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Article Dans Une Revue IEEE Access Année : 2023

A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications

Résumé

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.
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Dates et versions

hal-04213702 , version 1 (21-09-2023)

Identifiants

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Hamza Chakraa, Edouard Leclercq, François Guérin, Dimitri Lefebvre. A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications. IEEE Access, 2023, 11, pp.99935-99949. ⟨10.1109/ACCESS.2023.3315130⟩. ⟨hal-04213702⟩
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