D.S. Andrashitov1, A.A. Kostoglotov2, S.V. Lazarenko3, M.Y. Zvezdina4, Yu.A. Shokova5

1 Military Academy of Strategic Rocket Troops after Peter the Great (Balashikha, Moscow Region, Russia)

2–5 Don State Technical University (Rostov-on-Don, Russia)

1dima-andrahitov@rambler.ru, 2kostoglotov@icloud.com, 3lazarenkosv@icloud.com, 4zvezdina_m@mail.ru, 5shokova.julia@yandex.ru

The great interest in unmanned aerial vehicles observed in the last five years is due to the possibility of their use in solving a variety of tasks, which usually include surveillance and control tasks, telecommunications tasks, etc. One of the varieties of unmanned aerial vehicles are tethered drones (tethered unmanned aerial vehicles).

Tethered drones include unmanned aerial vehicles, in which rotors act as wings (from four to eight), energy is supplied on board using a flexible cable-a power cable. This circumstance removes the restriction on the time the device is in the air, and also eliminates the need to load an electronic terrain map into the onboard computer's memory. The length of the cable is adjustable from 50 m to 300 m, depending on the installed payload. The dimensions of the device do not exceed 500 mm, and the weight is 25 kg. The payload depends on the length of the cable. With the most commonly used length of 100 m, the payload should not exceed 5 kg.

The tethered drone can operate in two modes: vertical takeoff and landing, as well as movement in the horizontal plane. In both modes, the tethered drone unwinds the cable within its length when moving in space. Due to its low weight and structural dimensions, the device can carry out evolutions in space under gusts of wind, some of which are unacceptable from the point of view of aerodynamics of the rotor structure. The reason for their instability is the lack of a restoring moment for deviation relative to the center of mass and a small damping of this movement. The instability of the tethered drone makes it necessary to constantly control the process of stabilizing the position of the device in space by setting special rotation modes of the rotors. This function is performed by an on-board control system based on specially introduced TUAV location detection algorithms based on data from systems installed on board.

Due to the peculiarities of the layout of the tethered drone, its small weight and size, as well as meeting the requirement to minimize the cost, on-board systems are made in the form of OEM modules of standard configuration. The standard navigation board of an unmanned aerial vehicle includes a communication channel with a Global Navigation Satellite System and appropriate software for determining the location of the device. However, international experience in the operation of unmanned aerial vehicles has shown that this channel can be used to influence spoofing attacks, as a result of which an unplanned change in the trajectory of the vehicle is carried out. To eliminate this possibility, modern unmanned aerial vehicles determine their location without using navigation information from the Global Navigation Satellite System, i.e. in offline mode. In this case, the process of determining the location of an object includes two stages. At the first stage, the position of the object is determined based on information received from the sensors of the strapless inertial navigation system, data on the angles of roll, pitch and yaw are received. At the second stage, the location of the object is clarified, since the accuracy of the range data received from the free-form inertial navigation system decreases significantly as the device is in the air, and data redundancy is required to accurately determine the location of the device without using a Global Navigation satellite System. The location is specified based on the sensor data of the "technical vision" system. In modern unmanned aerial vehicles with vertical takeoff and landing, but not tethered, two types of sensors are most widely used: optoelectronic (OE) and infrared (IR). In this dual IR system, sensors are used to organize the landing of a drone at night and are switched on at a height of 4 m from the Ground surface. A large number of algorithms for determining the location of an unmanned aerial vehicle have been developed for this sensor configuration.

At the same time, at high altitudes (about 100−300 m) and the need to stabilize the position of the drone, the OE / IR sensor system is fundamentally unacceptable, which significantly reduces the capabilities of the device in terms of time spent in the air. In this regard, the article suggests using data from the radar radar antenna, which is part of the traditional navigation board, for example, the IWR1642 Single-Chip OEM module, as an additional source of information about the position of the reference point. The construction of the radar using MIMO technology, as well as the required small viewing area, allows it to function within the limits of the power supplied to the tethered drone via cable. An additional advantage of such a replacement is the simplification of the data processing process in the on-board computer, since the data from the radar is already received in binary form. This fact becomes relevant due to the requirements of low cost and small mass dimensions imposed on the tethered drone as a whole.

The selection / development of an algorithm for processing information received from on-board sensors, while ensuring the required reliability of estimating the location of the device, should be accompanied by an assessment of the influence of the accuracy characteristics of the sensors on the error value of the results obtained. This allows you to assemble a device design that meets the "price-quality" criterion.

The purpose of the article is to stabilize the position of a tethered drone in space by determining its three-dimensional coordinates based on data from a strapdown inertial navigation system and an on-board radar about the angular position of a radiocontrast reference point on the Earth's surface.

