RT info:eu-repo/semantics/article
T1 Improving Safety and Efficiency of Industrial Vehicles by Bio‐Inspired Algorithms
A1 Bayona Blanco, Eduardo
A1 Sierra Garcia, Jesús Enrique
A1 Santos Peñas, Matilde
K1 AGV
K1 Bio-inspired algorithms
K1 Industry 4.0
K1 Optimization
K1 Algoritmos computacionales
K1 Computer algorithms
K1 Diseño industrial
K1 Industrial design
AB 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.
PB Wiley
SN 0266-4720
YR 2025
FD 2025-03
LK https://hdl.handle.net/10259/11615
UL https://hdl.handle.net/10259/11615
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 15-may-2026