RT Journal Article
T1 Comparative analysis of metaheuristic optimization methods for trajectory generation of Automated Guided Vehicles
A1 Bayona, Eduardo
A1 Sierra-García, Jesús Enrique
A1 Santos Peñas, Matilde
AB This paper presents a comparative analysis of several metaheuristic optimization methods for generating trajectories of automated guided vehicles, which commonly operate in industrial environments. The goal is to address the challenge of efficient path planning for mobile robots, taking into account the specific capabilities and mobility limitations inherent to automated guided vehicles. To do this, three optimization techniques are compared: genetic algorithms, particle swarm optimization and pattern search. The findings of this study reveal the different efficiency of these trajectory optimization approaches. This comprehensive research shows the strengths and weaknesses of various optimization methods and offers valuable information for optimizing the trajectories of industrial vehicles using geometric occupancy maps.
PB Mdpi
YR 2024
FD 2024-02-11
LK https://hdl.handle.net/20.500.14352/111084
UL https://hdl.handle.net/20.500.14352/111084
LA eng
NO Bayona, E., Sierra-García, J. E., & Santos, M. (2024). Comparative Analysis of Metaheuristic Optimization Methods for Trajectory Generation of Automated Guided Vehicles. Electronics, 13(4), 728.
DS Docta Complutense
RD 11 abr 2025