Development of robotic grasping of object detection model

• Main Author: Koh, Aloysius Jun Jie
• Other Authors: Cheah Chien Chern
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering; Object detection; Robotic grasping
• Online Access: https://hdl.handle.net/10356/177230
• Institution: Nanyang Technological University

Robotic grasping stands as a foundational task within the realm of robotics, facilitating effective interaction and manipulation of objects in their environment. In recent years, the integration of machine learning has revolutionized robotic grasping, empowering robots to grasp directly from raw sensory data. This paper aims to explore a machine learning-based approach to robotic grasping, with a specific focus on leveraging basic shape features to discern potential grasping points. It provides a comprehensive documentation of the process involved in training the YOLOv5 robotic grasping model, starting from the stage of image collection, proceeding to the development of algorithms aimed at identifying feasible grasping points based on the detected data, and culminating in the implementation of the model and algorithms onto an actual robotic arm for simulation.