A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications
Recently, considerable attention has focused on enhancing the security and safety of industries with high-risk level activities in order to protect the equipment and environment. In particular, chemical processes and nuclear power generation may have a deep impact on their surroundings. In the case...
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Language: | English |
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IEEE
2023-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/10250421/ |
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author | Hamza Chakraa Edouard Leclercq Francois Guerin Dimitri Lefebvre |
author_facet | Hamza Chakraa Edouard Leclercq Francois Guerin Dimitri Lefebvre |
author_sort | Hamza Chakraa |
collection | DOAJ |
description | Recently, considerable attention has focused on enhancing the security and safety of industries with high-risk level activities in order to protect the equipment and environment. In particular, chemical processes and nuclear power generation may have a deep impact on their surroundings. In the case of major events, such as chemical spills, oil rig explosions, or nuclear accidents, collecting accurate and rapidly evolving data becomes a challenging task. So, coordinating a fleet of autonomous mobile robots is a very promising way to deal with unpredicted events and also prevent malicious actions. This paper addresses the problem of assigning optimally a set of tasks to a set of mobile robots equipped with different sensors to minimize a global objective function. The robots perform sensing tasks in order to monitor the area and to facilitate firefighters and inspectors work if a disaster occurs by providing the necessary measures. For this purpose, a centralized Genetic Algorithm (GA) is proposed to determine the task each robot will perform and the order of execution. The proposed approach is tested through a simulation scenario of a grid map environment that represents an industrial area of the city of Le Havre, France. Moreover, a comparative study is conducted with the Hybrid Filtered Beam Search (HFBS) approach and the Mixed-Integer Linear Programming (MILP) solver Cplex. The results demonstrate that the GA approach offers a favorable balance between optimality and execution time. |
first_indexed | 2024-03-11T23:36:36Z |
format | Article |
id | doaj.art-1176bb9878e74e698e88f52d06f17f82 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-03-11T23:36:36Z |
publishDate | 2023-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-1176bb9878e74e698e88f52d06f17f822023-09-19T23:01:50ZengIEEEIEEE Access2169-35362023-01-0111999359994910.1109/ACCESS.2023.331513010250421A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing SpecificationsHamza Chakraa0https://orcid.org/0000-0002-8776-4235Edouard Leclercq1https://orcid.org/0000-0003-2840-1378Francois Guerin2Dimitri Lefebvre3https://orcid.org/0000-0001-7060-756XUniversité Le Havre Normandie, GREAH, 75 Rue Bellot, Le Havre, FranceUniversité Le Havre Normandie, GREAH, 75 Rue Bellot, Le Havre, FranceUniversité Le Havre Normandie, GREAH, 75 Rue Bellot, Le Havre, FranceUniversité Le Havre Normandie, GREAH, 75 Rue Bellot, Le Havre, FranceRecently, considerable attention has focused on enhancing the security and safety of industries with high-risk level activities in order to protect the equipment and environment. In particular, chemical processes and nuclear power generation may have a deep impact on their surroundings. In the case of major events, such as chemical spills, oil rig explosions, or nuclear accidents, collecting accurate and rapidly evolving data becomes a challenging task. So, coordinating a fleet of autonomous mobile robots is a very promising way to deal with unpredicted events and also prevent malicious actions. This paper addresses the problem of assigning optimally a set of tasks to a set of mobile robots equipped with different sensors to minimize a global objective function. The robots perform sensing tasks in order to monitor the area and to facilitate firefighters and inspectors work if a disaster occurs by providing the necessary measures. For this purpose, a centralized Genetic Algorithm (GA) is proposed to determine the task each robot will perform and the order of execution. The proposed approach is tested through a simulation scenario of a grid map environment that represents an industrial area of the city of Le Havre, France. Moreover, a comparative study is conducted with the Hybrid Filtered Beam Search (HFBS) approach and the Mixed-Integer Linear Programming (MILP) solver Cplex. The results demonstrate that the GA approach offers a favorable balance between optimality and execution time.https://ieeexplore.ieee.org/document/10250421/Multi-robot system (MRS)task allocationgenetic algorithm (GA)combinatorial optimizationpath planningindustrial area |
spellingShingle | Hamza Chakraa Edouard Leclercq Francois Guerin Dimitri Lefebvre A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications IEEE Access Multi-robot system (MRS) task allocation genetic algorithm (GA) combinatorial optimization path planning industrial area |
title | A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications |
title_full | A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications |
title_fullStr | A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications |
title_full_unstemmed | A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications |
title_short | A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications |
title_sort | centralized task allocation algorithm for a multi robot inspection mission with sensing specifications |
topic | Multi-robot system (MRS) task allocation genetic algorithm (GA) combinatorial optimization path planning industrial area |
url | https://ieeexplore.ieee.org/document/10250421/ |
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