Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors

This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational cons...

Descripción completa

Detalles Bibliográficos
Autores: Freixes Puig, Alfons, Panadero Martínez, Javier, Juan, Angel A., Serrat Piè, Carles|||0000-0002-1504-5354
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/438777
Acceso en línea:https://hdl.handle.net/2117/438777
https://dx.doi.org/10.3390/a18060309
Access Level:acceso abierto
Palabra clave:Unmanned aerial vehicles
A* algorithm
Team orienteering problem
Artificial intelligence
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Sistemes d'informació geogràfica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
id ES_d47439b1aa82fa9db21179a8a684b649
oai_identifier_str oai:upcommons.upc.edu:2117/438777
network_acronym_str ES
network_name_str España
repository_id_str
spelling Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factorsFreixes Puig, AlfonsPanadero Martínez, JavierJuan, Angel A.Serrat Piè, Carles|||0000-0002-1504-5354Unmanned aerial vehiclesA* algorithmTeam orienteering problemArtificial intelligenceÀrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Sistemes d'informació geogràficaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThis paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To achieve this, we employ a simheuristic algorithm for the overall route optimization, while integrating the A* algorithm to determine feasible paths between nodes that avoid obstacles in a 2D grid-based environment. Then, a feedforward neural network estimates travel time based on UAV speed, wind conditions, trajectory distance, and payload weight. This estimation is incorporated into the optimization process to improve route planning accuracy. Numerical experiments evaluate the impact of various parameters, including obstacle placement, UAV speed, wind conditions, and payload weight. These experiments include maps with 30 to 100 points of interest and varying obstacle densities and show that our hybrid method improves solution quality by up to 15% in total profit compared to a baseline approach. Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency.This work has been also partially funded by the Spanish Ministry of Science (RED2022-134703-T)Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20252025-06-0120252025-07-15journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/438777https://dx.doi.org/10.3390/a18060309reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138860NB-I00 INTELIGENCIA ARTIFICIAL E INTERNET DE LAS COSAS PARA OPTIMIZAR EL CONSUMO ENERGETICO EN EL TRANSPORTE CON VEHICULOS ELECTRICOSopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4387772026-05-27T15:37:01Z
dc.title.none.fl_str_mv Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
title Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
spellingShingle Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
Freixes Puig, Alfons
Unmanned aerial vehicles
A* algorithm
Team orienteering problem
Artificial intelligence
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Sistemes d'informació geogràfica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
title_full Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
title_fullStr Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
title_full_unstemmed Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
title_sort Combining the A* algorithm with neural networks to solve the team orienteering problem with obstacles and environmental factors
dc.creator.none.fl_str_mv Freixes Puig, Alfons
Panadero Martínez, Javier
Juan, Angel A.
Serrat Piè, Carles|||0000-0002-1504-5354
author Freixes Puig, Alfons
author_facet Freixes Puig, Alfons
Panadero Martínez, Javier
Juan, Angel A.
Serrat Piè, Carles|||0000-0002-1504-5354
author_role author
author2 Panadero Martínez, Javier
Juan, Angel A.
Serrat Piè, Carles|||0000-0002-1504-5354
author2_role author
author
author
dc.subject.none.fl_str_mv Unmanned aerial vehicles
A* algorithm
Team orienteering problem
Artificial intelligence
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Sistemes d'informació geogràfica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Unmanned aerial vehicles
A* algorithm
Team orienteering problem
Artificial intelligence
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Sistemes d'informació geogràfica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To achieve this, we employ a simheuristic algorithm for the overall route optimization, while integrating the A* algorithm to determine feasible paths between nodes that avoid obstacles in a 2D grid-based environment. Then, a feedforward neural network estimates travel time based on UAV speed, wind conditions, trajectory distance, and payload weight. This estimation is incorporated into the optimization process to improve route planning accuracy. Numerical experiments evaluate the impact of various parameters, including obstacle placement, UAV speed, wind conditions, and payload weight. These experiments include maps with 30 to 100 points of interest and varying obstacle densities and show that our hybrid method improves solution quality by up to 15% in total profit compared to a baseline approach. Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-06-01
2025
2025-07-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/438777
https://dx.doi.org/10.3390/a18060309
url https://hdl.handle.net/2117/438777
https://dx.doi.org/10.3390/a18060309
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138860NB-I00 INTELIGENCIA ARTIFICIAL E INTERNET DE LAS COSAS PARA OPTIMIZAR EL CONSUMO ENERGETICO EN EL TRANSPORTE CON VEHICULOS ELECTRICOS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420548242538496
score 15.812429

Similares en LA Referencia