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

• Morteza Kiadi ^[1] ; José Ramón Villar ^[1] ; 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, Carlos Cambra Baseca, Daniel Urda Muñoz, Javier Sedano Franco, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 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.