Algoritms for determining the attitude position of an unmanned aerial vehicle relative to the landing platform by using computer vision
- Authors: Gainutdinova T.Y.1, Novikova S.V.1, Gainutdinov V.G.1, Trusfus M.V.1, Litvin V.M.1
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Affiliations:
- Kazan National Research Technical University named after A.N. Tupolev
- Issue: Vol 22, No 4 (2023)
- Pages: 37-51
- Section: AIRCRAFT AND SPACE ROCKET ENGINEERING
- URL: https://journals.ssau.ru/vestnik/article/view/27048
- DOI: https://doi.org/10.18287/2541-7533-2023-22-4-37-51
- ID: 27048
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Full Text
Abstract
Algorithms for determining the attitude position of an aircraft or helicopter-type unmanned aerial vehicle relative to the landing platform with special optical marks are considered. An assessment is made of the possibility of calculating the angular position, height and distance to the landing platform in real time based on image processing by a separate on-board processor combined with a digital optical camera into a single measuring unit. The results of calculating the aircraft attitude relative to the landing platform moving along a program trajectory using computer vision algorithms are presented. Simulation of the process of recognizing optical marks on a moving platform from a moving aircraft confirmed that using a processor with a program for recognizing and identifying optical marks by using computer vision and algorithms for calculating the position of the aircraft relative to landing platforms can assuredly provide reliable information about the positioning of an unmanned aerial vehicle relative to the landing platform in real time and can be used in conjunction with other navigation aids (or independently) to ensure accurate landing of unmanned aircraft.
About the authors
T. Yu. Gainutdinova
Kazan National Research Technical University named after A.N. Tupolev
Author for correspondence.
Email: tgainut@mail.ru
Candidate of Science (Engineering), Associate Professor of the Department of Construction and Design of Aircraft
Russian FederationS. V. Novikova
Kazan National Research Technical University named after A.N. Tupolev
Email: sweta72@bk.ru
Doctor of Science (Engineering), Professor of the Department of Construction and Design of Aircraft
Russian FederationV. G. Gainutdinov
Kazan National Research Technical University named after A.N. Tupolev
Email: gainut@mail.ru
Doctor of Science (Engineering), Professor, Head of the Department of Construction and Design of Aircraft
Russian FederationM. V. Trusfus
Kazan National Research Technical University named after A.N. Tupolev
Email: mtrusfus@yandex.ru
Engineer, Department of Construction and Design of Aircraft
Russian FederationV. M. Litvin
Kazan National Research Technical University named after A.N. Tupolev
Email: litwin@mail.ru
Engineer, Department of Construction and Design of Aircraft
Russian FederationReferences
- Sanchez-Lopez J.L., Pestana J., Saripalli S., Campoy P. An approach toward visual autonomous ship board landing of a VTOL UAV. Journal of Intelligent & Robotic Systems. 2014. V. 74. P. 113-127. doi: 10.1007/s10846-013-9926-3
- Xu G., Zhang Y., Ji S., Cheng Y., Tian Y. Research on computer vision-based for UAV autonomous landing on a ship. Pattern Recognition Letters. 2009. V. 30, Iss. 6. P. 600-605. doi: 10.1016/j.patrec.2008.12.011
- Truong Q.H., Rakotomamonjy T., Taghizad A., Biannic J.-M. Vision-based control for helicopter ship landing with handling qualities constraints. IFAC-PapersOnLine. 2016. V. 49, Iss. 17. P. 118-123. doi: 10.1016/j.ifacol.2016.09.021
- Holmes W.K., Langelaan J.W. Autonomous ship-board landing using monocular vision. Proceedings of the AHS 72th Annual Forum (May, 17-19, 2016, West Palm Beach, Florida). V. 2.
- Meng Y., Wang W., Han H., Ban J. A visual/inertial integrated landing guidance method for UAV landing on the ship. Aerospace Science and Technology. 2019. V. 85. P. 474-480. doi: 10.1016/j.ast.2018.12.030
- Yakimenko O.A., Kaminer I.I., Lentz W.J., Ghyzel P.A. Unmanned aircraft navigation for shipboard landing using infrared vision. IEEE Transactions on Aerospace and Electronic Systems. 2002. V. 38, Iss. 4. P. 1181-1200. doi: 10.1109/taes.2002.1145742
- Ageev A.M., Bondarev V.G., Protsenko V.V. Justification of the choice of radiation sources for a computer vision system in the problem of automatic landing of unmanned aerial vehicles. Computer Optics. 2022. V. 46, no. 2. P. 239-245. (In Russ.). doi: 10.18287/2412-6179-CO-875
- Lumsden B., Wilkinson C., Padfield G. Challenges at the helicopter-ship dynamic interface. 24th European Rotorcraft Forum (September, 15-17, 1998, Marseilles, France).
