Improving Safety and Efficiency of Industrial Vehicles by Bio‐Inspired Algorithms
In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing op-erational efficiency and safety. They must travel in crowded work areas and cross narrow corridors with strict safety and timerequirements. Bio-inspired optimization algorithms...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Burgos (UBU) |
| Repositorio: | Repositorio Institucional de la Universidad de Burgos (RIUBU) |
| OAI Identifier: | oai:dnet:riubu_______::d890994b5b7ae3ecad7bb3cb105ada19 |
| Acceso en línea: | https://hdl.handle.net/10259/11615 |
| Access Level: | acceso abierto |
| Palabra clave: | AGV Bio-inspired algorithms Industry 4.0 Optimization Computer algorithms Algoritmos computacionales Diseño industrial Industrial design |
| Sumario: | In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing op-erational efficiency and safety. They must travel in crowded work areas and cross narrow corridors with strict safety and timerequirements. Bio-inspired optimization algorithms have emerged as a promising approach to deal with complex optimiza-tion scenarios. Thus, this paper explores the ability of three novel bio-inspired algorithms: the Bat Algorithm (BA), the WhaleOptimization Algorithm (WOA) and the Gazelle Optimization Algorithm (GOA); to optimise the AGV path planning in complexenvironments. To do it, a new optimization strategy is described: the AGV trajectory is based on clothoid curves and a specialisedpiece-wise fitness function which prioritises safety and efficiency is designed. Simulation experiments were conducted acrossdifferent occupancy maps to evaluate the performance of each algorithm. WOA demonstrates faster optimization providingsuitable safety solutions 4 times faster than GOA. Meanwhile, GOA gives solutions with better safety metrics but demands morecomputational time. The study highlights the potential of bio-inspired approaches for AGV trajectory optimisation and suggestsavenues for future research, including hybrid algorithm development. |
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