Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform

This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage o...

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Autores principales:	Panwar Vikas Singh, Pandey Anish, Hasan Muhammad Ehtesham
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2021
Materias:	e-puck robot generalised regression neural network architecture virtual robot experimentation platform software scattered obstacle infra-red sensor Mechanics of engineering. Applied mechanics TA349-359
Acceso en línea:	https://doaj.org/article/630e43c1dbaa4d7a9422abc12d5be578
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id	oai:doaj.org-article:630e43c1dbaa4d7a9422abc12d5be578
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spelling	oai:doaj.org-article:630e43c1dbaa4d7a9422abc12d5be5782021-12-05T14:11:05ZGeneralised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform2300-531910.2478/ama-2021-0027https://doaj.org/article/630e43c1dbaa4d7a9422abc12d5be5782021-12-01T00:00:00Zhttps://doi.org/10.2478/ama-2021-0027https://doaj.org/toc/2300-5319This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage of this GRNN over the feedforward neural network is that it provides accurate results in a short period with minimal error. First, the designed GRNN architecture receives real-time obstacle information from the Infra-Red (IR) sensors of an E-puck robot. According to IR sensor data interpretation, this architecture sends the left and right wheel velocities command to the E-puck robot in the V-REP software platform. In the present study, the GRNN architecture includes the MIMO system, i.e., multiple inputs (IR sensors data) and multiple outputs (left and right wheel velocities). The three-dimensional (3D) motion and orientation results of the GRNN architecture-controlled E-puck robot are carried out in the V-REP software platform among scattered and wall-type obstacles. Further on, compared with the feedforward neural network, the proposed GRNN architecture obtains better navigation path length with minimum error results.Panwar Vikas SinghPandey AnishHasan Muhammad EhteshamSciendoarticlee-puck robotgeneralised regression neural network architecturevirtual robot experimentation platform softwarescattered obstacleinfra-red sensorMechanics of engineering. Applied mechanicsTA349-359ENActa Mechanica et Automatica , Vol 15, Iss 4, Pp 209-214 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-puck robot generalised regression neural network architecture virtual robot experimentation platform software scattered obstacle infra-red sensor Mechanics of engineering. Applied mechanics TA349-359
spellingShingle	e-puck robot generalised regression neural network architecture virtual robot experimentation platform software scattered obstacle infra-red sensor Mechanics of engineering. Applied mechanics TA349-359 Panwar Vikas Singh Pandey Anish Hasan Muhammad Ehtesham Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform
description	This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage of this GRNN over the feedforward neural network is that it provides accurate results in a short period with minimal error. First, the designed GRNN architecture receives real-time obstacle information from the Infra-Red (IR) sensors of an E-puck robot. According to IR sensor data interpretation, this architecture sends the left and right wheel velocities command to the E-puck robot in the V-REP software platform. In the present study, the GRNN architecture includes the MIMO system, i.e., multiple inputs (IR sensors data) and multiple outputs (left and right wheel velocities). The three-dimensional (3D) motion and orientation results of the GRNN architecture-controlled E-puck robot are carried out in the V-REP software platform among scattered and wall-type obstacles. Further on, compared with the feedforward neural network, the proposed GRNN architecture obtains better navigation path length with minimum error results.
format	article
author	Panwar Vikas Singh Pandey Anish Hasan Muhammad Ehtesham
author_facet	Panwar Vikas Singh Pandey Anish Hasan Muhammad Ehtesham
author_sort	Panwar Vikas Singh
title	Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform
title_short	Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform
title_full	Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform
title_fullStr	Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform
title_full_unstemmed	Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform
title_sort	generalised regression neural network (grnn) architecture-based motion planning and control of an e-puck robot in v-rep software platform
publisher	Sciendo
publishDate	2021
url	https://doaj.org/article/630e43c1dbaa4d7a9422abc12d5be578
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Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform

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