Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints

Purpose: We consider the problem of route planning of multiple rechargeable heterogeneous UAVs with multiple trips under mission time and payload carrying constraints. The goal is to determine the types and number of UAVs to be deployed and their flying paths that minimizes the monetary cost, which...

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Detalles Bibliográficos
Autores: Phalapanyakoon, Kriangsak, Siripongwutikorn, Peerapon
Tipo de recurso: artículo
Fecha de publicación:2023
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:2117/395108
Acceso en línea:https://hdl.handle.net/2117/395108
https://dx.doi.org/10.3926/jiem.4381
Access Level:acceso abierto
Palabra clave:Drone aircraft
Strategic planning
Airways
Genetic algorithms
Unmanned aerial vehicle (UAV)
Heterogenous rechargeable unmanned aerial vehicles
Route planning
Genetic algorithm (GA)
Avions no tripulats
Planificació estratègica
Aerovies
Algorismes genètics
Àrees temàtiques de la UPC::Aeronàutica i espai::Navegació aèria
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spelling Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraintsPhalapanyakoon, KriangsakSiripongwutikorn, PeeraponDrone aircraftStrategic planningAirwaysGenetic algorithmsUnmanned aerial vehicle (UAV)Heterogenous rechargeable unmanned aerial vehiclesRoute planningGenetic algorithm (GA)Avions no tripulatsPlanificació estratègicaAeroviesAlgorismes genèticsÀrees temàtiques de la UPC::Aeronàutica i espai::Navegació aèriaPurpose: We consider the problem of route planning of multiple rechargeable heterogeneous UAVs with multiple trips under mission time and payload carrying constraints. The goal is to determine the types and number of UAVs to be deployed and their flying paths that minimizes the monetary cost, which is a sum of the recharging energy cost of each UAV, the UAV rental cost, and the cost of violating the mission time deadline. Design/methodology/approach: The problem is formulated as a mixed integer programming (MIP). Then, the genetic algorithm (GA) is developed to solve the model and the solutions are compared to those obtained from the exact method (Branch-and-Bound). Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work. Test problems on grid networks and real terrains are used to evaluate the runtime efficiency and solution optimality, and the sensitivity of GA parameters is studied based on two-level factorial experiments. Findings: The proposed GA method can find optimal solutions for small problem sizes but with much less computation time than the exact method. For larger problem sizes, the exact method failed to find optimal solutions within the limits of time and disk space constraints (24 hours and 500 GB) while the GA method yields the solutions within a few minutes with as high as 49% better objective values. Also, the proposed GA method is shown to well explore the solution space based on the variation of the total costs obtained. Originality/value: The unique aspects of this work are that the model optimizes the sum of three different costs – the electricity recharging cost, the UAV rental cost, the penalty cost for mission deadline violation, and the recharging period based on the remaining energy, the payload capacity, and the heterogeneity of UAVs are incorporated into the model. The model is formulated as a mixed integer programming and the genetic algorithm is developed to solve the program. Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this workPeer ReviewedOmniaScience20232023-09-0120232023-10-19journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/395108https://dx.doi.org/10.3926/jiem.4381reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3951082026-05-27T15:37:01Z
dc.title.none.fl_str_mv Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
title Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
spellingShingle Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
Phalapanyakoon, Kriangsak
Drone aircraft
Strategic planning
Airways
Genetic algorithms
Unmanned aerial vehicle (UAV)
Heterogenous rechargeable unmanned aerial vehicles
Route planning
Genetic algorithm (GA)
Avions no tripulats
Planificació estratègica
Aerovies
Algorismes genètics
Àrees temàtiques de la UPC::Aeronàutica i espai::Navegació aèria
title_short Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
title_full Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
title_fullStr Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
title_full_unstemmed Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
title_sort Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints
dc.creator.none.fl_str_mv Phalapanyakoon, Kriangsak
Siripongwutikorn, Peerapon
author Phalapanyakoon, Kriangsak
author_facet Phalapanyakoon, Kriangsak
Siripongwutikorn, Peerapon
author_role author
author2 Siripongwutikorn, Peerapon
author2_role author
dc.subject.none.fl_str_mv Drone aircraft
Strategic planning
Airways
Genetic algorithms
Unmanned aerial vehicle (UAV)
Heterogenous rechargeable unmanned aerial vehicles
Route planning
Genetic algorithm (GA)
Avions no tripulats
Planificació estratègica
Aerovies
Algorismes genètics
Àrees temàtiques de la UPC::Aeronàutica i espai::Navegació aèria
topic Drone aircraft
Strategic planning
Airways
Genetic algorithms
Unmanned aerial vehicle (UAV)
Heterogenous rechargeable unmanned aerial vehicles
Route planning
Genetic algorithm (GA)
Avions no tripulats
Planificació estratègica
Aerovies
Algorismes genètics
Àrees temàtiques de la UPC::Aeronàutica i espai::Navegació aèria
description Purpose: We consider the problem of route planning of multiple rechargeable heterogeneous UAVs with multiple trips under mission time and payload carrying constraints. The goal is to determine the types and number of UAVs to be deployed and their flying paths that minimizes the monetary cost, which is a sum of the recharging energy cost of each UAV, the UAV rental cost, and the cost of violating the mission time deadline. Design/methodology/approach: The problem is formulated as a mixed integer programming (MIP). Then, the genetic algorithm (GA) is developed to solve the model and the solutions are compared to those obtained from the exact method (Branch-and-Bound). Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work. Test problems on grid networks and real terrains are used to evaluate the runtime efficiency and solution optimality, and the sensitivity of GA parameters is studied based on two-level factorial experiments. Findings: The proposed GA method can find optimal solutions for small problem sizes but with much less computation time than the exact method. For larger problem sizes, the exact method failed to find optimal solutions within the limits of time and disk space constraints (24 hours and 500 GB) while the GA method yields the solutions within a few minutes with as high as 49% better objective values. Also, the proposed GA method is shown to well explore the solution space based on the variation of the total costs obtained. Originality/value: The unique aspects of this work are that the model optimizes the sum of three different costs – the electricity recharging cost, the UAV rental cost, the penalty cost for mission deadline violation, and the recharging period based on the remaining energy, the payload capacity, and the heterogeneity of UAVs are incorporated into the model. The model is formulated as a mixed integer programming and the genetic algorithm is developed to solve the program. Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-09-01
2023
2023-10-19
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/395108
https://dx.doi.org/10.3926/jiem.4381
url https://hdl.handle.net/2117/395108
https://dx.doi.org/10.3926/jiem.4381
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

https://creativecommons.org/licenses/by-nc/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

https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv OmniaScience
publisher.none.fl_str_mv OmniaScience
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
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score 15,300724

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