Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo

  • Johannes Meyer
  • Alexander Sendobry
  • Stefan Kohlbrecher
  • Uwe Klingauf
  • Oskar von Stryk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7628)


Quadrotor UAVs have successfully been used both in research and for commercial applications in recent years and there has been significant progress in the design of robust control software and hardware. Nevertheless, testing of prototype UAV systems still means risk of damage due to failures. Motivated by this, a system for the comprehensive simulation of quadrotor UAVs is presented in this paper. Unlike existing solutions, the presented system is integrated with ROS and the Gazebo simulator. This comprehensive approach allows simultaneous simulation of diverse aspects such as flight dynamics, onboard sensors like IMUs, external imaging sensors and complex environments. The dynamics model of the quadrotor has been parameterized using wind tunnel tests and validated by a comparison of simulated and real flight data. The applicability for simulation of complex UAV systems is demonstrated using LIDAR-based and visual SLAM approaches available as open source software.


Inertial Measurement Unit Wind Tunnel Test Ultrasonic Sensor Comprehensive Simulation Robot Operating System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Achtelik, M., Bachrach, A., He, R., Prentice, S., Roy, N.: Autonomous navigation and exploration of a quadrotor helicopter in GPS-denied indoor environments. In: Robotics: Science and Systems Conference (2008)Google Scholar
  2. 2.
    Balakirsky, S.B., Kootbally, Z.: USARSim/ROS: A Combined Framework for Robotic Control and Simulation. In: ASME 2012 International Symposium on Flexible Automation (ISFA 2012). ASME (2012)Google Scholar
  3. 3.
    Bouabdallah, S., Siegwart, R.: Full control of a quadrotor. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 153–158 (November 2007)Google Scholar
  4. 4.
    Bristeau, P., Callou, F., Vissière, D., Petit, N., et al.: The navigation and control technology inside the AR Drone micro UAV. In: 18th IFAC World Congress, Milano, Italy, pp. 1477–1484 (2011)Google Scholar
  5. 5.
    Brown, R., Hwang, P., et al.: Introduction to random signals and applied Kalman filtering. Wiley, New York (1992)zbMATHGoogle Scholar
  6. 6.
    Bruyninckx, H.: Open robot control software: the OROCOS project. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 3, pp. 2523–2528. IEEE (2001)Google Scholar
  7. 7.
    Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: USARSim: a robot simulator for research and education. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1400–1405 (2007)Google Scholar
  8. 8.
    Ducard, G., D’Andrea, R.: Autonomous quadrotor flight using a vision system and accommodating frames misalignment. In: IEEE International Symposium on Industrial Embedded Systems (SIES), pp. 261–264. IEEE (2009)Google Scholar
  9. 9.
    Goel, R., Shah, S., Gupta, N., Ananthkrishnan, N.: Modeling, Simulation and Flight Testing of an Autonomous Quadrotor. In: IISc Centenary International Conference and Exhibition on Aerospace Engineering, ICEAE, Bangalore, India, pp. 18–22 (2009)Google Scholar
  10. 10.
    Hoffmann, G.M., Huang, H., Wasl, S.L., Tomlin, E.C.J.: Quadrotor helicopter flight dynamics and control: Theory and experiment. In: AIAA Guidance, Navigation, and Control Conference (2007)Google Scholar
  11. 11.
    Huang, H., Hoffmann, G., Waslander, S., Tomlin, C.: Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3277–3282 (May 2009)Google Scholar
  12. 12.
    Isermann, R.: Mechatronische Systeme: Grundlagen (German Edition). 1. Auflage 1999, 1. korrigierter Nachdruck - Studienausgabe edn. Springer (December 1999)Google Scholar
  13. 13.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR), pp. 225–234. IEEE (2007)Google Scholar
  14. 14.
    Kohlbrecher, S., Meyer, J., von Stryk, O., Klingauf, U.: A Flexible and Scalable SLAM System with Full 3D Motion Estimation. In: IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). IEEE, Kyoto (2011)Google Scholar
  15. 15.
    Leishman, G.: Principles of Helicopter Aerodynamics, 2nd edn. Cambridge Aerospace Series. Cambridge University Press (April 2006)Google Scholar
  16. 16.
    Meyer, J., Strobel, A.: A flexible real-time control system for autonomous vehicles. In: 41st International Symposium on Robotics (ISR) and 6th German Conference on Robotics (ROBOTIK). VDE (2010)Google Scholar
  17. 17.
    Michael, N., Mellinger, D., Lindsey, Q., Kumar, V.: The GRASP Multiple Micro-UAV Testbed. IEEE Robotics Automation Magazine 17(3), 56–65 (2010)CrossRefGoogle Scholar
  18. 18.
    Qiang, Y., Bin, X., Yao, Z., Yanping, Y., Haotao, L., Wei, Z.: Visual simulation system for quadrotor unmanned aerial vehicles. In: 30th Chinese Control Conference, pp. 454–459 (July 2011)Google Scholar
  19. 19.
    Rankin, J.: An error model for sensor simulation GPS and differential GPS. In: Position Location and Navigation Symposium, pp. 260–266. IEEE (1994)Google Scholar
  20. 20.
    Rodić, A., Mester, G.: The Modeling and Simulation of an Autonomous Quad-Rotor Microcopter in a Virtual Outdoor Scenario. Acta Polytechnica Hungarica 8(4) (2011)Google Scholar
  21. 21.
    Sendobry, A.: A Model Based Navigation Architecture for Small Unmanned Aerial Vehicles. In: European Navigation Conference. Royal Institute of Navigation (RIN) (November 2011)Google Scholar
  22. 22.
    Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-SLAM–based navigation for autonomous micro helicopters in GPS-denied environments. Journal of Field Robotics 28(6), 854–874 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Johannes Meyer
    • 1
  • Alexander Sendobry
    • 1
  • Stefan Kohlbrecher
    • 2
  • Uwe Klingauf
    • 1
  • Oskar von Stryk
    • 2
  1. 1.Department of Mechanical EngineeringTU DarmstadtGermany
  2. 2.Department of Computer ScienceTU DarmstadtGermany

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