Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas

graficas, tablas

Autores:
Grisales Ramírez, Efraín
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84744
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84744
https://repositorio.unal.edu.co/
Palabra clave:
620 - Ingeniería y operaciones afines
Algoritmo de Dijsktra
Algoritmo de mapeo de celdas
Doble integrador
Modelo dinámico
Optimización combinatoria
Optimización continua
Optimización discreta
Robot móvil de guiado diferencial
Teoría de control óptimo
Dijsktra algorithm
Cell mapping algorithm
Discrete optimization
Double integrator
Dynamic model
Combinatorial optimization
Continuous optimization
Differential guided mobile robot
Optimal control theory
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openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_02be04347539101ef02f4abc5f40278b
oai_identifier_str oai:repositorio.unal.edu.co:unal/84744
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
dc.title.translated.eng.fl_str_mv Optimal trajectory planning for mobile robots using cell mapping
title Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
spellingShingle Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
620 - Ingeniería y operaciones afines
Algoritmo de Dijsktra
Algoritmo de mapeo de celdas
Doble integrador
Modelo dinámico
Optimización combinatoria
Optimización continua
Optimización discreta
Robot móvil de guiado diferencial
Teoría de control óptimo
Dijsktra algorithm
Cell mapping algorithm
Discrete optimization
Double integrator
Dynamic model
Combinatorial optimization
Continuous optimization
Differential guided mobile robot
Optimal control theory
title_short Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
title_full Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
title_fullStr Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
title_full_unstemmed Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
title_sort Planificación de trayectorias óptimas de robots móviles empleando el mapeo de celdas
dc.creator.fl_str_mv Grisales Ramírez, Efraín
dc.contributor.advisor.none.fl_str_mv Osorio Londoño, Gustavo Adolfo
dc.contributor.author.none.fl_str_mv Grisales Ramírez, Efraín
dc.contributor.researchgroup.spa.fl_str_mv Percepción y Control Inteligente (Pci)
dc.contributor.orcid.spa.fl_str_mv Grisales Ramírez, Efraín [0009000814296566]
dc.subject.ddc.spa.fl_str_mv 620 - Ingeniería y operaciones afines
topic 620 - Ingeniería y operaciones afines
Algoritmo de Dijsktra
Algoritmo de mapeo de celdas
Doble integrador
Modelo dinámico
Optimización combinatoria
Optimización continua
Optimización discreta
Robot móvil de guiado diferencial
Teoría de control óptimo
Dijsktra algorithm
Cell mapping algorithm
Discrete optimization
Double integrator
Dynamic model
Combinatorial optimization
Continuous optimization
Differential guided mobile robot
Optimal control theory
dc.subject.proposal.spa.fl_str_mv Algoritmo de Dijsktra
Algoritmo de mapeo de celdas
Doble integrador
Modelo dinámico
Optimización combinatoria
Optimización continua
Optimización discreta
Robot móvil de guiado diferencial
Teoría de control óptimo
dc.subject.proposal.eng.fl_str_mv Dijsktra algorithm
Cell mapping algorithm
Discrete optimization
Double integrator
Dynamic model
Combinatorial optimization
Continuous optimization
Differential guided mobile robot
Optimal control theory
description graficas, tablas
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-10-03T02:17:55Z
dc.date.available.none.fl_str_mv 2023-10-03T02:17:55Z
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.content.spa.fl_str_mv DataPaper
Image
Text
format http://purl.org/coar/resource_type/c_db06
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/84744
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/84744
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Osorio Londoño, Gustavo Adolfoa8459e28e318014da878eb8be713aa83Grisales Ramírez, Efraínfbff2fe91554511b10ac9af3d55ea7a6Percepción y Control Inteligente (Pci)Grisales Ramírez, Efraín [0009000814296566]2023-10-03T02:17:55Z2023-10-03T02:17:55Z2023https://repositorio.unal.edu.co/handle/unal/84744Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasEl problema de planificación de trayectorias es de importancia dentro de la robótica móvil, puesto que es indispensable generar caminos de referencia que eviten obstáculos y de fácil seguimiento por los sistemas autónomos. La solución dada por los algoritmos de planificación puede ser de diferente naturaleza, aumentando o disminuyendo la complejidad computacional. En este documento se propone un método para la planificación de trayectorias de robots móviles empleando el algoritmo de mapeo de celdas (AMC), el cual contiene las restricciones cinemáticas y dinámicas del robot, así como las del