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Vision-based Autonomous Landing Control for Unmanned Helicopters

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Abstract

This paper presents the design of a complete control system for the autonomous landing of unmanned flybarless helicopters on known stationary visual landmarks. A state estimator based on the complementary filters notion, estimates the position, translational velocity and attitude vectors of the vehicle by fusing data acquired from the on–board camera and an Inertial Measurement Unit. A vision-aided nonlinear model predictive controller is designed for the landing motion of the helicopter, assuming that the on–board camera is rigidly (i.e., no additional Degrees of Freedom (DOF)) attached on the vehicle. Although the under–actuated character of the helicopter dynamics introduces counter–goals for minimizing the error between the vehicle and the landmark, the proposed control scheme guarantees, via hard nonlinear constraints, that the landmark will always be kept inside the camera field of view during the landing procedure. In order to simplify the derived algorithm without violating the robustness of the proposed controller, we reformulate the translational helicopter dynamics in order to reduce the number of the unknown model parameters to a minimum. Moreover, a parameter/disturbance observer is designed for estimating simultaneously the vehicle’s unknown dynamic parameters as well as the induced disturbances. The efficacy of the proposed landing scheme is evaluated via a set of experimental and simulation results, using a small–scale flybarless helicopter.

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References

  1. Cai, G., Chen, B.M., Lee, T.H.: Unmanned rotorcraft systems. Springer Science & Business Media, London (2011)

    Book  MATH  Google Scholar 

  2. Cesetti, A., Frontoni, E., Mancini, A., Zingaretti, P., Longhi, S.: A vision-based guidance system for uav navigation and safe landing using natural landmarks. J. Intell. Robot. Syst. Theory Appl. 57(1–4), 233–257 (2010). https://doi.org/10.1007/s10846-009-9373-3

    Article  MATH  Google Scholar 

  3. Duan, X., Dong, H., Wu, K., Tian, Z., Wang, Z.: UAV flight control based on RTX system simulation platform. Sensors Transducers 166(3), 1–6 (2014)

    Google Scholar 

  4. Gadewadikar, J., Lewis, F.L., Subbarao, K., Peng, K., Chen, B.M.: H ∞ static output-feedback control for rotorcraft. J. Intell. Robot. Syst. 54(4), 629–646 (2009)

    Article  Google Scholar 

  5. Garcia-Pardo, P., Sukhatme, G., Montgomery, J.: Towards vision-based safe landing for an autonomous helicopter. Robot. Autonom. Syst. 38(1), 19–29 (2002). https://doi.org/10.1016/S0921-8890(01)00166-X

    Article  MATH  Google Scholar 

  6. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Marín-Jiménez, M.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6), 2280–2292 (2014). https://doi.org/10.1016/j.patcog.2014.01.005

    Article  Google Scholar 

  7. Hamel, T., Mahony, R.: Visual servoing of an under-actuated dynamic rigid-body system: An image-based approach. IEEE Trans. Robot. Autom. 18(2), 187–198 (2002). https://doi.org/10.1109/TRA.2002.999647

    Article  Google Scholar 

  8. Higgins, W.: A comparison of complementary and Kalman filtering. IEEE Trans. Aerospace Electron. Syst. 11, 321–325 (1975). https://doi.org/10.1109/TAES.1975.308081

    Article  Google Scholar 

  9. Hooi, C.G., Lagor, F.D., Paley, D.A.: Flow sensing for height estimation and control of a rotor in ground effect: Modeling and experimental results. Annual Forum Proceedings - AHS International 3, 1878–1889 (2015)

    Google Scholar 

  10. Huang, H., Xu, Y.P., Deng, Z.W.: Real-time visual flight simulation system based on Flightgear simulator. Xitong Fangzhen Xuebao / J. Syst. Simul. 19(19), 4421–4423 (2007)

    Google Scholar 

  11. Isermann, R., Schaffnit, J., Sinsel, S.: Hardware-in-the-loop simulation for the design and testing of engine-control systems. Control Eng. Pract. 7(5), 643–653 (1999)

    Article  Google Scholar 

  12. Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol 3, pp. 2149–2154. (2004)

  13. Liu, C., Chen, W.H., Andrews, J.: Tracking control of small-scale helicopters using explicit nonlinear MPC augmented with disturbance observers. Control Eng. Pract. 20(3), 258–268 (2012). https://doi.org/10.1016/j.conengprac.2011.10.015

    Article  Google Scholar 

  14. Mahony, R., Hamel, T.: Robust trajectory tracking for a scale model autonomous helicopter. Int. J. Robust Nonlinear Control 14(12), 1035–1059 (2004). https://doi.org/10.1002/rnc.931

    Article  MathSciNet  MATH  Google Scholar 

  15. Marantos, P., Karras, G.C., Bechlioulis, C.P., Kyriakopoulos, K.J.: Autonomous model-free landing control of small-scale flybarless helicopters. In: Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA.2015.7139934 (2015)

  16. Marantos, P., Koveos, Y., Kyriakopoulos, K.J.: Uav state estimation using adaptive complementary filters. IEEE Trans. Control Syst. Technol. 24(4), 1214–1226 (2016). https://doi.org/10.1109/TCST.2015.2480012

