Distributed Control and Navigation System for Quadrotor UAVs in GPS-Denied Environments

  • Konstantin YakovlevEmail author
  • Vsevolod Khithov
  • Maxim Loginov
  • Alexander Petrov
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 323)


The problem of developing distributed control and navigation system for quadrotor UAVs operating in GPS-denied environments is addressed in the paper. Cooperative navigation, marker detection and mapping task solved by a team of multiple unmanned aerial vehicles is chosen as demo example. Developed intelligent control system complies with on 4D\RCS reference model and its implementation is based on ROS framework. Custom implementation of EKF-based map building algorithm is used to solve marker detection and map building task.


intelligent control system distributed architecture 4D/RCS visual navigation marker detection SLAM map building ROS AR.Drone 


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  1. 1.
    Robot Operation System,
  2. 2.
    Parker, L.E.: Distributed control of multi-robot teams: Cooperative baton-passing task. In: Proceedings of the 4th International Conference on Information Systems Analysis and Synthesis (ISAS 1998), vol. 3, pp. 89–94 (1998b)Google Scholar
  3. 3.
    Feddema, J., Lewis, C., Schoenwald, D.: Decentralized control of cooperative robotic vehicles: Theory and application. IEEE Trans. Robot. Automat. 18, 852–864 (2002)CrossRefGoogle Scholar
  4. 4.
    Huntsberger, T.L., Trebi-Ollennu, A., Aghazarian, H., Schenker, P.S., Pirjanian, P., Nayar, H.D.: Distributed control of multi-robot systems engaged in tightly coupled tasks. Autonomous Robots 17(1), 79–92 (2004)CrossRefGoogle Scholar
  5. 5.
    Michael, N., Fink, J., Kumar, V.: Cooperative grasping and transportation using multiple quadrotors. In: International Symposium on Distributed Autonomous Robotic Systems (DARS), Lausanne, Switzerland (2011)Google Scholar
  6. 6.
    Charrow, B., Michael, N., Kumar, V.: Cooperative multirobot estimation and control for radio source localization. In: Desai, J.P., Dudek, G., Khatib, O., Kumar, V. (eds.) Experimental Robotics. STAR, vol. 88, pp. 337–351. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Walter, M., Leonard, J.: An experimental investigation of cooperative SLAM. In: 5th International Symposium on Intelligent Autonomous Vehicles, Lisbon, July 5-7 (2004)Google Scholar
  8. 8.
    Özkucur, N.E., Akın, H.L.: Cooperative multi-robot map merging using fast-slam. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS (LNAI), vol. 5949, pp. 449–460. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Kim, B., et al.: Multiple Relative Pose Graphs for Robust Cooperative Mapping. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3185–3192. © Copyright 2010 IEEE (2010)Google Scholar
  10. 10.
    Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: Incremental smoothing and mapping. IEEE Trans. Robotics 24, 1365–1378 (2008)CrossRefGoogle Scholar
  11. 11.
    Nekkundi, P.S., Dulman, S.: A Framework for Cooperative 3D Mapping of Unstructured Environments. Master Thesis, Delft University of Technology (2011)Google Scholar
  12. 12.
    Jameson, S., Franke, J., Szczerba, R., Stockdale, S.: Collaborative Autonomy for Manned/Unmanned Teams. AHS International Forum 61. Grapevine, TX (2005)Google Scholar
  13. 13.
    Bonasso, R.P., Kortenkamp, D., Miller, D.P., Slack, M.: Experiences with an Architecture for Intelligent, Reactive Agents. In: Wooldridge, M., Müller, J.P., Tambe, M. (eds.) IJCAI-WS 1995 and ATAL 1995. LNCS, vol. 1037, pp. 187–202. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  14. 14.
    Gat, E.: Integrating planning and reacting in a heterogenous asynchronous architecture for controlling real-world mobile robots. In: National Conference for Artificial Intelligence (1992)Google Scholar
  15. 15.
    Arkin, R.: Motor schema based navigation for a mobile robot: An approach to programming by behavior. In: Proceedings of the IEEE International Conference on Robotics and Automation (1987)Google Scholar
  16. 16.
    Freed, M., et al.: An Architecture for Intelligent Management of Aerial Observation Missions. In: AIAA 2005, pp. 2005–6938 (2005)Google Scholar
  17. 17.
  18. 18.
    Bristeau, P.J., Callou, F., Vissière, D., Petit, N.: The navigation and control technology inside the AR.Drone micro UAV. In: 18th IFAC World Congress, vol. 18(1), pp. 1477–1484 (2011)Google Scholar
  19. 19.
    Albus, J., Huang, H.M., Messina, E., Murphy, K., Juberts, M., Lacaze, A., Finkelstein, R.: 4D/RCS Version 2.0: A reference model architecture for unmanned vehicle systems. National Institute of Standards and Technology, Gaithersburg (2002)Google Scholar
  20. 20.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ... Ng, A.Y.: ROS: an open-source Robot Operating System. In: ICRA Workshop on Open Source Software, vol. 3(3.2) (2009)Google Scholar
  21. 21.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: International Symposium on Mixed and Augmented Reality (2007)Google Scholar
  22. 22.
    Munoz-Salinas, R., Garrido-Jurado, S.: Aruco library,
  23. 23.
    Engel, J., Sturm, J., Cremers, D.: Camera-Based Navigation of a Low-Cost Quadrocopter. In: Proc. of the International Conference on Intelligent Robot Systems (IROS) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Konstantin Yakovlev
    • 1
    Email author
  • Vsevolod Khithov
    • 2
  • Maxim Loginov
    • 2
  • Alexander Petrov
    • 3
  1. 1.Institute for Systems Analysis of Russian Academy of SciencesMoscowRussia
  2. 2.Soloviev Rybinsk State Aviation Technical UniversityRybinskRussia
  3. 3.NPP SATEK plusRybinskRussia

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