PENERAPANAN MODEL DETEKSI OBJEK UNTUK ROBOT MENGGUNAKAN MODEL SSD DI LINGKUNGAN SIMULASI ROS - Tamadjoe, Ilham Rizqyakbar; Dewa, Chandra Kusuma (JATISI, 2024)
The rapid advancement of automation technologies, particularly robotics, has transformed industries and economies in Southeast Asia. As Indonesia aims for developed nation status by 2045, embracing automation is crucial for competitiveness. However, Indonesia trails behind its regional peers in automation and robotics adoption, as noted by the International Federation of Robotics. Initiatives like the Indonesian Robotics Contest (KRI) seek to bridge this gap by fostering innovation among students and preparing them for international competitions like RoboCup. Challenges in KRI, such as object detection using color segmentation methods, highlight the need for more advanced computer vision techniques, particularly through deep learning. This paper proposes using a single shot detector model in robotic soccer within the ROS Gazebo simulation environment. Models like MobileNet V2 offer real-time object detection essential for autonomous robotics. By focusing on predefined objects like balls and goals, the study aims to develop a robotic system capable of accurately identifying and measuring distances to objects based on visual characteristics. This research aims to enhance Indonesia’s robotics capabilities, address limitations in object recognition, and advance automation technology in the region.