Please use this identifier to cite or link to this item:
Title: Multi-terrain traversability of mobile robots using deep learning
Authors: Lee, Jerome Zhi Hao
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2020
Publisher: Nanyang Technological University
Project: B1191-191
Abstract: With robots being a growing topic in the world of technology, the need for more accuracy and precision is needed in its tasks. One such area is how these robots are becoming more able to navigate through various terrains to deliver and transport. In recent years, there has been a lot of projects and advancements explored to be used on such application. The possible functions that autonomous mobile robot navigation is limitless. With advancements in this research topic, we look to dig deeper at Deep Learning methods that can improve Traversability to manoeuvre any type of terrain. Various methods have been studied and put into effect as shown in recent advancements in delivery robots and the rise of autonomous self-driving cars. Even within deep learning methods, there are still much to discover as there are many factors to consider within the world of traversability due to various terrains and its unpredictability. In this project, the author aims to use a deep learning approach via convolutional neural networks to identify various terrains and use efficient algorithms for path planning. We will be using deep learning frameworks from TensorFlow and Keras, and input data from RGB images to help with terrain classification and path planning for multi-terrain traversability.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_FinalReport_LeeZhiHaoJerome - nosc.pdf
  Restricted Access
2.36 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 24, 2024


Updated on Jun 24, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.