LPV-MP planning for autonomous racing vehicles considering obstacles

In this paper, we present an effective online planning solution for autonomous vehicles that aims at improving the computational load while preserving high levels of performance in racing scenarios. The method follows the structure of the model predictive (MP) optimal strategy where the main objecti...

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Autores: Alcalá Baselga, Eugenio|||0000-0002-6023-0014, Puig Cayuela, Vicenç|||0000-0002-6364-6429, Quevedo Casín, Joseba Jokin|||0000-0002-7827-2896
Tipo de recurso: artículo
Fecha de publicación:2020
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:2117/340473
Acceso en línea:https://hdl.handle.net/2117/340473
https://dx.doi.org/10.1016/j.robot.2019.103392
Access Level:acceso abierto
Palabra clave:Autonomous vehicles
Predictive control
Nonlinear systems
Automatic control
Autonomous driving
Racing planning
MPCLPV
Obstacle avoidance
Vehicles autònoms
Control predictiu
Sistemes no lineals
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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oai_identifier_str oai:upcommons.upc.edu:2117/340473
network_acronym_str ES
network_name_str España
repository_id_str
spelling LPV-MP planning for autonomous racing vehicles considering obstaclesAlcalá Baselga, Eugenio|||0000-0002-6023-0014Puig Cayuela, Vicenç|||0000-0002-6364-6429Quevedo Casín, Joseba Jokin|||0000-0002-7827-2896Autonomous vehiclesPredictive controlNonlinear systemsAutomatic controlAutonomous drivingRacing planningMPCLPVObstacle avoidanceVehicles autònomsControl predictiuSistemes no linealsControl automàticÀrees temàtiques de la UPC::Informàtica::Automàtica i controlIn this paper, we present an effective online planning solution for autonomous vehicles that aims at improving the computational load while preserving high levels of performance in racing scenarios. The method follows the structure of the model predictive (MP) optimal strategy where the main objective is to maximize the velocity while smoothing the dynamic behavior and fulfilling varying constraints. We focus on reformulating the non-linear original problem into a pseudo-linear problem by convexifying the objective function and reformulating the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. In addition, the ability of avoiding obstacles is introduced in a simple way and with reduced computational cost. We test and compare the performance of the proposed strategy against its non-linear approach through simulations. We focus on testing the performance of the trajectory planning approach in a racing scenario. First, the case of free obstacles track and afterwards a scenario including static obstacles. Simulation results show the effectiveness of the proposed strategy by reducing the algorithm elapsed time while finding appropriate trajectories under several input/state constraints.Peer Reviewed20202020-02-0120212021-02-25journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/340473https://dx.doi.org/10.1016/j.robot.2019.103392reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3404732026-05-27T15:37:01Z
dc.title.none.fl_str_mv LPV-MP planning for autonomous racing vehicles considering obstacles
title LPV-MP planning for autonomous racing vehicles considering obstacles
spellingShingle LPV-MP planning for autonomous racing vehicles considering obstacles
Alcalá Baselga, Eugenio|||0000-0002-6023-0014
Autonomous vehicles
Predictive control
Nonlinear systems
Automatic control
Autonomous driving
Racing planning
MPCLPV
Obstacle avoidance
Vehicles autònoms
Control predictiu
Sistemes no lineals
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short LPV-MP planning for autonomous racing vehicles considering obstacles
title_full LPV-MP planning for autonomous racing vehicles considering obstacles
title_fullStr LPV-MP planning for autonomous racing vehicles considering obstacles
title_full_unstemmed LPV-MP planning for autonomous racing vehicles considering obstacles
title_sort LPV-MP planning for autonomous racing vehicles considering obstacles
dc.creator.none.fl_str_mv Alcalá Baselga, Eugenio|||0000-0002-6023-0014
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Quevedo Casín, Joseba Jokin|||0000-0002-7827-2896
author Alcalá Baselga, Eugenio|||0000-0002-6023-0014
author_facet Alcalá Baselga, Eugenio|||0000-0002-6023-0014
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Quevedo Casín, Joseba Jokin|||0000-0002-7827-2896
author_role author
author2 Puig Cayuela, Vicenç|||0000-0002-6364-6429
Quevedo Casín, Joseba Jokin|||0000-0002-7827-2896
author2_role author
author
dc.subject.none.fl_str_mv Autonomous vehicles
Predictive control
Nonlinear systems
Automatic control
Autonomous driving
Racing planning
MPCLPV
Obstacle avoidance
Vehicles autònoms
Control predictiu
Sistemes no lineals
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Autonomous vehicles
Predictive control
Nonlinear systems
Automatic control
Autonomous driving
Racing planning
MPCLPV
Obstacle avoidance
Vehicles autònoms
Control predictiu
Sistemes no lineals
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description In this paper, we present an effective online planning solution for autonomous vehicles that aims at improving the computational load while preserving high levels of performance in racing scenarios. The method follows the structure of the model predictive (MP) optimal strategy where the main objective is to maximize the velocity while smoothing the dynamic behavior and fulfilling varying constraints. We focus on reformulating the non-linear original problem into a pseudo-linear problem by convexifying the objective function and reformulating the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. In addition, the ability of avoiding obstacles is introduced in a simple way and with reduced computational cost. We test and compare the performance of the proposed strategy against its non-linear approach through simulations. We focus on testing the performance of the trajectory planning approach in a racing scenario. First, the case of free obstacles track and afterwards a scenario including static obstacles. Simulation results show the effectiveness of the proposed strategy by reducing the algorithm elapsed time while finding appropriate trajectories under several input/state constraints.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-02-01
2021
2021-02-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/340473
https://dx.doi.org/10.1016/j.robot.2019.103392
url https://hdl.handle.net/2117/340473
https://dx.doi.org/10.1016/j.robot.2019.103392
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
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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 15,301603

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