Hostname: page-component-65b85459fc-k6zl8 Total loading time: 0 Render date: 2025-10-18T20:41:40.677Z Has data issue: false hasContentIssue false
  • English
  • Français

A multi-sensor approach for precision UAV landing on dynamic platforms under challenging conditions

Published online by Cambridge University Press:  17 October 2025

C. Işilak Open the ORCID record for C. Işilak [Opens in a new window]  and
H. Oktal Open the ORCID record for H. Oktal [Opens in a new window]
C. Işilak*
Affiliation:
Department of Avionics, Erzincan Binali Yildirim University, Erzincan, Türkiye
H. Oktal
Affiliation:
Department of Avionics, Eskişehir Technical University, Eskisehir, Türkiye
*
Corresponding author: C. Işilak; Email: cemal.isilak@erzincan.edu.tr
Get access Rights & Permissions [Opens in a new window]

Abstract

This study presents the design and evaluation of an autonomous landing system for rotary-wing unmanned aerial vehicles (UAVs) targeting moving platforms. The proposed system leverages the UAV’s onboard positional data and the moving platform’s position, velocity and orientation information to execute high-precision landings. By incorporating the GPS coordinates provided by the mobile platform, the operational envelope of the landing procedure is significantly extended. A Kalman filter is employed to fuse the platform’s GPS data with the UAV’s inertial orientation measurements, enabling accurate estimation of a dynamic rendezvous point along the platform’s trajectory. This facilitates the generation of an optimised landing trajectory that minimises path length and enhances energy efficiency.

A YOLOv8-based object detection model is integrated into the system to detect the landing pad in real time. The effectiveness of the proposed method is validated through scenario-based simulations designed to evaluate landing performance under variable altitudes, crosswind disturbances and limited visibility due to fog.

Across 30 independent runs, the proposed method reduced total autonomous landing time by 12% (191 ± 2.0 s → 168 ± 1.6 s, p < 0.001), halved the landing phase (22.9 ± 0.7 s → 11.1 ± 0.7 s), shortened the path by ≍152 m (2035 ± 6.8 m → 1883 ± 3.1 m), and lowered battery consumption from 5.0 ± 0.1% to 4.0 ± 0.1%. The system maintained successful landings under variable wind (up to 6 m/s) and fog with a 7 m detection range, achieving sub-meter touchdown accuracy (RMSE ≍ 0.15 m); at a 5 m detection limit, landings failed, indicating a robustness boundary.

Compared to existing literature, the developed system introduces a novel 3D trajectory planning approach involving altitude variations and dynamic target prediction. The framework is modular and compatible with various UAV and ground vehicle platforms, making it suitable for diverse mission profiles in both civilian and defense applications.

Keywords

autonomous landingtrajectory predictionunmanned aerial vehicles (UAVs)visual object detection

