Skip to main content
Log in

Design and Experimental Validation of an Adaptive Landing Gear for Safe Landing on Uneven Grounds of VTOL UAVs in the Context of Lightweight and Fast Adaptations

  • Research Article-Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

One of the most fundamental problems of rotary-wing aircraft that can take off and land vertically is that they cannot land safely when the ground on which they will land is too sloped. In case they have to make their landing, they inevitably face the risk of rolling over. The main source of this problem is that these vehicles have fixed landing gear. In this study, an important solution proposal is presented by performing an adaptive passive landing gear design and control for a four-rotor UAV with low takeoff weight, within the framework of the principle of fast adaptation to the ground, low cost, and weight. The experimental studies’ results indicated the usefulness of the proposed landing gear. This adaptive landing gear, produced within the scope of this study, has a total mass of 170 g, and it got the UAV to safely land on both sloped surfaces with 15 degrees and randomly uneven surfaces.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

\(\varphi\) :

Roll angle [°]

\(\theta\) :

Pitch angle [°]

\(\psi\) :

Yaw angle [°]

[B]:

Body fixed frame

[E]:

Earth fixed frame

\({{}^{B}R}_{\mathrm{E}}\) :

Rotation matrix from the body frame to the earth frame

\({M}_{\varphi }\) :

Roll moment [Nm]

\({M}_{\theta }\) :

Pitch moment [Nm]

\({M}_{\psi }\) :

Yaw moment [Nm]

\({F}_{t}\) :

Total thrust force [N]

\({\mathrm{F}}_{b}\) :

Acting on the fuselage of the UAV [N]

\({\mathrm{F}}_{g}\) :

Gravitational force acting on the UAV [N]

\({\mathrm{F}}_{d}\) :

Drag force [N]

\(K\) :

Coefficient matrix

\(m\) :

Mass of the UAV

\(g\) :

Gravitational acceleration [\(\left[ {{\raise0.7ex\hbox{${\text{m}}$} \!\mathord{\left/ {\vphantom {{\text{m}} {{\text{s}}^{{\text{2}}} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${{\text{s}}^{{\text{2}}} }$}}} \right]\)]

\({I}_{\mathrm{xx}}\) :

Moment of inertia in the x-axis of UAV [kg m2]

\({I}_{\mathrm{yy}}\) :

Moment of inertia in the y-axis of UAV [kg m2]

\({I}_{\mathrm{zz}}\) :

Moment of inertia in the z-axis of UAV [kg m2]

\({J}_{\mathrm{r}}\) :

Moment of inertia of rotor [kg m2]

\({\Omega }_{\mathrm{r}}\) :

Angular velocity vector of rotors

\(l\) :

Distance between two opposing legs [m]

\(d\) :

Maximum distance that one leg moves on the vertical axis [m]

\(h\) :

Minimum distance between the center of gravity of the UAV and the ground [m]

\({F}_{\mathrm{f}}\) :

Friction force [N]

\({\mu }_{\mathrm{k}}\) :

Kinetic friction coefficient [-]

\({\mu }_{\mathrm{s}}\) :

Static friction coefficient [-]

\({m}_{\mathrm{l}}\) :

Mass of one leg [kg]

\({F}_{\mathrm{f}\varphi }\) :

Roll axis component of friction force [N]

\({F}_{\mathrm{f}\theta }\) :

Pitch axis component of friction force [N]

\(\upgamma\) :

Critical angle of rolling over situations [°]

\({I}_{0}\) :

Inertia of UAV around the center of mass [kg m2]

ESC:

Electronic speed controller

GPS:

Global positioning system

EKF:

Extended Kalman filter

PLA:

Polylactic acid

ABS:

Acrylonitrile butadiene styrene

References

  1. Yıldırım, Ş; Çabuk, N.; Bakırcıoğlu, V.: Modelling and control of proposed two dodecacopter systems. Int. J. Appl. Math. Electron. Comput. 8, 34–8 (2020). https://doi.org/10.18100/ijamec.698462

    Article  Google Scholar 

  2. Bakırcıoğlu, V.; Çabuk, N.; Yıldırım, Ş: Experimental comparison of the effect of the number of redundant rotors on the fault tolerance performance for the proposed multilayer UAV. Rob. Auton. Syst. 149, 103977 (2022). https://doi.org/10.1016/j.robot.2021.103977

    Article  Google Scholar 

  3. Verbeke, J.; Hulens, D.; Ramon, H.; Goedeme, T.; De Schutter, J.: The design and construction of a high endurance hexacopter suited for narrow corridors. In: 2014 international conference proceedings on unmanned aircraft systems ICUAS, 2014:543–51

  4. Çabuk, N.; Yıldırım, Ş: Design, modelling and control of an eight-rotors UAV with asymmetric configuration for use in remote sensing systems. J. Aviat. 5, 72–81 (2021). https://doi.org/10.30518/jav.943804

