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

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Summary

Introduction

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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