Learning based robotic grasping

• Main Author: Wang, Chongyu
• Other Authors: Cheah Chien Chern
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/158933
• Institution: Nanyang Technological University

Nowadays, with the rapid development of artificial intelligence, machine learning has made great strides in making computers behave more intelligently. In this context, machine learning has been applied to robots to make them work in a more reasonable way. This has enabled rapid advances in robotic work planning. At the same time, with the transformation and upgrading of manufacturing, intelligent industrial robots with vision systems are widely used in modern factories. In order to deploy robots rapidly in flexible manufacturing and make robots operate objects accurately, this dissertation studied a robot that applies machine learning to robot object detecting. The content and results of this report mainly include the following aspects: Firstly, this report presents a grasping robot that is able to detect objects accurately by using a model trained by YOLO (You only look once). In order to make the robot grasp objects while avoiding the sharp points on their edge, YOLO is also used to train the model to detect the sharp points on objects' edges. Training results show that the mean average precision of detecting different objects in the lab can reach as high as 91.8\%. Secondly, this report presents a method to calculate the position of the robot tool for grasping objects. The experimental results show that the robot is able to grasp the objects accurately and steadily with the detecting outcomes and robot motion commands.