Autonomous Robots

, 27:165 | Cite as

An adaptive vision-based autopilot for mini flying machines guidance, navigation and control

  • Farid KendoulEmail author
  • Kenzo Nonami
  • Isabelle Fantoni
  • Rogelio Lozano


The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This paper addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks such as take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.


Visual navigation Adaptive control Rotorcraft UAV Visual odometry Visual servoing 

Supplementary material

Below is the link to the supplementary material. (15.3 MB)

Below is the link to the supplementary material. (8.74 MB)

Below is the link to the supplementary material. (2.83 MB)

Below is the link to the supplementary material. (8.17 MB)

Below is the link to the supplementary material. (6.88 MB)


  1. Ahrens, S., Levine, D., Andrews, G., & How, J. P. (2009). Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments. In Proceedings of the IEEE international conference on robotics and automation (pp. 2643–2648), Kobe, Japan, May 2009. Google Scholar
  2. Altug, E., Ostrowski, J. P., & Taylor, C. J. (2005). Control of a quadrotor helicopter using dual camera visual feedback. International Journal of Robotics Research, 24(5), 329–341. CrossRefGoogle Scholar
  3. Amidi, O., Kanade, T., & Fujita, K. (1999). A visual odometer for autonomous helicopter flight. Robotics and Autonomous Systems, 28(2–3), 185–193. CrossRefGoogle Scholar
  4. Astrom, K. J., & Wittenmark, B. (1989). Adaptive control. Reading: Addison-Wesley. Google Scholar
  5. Barrows, G. L. (1999). Mixed-mode VLSI optic flow sensors for micro air vehicles. Ph.D. Dissertation, Department of Electrical Engineering, University of Maryland. Google Scholar
  6. Caballero, F., Merino, L., Ferruz, J., & Ollero, A. (2009). Vision-based odometry and slam for medium and high altitude flying UAVs. Journal of Intelligent Robotic Systems, 54, 137–161. CrossRefGoogle Scholar
  7. Chahl, J. S., Srinivasan, M. V., & Zhang, S. W. (2004). Landing strategies in honeybees and applications to uninhabited airborne vehicles. International Journal of Robotics Research, 23(2), 101–110. CrossRefGoogle Scholar
  8. Frew, E., McGee, T., ZuWhan, K., Xiao, X., Jackson, S., Morimoto, M., Rathinam, S., Padial, J., & Sengupta, R. (2004). Vision-based road-following using a small autonomous aircraft. In Proceedings of the IEEE aerospace conference (Vol. 5, pp. 3006–3015), March 2004. Google Scholar
  9. Ettinger, S. M., Nechyba, M. C., Ifju, P. G., & Waszak, M. (2002). Towards flight autonomy: Vision-based horizon detection for micro air vehicles. In Proceedings of the Florida conference on recent advances in robotics, Miami, May 2002. Google Scholar
  10. Fowers, S. G., Lee, D.-J., Tippetts, B. J., Lillywhite, K. D., Dennis, A. W., & Archibald, J. K. (2007). Vision aided stabilization and the development of a quad-rotor micro uav. In Proceedings of the IEEE international symposium on computational intelligence in robotics and automation (pp. 143–148), Florida, USA, June 2007. Google Scholar
  11. Garratt, M. A., & Chahl, J. S. (2008). Vision-based terrain following for an unmanned rotorcraft. Journal of Field Robotics, 25(4–5), 284–301. CrossRefGoogle Scholar
  12. Goodwin, G. C., & Sin, K. C. (1984). Adaptive filtering prediction and control. Englewood Cliffs: Prentice Hall. zbMATHGoogle Scholar
  13. Green, W. E., Oh, P. Y., Sevcik, K., & Barrows, G. (2003). Autonomous landing for indoor flying robots using optic flow. In Proceedings of the 2003 ASME international mechanical engineering congress, Washington, 15–21 November 2003. Google Scholar
  14. Guenard, N., Hamel, T., & Mahony, R. (2008). A practical visual servo control for a unmanned aerial vehicle. IEEE Transactions on Robotics, 24(2), 331–341. CrossRefGoogle Scholar
  15. He, R., Prentice, S., & Roy, N. (2008). Planning in information space for a quadrotor helicopter in a GPS-denied environment. In Proceedings of the IEEE international conference on robotics and automation (pp. 1814–1820), California, USA, May 2008. Google Scholar