Information

Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 27
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Feng, Y., Zhang, C., Baek, S., Rawashdeh, S. and Mohammadi, A. Autonomous landing of a UAV on a moving platform using model predictive control, Drones, 2018, 2, (4), p 34.10.3390/drones2040034CrossRefGoogle Scholar
Iris- The Ready to Fly UAV Quadcopter. http://www.arducopter.co.uk/iris-quadcopter-uav.html Google Scholar
Baca, T., Stepan, P., Spurny, V., Hert, D., Penicka, R., Saska, M., and Kumar, V. Autonomous landing on a moving vehicle with an unmanned aerial vehicle, Journal of Field Robotics, 2019, 36, (5), pp 874–891.10.1002/rob.21858CrossRefGoogle Scholar
Borowczyk, A., Nguyen, D.T., Nguyen, A.P.V., Nguyen, D.Q., Saussié, D. and Le Ny, J. Autonomous landing of a quadcopter on a high-speed ground vehicle, J. Guid. Control Dyn., 2017, 40, (9), pp 2378–2385.10.2514/1.G002703CrossRefGoogle Scholar
Neves, F.S., Branco, L.M., Pereira, M.I., Claro, R.M. and Pinto, A.M. A multimodal learning-based approach for autonomous landing of uav, 2024 20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), IEEE, 2024, pp. 1–8.10.1109/MESA61532.2024.10704866CrossRefGoogle Scholar
Falanga, D., Zanchettin, A., Simovic, A., Delmerico, J. and Scaramuzza, D. Vision-based autonomous quadrotor landing on a moving platform, 2017, IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE, 2017, pp. 200–207.10.1109/SSRR.2017.8088164CrossRefGoogle Scholar
Lee, B., Saj, V., Benedict, M. and Kalathil, D. A vision-based control method for autonomous landing of vertical flight aircraft on a moving platform without using GPS, 2020. arXiv preprint arXiv:2008.05699.Google Scholar
Paris, A., Lopez, B.T. and How, J.P. Dynamic landing of an autonomous quadrotor on a moving platform in turbulent wind conditions, 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020, pp. 9577–9583.10.1109/ICRA40945.2020.9197081CrossRefGoogle Scholar
Polvara, R., Patacchiola, M., Sharma, S., Wan, J., Manning, A., Sutton, R. and Cangelosi, A. Toward end-to-end control for UAV autonomous landing via deep reinforcement learning, The 2018 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2018, pp. 115–123.10.1109/ICUAS.2018.8453449CrossRefGoogle Scholar
Rodriguez-Ramos, A., Sampedro, C., Bavle, H., De La Puente, P. and Campoy, P. A deep reinforcement learning strategy for UAV autonomous landing on a moving platform, J. Intell. Rob. Syst., 2019, 93, (1-2), pp 351–366.10.1007/s10846-018-0891-8CrossRefGoogle Scholar
Chen, X., Phang, S.K. and Chen, B.M. System integration of a vision-guided UAV for autonomous tracking on moving platform in low illumination condition, Proceedings of the ION 2017 Pacific PNT Meeting, 2017, pp. 1082–1092.10.33012/2017.15022CrossRefGoogle Scholar
Lin, S., Jin, L. and Chen, Z. Real-time monocular vision system for UAV autonomous landing in outdoor low-illumination environments, Sensors, 2021, 21, (18), p 6226.10.3390/s21186226CrossRefGoogle ScholarPubMed
Jin, S., Zhang, J., Shen, L. and Li, T. Onboard vision autonomous landing techniques for quadrotor: A survey, 2016, the 35th Chinese Control Conference (CCC), IEEE, 2016, pp. 10284–10289.10.1109/ChiCC.2016.7554984CrossRefGoogle Scholar
Araar, O., Aouf, N. and Vitanov, I. Vision-based autonomous landing of a multirotor UAV on a moving platform, J. Intell. Rob. Syst., 2017, 85, (2), pp 369–384.10.1007/s10846-016-0399-zCrossRefGoogle Scholar
Vankadari, M.B., Das, K., Shinde, C. and Kumar, S. A reinforcement learning approach for autonomous control and landing of a quadrotor, 2018 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2018, pp. 676–683.10.1109/ICUAS.2018.8453468CrossRefGoogle Scholar
