The Design of People Follower Robot to Transport Harvests Using Yolo V5 Detection

Abstract

Robots transporting harvested products is commonly use automatic navigation systems with sensors such as lidar, cameras and ultrasonic proximity sensors are important for detecting obstacles and avoiding collisions. Mapping and positioning algorithm are crucial for precise the robot determine position in the field. A dependable drive system, adaptable to diverse terrains, and a robust motor are essential components. Efficient energy requirements must be addressed by using long-lasting battery systems and, where possible, automatic charging solutions. Robots picking mechanisms that can adapt to different types of plants, as well as storage systems that protect vegetables from damage during transport, also need to be considered. This writing utilizes does not huge robot harvest hardware as a prototype due to research scope limitations. Instead, it involves instructing a Jetson Nano and Arduino Uno to direct the camera towards the tracked object using coding through python and Arduino IDE. Computer mini used Jetson Nano for running the best model object tracking. Output serial connected with Arduino for control actuator like motor DC and servo motor. The method used is YOLOv5, while the image processing technique employs object detection, which likely serves for object identification and localization. The additional computer vision facilitates the camera to adapt to the objects. Through testing, the system successfully detects objects, particularly vest, with an average confidence value of 0.91%, indicating that the detection system has a very high level of accuracy in identifying and detecting vest.
 
 Keywords; Robot, Jetson nano, Arduino Uno, Object Detection, Yolov5