Development of robotic grasping of object detection model
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 r...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177230 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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