Abstract
An unmanned aerial vehicle (UAV) is a small, fast aircraft with many useful features. It is widely used in military reconnaissance, aerial photography, searches, and other fields; it also has very good practical-application and development prospects. Since the UAV’s flight orientation is easily changeable, its orientation and flight path are difficult to control, leading to its high damage rate. Therefore, UAV flight-control technology has become the focus of attention. This study focuses on simulating a UAV’s flight and orientation control, and detecting collisions between a UAV and objects in a complex virtual environment. The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment. A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper, to improve the system performance and accelerate the collision-detection process. This provides a practical method for future studies on UAV flight position and orientation control, collision detection, etc.
Highlights
An unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard
UAV orientation and position control Orientation control is the premise for realizing many complex UAV functions; i.e., it is the core of UAV control
The current angle is measured by the UAV simulation system, where the angle refers to the Euler angle
Summary
An unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard. It can climb, fall, hover, yaw, etc. The UAV is an underactuated system [1] that has six degrees-of-freedom (position and orientation) and multiple control inputs (e.g., rotor speed). It has multivariable, non-linear, and strong coupling characteristics, all of which make its flight-control design very difficult. Accurate collision detection can improve the authenticity and reliability of the UAV simulation system, giving the user a better sense of immersion
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