Implementation of Deep Learning on Obstacle Avoidance System PENSHIP Ship Robot

Abstract

This study proposes an autonomous system for detecting objects in the form of a ball which is assumed to be the trajectory of the PENSHIP ship robot. In following the trajectory, the ship robot is prohibited from being operated manually by remote control or using additional sensors such as ultrasonic sensors, laser sensors, proximity sensors or GPS tracking. This study uses an image/camera detection option using Deep Learning with YOLO model to identify obstacles in the ship robot’s track area, which will be implemented on the PENSHIP ship robot. The resistance data will be labeled and use the YOLO model. The results of this object recognition system succeeded in detecting the type of obstacle in the form of a ball with a YOLOv5 model with an accuracy of 90% at a distance of 2-5 meters from the obstacle. In the process of forming the Obstacle Avoidance System, the centroid value was successfully detected for each object bounding box and the simulation test results from the Obstacle Avoidance System that classified the movement direction of the ship was successful with 100% accuracy with BLDC motor speed approximately 1 m/s and the distance between the object at 2-5 meters. With this system, the PENSHIP Robot Ship will continue to follow the trajectory and avoid crash with the obstacles in front of it.