The Development of 3D Aircraft Flight Path Models by NURBS Parametric Equations
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Abstract
There are a variety of methods for creating aircraft flight routes or predicting flight routes such as prediction by the neural network method or prediction with relative motion between positions. In this research, aircraft flight paths will be created using the Non-Uniform Rational Basic Spline parametric (NURBS) equation. The NURBS equation is a mathematical model based on spline method commonly used in various computer graphics applications which are used to create curves. The nature of an aircraft's flight path also has curves, which are suitable for creating a flight path by using the three dimensions data of the flight path to calculate the appropriate parameters for each flight path. One of the most important parameters for constructing a NURBS curve is the Control Point. To test the NURBS parametric equation, the parameters of the equation are adjusted and then the errors are compared
between the obtained aircraft position data set and the curve position obtained from the NURBS equation to give the smallest error value of the curve. Then, those parameters defined as NURBS parametric equations show the flight path of the next aircraft.
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References
Liu Y. & Hansen M. 2018. Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach, Institutes of Transportation Studies, University of California, Berkeley
Pang Y., Xu N & Liu Y. 2019. Aircraft Trajectory Prediction using LSTM Neural Network with Embedded Convolutional Layer. Annul Conference of The Prognostics and Health Management Society.
Xu X., Yang R., Zhang T. & Yang B. 2019. Trajectory Prediction of Target Aircraft in Air Combat Based on GA-OIF-Elman Neural
Network. IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA).
Rodin E.Y., & Amin S.M. 1992. Maneuver prediction in air combat via artificial neural network. Computer Mat. Applic, Vol. 24,
No.3, pp. 95-112.
Lin Y., Zhang J. & Liu H. 2018. An algorithm for trajectory prediction of flight plan based on relative motion between positions. Front Inform Technol Electron Eng, 19 (7) :905-916.
Ulf A. 2020. Computer Graphics Curves and Surfaces.
Lei W.T., Sung M.P., Lin L.Y. & Huang J.J. 2007. Fast real-time NURBS path interpolation for CNC machine tools.Int. J. Mach. Tool Manufact., 47 (10) pp. 1530-1541.
Lei W.T. & Sung M.P. 2008. NURBSbased fast geometric error compensation for CNC machine tools. Int. J. Mach. Tool Manufact., 48 (3-4) , pp. 307-319.
Liang F., Kang Ch. & Fang F. 2020. A smooth tool path planning method on NURBS surface based on the shortest boundary Defence Technology Academic Journal, Volume 3, Issue 8 / July - December 2021 59 geodesic map. Int. J. Manufact Proc., 58 pp. 646-658.
Ji Sh., Lei L., Zhao J., Lu X. & Gao H. 2021. An adaptive real-time NURBS curve interpolation for 4-axis polishing machine tool. Robot. Comput. Integrat. Manuf., 67
John, F., J.L. Lowther, & C.K. Shene. 2004. If You Know BSplines Well, You Also Know NURBS!. ACM SIGCSE Bulletin 36(1):343-347.
Johnson D.L. 1996. Multi-Command Handbook 11-F16. Vol.5.
Chouychai B. 2015. Point cloud data reduction with tangent function method for nurbs curve and surface fitting.
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