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...

Full description

Bibliographic Details
Main Authors: Hamza Chakraa, Edouard Leclercq, Francois Guerin, Dimitri Lefebvre
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10250421/
_version_ 1797680874417815552
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/
work_keys_str_mv AT hamzachakraa acentralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT edouardleclercq acentralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT francoisguerin acentralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT dimitrilefebvre acentralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT hamzachakraa centralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT edouardleclercq centralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT francoisguerin centralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications
AT dimitrilefebvre centralizedtaskallocationalgorithmforamultirobotinspectionmissionwithsensingspecifications