    Article  Google Scholar 

  17. Marantos, P., Karras, G.C., Vlantis, P., Kyriakopoulos, K.J.: Simulation & Experimental Results Video - Vision-based Autonomous Landing Control for Unmanned Helicopters. https://youtu.be/pmjG6EMkN7c (2017)

  18. Marconi, L., Naldi, R.: Robust full degree-of-freedom tracking control of a helicopter. Automatica 43(11), 1909–1920 (2007). https://doi.org/10.1016/j.automatica.2007.03.028

    Article  MathSciNet  MATH  Google Scholar 

  19. Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: Stability and optimality. Automatica 36, 789–814 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  20. Merz, T., Duranti, S., Conte, G.: Autonomous landing of an unmanned helicopter based on vision and inertial sensing. Springer Tracts Adv. Robot. 21, 343–352 (2006). https://doi.org/10.1007/11552246_33

    Article  Google Scholar 

  21. Morari, M., Leeb, J.H.: Model predictive control: Past, present and future. Comput. Chem.l Eng. 23, 667–682 (1999)

    Article  Google Scholar 

  22. Olivares-Mendez, M., Mondragon, I., Campoy, P.: Autonomous landing of an unmanned aerial vehicle using image-based fuzzy control. 2, 79–86. (2013). https://doi.org/10.3182/20131120-3-FR-4045.00011

  23. Patel, K., Barve, J.: Modeling, simulation and control study for the quad-copter UAV. In: 2014 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. (2014). https://doi.org/10.1109/ICIINFS.2014.7036590

  24. Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: An open-source robot operating system. In: ICRA Workshop on Open Source Software vol. 3, p. 5 (2009)

  25. Raptis, I.A., Valavanis, K.P.: Linear and nonlinear control of small-scale unmanned helicopters, vol. 25. Springer Science & Business Media, Dordrecht (2010)

    Google Scholar 

  26. Ruffier, F., Franceschini, N.: Optic flow regulation in unsteady environments: A tethered MAV achieves terrain following and targeted landing over a moving platform. J. Intell. Robot. Syst. Theory Appl. 79(2), 275–293 (2015). https://doi.org/10.1007/s10846-014-0062-5

    Article  Google Scholar 

  27. Saripalli, S., Sukhatme, G.S.: Landing a helicopter on a moving target. In: Proceedings of the 2007 IEEE International Conference on Robotics & Automation (ICRA), pp 2030–2035. IEEE, Rome (2007). https://doi.org/10.1109/ROBOT.2007.363620

  28. Saripalli, S., Montgomery, J., Sukhatme, G.: Visually guided landing of an unmanned aerial vehicle. IEEE Trans.on Robot. Autom. 19(3), 371–380 (2003). https://doi.org/10.1109/TRA.2003.810239

    Article  Google Scholar 

  29. Templeton, T., Shim, D., Geyer, C., Sastry, S.: Autonomous vision-based landing and terrain mapping using an mpc-controlled unmanned rotorcraft. pp 1349–1356. (2007). https://doi.org/10.1109/ROBOT.2007.363172

  30. Vaerenbergh, S.V., Vía, J., Santamaría, I.: Nonlinear system identification using a new sliding-window kernel rls algorithm. J. Commun. 2(3), 1–8 (2007)

    Article  Google Scholar 

  31. Vlantis, P., Marantos, P., Bechlioulis, C.P., Kyriakopoulos, K.J.: Quadrotor landing on an inclined platform of a moving ground vehicle. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2202–2207. (2015). https://doi.org/10.1109/ICRA.2015.7139490

  32. Vogeltanz, T.: A Survey of Free Software for the Design, Analysis, Modelling, and Simulation of an Unmanned Aerial Vehicle. Arch. Comput. Meth. Eng. 23(3), 449–514 (2016). https://doi.org/10.1007/s11831-015-9147-y

    Article  MATH  Google Scholar 

  33. Wenzel, K., Masselli, A., Zell, A.: Automatic take off, tracking and landing of a miniature uav on a moving carrier vehicle. J. Intell. Robot. Syst. Theory Appl. 61(1-4), 221–238 (2011)

    Article  Google Scholar 

  34. Yili, S., Ziyang, Z., Chaojie, O., Huangzhong, P.: 3D scene simulation of UAVs formation flight based on FlightGear simulator. In: 2014 IEEE Chinese on Guidance, Navigation and Control Conference (CGNCC), pp. 1978–1982. (2014). https://doi.org/10.1109/CGNCC.2014.7007481

  35. Yu, Z., Nonami, K., Shin, J., Celestino, D.: 3d vision based landing control of a small scale autonomous helicopter. Int. J. Adv. Robot. Syst. 4(1), 51–56 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Control Systems Lab, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou, Athens, 15780, Greece

    Panos Marantos, George C. Karras, Panagiotis Vlantis & Kostas J. Kyriakopoulos

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  1. Panos Marantos
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  2. George C. Karras
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  3. Panagiotis Vlantis
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  4. Kostas J. Kyriakopoulos
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Correspondence to Panos Marantos.

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Marantos, P., Karras, G.C., Vlantis, P. et al. Vision-based Autonomous Landing Control for Unmanned Helicopters. J Intell Robot Syst 92, 145–158 (2018). https://doi.org/10.1007/s10846-017-0702-7

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