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Optimized execution of PDDL plans using behavior trees

  • Francisco Martín Rico
  • , Matteo Morelli
  • , Huascar Espinoza
  • , Francisco J. Rodríguez-Lera
  • , Vicente Matellán Olivera
  • Universidad Rey Juan Carlos
  • List
  • University of Leon

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

10 Citas (Scopus)

Resumen

Robots need task planning to sequence and execute actions toward achieving their goals. On the other hand, Behavior Trees provide a mathematical model for specifying plan execution in an intrinsically composable, reactive, and robust way. PDDL (Planning Domain Definition Language) has become the standard description language for most planners. In this paper, we present a novel algorithm to systematically create behavior trees from PDDL plans to execute them. This approach uses the execution graph of the plan to generate a behavior tree. The most remarkable contribution of this approach is the algorithm to build a Behavior Tree that optimizes its execution by paralyzing actions, applicable to any plan, taking into account the actions' causal relationships. We demonstrate the improvement in the execution of plans in mobile robots using the ROS2 Planning System framework.

Idioma originalInglés
Título de la publicación alojada20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
EditorialInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Páginas1584-1586
Número de páginas3
ISBN (versión digital)9781713832621
EstadoPublicada - 2021
Publicado de forma externa
Evento20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021 - Virtual, Online
Duración: 3 may 20217 may 2021

Serie de la publicación

NombreProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volumen3
ISSN (versión impresa)1548-8403
ISSN (versión digital)1558-2914

Conferencia

Conferencia20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
CiudadVirtual, Online
Período3/05/217/05/21

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