Real Time Object Edge Detection System to Assist RPLidar Sensor Performance

Abstract

The covid-19 virus really brings problems to human life, one of which is in the health sector. To overcome this problem, innovations in technology are needed. Today's robotics industry has been able to improve the quality and quantity of human life today. One proof is the presence of a robot that can navigate by itself, this is called an autonomous robot. However, in the navigation path of the robot itself, sometimes it will be faced with objects that are in its path. The object is not predictable by the 2D LiDAR sensor, so when it hits an object, the sensor gets messed up. In this study, a visual object detection method was developed to assist the performance of the RPLiDAR sensor so that the scanning carried out by the RPLiDAR sensor remains stable. This visual object detection method uses the OpenCV library which has functions to process images. The image obtained is a frame of the surrounding environment in real-time. The software used to use this method is the PyCharm IDE. By implementing this method in covid robot research, the covid robot could detect obstacles or detect objects. So that this covid robot could know that there was an object in its navigation path so that the performance of the RPlidar sensor could be maximized.