entorno, permitiendo generar trayectos de fácil seguimiento por el sistema. También contiene la información necesaria con el fin de dar solución al problema de optimización multiobjetivo. El algoritmo consiste en dividir el espacio de estados en celdas y luego simular la evolución del sistema dinámico del móvil para cada condición inicial; con esto se generan grafos que contienen la información de las conexiones entre celdas en términos de distancia, tiempo, esfuerzo de control y energía. Con esta información se solucionan los problemas de optimización simple o multiobjetivo, según sea la función de costo empleada para encontrar el camino más corto entre dos puntos con el algoritmo de Dijkstra. Además, en este documento se introducen algunos conceptos de control óptimo que serán empleados en la planificación de trayectorias de robots móviles. Estos conceptos se aplican inicialmente al doble integrador (DI) en tiempo continuo, debido a que éste emula el comportamiento de un robot sin masa. Posteriormente se implementa el AMC para solucionar los mismos problemas de optimización en forma discreta para el movimiento en 1 y 2 dimensiones, y se extiende el concepto a la aplicación en robots móviles de guiado diferencial, así como para el Robotino de FESTO. (Texto tomado de la fuente)The trajectory planning problem is of importance in mobile robotics since it is essential to generate reference paths that avoid obstacles and are easy to follow for autonomous systems. The solutions provided by planning algorithms can vary in nature, either increasing or decreasing computational complexity. This paper presents a method for planning mobile robot trajectories using the Cell Mapping Algorithm (CMA), which incorporates the kinematic and dynamic constraints of the robot as well as those of the environment. This approach enables the generation of trajectories that are easy for the system to follow and includes the necessary information to address multi-objective optimization problems. The algorithm involves dividing the state space into cells and then simulating the evolution of the mobile robot's dynamic system for each initial condition. This process generates graphs that contain information about the connections between cells in terms of distance, time, control effort, and energy. With this information, simple or multi-objective optimization problems can be solved, depending on the cost function used to find the shortest path between two points, typically implemented with Dijkstra's algorithm. Also, this thesis introduces some optimal control concepts that will be used in path planning of mobile robots. These concepts are initially applied to continuous-time double integrator (DI), because it emulates behavior of a massless robot. Subsequently, the AMC is implemented to solve same optimization problems in discrete form for motion in 1 and 2 dimensions, and concept is extended to the application in differential guided mobile robots, as well as for FESTO Robotino.DoctoradoDoctor en IngenieríaSistemas de Control y RobóticaEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesxxiv, 136 páginasapplication/pdfspaUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales620 - Ingeniería y operaciones afinesAlgoritmo de DijsktraAlgoritmo de mapeo de celdasDoble integradorModelo dinámicoOptimización combinatoriaOptimización continuaOptimización discretaRobot móvil de guiado diferencialTeoría de control óptimoDijsktra algorithmCell mapping algorithmDiscrete optimizationDouble integratorDynamic modelCombinatorial optimizationContinuous optimizationDifferential guided mobile robotOptimal control theoryPlanificación de trayectorias óptimas de robots móviles empleando el mapeo de celdasOptimal trajectory planning for mobile robots using cell mappingTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesishttp://purl.org/coar/resource_type/c_db06http://purl.org/coar/version/c_71e4c1898caa6e32DataPaperImageTextA. 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Proceedings of the 4th IEEE Colombian Conference on Automatic Control (CCAC), 2019.BibliotecariosEstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84744/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1053781686.2023.pdf1053781686.2023.pdfTesis de Doctorado en Ingeniería - Automáticaapplication/pdf3689705https://repositorio.unal.edu.co/bitstream/unal/84744/2/1053781686.2023.pdff31ff8a0b92c5164dde39c0f94f15786MD52THUMBNAIL1053781686.2023.pdf.jpg1053781686.2023.pdf.jpgGenerated Thumbnailimage/jpeg4388https://repositorio.unal.edu.co/bitstream/unal/84744/3/1053781686.2023.pdf.jpg63a4be8ddb9acf2ec5b9dd3e576ff867MD53unal/84744oai:repositorio.unal.edu.co:unal/847442023-10-02 23:03:42.776Repositorio Institucional Universidad Nacional de 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