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Simulation and optimization of robotic tasks for UV treatment of diseases in horticulture

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Abstract

Robotization is increasingly used in the agriculture since the last few decades. It is progressively replacing the human workforce that is deserting the agricultural sector, partly because of the harshness of its activities and health risks they may present. Moreover, robotization aims to improve efficiency and competitiveness of the agricultural sector. However, it leads to several research and development challenges regarding robots supervision, control and optimization. This paper presents a simulation and optimization approach for the optimization of robotized treatment tasks using type-c ultraviolet radiation in horticulture. The optimization of tasks scheduling problem is formalized and a heuristic and a genetic algorithms are proposed to solve it. These algorithms are evaluated compared to an exact method using a multi-agent-based simulation approach. The simulator takes into account the evolution of the disease during time and simulates the execution of treatment tasks by the robot.

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Acknowledgements

This research was made possible thanks to €1.35 million financial support from the European Regional Development Fund provided by the Interreg North-West Europe Programme in context of UV-ROBOT.

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Authors and Affiliations

  1. LINEACT - CESI. 80, rue Edmund Halley, Rouen Madrillet Innovation, 76800, Saint-Étienne-du-Rouvray, France

    Merouane Mazar, M’hammed Sahnoun & Anne Louis

  2. LINEACT - CESI. 8, boulevard Louis XIV, 59046, Lille, France

    Belgacem Bettayeb

  3. LISPEN, Arts et Métiers, HeSam. École Nationale Supérieure d’Arts et Métiers. 8, boulevard Louis XIV, 59046, Lille, France

    Nathalie Klement

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  1. Merouane Mazar
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  2. M’hammed Sahnoun
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  3. Belgacem Bettayeb
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  4. Nathalie Klement
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  5. Anne Louis
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Correspondence to Merouane Mazar.

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Mazar, M., Sahnoun, M., Bettayeb, B. et al. Simulation and optimization of robotic tasks for UV treatment of diseases in horticulture. Oper Res Int J 22, 49–75 (2022). https://doi.org/10.1007/s12351-019-00541-w

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  • DOI: https://doi.org/10.1007/s12351-019-00541-w

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