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Communication Dans Un Congrès Année : 2022

Collective obstacle avoidance strategy - an agent-based simulation approach

Résumé

The context of factory 4.0 leads more and more to decentralised solutions, as centralisation shows its limits. One of the research areas of Industry 4.0 is the use of autonomous guided vehicles (AGVs), autonomous industrial vehicles (AIVs). We want to show that cooperation is useful and necessary to increase their autonomy. We propose in this paper an agent model to test scenarios in Industry 4.0 environments with a fleet of AIVs. In addition, we are interested in the resolution of global obstacle avoidance by AIVs with a collective strategy. The results of the simulation will be evaluated by performance indicators such as distance and time in order to compare the different proposed approaches.
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Dates et versions

hal-03958445 , version 1 (26-01-2023)

Identifiants

  • HAL Id : hal-03958445 , version 1

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Juliette Grosset, Alain-Jérôme Fougères, Moïse Djoko-Kouam, Jean-Marie Bonnin. Collective obstacle avoidance strategy - an agent-based simulation approach. ASPAI 2022: 4th International Conference on Advances in Signal Processing and Artificial Intelligence, Oct 2022, Corfu, Greece. ⟨hal-03958445⟩
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