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Vision Based Autonomous Landing of Multirotor UAV on Moving Platform

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Abstract

This paper investigates solutions for the fundamental yet challenging problem of autonomous landing of multirotor Unammaned Aerial Vehicles UAVs. In addition to landing on static targets, tracking and landing on a moving platform is addressed, as a solution to facilitate the deployment of the UAV. The paper presents the design of a new landing pad and its relative pose estimation. The fusion of inertial measurement with the estimated pose is considered to ensure a high sampling rate, and to increase the manoeuvrability of the vehicle. Two filters are designed to conduct the fusion, an Extended Kalman Filter (EKF) and an Extended H∞ (EH∞). The extensive simulation and practical tests permitted identification of the challenges of the landing task. Adequate solutions to these challenges are proposed to lessen their impact on landing precision.

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References

  1. Microdrones md4-1000: The tough workhorse. http://www.microdrones.com/products/products.php

  2. Amazon testing drones for deliveries. http://www.bbc.co.uk/news/technology-25180906. Last updated: 02-12- 2013

  3. Cowling, I.: Towards Autonomy of a Quadrotor UAV. PhD thesis, Cranfield Univesity (2008)

  4. Helicopter could be ’scout’ for mars rovers. http://www.jpl.nasa.gov/news/news.php?feature=4457

  5. Lange, S., Sunderhauf, N., Protzel, P.: A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. In: International Conference on Advanced Robotics, ICAR., pp. 1–6 (2009)

  6. Saripalli, S.: Vision-based autonomous landing of an unmanned aerial vehicle. In: IEEE 3rd international conference on robotics & automation, pp. 3–8 (2002)

  7. Saripalli, S.: Visually guided landing of an unmanned aerial vehicle. IEEE Trans. Robot. Autom. 19(3), 371–380 (2003)

    Article  Google Scholar 

  8. Merz, T., Duranti, S., Conte, G.: Autonomous landing of an unmanned helicopter based on vision and inertial sensing. In: The 9th international symposium on experimental robotics, pp. 343–352 (2006)

  9. Rotary wing uavs full automatic landing. https://www.thalesgroup.com/ (2011)

  10. Richardson, T., Jones, C.: Automated Vision-based Recovery of a Rotary Wing Unmanned Aerial Vehicle onto a Moving Platform. J. Field Rob. 30(5), 667–684 (2013)

    Article  Google Scholar 

  11. Harris, C., Stennett, C.: Rapid-a video rate object tracker. In: BMVC, pp. 1–6 (1990)

  12. Yang, S., Scherer, S., Zell, A.: An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. J. Intell. Robot. Syst. 69, 499–515 (2013)

    Article  Google Scholar 

  13. Li, W., Zhang, T., Kuhnlenz, K.: A vision-guided autonomous quadrotor in an air-ground multi-robot system. In: IEEE International Conference on Robotics and Automation, pp. 2980–2985 (2011)

  14. Brockers, R., Bouffard, P., Ma, J., Matthies, L., Tomlin, C.: Autonomous landing and ingress of micro-air-vehicles in urban environments based on monocular vision (2011)

  15. Yang, S., Scherer, S.a., Schauwecker, K., Zell, A.: Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision. J. Intell. Robot. Syst. 74, 27–43 (2013)

    Article  Google Scholar 

  16. Wenzel, K.E., Masselli, A., Zell, A.: Automatic Take Off, Tracking and Landing of a Miniature UAV on a Moving Carrier Vehicle. J. Intell. Robot. Syst. 61, 221–238 (2010)

    Article  Google Scholar 

  17. Lee, D., Ryan, T., Kim, H.J.. In: IEEE International Conference on Robotics and Automation, pp. 971–976 (2012)

  18. Vicon motion tracking system. http://www.vicon.com/

  19. Olson, E.: AprilTag: A robust and flexible visual fiducial system. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3400–3407 (2011)

  20. Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 6, 24 (1981)

    MathSciNet  Google Scholar 

  21. Lu, C., Hager, G., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 610–622 (2000)

    Article  Google Scholar 

  22. Tahri, O., Araujo, H.: Efficient Iterative Pose Estimation using an Invariant to Rotations. IEEE Trans. Cybernetics 44(2), 199–207 (2014)

    Article  Google Scholar 

  23. Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: An Accurate O(n) Solution to the PnP Problem. Int. J. Comput. Vis. 81, 155–166 (2008)

    Article  Google Scholar 

  24. Quan, L., Lan, Z.: Linear n-point camera pose determination. IEEE Trans. Pattern Anal. Mach. Intell. 21(7), 1–7 (1999)

    Google Scholar 

  25. Ansar, A., Daniilidis, K.: Linear pose estimation from points or lines. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 578–589 (2003)

    Article  MATH  Google Scholar 

  26. Li, S., Xu, C., Xie, M.: A robust O (n) solution to the perspective-n-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1444–1450 (2012)

    Article  Google Scholar 

  27. Janabi-Sharifi, F., Marey, M.: A kalman-filter-based method for pose estimation in visual servoing. IEEE Trans. Robot. 26(5), 939–947 (2010)

    Article  Google Scholar 

  28. Schweighofer, G., Pinz, A.: Robust pose estimation from a planar target. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2024–30 (2006)

    Article  Google Scholar 

  29. Corke, P., Lobo, J., Dias, J.: An Introduction to Inertial and Visual Sensing. Int. J. Robot. Res. 26, 519–535 (2007)

    Article  Google Scholar 

  30. Angelino, C.: UAV position and attitude estimation using IMU. GNSS and camera, in 15th International Conference on Information Fusion (FUSION), 735–742 (2012)

  31. Kyriakoulis, N., Gasteratos, A.: On Visuo-Inertial Fusion for Robot Pose Estimation Using Hierarchical Fuzzy Systems. Int. J. Optomechatronics 6, 17–36 (2012)

    Article  Google Scholar 

  32. Gazebo simulator. http://gazebosim.org/

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Authors and Affiliations

  1. Centre for Electronic Warfare, Cranfield University, Shrivenham, SN6 8LA, UK

    Oualid Araar, Nabil Aouf & Ivan Vitanov

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  1. Oualid Araar
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  2. Nabil Aouf
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  3. Ivan Vitanov
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Correspondence to Oualid Araar.

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Araar, O., Aouf, N. & Vitanov, I. Vision Based Autonomous Landing of Multirotor UAV on Moving Platform. J Intell Robot Syst 85, 369–384 (2017). https://doi.org/10.1007/s10846-016-0399-z

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  • DOI: https://doi.org/10.1007/s10846-016-0399-z

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