    Article  Google Scholar 

  5. Cardoso, D.N.; Esteban, S.; Raffo, G.V.: A new robust adaptive mixing control for trajectory tracking with improved forward flight of a tilt-rotor UAV. ISA Trans. 110, 86–104 (2021). https://doi.org/10.1016/j.isatra.2020.10.040

    Article  Google Scholar 

  6. Heidari, H.; Saska, M.: Collision-free trajectory planning of multi-rotor UAVs in a wind condition based on modified potential field. Mech. Mach. Theory 156, 104140 (2021). https://doi.org/10.1016/j.mechmachtheory.2020.104140

    Article  Google Scholar 

  7. Alkamachi, A.; Erçelebi, E.: Modelling and genetic algorithm based-PID control of H-shaped racing quadcopter. Arab. J. Sci. Eng. 42, 2777–2786 (2017). https://doi.org/10.1007/s13369-017-2433-2

    Article  Google Scholar 

  8. Noordin, A.; Mohd Basri, M.A.; Mohamed, Z.; Mat, L.I.: Adaptive PID controller using sliding mode control approaches for quadrotor UAV attitude and position stabilization. Arab. J. Sci. Eng. 46, 963–981 (2021). https://doi.org/10.1007/s13369-020-04742-w

    Article  Google Scholar 

  9. Tran, H.K.; Nguyen, T.N.: Flight motion controller design using genetic algorithm for a quadcopter. Meas. Control (United Kingdom) 51, 59–64 (2018). https://doi.org/10.1177/0020294018768744

    Article  Google Scholar 

  10. Miranda-Colorado, R.; Aguilar, L.T.: Robust PID control of quadrotors with power reduction analysis. ISA Trans. 98, 47–62 (2020). https://doi.org/10.1016/j.isatra.2019.08.045

    Article  Google Scholar 

  11. Zhang, B.; Xie, Y.; Zhou, J.; Wang, K.; Zhang, Z.: State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: a review. Comput. Electron. Agric. 177, 105694 (2020). https://doi.org/10.1016/j.compag.2020.105694

    Article  Google Scholar 

  12. Ling, X.; Zhao, Y.; Gong, L.; Liu, C.; Wang, T.: Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision. Rob. Auton. Syst. 114, 134–143 (2019). https://doi.org/10.1016/j.robot.2019.01.019

    Article  Google Scholar 

  13. Savsani, P.; Jhala, R.L.; Savsani, V.J.: Comparative study of different metaheuristics for the trajectory planning of a robotic arm. IEEE Syst. J. 10, 697–708 (2016). https://doi.org/10.1109/JSYST.2014.2342292

    Article  Google Scholar 

  14. Liu, S.; Dong, W.; Ma, Z.; Sheng, X.: Adaptive aerial grasping and perching with dual elasticity combined suction cup. IEEE Robot. Autom. Lett. 5, 4766–4773 (2020). https://doi.org/10.1109/LRA.2020.3003879

    Article  Google Scholar 

  15. McLaren, A.; Fitzgerald, Z.; Gao, G.; Liarokapis, M.: A passive closing, tendon driven, adaptive robot hand for ultra-fast, aerial grasping and perching. In: 2019 IEEE/RSJ international conference on intelligent robots systems, IEEE; 2019, p. 5602–7

  16. Villa, D.K.D.; Brandão, A.S.; Sarcinelli-Filho, M.: A survey on load transportation using multirotor UAVs. J Intell. Robot. Syst. Theory Appl. 98, 267–296 (2020). https://doi.org/10.1007/s10846-019-01088-w

    Article  Google Scholar 

  17. Bonyan Khamseh, H.; Janabi-Sharifi, F.; Abdessameud, A.: Aerial manipulation—a literature survey. Rob. Auton. Syst. 107, 221–235 (2018). https://doi.org/10.1016/j.robot.2018.06.012

    Article  Google Scholar 

  18. Acosta, J.; de Cos, C.R.; Ollero, A.: Accurate control of aerial manipulators outdoors. A reliable and self-coordinated nonlinear approach. Aerosp. Sci. Technol. 99, 105731 (2020). https://doi.org/10.1016/j.ast.2020.105731

    Article  Google Scholar 

  19. DARPA: Robotic landing gear could enable future helicopters to take off and land almost anywhere 2015. https://www.darpa.mil/news-events/2015-09-10 (Accessed June 7, 2021)

  20. Bodie, K.; Tognon, M.; Siegwart, R.: Dynamic end effector tracking with an omnidirectional parallel aerial manipulator. IEEE Robot. Autom. Lett. 6, 8165–8172 (2021). https://doi.org/10.1109/LRA.2021.3101864

    Article  Google Scholar 

  21. Ikura, M.; Miyashita, L.; Ishikawa, M.: Real-time landing gear control system based on adaptive 3D sensing for safe landing of UAV. In: Proceedings 2020 IEEE/SICE international symposium on system integration SII 2020 2020:759–64.