  16. Herisse, B., Russotto, F.-X., Hamel, T., & Mahony, R. (2008). Hovering flight and vertical landing control of a VTOL unmanned aerial vehicle using optical flow. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 801–806), Nice, France, September 2008. Google Scholar
  17. Hrabar, S., Sukhatme, G. S., Corke, P., Usher, K., & Roberts, J. (2005). Combined optic-flow and stereo-based navigation of urban canyons for a UAV. In Proceedings of the IEEE international conference on intelligent robots and systems (pp. 302–309), Canada, 2005. Google Scholar
  18. Ioannou, P., & Sun, J., (1996). Robust adaptive control. Englewood Cliffs: Prentice Hall Inc. zbMATHGoogle Scholar
  19. Johnson, A., Montgomery, J., & Matthies, L. (2005). Vision guided landing of an autonomous helicopter in hazardous terrain. In Proceedings of the IEEE international conference on robotics and automation (pp. 4470–4475), Barcelona, Spain, April 2005. Google Scholar
  20. Calise, A. J., Watanabe, Y., Ha, J., Neidhoefer, J. C., & Johnson, E. N. (2007). Real-time vision-based relative aircraft navigation. AIAA Journal of Aerospace Computing, Information, and Communication, 4(4), 707–738. CrossRefGoogle Scholar
  21. Kanade, T., Amidi, O., & Ke, Q. (2004). Real-time and 3d vision for autonomous small and micro air vehicles. In Proceedings of the 43rd IEEE conference on decision and control (pp. 1655–1662), Atlantis, Bahamas, December 2004. Google Scholar
  22. Kendoul, F. (2007). Modelling and control of unmanned aerial vehicles, and development of a vision-based autopilot for small rotorcraft navigation. Ph.D. Thesis Report, CNRS Heudiasyc Laboratory, University of Technology of Compiegne, France. Google Scholar
  23. Kendoul, F., Fantoni, I., & Lozano, R. (2008). Adaptive vision-based controller for small rotorcraft uavs control and guidance. In Proceedings of the 17th IFAC world congress (pp. 797–802), Seoul, Korea, 6–11 July 2008. Google Scholar
  24. Kendoul, F., Fantoni, I., & Nonami, K. (2009a). Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles. Robotics and Autonomous Systems (Elsevier), 57, 591–602. CrossRefGoogle Scholar
  25. Kendoul, F., Zhenyu, Y., & Nonami, K. (2009b). Embedded autopilot for accurate waypoint navigation and trajectory tracking: Application to miniature rotorcraft uavs. In Proceedings of the IEEE international conference on robotics and automation (pp. 2884–2890), Kobe, Japan, May 2009. Google Scholar
  26. Kima, J., & Sukkarieh, S. (2007). Real-time implementation of airborne inertial-SLAM. Robotics and Autonomous Systems, 55, 62–71. CrossRefGoogle Scholar
  27. Lacroix, S., Jung, I. K., Soueres, P., Hygounenc, E., & Berry, J. P. (2002). The autonomous blimp project of LAAS/CNRS: Current status and research challenges. In Proceedings of the workshop WS6 aerial robotics, IEEE/RSJ international conference on intelligent robots and systems (pp. 35–42), Lausanne, Switzerland, 2002. Google Scholar
  28. Landau, I. D., Lozano, R., & M’Saad, M. (1998). Adaptive control. Communications and control engineering. Berlin: Springer. Google Scholar
  29. Lucas, B., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings of the DARPA IU workshop (pp. 121–130). Google Scholar
  30. Mejias, L., Saripalli, S., Campoy, P., & S Sukhatme, G. (2006). Visual servoing of an autonomous helicopter in urban areas using feature tracking. Journal of Field Robotics, 23(3/4), 185–199. CrossRefGoogle Scholar
  31. Mori, R., Hirata, K., & Kinoshita, T. (2007). Vision-based guidance control of a small-scale unmanned helicopter. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 2648–2653), San Diego, CA, USA, Oct. 29–Nov. 2007. Google Scholar
  32. Olfati-Saber, R. (2001). Nonlinear control of underactuated mechanical systems with application to robotics and aerospace vehicles. Ph.D. Thesis Report, Department of Electrical Engineering and Computer Science, MIT, USA, February 2001. Google Scholar
