Perez-Carabaza, SaraBesada Portas, EvaLópez Orozco, José AntonioCruz García, Jesús Manuel De La2024-01-222024-01-222017Sara Perez-Carabaza, Eva Besada-Portas, Jose A. Lopez-Orozco, Jesus M. de la Cruz, Ant colony optimization for multi-UAV minimum time search in uncertain domains, Applied Soft Computing, Volume 62, 2018, Pages 789-806, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2017.09.009.1568-494610.1016/j.asoc.2017.09.009https://hdl.handle.net/20.500.14352/94255El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital.This paper presents a new approach based on ant colony optimization (ACO) to determine the trajectories of a fleet of unmanned air vehicles (UAVs) looking for a lost target in the minimum possible time. ACO is especially suitable for the complexity and probabilistic nature of the minimum time search (MTS) problem, where a balance between the computational requirements and the quality of solutions is needed. The presented approach includes a new MTS heuristic that exploits the probability and spatial properties of the problem, allowing our ant based algorithm to quickly obtain high-quality high-level straight segmented UAV trajectories. The potential of the algorithm is tested for different ACO parameterizations, over several search scenarios with different characteristics such as number of UAVs, or target dynamicsand location distributions. The statistical comparison against other techniques previously used for MTS( ad hoc heuristics, cross entropy optimization, bayesian optimization algorithm and genetic algorithms) shows that the new approach outperforms the others. (C) 2017 Elsevier B.V. All rights reserved.engjournal articlehttps://doi.org/10.1016/j.asoc.2017.09.009open access007.52Ant colony optimizationProbabilistic path planningUAVsMinimum time searchRobótica3311.01 Tecnología de la Automatización