Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem

Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate...

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Autores principales:	Javier Maldonado-Romo, Mario Aldape-Pérez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	path planning machine learning indoor navigation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/00c9ea5077d446e6831e2084e9324446
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id	oai:doaj.org-article:00c9ea5077d446e6831e2084e9324446
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spelling	oai:doaj.org-article:00c9ea5077d446e6831e2084e93244462021-11-11T15:24:16ZInteroperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem10.3390/app1121104452076-3417https://doaj.org/article/00c9ea5077d446e6831e2084e93244462021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10445https://doaj.org/toc/2076-3417Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate objects in motion, and build the planner module’s restrictions. On the other hand, the second module controls the flight of the system. This process is computationally expensive and requires adequate performance to avoid accidents. For this reason, we propose a novel solution to improve conventional robotic systems’ functions, such as systems having a small-capacity battery, a restricted size, and a limited number of sensors, using fewer elements. A navigation dataset was generated through a virtual simulator and a generative adversarial network to connect the virtual and real environments under an end-to-end approach. Furthermore, three path generators were analyzed using deep-learning solutions: a deep convolutional neural network, hierarchical clustering, and an auto-encoder. Since the path generators share a characteristic vector, transfer learning approaches complex problems by using solutions with fewer features, minimizing the costs and optimizing the resources of conventional system architectures, thus improving the limitations with respect to the implementation in embedded devices. Finally, a visualizer applying augmented reality was used to display the path generated by the proposed system.Javier Maldonado-RomoMario Aldape-PérezMDPI AGarticlepath planningmachine learningindoor navigationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10445, p 10445 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	path planning machine learning indoor navigation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	path planning machine learning indoor navigation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Javier Maldonado-Romo Mario Aldape-Pérez Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
description	Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate objects in motion, and build the planner module’s restrictions. On the other hand, the second module controls the flight of the system. This process is computationally expensive and requires adequate performance to avoid accidents. For this reason, we propose a novel solution to improve conventional robotic systems’ functions, such as systems having a small-capacity battery, a restricted size, and a limited number of sensors, using fewer elements. A navigation dataset was generated through a virtual simulator and a generative adversarial network to connect the virtual and real environments under an end-to-end approach. Furthermore, three path generators were analyzed using deep-learning solutions: a deep convolutional neural network, hierarchical clustering, and an auto-encoder. Since the path generators share a characteristic vector, transfer learning approaches complex problems by using solutions with fewer features, minimizing the costs and optimizing the resources of conventional system architectures, thus improving the limitations with respect to the implementation in embedded devices. Finally, a visualizer applying augmented reality was used to display the path generated by the proposed system.
format	article
author	Javier Maldonado-Romo Mario Aldape-Pérez
author_facet	Javier Maldonado-Romo Mario Aldape-Pérez
author_sort	Javier Maldonado-Romo
title	Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
title_short	Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
title_full	Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
title_fullStr	Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
title_full_unstemmed	Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
title_sort	interoperability between real and virtual environments connected by a gan for the path-planning problem
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/00c9ea5077d446e6831e2084e9324446
work_keys_str_mv	AT javiermaldonadoromo interoperabilitybetweenrealandvirtualenvironmentsconnectedbyaganforthepathplanningproblem AT marioaldapeperez interoperabilitybetweenrealandvirtualenvironmentsconnectedbyaganforthepathplanningproblem
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Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem

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