Abstract
A quadrotor, or quadrotor helicopter, is an aircraft that becomes airborne due to the lift force provided by four rotors usually mounted in cross configuration, hence its name. They are of particular interest due to their small size, great maneuverability and hovering capability. However quadrotors have a limited payload capacity because they require four motors for operation, which consumes more power than conventional Unmanned Aerial Vehicles. This reduces the number of sensors and mission critical equipment they are able to carry. Due to this limitation researchers have turned to vision-based techniques for navigation of UAVs. Vision-based navigation provides a large number of useful features that extend quadrotors capabilities. They include the basic control functions of keeping the quadrotor roll and pitch angles stable (attitude control), controlling the vehicle trajectories when flying in free spaces (course stabilization), and keeping the altitude at proper height over ground. A color camera tracks the image features under the quadrotor and above ground. These blobs are located on a known geometric shape.
Highlights
A quadrotor known as quadrotor unmanned aerial vehicle (UAV) consists of two pairs of counter-rotating rotors and propellers, located at the vertices of a square frame
It has four rotors and is capable of vertical takeoff and landing (VTOL), and does not require complex mechanical linkages, such as swash plates or teeter hinges that commonly appear in typical helicopters
The force vector is split into a horizontal component and a vertical component
Summary
A quadrotor known as quadrotor unmanned aerial vehicle (UAV) consists of two pairs of counter-rotating rotors and propellers, located at the vertices of a square frame. To roll or pitch, one rotor's thrust is decreased and the opposite rotor‟s thrust is increased by the same amount A quadrotor is equipped with a fixed vision camera looking ahead This camera captures the image of a pattern mounted on the target vehicle that features four marks on a square of known side length. The positions of the pattern‟s marks on the image are expressed in pixels From these image features, as measured by the vision system, it is possible to compute the posture of the target vehicle, measured on a coordinate system associated to the camera. Figure 1.shows a diagram with the horizontal projection of the vision system, showing the posture of the target vehicle on the camera‟s coordinates [1]
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