Aerodynamic model of a group of uavs in aircraft space.

Keywords: unmanned aerial vehicle, obstacles, space, aerodynamic model, flight.


The article examines the aerodynamic model of a group of unmanned aerial vehicles in a space with obstacles, the model is built on the basis of the methods for forming the Dubins trajectory and the Pythagorean spatial theorem, according to the hodograph. The article determines that one of the classical trajectories that is used to maneuver an unmanned aerial vehicle from one height to another is the intersection of a circular spiral, which is projected onto the X-Y plane in the form of a circle. Compared with the Pythagorean theorem, according to the hodograph, the length of the spiral path will be longer than any other and more accurate in the shape of the path. The problem of avoiding obstacles is identified and the Dubins diagram of the paths of two unmanned aerial vehicles in an environment with obstacles is given. Based on this scheme, an algorithm for redevelopment of the UAV2 path with curvature adjustment using an intermediate point is described in the second scheme. It is noted that for the use of UAVs, it is important that the continuity of curvature is proportional to the lateral acceleration of the UAV, as a result, it is necessary to have controlled curvature at the boundaries of the interpolation curves, as well as impose restrictions on the maximum curvature.


The Technique of Building an Intelligent System for Automatic Control of an Unmanned Aerial Vehicle / RO Bieliakov, HD Radzivilov, OD Fesenko, VV Vasylchenko, OH Tsaturian, AV Shyshatskyi, VP Romanenko. [Text]: Radio Electronics, Computer Science, Control. – 2019. – No. 1– P. 218-229.

Bondarev DI, Kucherov, DP, Shmelova TF. Group Flight Models of Unmanned Aerial Vehicles Using Graph Theory . [Text]: Science and Technology of the Air Force of Ukraine. – 2015. – Is. 3 (20). – P. 68–75.

Bondariev DI, Dzhafarzade RT, Kozub, AM. Efficiency of Group Flights of Unmanned Aerial Vehicles. [Text]: Information Processing Systems, KNAFU. – 2014. – Is. 6 (122). –Pp. 9-14.

Danik YuH, Balytskyi II. Methods of Determining the Safety Environment of Unmanned Aerial Vehicles // Science-based Technologies. – 2018. – No. 4 (40). – P. 526–534.

Danyk YuH, Katerynchuk IS, Balytskyi II. Methods of Ensuring the Safety of the Use of UAVs when Performing Special Tasks in Difficult Conditions / YuH Danyk, IS Katerynchuk, II Balytskyi. Modern Information Technologies in the Sphere of Security and Defence. – 2017. – No. 3 (30). – P. 80–89.

Mu C. Neural-network-based adaptive guaranteed cost control of nonlinear dynamical systems with matched uncertainties / C Mu, D. Wang. Neurocomputing. – 2017. – Vol. 245. – P. 46–54.

Lin Z. Relative ordering learning in spiking neural network for pattern recognition / Z Lin, D Ma, J Meng, L Chen . Neurocomputing. – 2018. – Vol. 275. – P. 94–106.

Yu J. Machine learning and signal processing for big multimedia analysis / J. Yu, J. Sang, X. Gao. Neurocomputing. – 2017. – Vol. 257. – P. 1–4.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics / Y Lv, J Na, Q Yang et al. International Journal of Control. – 2016. – Vol. 89. – P. 99–112.

Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification / Y Sun, B Xue, M Zhang, GG. Yen] . Cornell University Libreri. – Electronic data. – 2018. – Mode of access:

Choice of the Optimal Flight Path of an Aircraft [Electronic resource] / VS Palamarchuk, OV Poliukhovych, OYe Luppo . Measurement and Computing Hardware in Technological Processes, 2015. – No. 4’(53). – P. 180-185.

Abstract views: 29
PDF Downloads: 16
How to Cite
Romaniuk, L., & Chykhira, I. (2020). Aerodynamic model of a group of uavs in aircraft space . COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (38), 59-66.