SURFACE DETECTION FOR QUADRUPED ROBOT USING YOLO-V3 TINY

Abstract

Robotics has become a very important field for engineers, as robots can perform various assigned tasks quickly and efficiently. specifically in the field of robotic with legs. In the Indonesian robot competition there are many categories, SAR is the one of them. In the SAR (Search and Rescue) category there are many surfaces that robot has to pass through, such as coral, and marbles. Different surfaces have different movements to pass through. The researchers have designed a quadruped with auto changed movement when pass through the surfaces, with YOLOv3-Tiny model on a raspberry pi 5 was placed on a quadruped robot. YOLOv3-Tiny model can detect surfaces according to dataset that has been trained. The model that has been trained with YOLOv3-Tiny is capable of detecting surfaces. In this research has an accuracy of detecting  up to 100% in 80 pictures. The precision value up to 1, the recall value up to 1, and the F1-score is up to 1.