Abstract
A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.
Highlights
Due to their strong maneuverability and high flexibility, unmanned aerial vehicles (UAVs) have gained great application prospects in many fields and scenarios in recent years
This study aims to establish the unsteady aerodynamic characteristics considering the prototype the remaining sections are as follows: Section 2 gives the aerodynamic influence of a ducted fan in and corner environment, and use a neural network to model them comprehensively
Sometimes the ducted fan aerial robot will hover near the corner, and the interference of the corner effect will prevent the prototype from working near the object in the corner environment
Summary
Due to their strong maneuverability and high flexibility, unmanned aerial vehicles (UAVs) have gained great application prospects in many fields and scenarios in recent years. The above two methods are often computationally intensive and require powerful processors to perform calculations, which are not suitable for aircraft operating in complex environments Traditional theories such as the linear aerodynamic coefficient cannot simulate the behavior of a UAV in a complex environment. [27] used neural networks to model the aerodynamic characteristics of UAV at high attack angles, compared the modeling effects of feedforward neural networks and recurrent neural networks, and performed wind tunnel experiments on prototypes to verify the prediction accuracy of the neural network model. This study aims to establish the unsteady aerodynamic characteristics considering the prototype the first to integratedly model the UAV and the environment. This study aims to establish the unsteady aerodynamic characteristics considering the prototype the remaining sections are as follows: Section 2 gives the aerodynamic influence of a ducted fan in and corner environment, and use a neural network to model them comprehensively.
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