2026-05-15T03:37:10Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/116152026-05-14T12:06:58Zcom_10259_8983com_10259_5086com_10259_2604col_10259_8984

Bayona Blanco, Eduardo Sierra Garcia, Jesús Enrique Santos Peñas, Matilde 2026-05-13T10:56:41Z 2026-05-13T10:56:41Z 2025-03 0266-4720 https://hdl.handle.net/10259/11615 10.1111/exsy.13836 1468-0394 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. Open access funding provided by FEDER European Funds and the Junta De Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027 eng Wiley Expert Systems. 2025, V. 42, n. 3, e13836 https://doi.org/10.1111/exsy.13836 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess AGV Bio-inspired algorithms Industry 4.0 Optimization Computer algorithms Algoritmos computacionales Diseño industrial Industrial design Improving Safety and Efficiency of Industrial Vehicles by Bio‐Inspired Algorithms info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Expert Systems 42 3https://ror.org/051jb1k20Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE)hybrid-oa12488,66 EUR9100199480John Wiley & Sons2025-04-302025-11-142025-01-012028-12-3110.1111/exsy.13836