Andrashitov D.S., Kostoglotov A.A., Lazarenko S.V., Zvezdina M.Y., Shokova Yu.A. Tethered drone space stabilization algorithm. Information-measuring and Control Systems. 2024. V. 22. № 1. P. 44−52. DOI: https://doi.org/10.18127/j20700814-202401-06 (in Russian)

1. López J.J., Mulero-Pázmány M. Drones for conservation in protected areas: Present and future. Drones. 2019. V. 3. № 1. DOI: https://doi.org/10.3390/drones3010010.
2. Taddia Y., Corbau C., Buoninsegni J., Simeoni U., Pellegrinelli A. UAV approach for detecting plastic marine debris on the beach: A case study in the Po river delta (Italy). Drones. 2021. V. 5. № 4. DOI: https://doi.org/10.3390/drones5040140.
3. Shukla D., Komerath N. Multirotor Aerodynamic interaction investigation. Drones. 2018. V. 2. № 4. DOI: https://doi.org/10.3390/drones2040043.
4. Sarkar N.I., Gul S. Artificial intelligence-based autonomous UAV networks: A survey. Drones. 2023. V. 7. № 5. DOI: https://doi.org/10.3390/drones7050322.
5. Saeed Z., Yousaf M.H., Ahmed R., Velastin S.A., Viriri S. On-board small-scale object detection for unmanned aerial vehicles (UAVs). Drones. 2023. V. 7. № 5. DOI: https://doi.org/10.3390/drones7050310.
6. Al-Radaideh A., Sun L. Self-localization of tethered drones without a cable force sensor in GPS-denied environments. Drones. 2021. V. 5. № 4. DOI: https://doi.org/10.3390/drones5040135.
7. Popov N.I., Emelyanov O.V., Yatsun S.F., Savin A.I. Issledovanie dvizheniya kvadrokoptera pri vneshnem periodicheskom vozdeistvii. Spravochnik. Inzhenernyi zhurnal. 2014. № 4. S. 17−21. DOI: https://doi.org/10.14489/hb.supp.2014.04.pp.017-021. (in Russian)
8. Beard R.W., McLain T.W. Small unmanned aircraft: Theory and Practice. Princeton and Oxford: Princeton University Press. 2012. 320 p.
9. Siti L., Mjahed M., Ayad H., A. Kari El New trajectory tracking approach for a quadrocopter using genetic algorithm and reference model methods. Appl. Sci. 2019. V. 9. № 9. DOI: https://doi.org/10.3390/app9091780.
10. Basan E., Basan A., Nekrasov A., Fidge C., Sushkin N., Peskova O. GPS-spoofing attack detection technology for UAVs based on Kullback-Leibler divergence. Drones. 2022. V. 6. № 1. DOI: https://doi.org/10.3390/drones6010008.
11. Tsuyang W., Xinglan G., Yehcheng C., Saru K., Chienming C. Amassing the security: An enhanced authentication protocol for drone communications over 5 G networks. Drones. 2022. V. 6. № 1. DOI: https://doi.org/10.3390/drones6010010.
12. Titterton D., Weston J.L., IEE. Srapdown inertial navigation technology. IEEE: 2004. 558 p.
13. Chang W., Lo L.Y., Cheung H.C., Feng Y., Yang A.S., Wen C.Y., Zhou W. Proactive guidance for accurate UAV landing on a dynamic platform: A visual-inertial approach. Sensors. 2022. V. 22. № 1. DOI: https://doi.org/10.3390/s22010404.
14. Kompleksy s bespilotnymi letatelnymi apparatami. Printsipy postroeniya i osobennosti primeneniya kompleksov s BLA. Pod red. V.S. Verby i B.G. Tatarskogo. M.: Radiotekhnika. 2017. 512 s. (in Russian)
15. Durrant-Whyte H., Bailey T. Simultaneous localization and mapping: Part I. IEEE Robot. Autonom. Mag. 2006. V. 13. P. 99−110. DOI: https://doi.org/10.1109/MRA.2006.1638022
16. González-Hernádez I., Salazar S., Lozano R., Ramirez-Ayala O. Real-time improvement of a trajectory-tracking control based on super-twisting algorithm for quadrotor aircraft. Drones. 2022. V. 6. № 2. DOI: https://doi.org/10.3390/drones6020036.
17. Rocamora M.B. Jr., Lima R.R., Samarakoon K., Rathjen J., Gross J.N., Pereira G.A.S. Oxpecker: A Tethered UAV for Inspection of Stone-Mine Pillars. Drones. 2023. V. 7. № 2. DOI: https://doi.org/10.3390/drones7020073.
18. Tong X., Liu X., Chen P., Liu S., Luan K., Li L., Liu S., Liu X., Xie H., Jin Y., Hong Z. Integration of UAV-based photogrammetry and terrestrial laser scanning for the three-dimensional mapping and monitoring of open-pit mine areas. Remote Sensing. 2015. V. 7. № 6. P. 6635−6662. DOI: https://doi.org/10.3390/rs70606635.
19. Demirhan M., Premachandra C. Development of an automated camera-based drone landing system. IEEE Access. 2020. V. 8. P. 202111−202121. DOI: https://doi.org/10.1109/ACCESS.2020.3034948
20. Wang L., Bai X. QuadrocopterAutonomous Approaching and Landing on a Vessel Deck. J. Intell. Robot. Syst. 2017. V. 92. P. 125−143. DOI: https://doi.org/10.1007/s10846-017-0757-5.
21. IWR1642 Single-Chip 76-to81-GHz mmWave Sensor. URL: http://www.ti.com/products/IWR1642 (date of access 20.12.2023).
22. Bialer O., Jonas A., Tirer T. Super resolution wide aperture automative radar. IEEE Sensor Journal. 2021. V. 21. № 16. P. 17846−17858. DOI: https://doi.org/ 10.1109/JSEN.2021.3085677.