Development of the unmanned aerial vehicle safe flight trajectorycalculation and adjustment process immitation model

DOI: 10.31673/2412-4338.2020.048794

Abstract

The article is devoted to the development and research the unmanned aerial vehicle safe flight trajectory calculation and adjustment process imitation model. The main distinguishing feature of the presented model is taking into account the signals of radar stations during the determination and adjustment the location of the aerial vehicle in the case of an autonomous flight and adapting the route based on possible obstacles and deviations. This will improve the safety of the unmanned aerial vehicle flight trajectory. Main studies of path planning methods approaches are analyzed. It was concluded that two-dimensional representation and visualization has disadvantages. Based on disadvantages, the three-dimensional calculated trajectory visualization of a flight mission by an unmanned aerial vehicle simulation model is developed. The simulation model combines the solution of following subtasks: a) modeling a three-dimensional environment with obstacles; b) building an unmanned aerial vehicle trajectory in such environment with avoidance the obstacles. Missions simulation is performed based on developed software system, sequentially, step by step. The following results of the simulation are achieved: 1) a special graphical interface for entering input data and displaying the results is developed; 2) the subsystem for simulation the space for performing a flight task taking into account the location and signals of external influences (for example, radar stations) was developed; 3) the subsystem for flight trajectory simulation calculation and visualization is developed. The software system is built using Delphi based on modular basis. The functional software system structure and the order of each functional module operation are described in article. The system’s graphical interface structure is discussed separately. An example of calculating and adjusting the safe flight trajectory of an unmanned aerial vehicle is illustrated.

Keywords: unmanned aerial vehicle, simulation model, safety, flight trajectory.

References
1. Khatib O. (1986) Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research. Vol. 5. No. 1. P. 90–98.
2. Barabash O., Shevchenko G., Dakhno N., Neshcheret O., Musienko A. (2017) Information Technology of Targeting: Optimization of Decision Making Process in a Competitive Environment. International Journal of Intelligent Systems and Applications. Vol. 9. № 12. Hong Kong: MECS Publisher. P. 1 – 9.
3. Barabash O.V., Dakhno N.B., Shevchenko H.V. (2017) Modified gradient method in a decision support system for control unmanned aerial vehicles. Science and Education a New Dimension. Natural and Technical Sciences, Vol. V (16), No 148, Budapest, Hungary, pp. 60 – 62.
4. Tuyrin V., Barabash O., Openko P., Sachuk I., Dudush A. (2017) Informational Support System for Technical State Control of Military Equipment. Proceedings of 2017 IEEE 4th International Conference “Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD)”, October 17-19, 2017, Kyiv, Ukraine. National Aviation University. P. 242 – 244.
5. Mashkov O.A., Sobchuk V.V., Barabash O.V., Dakhno N.B., Shevchenko H.V., Maisak T.V. (2019) Improvement of variational-gradient method in dynamical systems of automated control for integro-differential models. Mathematical Modeling and Computing, Vol. 6, No. 2. P. 344 – 357.
6. Xuming He, Peide Shi. (1997) Monotone B - Spline Smoothing. Journal of the American Statistical Association. October 1997. DOI: 10.2307/2670115 ·
7. Semenov S., Voloshyn D., Ahmed A.N. (2019) Mathematical model of the implementation process of flight task of unmanned aerial vehicle in the conditions of external impact. International Journal of Advanced Trends in Computer Science and Engineering, No 8(1), РР. 7–13
8. Semenov, S., Sira, O., Kuchuk, N. (2018) Development of graphicanalytical models for the software security testing algorithm. Eastern-European Journal of Enterprise Technologies, No 2(4-92), PP. 39–46
9. Park S., Deyst J., How P.J. (2004) 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.
10. Sujit B.P., Saripalli S., Borges Sousa J. Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless // Control Systems. 2014. Vol. 34. No. 1. P. 42–59.
11. Barabash O., Dakhno N., Shevchenko H., Sobchuk V. (2019) Unmanned Aerial Vehicles Flight Trajectory Optimisation on the Basis of Variational Enequality Algorithm and Projection Method. Proceeding. 2019 IEEE 5th International Conference “Actual Problems of Unmanned Aerial Vehicles Developments” (APUAVD). 22-24 October, National Aviation University, Kyiv, Ukraine. P. 136 – 139.
12. Fan X., Li S., Chen Tefang T. (2005) Dynamic obstacle avoiding path plan for robots based on a new artificial potential field function. Control Theory & Applications. Vol. 22. No. 5. P. 703–707
13. Dijkstra E. W. (1959) A note on two problems in connexion with graphs. Numer. Math / F. Brezzi – Springer-Verlag. Vol. 1, Iss. 1. P. 269–271.