  22. Çabuk, N.: Design and kinematic analysis of proposed adaptive landing gear for multirotor UAV. El-Cezeri J. Sci. Eng. 9, 159–70 (2022). https://doi.org/10.31202/ecjse.952728

    Article  Google Scholar 

  23. Tang, H.; Zhang, D.; Tian, C.: An approach for modeling and performance analysis of three-leg landing gear mechanisms based on the virtual equivalent parallel mechanism. Mech. Mach. Theory 169, 104617 (2022). https://doi.org/10.1016/j.mechmachtheory.2021.104617

    Article  Google Scholar 

  24. Tang, H.; Zhang, D.; Tian, C.: A method for comprehensive performance optimization of four-leg landing gear based on the virtual equivalent parallel mechanism. Mech. Mach. Theory 174, 104924 (2022). https://doi.org/10.1016/j.mechmachtheory.2022.104924

    Article  Google Scholar 

  25. Hang, K.; Lyu, X.; Song, H.; Stork, J.A.; Dollar, A.M.; Kragic, D., et al.: Perching and resting-A paradigm for UAV maneuvering with modularized landing gears. Sci. Robot. (2019). https://doi.org/10.1126/scirobotics.aau6637

    Article  Google Scholar 

  26. Ramon-Soria, P.; Gomez-Tamm, A.E.; Garcia-Rubiales, F.J.; Arrue, B.C.; Ollero, A.: Autonomous landing on pipes using soft gripper for inspection and maintenance in outdoor environments. In: 2019 IEEE/RSJ international conference on intelligent robots and systems, IEEE; 2019, p. 5832–9

  27. Sarkisov, Y.S.; Yashin, G.A.; Tsykunov, E.V.; Tsetserukou, D.: DroneGear: a novel robotic landing gear with embedded optical torque sensors for safe multicopter landing on an uneven surface. IEEE Robot. Autom. Lett. 3, 1912–1917 (2018). https://doi.org/10.1109/LRA.2018.2806080

    Article  Google Scholar 

  28. Nadan, P.M.; Anthony, T.M.; Michael, D.M.; Pflueger, J.B.; Sethi, M.S.; Shimazu, K.N., et al.: A bird-inspired perching landing gear system. J. Mech. Robot. (2019). https://doi.org/10.1115/1.4044416

    Article  Google Scholar 

  29. Tang, H.; Zhang, D.; Gan, Z.: Control system for vertical take-off and landing vehicle’s adaptive landing based on multi-sensor data fusion. Sensors (Switzerland) 20, 1–21 (2020). https://doi.org/10.3390/s20164411

    Article  Google Scholar 

  30. Yashin, G.; Egorov, A.; Darush, Z.; Zherdev, N.; Tsetserukou, D.: LocoGear: locomotion analysis of robotic landing gear for multicopters. IEEE J. Miniaturizat. Air Sp. Syst. 1, 138–147 (2020). https://doi.org/10.1109/jmass.2020.3015525

    Article  Google Scholar 

  31. Choi, J.; Cheon, D.; Lee, J.: Robust landing control of a quadcopter on a slanted surface. Int. J. Precis. Eng. Manuf. 22, 1147–1156 (2021). https://doi.org/10.1007/s12541-021-00523-z

    Article  Google Scholar 

  32. Ikura, M.; Miyashita, L.; Ishikawa, M.: Stabilization system for UAV landing on rough ground by adaptive 3d sensing and high-speed landing gear adjustment. J. Robot. Mechatr. 33, 108–18 (2021). https://doi.org/10.20965/jrm.2021.p0108

    Article  Google Scholar 

  33. Liu, J.; Zhang, D.; Wu, C.; Tang, H.; Tian, C.: A multi-finger robot system for adaptive landing gear and aerial manipulation. Rob. Auton. Syst. (2021). https://doi.org/10.1016/j.robot.2021.103878

    Article  Google Scholar 

  34. Paul, H.; Miyazaki, R.; Ladig, R.; Shimonomura, K.: TAMS: development of a multipurpose three-arm aerial manipulator system. Adv. Robot. 35, 31–47 (2021). https://doi.org/10.1080/01691864.2020.1845237

    Article  Google Scholar 

  35. Liu, J.; Zhang, D.; Chen, Y.; Xia, Z.; Wu, C.: Design of a class of generalized parallel mechanisms for adaptive landing and aerial manipulation. Mech. Mach. Theory (2022). https://doi.org/10.1016/j.mechmachtheory.2021.104692

