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

Morteza Kiadi; José Ramón Villar; Qing Tan

doi:10.1007/978-3-030-57802-2_56

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

Morteza Kiadi, José Ramón Villar, Qing Tan

School of Computing and Information systems

University of Oviedo

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020
Editors	Álvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado
Pages	580-589
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-030-57802-2_56
Publication status	Published - 2021
Event	15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 - Burgos, Spain Duration: 16 Sep. 2020 → 18 Sep. 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1268 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020
Country/Territory	Spain
City	Burgos
Period	16/09/20 → 18/09/20

Keywords

A* algorithm
Fog computing
Kubernetes
Pathfinding

Access to Document

10.1007/978-3-030-57802-2_56

Cite this

Kiadi, M., Villar, J. R., & Tan, Q. (2021). Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing. In Á. Herrero, C. Cambra, D. Urda, J. Sedano, H. Quintián, & E. Corchado (Eds.), 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 (pp. 580-589). (Advances in Intelligent Systems and Computing; Vol. 1268 AISC). https://doi.org/10.1007/978-3-030-57802-2_56

Kiadi, Morteza ; Villar, José Ramón ; Tan, Qing. / Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing. 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. editor / Álvaro Herrero ; Carlos Cambra ; Daniel Urda ; Javier Sedano ; Héctor Quintián ; Emilio Corchado. 2021. pp. 580-589 (Advances in Intelligent Systems and Computing).

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

abstract = "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.",

keywords = "A* algorithm, Fog computing, Kubernetes, Pathfinding",

author = "Morteza Kiadi and Villar, {Jos{\'e} Ram{\'o}n} and Qing Tan",

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doi = "10.1007/978-3-030-57802-2_56",

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Kiadi, M, Villar, JR & Tan, Q 2021, Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing. in Á Herrero, C Cambra, D Urda, J Sedano, H Quintián & E Corchado (eds), 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. Advances in Intelligent Systems and Computing, vol. 1268 AISC, pp. 580-589, 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020, Burgos, Spain, 16/09/20. https://doi.org/10.1007/978-3-030-57802-2_56

Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing. / Kiadi, Morteza; Villar, José Ramón; Tan, Qing.
15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. ed. / Álvaro Herrero; Carlos Cambra; Daniel Urda; Javier Sedano; Héctor Quintián; Emilio Corchado. 2021. p. 580-589 (Advances in Intelligent Systems and Computing; Vol. 1268 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

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T1 - Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing

AU - Kiadi, Morteza

AU - Villar, José Ramón

AU - Tan, Qing

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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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Kiadi M, Villar JR, Tan Q. Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing. In Herrero Á, Cambra C, Urda D, Sedano J, Quintián H, Corchado E, editors, 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. 2021. p. 580-589. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-57802-2_56

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

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Keywords

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