Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can...

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Main Author:	Mahadhir, Khairul Azmi
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Control systems Computer simulation
Online Access:	https://ir.uitm.edu.my/id/eprint/27619/1/TM_KHAIRUL%20AZMI%20MAHADHIR%20EM%2015.pdf
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spelling	my-uitm-ir.276192020-02-05T06:37:31Z Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir 2015 Mahadhir, Khairul Azmi Control systems Computer simulation Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision. 2015 Thesis https://ir.uitm.edu.my/id/eprint/27619/ https://ir.uitm.edu.my/id/eprint/27619/1/TM_KHAIRUL%20AZMI%20MAHADHIR%20EM%2015.pdf text en public masters Universiti Teknologi MARA Faculty of Mechanical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Control systems Computer simulation
spellingShingle	Control systems Computer simulation Mahadhir, Khairul Azmi Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir
description	Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision.
format	Thesis
qualification_level	Master's degree
author	Mahadhir, Khairul Azmi
author_facet	Mahadhir, Khairul Azmi
author_sort	Mahadhir, Khairul Azmi
title	Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir
title_short	Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir
title_full	Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir
title_fullStr	Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir
title_full_unstemmed	Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir
title_sort	development of track-driven agriculture robot with terrain classification functionality / khairul azmi mahadhir
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Mechanical Engineering
publishDate	2015
url	https://ir.uitm.edu.my/id/eprint/27619/1/TM_KHAIRUL%20AZMI%20MAHADHIR%20EM%2015.pdf
_version_	1783733985027817472

Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

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