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Title: Grasping module of autonomous item picking robotic arm
Authors: Khoo, Jie Xiong
Keywords: DRNTU::Engineering::Industrial engineering::Automation
Issue Date: 2017
Abstract: The demand for robotic automation has grown past simple and repetitive tasks. Its applications are now pushing past simple repetitive tasks such as in mass manufacturing to more complexed tasks with higher variability. This project attempts to address the need for robotic automation applied to the warehousing and electronic commerce industry. The aim of this project is to autonomously pick and stow items according to the Amazon Picking Challenge (APC) 2017 rules by configuring a Universal Robot 5 (UR5) robotic arm. This project is split into 4 segments. This report covers only the grasping segment (Grasping Module) of the project, which aims to determine the best possible strategy to prehend the target item and return the position and orientation of the end-effector (e-e) to the System Manager. The Grasping Module should also cater to the other grasping related requirements of the project as specified in the APC 2017 rules. The Grasping Module was written in Python 2.7, from the overall framework to the functions which handle the algebraic manipulations, entirely from scratch. Physical grasp testing and data collection was also conducted to determine the best possible grasping strategy to use for each item.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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FYP Final Report - Khoo Jie Xiong.pdf
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