<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282024000100027</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación de Estrategias Metaheurísticas en la Planificación de Rutas para Robots]]></article-title>
<article-title xml:lang="en"><![CDATA[Evaluation of Metaheuristic Strategies in Robot Path Planning.]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sehuveret Hernández]]></surname>
<given-names><![CDATA[Dizahab]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Muñoz]]></surname>
<given-names><![CDATA[Jorge A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barranco Gutiérrez]]></surname>
<given-names><![CDATA[Alejandro I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de México en Celaya  ]]></institution>
<addr-line><![CDATA[Celaya, Gto ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<volume>45</volume>
<numero>1</numero>
<fpage>27</fpage>
<lpage>37</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282024000100027&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282024000100027&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282024000100027&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Los Vehículos Autónomos Terrestres son fundamentales para diversas industrias, requiriendo una planificación de rutas eficiente para el éxito de la misión sin intervención humana. Este estudio evalúa dos enfoques de optimización metaheurística, Algoritmos Genéticos y Optimización por Colonias de Hormigas, en el contexto de la planificación de rutas para estas estructuras robóticas. La metodología integral emplea diseños de estos métodos metaheurísticos, y se lleva a cabo un análisis comparativo mediante experimentos simulados en entornos diversos. Los resultados revelan que los Algoritmos Genéticos sobresalen en terrenos simples, mientras que la Optimización por Colonia de Hormigas demuestra capacidades de exploración robustas en entornos complejos con obstáculos y restricciones adicionales. Esto subraya la importancia de seleccionar la metaheurística adecuada basada en requisitos específicos de la misión y condiciones ambientales. La investigación brinda información a diseñadores y operadores, permitiendo tomar decisiones fundamentadas para la selección de estrategias de planificación de rutas, mejorando así la eficiencia y seguridad de las operaciones robóticas autónomas en diversos escenarios.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Autonomous Ground Vehicles play a vital role in various industries, necessitating efficient route planning for mission success without human intervention. This study examines two metaheuristic optimization approaches, Genetic Algorithms and Ant Colony Optimization, within the context of route planning for these robotic entities. The comprehensive methodology utilizes the designs of these metaheuristic methods, and a comparative analysis is conducted through simulated experiments in diverse environments. The findings unveil that Genetic Algorithms excel in simple terrains; while Ant Colony Optimization demonstrates robust exploration capabilities in complex environments with obstacles and additional constraints. This underscores the importance of selecting the appropriate metaheuristic based on specific mission requirements and environmental conditions. The research furnishes insights to designers and operators, enabling informed decisions in route planning strategy selection, thereby enhancing the efficiency and safety of autonomous robotic operations across various scenarios.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Algoritmos genéticos]]></kwd>
<kwd lng="es"><![CDATA[Metaheurística]]></kwd>
<kwd lng="es"><![CDATA[Optimización por colonia de hormigas]]></kwd>
<kwd lng="es"><![CDATA[Planificación de caminos]]></kwd>
<kwd lng="es"><![CDATA[Robots móviles]]></kwd>
<kwd lng="es"><![CDATA[Vehículos autónomos terrestres]]></kwd>
<kwd lng="en"><![CDATA[Ant colony optimization]]></kwd>
<kwd lng="en"><![CDATA[Autonomous ground vehicles]]></kwd>
<kwd lng="en"><![CDATA[Genetic algorithms]]></kwd>
<kwd lng="en"><![CDATA[Metaheuristic]]></kwd>
<kwd lng="en"><![CDATA[Movil robots]]></kwd>
<kwd lng="en"><![CDATA[Path planning]]></kwd>
</kwd-group>
</article-meta>
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