Path guidance and obstacle detection techniques using monocular vision systems for automated guided vehicles (AGVS)

There are many problems with object detection for autonomous robots and automated guided vehicles (AGVs) used in factories. The main problems consist of issues such as routing and obstacle recognition. For the solution of these, laser, sight, vision based sensors have been practically used. In this study, the methods used through vision-based sensors, such as camera, are in the field of interest. These methods consist of two categories: traditional or machine learning based. Recently, the most popular of these techniques are deep learning-based techniques, which is a sub-field of machine learning. Deep learning techniques such as YOLO, SSD, R-CNN have left many other techniques out of use with their performance. However, these techniques may occasionally experience performance degradation especially during video processing. Better results can be obtained by adding a novel Sequential Decision Theory layer on top of the available deep learning techniques.