  33. Proctor, A. A., Johnson, E. N., & Apker, T. B. (2006). Vision-only control and guidance for aircraft. Journal of Field Robotics, 23(10), 863–890. CrossRefGoogle Scholar
  34. Roberts, J. F., Stirling, T., Zufferey, J. C., & Floreano, D. (2007). Quadrotor using minimal sensing for autonomous indoor flight. In European micro air vehicle conference and flight competition (EMAV), Toulouse, France, September 2007. Google Scholar
  35. Ruffier, F., & Franceschini, N. (2005). Optic flow regulation: the key to aircraft automatic guidance. Robotics and Autonomous Systems, 50(4), 177–194. CrossRefGoogle Scholar
  36. Saripalli, S., Montgomery, J. F., & Sukhatme, G. S. (2003). Visually-guided landing of an unmanned aerial vehicle. IEEE Transactions on Robotics and Automation, 19(3), 371–381. CrossRefGoogle Scholar
  37. Scherer, S., Singh, S., Chamberlain, L., & Elgersma, M. (2008). Flying fast and low among obstacles: Methodology and experiments. International Journal of Robotics Research, 27(5), 549–574. CrossRefGoogle Scholar
  38. Sepulcre, R., Jankovic, M., & Kokotovic, P. (1997). Constructive nonlinear control. Communications and control engineering series. Berlin: Springer. Google Scholar
  39. Serres, J., Dray, D., Ruffier, F., & Franceschini, N. (2008). A vision-based autopilot for a miniature air vehicle: joint speed control and lateral obstacle avoidance. Autonomous Robots, 25, 103–122. CrossRefGoogle Scholar
  40. Shakernia, O., Sharp, C. S., Vidal, R., Shim, D. H., Ma, Y., & Sastry, S. (2002). Multiple view motion estimation and control for landing an unmanned aerial vehicle. In Proceedings of the IEEE conference on robotics and automation (Vol. 3, pp. 2793–2798). Google Scholar
  41. Shi, J., & Tomasi, C. (1994). Good features to track. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 593–600), Seattle, WA, USA. Google Scholar
  42. Sontag, E. D. (1988). Smooth stabilization implies coprime factorization. IEEE Transactions on Automatic Control, 34, 435–443. CrossRefMathSciNetGoogle Scholar
  43. Srinivasan, M. V., Miles, F. A., & Wallman, J. (1993). Visual motion and its role in the stabilisation of gaze. Amsterdam: Elsevier. Google Scholar
  44. Srinivasan, M. V., Zhang, S. W., Lehrer, M., & Collett, T. S. (1996). Honeybee navigation en route to the gaoal: Visual flight control and odometry. The Journal of Experimental Biology, 199(1), 237–244. Google Scholar
  45. Srinivasan, M. V., Zhang, S. W., & Bidwell, N. J. (1997). Visually mediated odometry in honeybees. The Journal of Experimental Biology, 200, 2513–2522. Google Scholar
  46. Tammero, L. F., & Dickinson, M. H. (2002). The influence of visual landscape on the free flight behavior of the fruit fly drosophila melanogaster. Journal of Experimental Biology, 205, 327–343. Google Scholar
  47. Wallace, G. K. (1959). Visual scanning in the desert locust schistocerca gregaria. The Journal of Experimental Biology, 36, 512–525. Google Scholar
  48. Yamada, H., Ichikawa, M., & Takeuchi, J. (2001). Flying robot with biologically inspired vision. Journal of Robotics and Mechatronics, 13(6), 621–624. Google Scholar
  49. Yu, Z., Nonami, K., Shin, J., & Celestino, D. (2007). 3D vision based landing control of a small scale autonomous helicopter. International Journal of Advanced Robotic Systems, 4(1), 51–56. Google Scholar
  50. Zufferey, J. C., & Floreano, D. (2006). Fly-inspired visual steering of an ultralight indoor aircraft. IEEE Transactions On Robotics, 22(1), 137–146. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Farid Kendoul
    • 1
    Email author
  • Kenzo Nonami
    • 1
  • Isabelle Fantoni
    • 2
  • Rogelio Lozano
    • 2
  1. 1.Robotics and Control Lab., Electronics and Mechanical Engineering Dept.Chiba UniversityChiba CityJapan
  2. 2.Laboratoire Heudiasyc, UMR CNRS 6599, Computer Science Dept.Universite de Technologie de CompiegneCompiègneFrance

Personalised recommendations