Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments

Increasingly, robotic systems require a level of perception of the scenario to interact in real-time, but they also require specialized equipment such as sensors to reach high performance standards adequately. Therefore, it is essential to explore alternatives to reduce the costs for these systems....

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Autores principales:	Javier Maldonado-Romo, Mario Aldape-Pérez, Alejandro Rodríguez-Molina
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	autonomous driving machine learning computer vision virtual training Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/cb512d13ea464eec8fa6b3647cdc64d2
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spelling	oai:doaj.org-article:cb512d13ea464eec8fa6b3647cdc64d22021-11-25T18:58:19ZPath Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments10.3390/s212276671424-8220https://doaj.org/article/cb512d13ea464eec8fa6b3647cdc64d22021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7667https://doaj.org/toc/1424-8220Increasingly, robotic systems require a level of perception of the scenario to interact in real-time, but they also require specialized equipment such as sensors to reach high performance standards adequately. Therefore, it is essential to explore alternatives to reduce the costs for these systems. For example, a common problem attempted by intelligent robotic systems is path planning. This problem contains different subsystems such as perception, location, control, and planning, and demands a quick response time. Consequently, the design of the solutions is limited and requires specialized elements, increasing the cost and time development. Secondly, virtual reality is employed to train and evaluate algorithms, generating virtual data. For this reason, the virtual dataset can be connected with the authentic world through Generative Adversarial Networks (GANs), reducing time development and employing limited samples of the physical world. To describe the performance, metadata information details the properties of the agents in an environment. The metadata approach is tested with an augmented reality system and a micro aerial vehicle (MAV), where both systems are executed in an authentic environment and implemented in embedded devices. This development helps to guide alternatives to reduce resources and costs, but external factors limit these implementations, such as the illumination variation, because the system depends on only a conventional camera.Javier Maldonado-RomoMario Aldape-PérezAlejandro Rodríguez-MolinaMDPI AGarticleautonomous drivingmachine learningcomputer visionvirtual trainingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7667, p 7667 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	autonomous driving machine learning computer vision virtual training Chemical technology TP1-1185
spellingShingle	autonomous driving machine learning computer vision virtual training Chemical technology TP1-1185 Javier Maldonado-Romo Mario Aldape-Pérez Alejandro Rodríguez-Molina Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments
description	Increasingly, robotic systems require a level of perception of the scenario to interact in real-time, but they also require specialized equipment such as sensors to reach high performance standards adequately. Therefore, it is essential to explore alternatives to reduce the costs for these systems. For example, a common problem attempted by intelligent robotic systems is path planning. This problem contains different subsystems such as perception, location, control, and planning, and demands a quick response time. Consequently, the design of the solutions is limited and requires specialized elements, increasing the cost and time development. Secondly, virtual reality is employed to train and evaluate algorithms, generating virtual data. For this reason, the virtual dataset can be connected with the authentic world through Generative Adversarial Networks (GANs), reducing time development and employing limited samples of the physical world. To describe the performance, metadata information details the properties of the agents in an environment. The metadata approach is tested with an augmented reality system and a micro aerial vehicle (MAV), where both systems are executed in an authentic environment and implemented in embedded devices. This development helps to guide alternatives to reduce resources and costs, but external factors limit these implementations, such as the illumination variation, because the system depends on only a conventional camera.
format	article
author	Javier Maldonado-Romo Mario Aldape-Pérez Alejandro Rodríguez-Molina
author_facet	Javier Maldonado-Romo Mario Aldape-Pérez Alejandro Rodríguez-Molina
author_sort	Javier Maldonado-Romo
title	Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments
title_short	Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments
title_full	Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments
title_fullStr	Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments
title_full_unstemmed	Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments
title_sort	path planning generator with metadata through a domain change by gan between physical and virtual environments
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cb512d13ea464eec8fa6b3647cdc64d2
work_keys_str_mv	AT javiermaldonadoromo pathplanninggeneratorwithmetadatathroughadomainchangebyganbetweenphysicalandvirtualenvironments AT marioaldapeperez pathplanninggeneratorwithmetadatathroughadomainchangebyganbetweenphysicalandvirtualenvironments AT alejandrorodriguezmolina pathplanninggeneratorwithmetadatathroughadomainchangebyganbetweenphysicalandvirtualenvironments
_version_	1718410490096386048

Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments

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