An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles
The automated guided vehicle (AGV) is a key component for the successful implementation of flexible manufacturing systems (FMS). AGVs are wheeled mobile robots (WMR) employed for material handling in the constantly evolving layouts of these modern factory shop floors. As such their ability to naviga...
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oai:eprints.oum.edu.my:6102011-10-14T06:12:21Z An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles Murelitharan, Muniandy Kanesan , Muthusamy TS Manufactures The automated guided vehicle (AGV) is a key component for the successful implementation of flexible manufacturing systems (FMS). AGVs are wheeled mobile robots (WMR) employed for material handling in the constantly evolving layouts of these modern factory shop floors. As such their ability to navigate autonomously is an equally important aspect to sustain an efficient manufacturing process. However, their mobility efficiency is inherently affected by the unproductive systematic and non-systematic odometry errors. Odometry errors mainly occur due to the mobility configuration of the AGV drive train and the surface characteristics the robot is interacting with. Odometry error accumulates over the distance traveled and leads to severe dead reckoning inaccuracy if the robot’s feedback control mechanism is unable to correct the error fast. This paper proposes an innovative drive train mechanism called dual planetary drive (DPD) that will minimize odometry errors without the need for complex electronic feedback control systems. (Abstract by authors) Trans Tech 2012 Article PeerReviewed Murelitharan, Muniandy and Kanesan , Muthusamy (2012) An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles. Advanced Materials Research (AMR), 383-390. ISSN 1022-6680 http://library.oum.edu.my/repository/610/ |
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TS Manufactures Murelitharan, Muniandy Kanesan , Muthusamy An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles |
description |
The automated guided vehicle (AGV) is a key component for the successful implementation of flexible manufacturing systems (FMS). AGVs are wheeled mobile robots (WMR) employed for material handling in the constantly evolving layouts of these modern factory shop floors. As such their ability to navigate autonomously is an equally important aspect to sustain an efficient manufacturing process. However, their mobility efficiency is inherently affected by the unproductive systematic and non-systematic odometry errors. Odometry errors mainly occur due to the mobility configuration of the AGV drive train and the surface characteristics the robot is interacting with. Odometry error accumulates over the distance traveled and leads to severe dead reckoning inaccuracy if the robot’s feedback control mechanism is unable to correct the error fast. This paper proposes an innovative drive train mechanism called dual planetary drive (DPD) that will minimize odometry errors without the need for complex electronic feedback control systems. (Abstract by authors) |
format |
Article |
author |
Murelitharan, Muniandy Kanesan , Muthusamy |
author_facet |
Murelitharan, Muniandy Kanesan , Muthusamy |
author_sort |
Murelitharan, Muniandy |
title |
An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles |
title_short |
An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles |
title_full |
An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles |
title_fullStr |
An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles |
title_full_unstemmed |
An Innovative Drive Train Design for Improved Dead Reckoning Accuracy in Automated Guided Vehicles |
title_sort |
innovative drive train design for improved dead reckoning accuracy in automated guided vehicles |
publisher |
Trans Tech |
publishDate |
2012 |
url |
http://library.oum.edu.my/repository/610/ |
first_indexed |
2018-09-06T17:08:30Z |
last_indexed |
2018-09-06T17:08:30Z |
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1610878770651070464 |