellado-Bataller
  • Pascual Campoy
  • Miguel A. Olivares-Mendez
  • Luis Mejias
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drone’s video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW

Keywords

MAV UAV communications software framework 

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References

  1. 1.
    Parrot AR.Drone, http://ardrone.parrot.com
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Visser, A., Dijkshoorn, N., van der Veen, M., Jurriaans, R.: Closing the gap between simulation and reality in the sensor and motion models of an autonomous AR.Drone. In: Proc. International Micro Air Vehicle Conference and Flight Competition (IMAV 2011), pp. 40–47 (September 2011)Google Scholar
  8. 8.
    Bills, C., Chen, J., Saxena, A.: Autonomous MAV flight in indoor environments using single image perspective cues. In: Int. Conf. Robotics and Automation (ICRA), Shanghai, China, pp. 5776–5783 (May 2011)Google Scholar
  9. 9.
    Koval, M.C., Mansley, C.R., Littman, M.L.: Autonomous quadrotor control with reinforcement learning, http://mkoval.org/projects/quadrotor/files/quadrotor-rl.pdf
  10. 10.
    ARDRONE open API platform, https://projects.ardrone.org
  11. 11.
    See and avoid with a fuzzy controller, http://vision4uav.eu/?q=researchline/SeeAndAvoidCE
  12. 12.
    Branicky, M.S., Phillips, S.M., Zhang, W.: Stability of networked control systems: explicit analysis of delay. In: Proc. American Control Conf, pp. 2352–2357 (June 2000)Google Scholar
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ignacio Mellado-Bataller
    • 1
  • Pascual Campoy
    • 1
  • Miguel A. Olivares-Mendez
    • 1
  • Luis Mejias
    • 2
  1. 1.Centre for Automation and Robotics (CAR)Universidad Politécnica de MadridMadridSpain
  2. 2.Australian Research Centre for Aerospace Automation (ARCAA)Queensland University of TechnologyBrisbaneAustralia

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