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Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing

  • Morteza Kiadi [1] ; José Ramón Villar [1] Árbol académico ; Qing Tan [2]
    1. [1] Universidad de Oviedo

      Universidad de Oviedo

      Oviedo, España

    2. [2] Athabasca University

      Athabasca University

      Canadá

  • Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío Árbol académico, Carlos Cambra Baseca Árbol académico, Daniel Urda Muñoz Árbol académico, Javier Sedano Franco Árbol académico, Héctor Quintián Pardo Árbol académico, Emilio Santiago Corchado Rodríguez Árbol académico, 2021, ISBN 978-3-030-57802-2, págs. 580-589
  • Idioma: inglés
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • Finding the shortest path for an autonomous robot in static environments has been studied for many years and many algorithms exist to solve that problem. While path finding in the static setting is very useful, it is very limiting in real world scenarios due to collisions with dynamic elements in an environment. As a result, many static path planning algorithms have been extended to cover dynamic settings, in which there are more than one moving objects in the environment. In this research, we propose a new implementation of multi agent path finding setting through A* that emphasizes on the path finding through a centralized meta-planner that operates on the base of Bag of Tasks (BoT), running on the distributed computing platforms on the cloud or fog infrastructures and avoiding dynamic obstacles during the planning. We also propose a model to offer a “Multi-Agent A* path planning as-a-Service” to abstract the details of the algorithm to make it more accessible.


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