    Article  Google Scholar 

  36. Paul, H.; Miyazaki, R.; Kominami, T.; Ladig, R.; Shimonomura, K.: A versatile aerial manipulator design and realization of uav take-off from a rocking unstable surface. Appl. Sci. (2021). https://doi.org/10.3390/app11199157

    Article  Google Scholar 

  37. Luo, C.; Zhao, W.; Du, Z.; Yu, L.: A neural network based landing method for an unmanned aerial vehicle with soft landing gears. Appl. Sci. (2019). https://doi.org/10.3390/app9152976

    Article  Google Scholar 

  38. Yıldırım, Ş; Çabuk, N.; Bakırcıoğlu, V.: Comparison of flight performances of unmanned air vehicle with six rotors and eight rotors under different disturbance effects. Konya J. Eng. Sci. 8, 552–62 (2020). https://doi.org/10.36306/konjes.594701

    Article  Google Scholar 

  39. Oktay, T.; Köse, O.: Dynamic modeling and simulation of quadrotor for different flight conditions. Eur. J. Sci. Technol. (2019). https://doi.org/10.31590/ejosat.507222

    Article  Google Scholar 

  40. Yıldırım, Ş; Çabuk, N.; Bakırcıoğlu, V.: Design and trajectory control of universal drone system. Measurement 147, 106834 (2019). https://doi.org/10.1016/j.measurement.2019.07.062

    Article  Google Scholar 

  41. Jiang, F.; Pourpanah, F.; Hao, Q.: Design, implementation, and evaluation of a neural-network-based quadcopter UAV system. IEEE Trans. Ind. Electron. 67, 2076–2085 (2020). https://doi.org/10.1109/TIE.2019.2905808

    Article  Google Scholar 

  42. Maleki Roudposhti, M.; Haghzad, K.S.: Development of a novel wheeled parallel robot with six degrees of freedom. Arab. J. Sci. Eng. (2022). https://doi.org/10.1007/s13369-022-06950-y

    Article  Google Scholar 

  43. Debnath, D.; Malla, P.; Roy, S.: Position control of a DC servo motor using various controllers: a comparative study. Mater. Today Proc. 58, 484–488 (2022). https://doi.org/10.1016/j.matpr.2022.03.008

    Article  Google Scholar 

  44. Zheng, X.; Li, J.; Wang, Q.; Liao, Q.: A methodology for modeling and simulating frictional translational clearance joint in multibody systems including a flexible slider part. Mech. Mach. Theory 142, 103603 (2019). https://doi.org/10.1016/j.mechmachtheory.2019.103603

    Article  Google Scholar 

  45. Marques, F.; Woliński, Ł; Wojtyra, M.; Flores, P.; Lankarani, H.M.: An investigation of a novel LuGre-based friction force model. Mech. Mach. Theory (2021). https://doi.org/10.1016/j.mechmachtheory.2021.104493

    Article  Google Scholar 

  46. Lee, T.H.; Tan, K.K.; Huang, S.: Adaptive friction compensation with a dynamical friction model. IEEE/ASME Trans. Mechatr. 16, 133–140 (2011)

    Article  Google Scholar 

  47. Tian, Y.; Huo, Z.; Wang, F.; Liang, C.; Shi, B.; Zhang, D.: A novel friction-actuated 2-DOF high precision positioning stage with hybrid decoupling structure. Mech. Mach. Theory 167, 104511 (2022). https://doi.org/10.1016/j.mechmachtheory.2021.104511

    Article  Google Scholar 

  48. Alzaher, H.A.; Alghamdi, M.K.: An all-digital low-noise switching DC–DC buck converter based on a multi-sampling frequency delta-sigma modulation with enhanced light-load efficiency. Arab. J. Sci. Eng. 45, 1411–1419 (2020). https://doi.org/10.1007/s13369-019-03955-y

    Article  Google Scholar 

  49. Ebeid, E.; Skriver, M.; Terkildsen, K.H.; Jensen, K.; Schultz, U.P.: A survey of open-source UAV flight controllers and flight simulators. Microprocess. Microsyst. 61, 11–20 (2018). https://doi.org/10.1016/j.micpro.2018.05.002

    Article  Google Scholar 

  50. Yang, B.; Yang, E.; Yu, L.; Niu, C.: Adaptive extended Kalman filter-based fusion approach for high-precision UAV positioning in extremely confined environments. IEEE/ASME Trans. Mechatr. (2022). https://doi.org/10.1109/TMECH.2022.3203875

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nihat Çabuk.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (MP4 9011 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Çabuk, N. Design and Experimental Validation of an Adaptive Landing Gear for Safe Landing on Uneven Grounds of VTOL UAVs in the Context of Lightweight and Fast Adaptations. Arab J Sci Eng 48, 12331–12344 (2023). https://doi.org/10.1007/s13369-023-07731-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-023-07731-x

Keywords

Navigation