Abstract
We propose an intelligent vision-based Automated Guided Vehicle (AGV) system using fiduciary markers. In this paper, we explore a low-cost, efficient vehicle guiding method using a consumer grade web camera and fiduciary markers. In the proposed method, the system uses fiduciary markers with a capital letter or triangle indicating direction in it. The markers are very easy to produce, manipulate, and maintain. The marker information is used to guide a vehicle. We use hue and saturation values in the image to extract marker candidates. When the known size fiduciary marker is detected by using a bird's eye view and Hough transform, the positional relation between the marker and the vehicle can be calculated. To recognize the character in the marker, a distance transform is used. The probability of feature matching was calculated by using a distance transform, and a feature having high probability is selected as a captured marker. Four directional signals and 10 alphabet features are defined and used as markers. A 98.87% recognition rate was achieved in the testing phase. The experimental results with the fiduciary marker show that the proposed method is a solution for an indoor AGV system.
Highlights
An Automated Guided Vehicle (AGV) is an intelligent mobile robot widely used to move objects or perform tasks in various places such as the industrial field, harbors, warehouses or dangerous working areas where human could not work in
We have proposed a new approach for an AGV system
An AGV system based on marker recognition has some advantages in terms of the cost of installation and changing routes
Summary
An Automated Guided Vehicle (AGV) is an intelligent mobile robot widely used to move objects or perform tasks in various places such as the industrial field, harbors, warehouses or dangerous working areas where human could not work in. Tried to detect some numbers beside the guide lane to check the location of the AGV They used a neural network algorithm to recognize the numbers. Kim et al [16] used a marker recognition method to locate the AGV without guideline. They applied Artificial Neural Networks (ANNs) to recognize letters of the alphabet on the marker. Jung et al [17] used the Hough transform to recognize the marker We modified their approach to obtain a more robust recognition result even if there are some occlusion problems and to determine the rotation angle of the marker at the same time.
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