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ISSN:2164-6457 (print)
ISSN:2164-6473 (online)
Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu

Design and Implementation of Motion Simulation for Precision Drop of Unmanned Aerial Vehicle Projectiles

Journal of Applied Nonlinear Dynamics 15(3) (2026) 739--750 | DOI:10.5890/JAND.2026.09.015

Caifeng Zou$^{1}$, Chuan Zhang$^{1,2,3}$, Xinlong Zhang$^{1}$

$^1$ School of Business, Shanghai DianJi University, Shanghai 201306, China

$^2$ School of Economics & Management, Shanghai Maritime University, Shanghai 200135, China

$^3$ School of Economics & Management, Shanghai University of Electric Power, Shanghai 200090, China

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Abstract

The accuracy of UAV deployment is influenced by operational techniques, flight conditions, and environmental factors (e.g., altitude, speed, wind speed). This paper conducts research using kinematics, aerodynamics, differential equations, and optimization methods. A 3D coordinate system and differential equation system are established to analyze the relationship between deployment distance and key factors, with distances calculated under three wind directions (0${^\circ}$, 90${^\circ}$, 180${^\circ}$). Based on kinematics and aerodynamics, a model with four variables (launch distance, altitude, dive angle, landing time) is built. Using linear programming combined with entropy weight method, the optimal strategy is obtained: dive angle 3${^\circ}$, launch distance 1005.01 m, flight altitude 717 m, landing time 10.9 s. Introducing dive/roll angles as attitude parameters, a target programming model solved by genetic algorithm yields optimal angles (2.56${^\circ}$, 3.48${^\circ}$). Verified via Lyapunov function and regression ($R^2=0.8388$), the random forest model achieves a higher $R^2$ of 0.9899.

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