Method of an unmanned aerial vehicle composition route in space
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Abstract
The article is devoted to the development of a method of composition of the route of an unmanned aerial vehicle in three-dimensional space. The main difference of the presented method is the complex consideration of the features of the environment, which reflects the possible obstacles (active or passive) and other limitations of the problem when composing the route of the unmanned aerial vehicle in three-dimensional space. This allowed to increase the safety of the task in autonomous flight conditions. The article analyzes the main approaches to the composition of unmanned aerial vehicle routes in space. The conclusion about the shortcomings of the two-dimensional representation is made. The method presents four stages of the task. This is the stage of modeling the environment that reflects possible obstacles (active or passive) and other limitations of the task. Stage of construction of an extended graph of unmanned aerial vehicle routes in space. The difference of this stage is the adaptive consideration of the spatial location of active obstacles in space. The next stage is the route search stage, which connects the starting point with the end and bypasses all obstacles and allows you to build a starting route in the form of a broken line, which is formed by a sequence of waypoints, and connects the starting point with the end, bypassing obstacles. The last is the stage of obtaining the final result, which is provided by smoothing the obtained broken line. In this part of the composition method, to solve the problem of smoothing the trajectory of the unmanned aerial vehicle in space on the selected route, the expediency of using the method of non-uniform cubic B-spline is proved. With the help of this method the task of selection and optimization of the smoothing parameter is set and solved.
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References
S. Huang and R. S. H. Teo, "Computationally Efficient Visibility Graph-Based Generation Of 3D Shortest Collision-Free Path Among Polyhedral Obstacles For Unmanned Aerial Vehicles," ICUAS, 2019, pp. 1218-1223, doi: 10.1109/ICUAS.2019.8798322.
Puente-Castro, A., Rivero, D., Pazos, A. et al. A review of artificial intelligence applied to path planning in UAV swarms. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-06569-4
Semenov, S., Voloshyn, D., Ahmed, A.N. Mathematical model of the implementation process of flight task of un-manned aerial vehicle in the conditions of external impact. Int. J. of Adv. Trends in Com. Sc. and Engineer-ing, 2019, 8(1), рр. 7–13.
Semenov, S., Sira, O., Kuchuk, N. Development of graphicanalytical models for the software security testing algorithm // Eastern-European Journal of Enterprise Technologies, 2018, 2(4-92), рр. 39–46
Park S., Deyst J., How P.J. A new nonlinear guidance logic for trajectory tracking // AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, Rhode Island, 16–19 August 2004. Art. No. AIAA 2004-4900
Sujit B.P., Saripalli S., Borges Sousa J. Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algo-rithms for Fixed-Wing Unmanned Aerial Vehicless // Control Systems. 2014. Vol. 34. No. 1. P. 42–59.
Jung L.F. Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization // 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, VA, USA. 992-1001. P. 2006.
Fan X., Li S., Chen Tefang T. Dynamic obstacle avoiding path plan for robots based on a new artificial potential field function // Control Theory & Applications. 2005. Vol. 22. No. 5. P. 703–707
Ramírez-Torres, José & Larranaga-Cepeda, Ander. (2015). Real-Time Reconstruction of Heightmaps from Images Taken with an UAV. Mechanisms and Machine Science. 37. 221-231. 10.1007/978-3-319-22368-1_22.
Li, Y. & Kobayashi, Y. & Wonka, P. & Zhang, E.. (2010). Editing Operations for Irregular Vertices in Triangle Meshes. ACM Transactions on Graphics. 29. 1-12. 10.1145/1882261.1866179.
Кожухов Д. Генерация трехмерных ландшафтов. URL: https://www.ixbt.com/video/3dterrains-generation.shtml
Curtis, Christopher & Deconinck, Bernard. (2010). On the convergence of Hill's method. Math. Comput.. 79. 169-187.
Y. Fu, Z. Zheng, D. Fu and Y. Tong, "Comparison of two fractal surface modeling methods," 2016 12th World Congress on Intelligent Control and Automation (WCICA), 2016, pp. 417-421, doi: 10.1109/WCICA.2016.7578280.
Hyttinen, Tuomo & Mäkinen, Erkki & Poranen, Timo. (2017). Terrain synthesis using noise by examples. 17-25.
Ladd A.M., Kavraki L.E. Measure theoretic analysis of probabilistic path planning. IEEE Trans. on Robotics and Au-tomation, 2004, vol. 20, no. 2, pp. 229–242.
Titas Bera, M. Seetharama Bhat, Debasish Ghose, Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability, IFAC Proc., Vol/ 47, Issue 1, 2014, Pages 462-469, https://doi.org/10.3182/20140313-3-IN-3024.00245.
Wei L., Zheng Zheng, Kaiyuan Cai, Adaptive path planning for unmanned aerial vehicles based on bi-level program-ming and variable planning time interval, Chinese J. of Aeronautics, Vol. 26, Is. 3, 2013, pp 646-660, https://doi.org/10.1016/j.cja.2013.04.041.
Dijkstra E. W. A note on two problems in connexion with graphs // Numer. Math / F. Brezzi – Springer-Verlag, 1959. – Vol. 1, Iss. 1. – P. 269–271. – ISSN 0029-599X; 0945-3245 – doi:10.1007/BF01386390
Золотухіна О. А., Волошин Д. Г., Давидов В. В., Бречко В. О. Розробка імітаційної моделі процесу розрахунку і коригування безпечної польотної траєкторії безпілотного літального апарату // Телекомунікаційні та інформаційні технології. 2020. № 4 (69). С. 87–94.