Quadrotor is one of the types of flying robots that has attracted the attention of researchers due to its simple structure and perpendicular flight capability. This paper presents a new method based on machine vision for correct window detection, in smoothly unknown env
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Quadrotor is one of the types of flying robots that has attracted the attention of researchers due to its simple structure and perpendicular flight capability. This paper presents a new method based on machine vision for correct window detection, in smoothly unknown environments. One of the challenges of controlling the Quadrotor path in unknown environments is actually accurate window identification for passing through it. In this study, quadrotor Parrot Bebop2 is used which is equipped with a camera. Also, an algorithm is proposed to perform image processing to identify the window in the environment and control the quadrotor's trajectory, which is implemented on the quadrotor. This method consists of three parts: preprocessing, diagnosis and identification. First, by applying image processing algorithms, we improve the image and delete the data unrelated to the target, and then we use a smart machine vision algorithm to extract information. Furthermore, to control the quadrotor route, a proportional-integral-derivative controller is designed and implemented using Ziegler and Nichols method, which will take place during a real indoor flight in an automated tracking. According to the obtained results, it can be concluded that the use of flying robots can have positive results in military processes and assistance to people in a short time.
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