Bicer, Y., Moghadam, M., Sahin, C., Eroglu, B. and Üre, N.K. Vision-based uav guidance for autonomous landing with deep neural networks, AIAA Scitech 2019 Forum, 2019, p 0140.10.2514/6.2019-0140CrossRefGoogle Scholar
Ciabatti, G., Daftry, S. and Capobianco, R. Autonomous planetary landing via deep reinforcement learning and transfer learning, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2031–2038.10.1109/CVPRW53098.2021.00231CrossRefGoogle Scholar
Cui, Q., Liu, M., Huang, X. and Gao, M. Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform, Biomimetic Intell. Rob., 3(1), 100088.10.1016/j.birob.2023.100088CrossRefGoogle Scholar
Mu, L., Li, Q., Wang, B., Zhang, Y., Feng, N., Xue, X. and Sun, W. A vision-based autonomous landing guidance strategy for a micro-uav by the modified camera view, Drones, 2023, 7, (6), p 400.10.3390/drones7060400CrossRefGoogle Scholar
Morales, J., Castelo, I., Serra, R., Lima, P.U. and Basiri, M. Vision-based autonomous following of a moving platform and landing for an unmanned aerial vehicle, Sensors, 2023, 23, (2), p 829.10.3390/s23020829CrossRefGoogle ScholarPubMed
Yuan, B., Ma, W. and Wang, F. High speed safe autonomous landing marker tracking of fixed wing drone based on deep learning, IEEE Access, 2022, 10, pp 80415–80436.10.1109/ACCESS.2022.3195286CrossRefGoogle Scholar
Xu, Y., Chen, Z., Deng, C., Wang, S. and Wang, J. Lcdl: Towards dynamic localization for autonomous landing of unmanned aerial vehicle based on lidar-camera fusion, IEEE Sens. Journal, 2024 , 24, (16), pp. 26407–26415.10.1109/JSEN.2024.3424218CrossRefGoogle Scholar
Pessanha Santos, N., Lobo, V. and Bernardino, A. Fixed-wing unmanned aerial vehicle 3d-model-based tracking for autonomous landing, Drones, 2023, 7, (4), p 243.10.3390/drones7040243CrossRefGoogle Scholar
Alam, M.S. and Oluoch, J. A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs), Expert Syst. Appl., 2021, 179, p 115091.10.1016/j.eswa.2021.115091CrossRefGoogle Scholar
Qi, Y., Jiang, J., Wu, J., Wang, J., Wang, C. and Shan, J. Autonomous landing solution of low-cost quadrotor on a moving platform, Rob. Auton. Syst., 2019, 119, pp 64–76.10.1016/j.robot.2019.05.004CrossRefGoogle Scholar
Xing, Z., Zhang, Y., Su, C.Y., Qu, Y. and Yu, Z. Kalman filter-based wind estimation for forest fire monitoring with a quadrotor UAV, 2019, the IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2019, pp. 783–788.10.1109/CCTA.2019.8920637CrossRefGoogle Scholar
Mohammadi, A., Feng, Y., Zhang, C., Rawashdeh, S. and Baek, S. Vision-based autonomous landing using an MPC-controlled micro UAV on a moving platform, 2020 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2020, pp. 771–780.10.1109/ICUAS48674.2020.9214043CrossRefGoogle Scholar
Nogar, S.M. Autonomous landing of a UAV on a moving ground vehicle in a GPS denied environment, 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE, 2020, pp. 77–83.10.1109/SSRR50563.2020.9292607CrossRefGoogle Scholar
Xuan-Mung, N., Nguyen, N.P., Nguyen, T., Pham, D.B., Vu, M.T., Thanh, H.L.N.N. and Hong, S.K. Quadcopter precision landing on moving targets via disturbance observer-based controller and autonomous landing planner, IEEE Access, 2022, 10, pp 83580–83590.10.1109/ACCESS.2022.3197181CrossRefGoogle Scholar
Oktal, H. and Yaman, K. Uyduya dayalı konum belirleme: GPS–GLONASS–GALILEO–BEIDOU, Nisan Kitapevi, 2019, Eskişehir, Turkey.Google Scholar
Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C. and Vallejo, D. Choosing good distance metrics and local planners for probabilistic roadmap methods, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146), vol. 1, IEEE, 1998, pp. 630–637.Google Scholar
Ultralytics. ultralytics/ultralytics (v8.0) [GitHub repository], 2023. https://github.com/ultralytics/ultralytics Google Scholar
Mori, K. ultralytics_ros [Computer software], 2023. https://github.com/Alpaca-zip/ultralytics_ros Google Scholar