Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique

Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step towar...

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Autores principales:	Karoline Kamil A. Farag, Hussein Hamdy Shehata, Hesham M. El-Batsh
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/5578e599ab754a36baf0053a18f02c68
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spelling	oai:doaj.org-article:5578e599ab754a36baf0053a18f02c682021-11-15T01:19:06ZMobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique1687-961910.1155/2021/1129872https://doaj.org/article/5578e599ab754a36baf0053a18f02c682021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1129872https://doaj.org/toc/1687-9619Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step toward a specific goal; sensing and avoiding the obstacles coming across its way toward the target by training to take the corrective action for every possible offset is one of the most challenging problems in the field of robotics. This problem is solved by proposing an Artificial Intelligence System (AIS), which works on the behaviour of Intelligent Autonomous Vehicles (IAVs) like humans in recognition, learning, decision making, and action. First, the use of the AIS and some navigation methods based on Artificial Neural Networks (ANNs) to training datasets provided high Mean Square Error (MSE) from training on MATLAB Simulink tool. Standardization techniques were used to improve the performance of results from the training network on MATLAB Simulink. When it comes to knowledge-based systems, ANNs can be well adapted in an appropriate form. The adaption is related to the learning capacity since the network can consider and respond to new constraints and data related to the external environment.Karoline Kamil A. FaragHussein Hamdy ShehataHesham M. El-BatshHindawi LimitedarticleMechanical engineering and machineryTJ1-1570ENJournal of Robotics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mechanical engineering and machinery TJ1-1570
spellingShingle	Mechanical engineering and machinery TJ1-1570 Karoline Kamil A. Farag Hussein Hamdy Shehata Hesham M. El-Batsh Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique
description	Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step toward a specific goal; sensing and avoiding the obstacles coming across its way toward the target by training to take the corrective action for every possible offset is one of the most challenging problems in the field of robotics. This problem is solved by proposing an Artificial Intelligence System (AIS), which works on the behaviour of Intelligent Autonomous Vehicles (IAVs) like humans in recognition, learning, decision making, and action. First, the use of the AIS and some navigation methods based on Artificial Neural Networks (ANNs) to training datasets provided high Mean Square Error (MSE) from training on MATLAB Simulink tool. Standardization techniques were used to improve the performance of results from the training network on MATLAB Simulink. When it comes to knowledge-based systems, ANNs can be well adapted in an appropriate form. The adaption is related to the learning capacity since the network can consider and respond to new constraints and data related to the external environment.
format	article
author	Karoline Kamil A. Farag Hussein Hamdy Shehata Hesham M. El-Batsh
author_facet	Karoline Kamil A. Farag Hussein Hamdy Shehata Hesham M. El-Batsh
author_sort	Karoline Kamil A. Farag
title	Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique
title_short	Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique
title_full	Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique
title_fullStr	Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique
title_full_unstemmed	Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique
title_sort	mobile robot obstacle avoidance based on neural network with a standardization technique
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/5578e599ab754a36baf0053a18f02c68
work_keys_str_mv	AT karolinekamilafarag mobilerobotobstacleavoidancebasedonneuralnetworkwithastandardizationtechnique AT husseinhamdyshehata mobilerobotobstacleavoidancebasedonneuralnetworkwithastandardizationtechnique AT heshammelbatsh mobilerobotobstacleavoidancebasedonneuralnetworkwithastandardizationtechnique
_version_	1718428982836199424

Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique

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