The research on autonomous landing of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) is well established. However, the research on autonomous deck landing using visual methods is relatively not so mature and many of them require the support of ground infrastructures. In order to reduce such dependencies, a ship landing guidance strategy based on on-board vision is studied. Considering the characteristics of the ship landing issue, we propose a three-phase landing scheme and a decision-making method to ensure landing safety is also studied. For improving the traditional two-dimensional (2D) optical-flow method, a three-dimensional (3D) velocity vector estimation method using image spherical optical flow is studied. Furthermore, a guidance law based on the tau theory is employed by only using the visual information of the line of sight to the target. In this phase, a trajectory-tracking controller is applied to generate the velocity commands of the UAV. Finally, the whole algorithm is validated by simulation in different wave conditions developed with Unity3D. Compared with traditional trajectory planning methods, our method does not require complex optimization iterations and can meet both the real-time and accuracy requirements of deck landing. The average tracking error of our method maintains in 0.2 m. Moreover, the whole algorithm runs efficiently at around 30 fps on a Raspberry-Pi 3B+ microcomputer which meets the real-time requirements.
AggarwalSKumarN (2020) Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications2020: 149270–149299.
2.
AcunaRZhangDWillertV (2018) Vision-Based UAV landing on a moving platform in GPS denied environments using motion prediction. In: 2018 Latin American robotic symposium, 2018 Brazilian symposium on robotics (SBR) and 2018 workshop on robotics in education (WRE), Pessoa, Brazil, 6–10 November, pp. 515–521. New York: IEEE.
3.
BlachutKSzolcHWasalaM, et al. (2020) Vision based hardware-software real-time control system for autonomous landing of an UAV. In: International conference on computer vision and graphics (ICCVG), Warsaw, 14–16 September, pp. 13–24. Cham: Springer.
4.
BruhnAWeickertJSchnörrC (2005) Lucas Kanade meets Horn/Schunck: Combining local and global optic flow methods. International Journal of Computer Vision61(3): 211–231.
5.
ChahlJSSrinivasanMVZhangSW (2004) Landing strategies in honeybees and applications to uninhabited airborne vehicles. The International Journal of Robotics Research23(2): 101–110.
6.
CuiKHanWLiuY, et al. (2021) Model predictive control for automatic carrier landing with time delay. International Journal of Aerospace Engineering2021(1): 1–19.
7.
DonaldEK (2004) Optimal Control Theory: An Introduction. New York: Dover Publications.
8.
DoughertyJLeeDLeeT (2014) Laser-based guidance of a quadrotor UAV for precise landing on an inclined surface. In: 2014 American control conference, Portland, OR, 4–6 June, pp. 1210–1215. New York: IEEE.
9.
FaridKAhmedAB (2012) Bio-inspired TauPilot for automated aerial 4D docking and landing of Unmanned Aircraft Systems. In: IEEE/RSJ international conference on intelligent robots & systems, Vilamoura-Algarve, 7–12 October, pp. 480–487. New York: IEEE.
10.
FalangaDZanchettinASimovicA, et al. (2017) Vision-based autonomous quadrotor landing on a moving platform. In: 2017 IEEE international symposium on safety, security and rescue robotics (SSRR), Shanghai, China, 11–13 October, pp. 200–207. New York: IEEE.
11.
FerrierBErnstR (2017) Fire scout launch and recovery considerations in unexpected ship roll motion conditions. Naval Engineers Journal129(4): 87–98.
12.
GreenWOhPBarrowsG (2004) Flying insect inspired vision for autonomous aerial robot maneuvers in near-earth environments. In: IEEE international conference on robotics and automation, New Orleans, LA, 26 April–1 May, vol. 3, pp. 2347–2352. New York: IEEE.
13.
KhanJT (2009) Image-based visual servoing of a VTOL UAV. Doctoral Dissertation, Lakehead University, Orillia, ON, Canada.
14.
LangeSSunderhaufNProtzelP (2009) A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. In: 2009 international conference on advanced robotics, Munich, 22–26 June, pp. 1–6. New York: IEEE.
15.
LeeDRyanTKimHJ (2012) Autonomous landing of a VTOL UAV on a moving platform using image-based visual servoing. In: 2012 IEEE international conference on robotics and automation, Saint Paul, MN, 14–18 May, pp. 971–976. New York: IEEE.
16.
LeeDN (1976) A theory of visual control of braking based on information about time-to-collision. Perception5(4): 437–459.
17.
MahonyRCorkePIHamelT (2008) Dynamic image-based visual servo control using centroid and optic flow features. Journal of Dynamic Systems Measurement and Control130(1): 141–148.
18.
SerraPCunhaRHamelT, et al. (2016) Landing of a quadrotor on a moving target using dynamic image-based visual servo control. IEEE Transactions on Robotics32(6): 1524–1535.
19.
VassalloRFSantosvictorJSchneebeliHJ (2002) A general approach for egomotion estimation with omnidirectional images. In: Proceedings of the IEEE workshop on omnidirectional vision 2002, Copenhagen, 2 June, pp. 97–103. New York: IEEE.
20.
VetrellaARSaIPopovicM, et al. (2017) Improved tau-guidance and vision-aided navigation for robust autonomous landing of UAVs. Field and Service Robotics5: 115–128.
21.
WangL (2009) Model Predictive Control System Design and Implementation Using MATLAB. London: Springer.
22.
WangXLiuJXichaoSU, et al. (2020) A review on carrier aircraft dispatch path planning and control on deck. Chinese Journal of Aeronautics33(12): 19.
23.
OlejnikDADuisterhofBPKarásekM, et al. (2020) A tailless flapping wing MAV performing monocular visual servoing tasks. Unmanned Systems8(4): 287–294.
24.
WynnJSMclainTW (2019) Visual servoing with feed-forward for precision shipboard landing of an autonomous multirotor. In: 2019 American control conference (ACC), Philadelphia, PA, 10–12 July, pp. 3928–3935. New York: IEEE.
25.
YamasakiTSakaidaHEnomotoK, et al. (2007) Robust trajectory-tracking method for UAV guidance using proportional navigation. In: 2007 international conference on control, automation and systems, Seoul, South Korea, 17–20 October, pp. 1404–1409. New York: IEEE.
26.
YangZFangZLiP (2016) Bio-inspired collision-free 4D trajectory generation for UAVs using tau strategy. Journal of Bionic Engineering13(1): 84–97.
27.
YoonSKimYKimS (2008) Pursuit guidance law and adaptive backstepping controller design for vision-based net-recovery UAV. In: AIAA guidance, navigation and control conference, Honolulu, HI, 18–21 August.
28.
ZhangCJYeZB (2010) Falling probability analysis of anti-ship missile caused by sea wave. Tactical Missile Technology11(6): 29–32.
29.
ZhangHBoCZhaoJ (2018) Optimal trajectory generation for time-to-contact based aerial robotic perching. Bioinspiration & Biomimetics14(1): 016008.
30.
ZhangZXiePMaO (2014) Bio-inspired trajectory generation for UAV perching movement based on tau theory. International Journal of Advanced Robotic Systems11(9): 141–152.
31.
ZhenZJiangSMaK (2018) Automatic carrier landing control for unmanned aerial vehicles based on preview control and particle filtering. Aerospace Science and Technology81(10): 99–107.
32.
ZhenZPuXOuM (2013) Bio-inspired trajectory generation for UAV perching. In: 2013 IEEE/ASME international conference on advanced intelligent mechatronics, Wollongong, NSW, Australia, 9–12 July, pp. 997–1002. New York: IEEE.
