KINOVA ROBOTIC ARM MANIPULATION WITH PYTHON PROGRAMMING

File
Publisher
Florida Atlantic University
Date Issued
2022
EDTF Date Created
2022
Description
As artificial intelligence (AI), such as reinforcement learning (RL), has continued to grow, the introduction of AI for use in robotic arms in order to have them autonomously complete tasks has become an increasingly popular topic. Robotic arms have recently had a drastic spike in innovation, with new robotic arms being developed for a variety of tasks both menial and complicated. One robotic arm recently developed for everyday use in close proximity to the user is the Kinova Gen 3 Lite, but limited formal research has been conducted about controlling this robotic arm both with an AI and in general. Therefore, this thesis covers the implementation of Python programs in controlling the robotic arm physically as well as the use of a simulation to train an RL based AI compatible with the Kinova Gen 3 Lite. Additionally, the purpose of this research is to identify and solve the difficulties in the physical instance and the simulation as well as the impact of the learning parameters on the robotic arm AI. Similarly, the issues in connecting two Kinova Gen 3 Lites to one computer at once are also examined.
This thesis goes into detail about the goal of the Python programs created to move the physical robotic arm as well as the overall setup and goal of the robotic arm simulation for the RL method. In particular, the Python programs for the physical robotic arm pick up the object and place it at a different location, identifying a method to prevent the gripper from crushing an object without a tactile sensor in the process. The thesis also covers the effect of various learning parameters on the accuracy and steps to goal curves of an RL method designed to make a Kinova Gen 3 Lite grab an object in a simulation. In particular, a neural network implementation of RL method with one of the learning parameters changed in comparison to the optimal learning parameters. The neural network is trained using Python Anaconda to control a Kinova Gen 3 Lite robotic arm model for a simulation made in the Unity compiler.
Note

Includes bibliography.

Language
Type
Extent
77 p.
Identifier
FA00014022
Rights

Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

Additional Information
Includes bibliography.
Thesis (MS)--Florida Atlantic University, 2022.
FAU Electronic Theses and Dissertations Collection
Date Backup
2022
Date Created Backup
2022
Date Text
2022
Date Created (EDTF)
2022
Date Issued (EDTF)
2022
Extension


FAU

IID
FA00014022
Person Preferred Name

Veit, Cameron

author

Graduate College
Physical Description

application/pdf
77 p.
Title Plain
KINOVA ROBOTIC ARM MANIPULATION WITH PYTHON PROGRAMMING
Use and Reproduction
Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
http://rightsstatements.org/vocab/InC/1.0/
Origin Information

2022
2022
Florida Atlantic University

Boca Raton, Fla.

Place

Boca Raton, Fla.
Title
KINOVA ROBOTIC ARM MANIPULATION WITH PYTHON PROGRAMMING
Other Title Info

KINOVA ROBOTIC ARM MANIPULATION WITH PYTHON PROGRAMMING