AGV (Automated Guided Vehicle) Path Recognition System

In this project, we implemented an AGV (Automated Guided Vehicle) path recognition system using MATLAB. The system employs a comprehensive suite of image processing techniques to achieve robust path identification. Initially, we perform grayscale conversion using MATLAB's rgb2gray() function to simplify subsequent processing by reducing color dimensionality. Next, threshold segmentation is applied through adaptive thresholding algorithms to separate foreground (path) from background elements. We then implement binarization using imbinarize() function to create a binary image that clearly distinguishes the path from surrounding areas. To enhance path quality, morphological operations including opening (imopen()) and closing (imclose()) are employed to eliminate noise particles and fill gaps in the path structure. Finally, edge detection algorithms such as Canny or Sobel operators are implemented using edge() function to precisely delineate path boundaries. Through this systematic approach combining multiple image processing techniques, we successfully developed a MATLAB-based AGV path recognition system that provides the fundamental framework for automated vehicle navigation.