- Colwell J. Maritime helicopter ship motion criteria. Challenges for operational guidance. Available at: http://resolver.tudelft.nl/uuid:01b52b50-e242-457d-854d-907b5e04faf1
- Stingl A.L. Vtol aircraft flight system. US Patent, no. 3473232, 1969. (Publ. 21.10.1969)
- MPP-02 V. I. Helicopter operations from ships other than aircraft carriers (HOSTAC). NATO, 2017.
- Girshick R., Donahue J., Darrell T., Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (June, 23-28, 2014, Columbus, OH, USA). doi: 10.1109/cvpr.2014.81
- Girshick R. Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (December, 07-13, 2015, Santiago, Chile). doi: 10.1109/iccv.2015.169
- Ren S., He K., Girshick R., Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017. V. 39, Iss. 6. P. 1137-1149. doi: 10.1109/tpami.2016.2577031
- Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C.-Y., Berg A.C. SSD: Single shot multibox detector. Lecture Notes in Computer Science. 2016. V. 9905. P. 21-37. doi: 10.1007/978-3-319-46448-0_2
- Redmon J., Divvala S., Girshick R., Farhadi A. You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (June, 27-30, 2016, Las Vegas, NV, USA). doi: 10.1109/cvpr.2016.91
- Redmon J., Farhadi A. YOLO9000: better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (July, 21-26, 2017, Honolulu, HI, USA). doi: 10.1109/cvpr.2017.690
- Redmon J., Farhadi A. Yolov 3: An incremental improvement. Tech. report, arXiv: 1804.02767 [cs.CV], 2018. doi: 10.48550/arXiv.1804.02767
- Benjdira B., Khursheed T., Koubaa A., Ammar A., Ouni K. Car detection using unmanned aerial vehicles: Comparison between faster R-CNN and YOLOv3. 1st International Conference on Unmanned Vehicle Systems-Oman (UVS) (February, 05-07, 2019, Muscat, Oman). doi: 10.1109/uvs.2019.8658300
- Zishan W., Shunliang P., Zishan S., Weiqun S. Computer vision scheme for autonomous landing of unmanned helicopter on ship deck. Journal of Beijing University of Aeronautics and Astronautics. 2007. V. 33, Iss. 6.
- Wang X.-J., Pan S.-L., Song Z.-S., Shen W.-Q. Vision based analytic 3D measurement algorithm for the autonomous landing of unmanned helicopter on ship deck. Optical Technique. 2007. V. 33. P. 264-267.
- Sharp C.S., Shakernia O., Sastry S.S. A vision system for landing an unmanned aerial vehicle. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2001) (May, 21-26, 2001, Seoul, Korea (South)). doi: 10.1109/robot.2001.932859
- Shiqing L., Chunhua H., Jihong Z. A Method for estimating position and orientation of an unmanned helicopter based on vanishing line information. Computer Engineering and Applications. 2004. V. 9.
- Lukyanov O.E., Zolotov D.V., Espinosa Barsenas O.U., Komarov V.A. Determining aerodynamic characteristics of small unmanned aerial vehicles involving flight experiment. Vestnik of Samara University. Aerospace and Mechanical Engineering. 2023. V. 22, no. 3. P. 59-74. (In Russ.). doi: 10.18287/2541-7533-2023-22-3-59-74
- Tapia E. A note on the computation of high-dimensional integral images. Pattern Recognition Letters. 2011. V. 32, Iss. 2. P. 197-201. doi: 10.1016/j.patrec.2010.10.007
- Jähne B., Scharr H., Körkel S. Principles of filter design. Handbook of Computer Vision and Applications. V. 2. Signal Processing and Pattern Recognition. Academic Press, 1999. P. 125-151.
- Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986. V. 8, Iss. 6. P. 679-698. doi: 10.1109/tpami.1986.4767851
- Gainutdinova T.Yu., Gainutdinov V.G., Latypov R.R., Mukhametzianov F.F. Sposob tochnoy posadki bespilotnogo letatel'nogo apparata i ustroystvo dlya realizatsii sposoba [Method for accurate landing of an unmanned aerial vehicle and device for implementing the method]. Patent RF, no. 2773978, 2022. (Publ. 14.06.2022, bull. no. 17)