Abstract: The focus of this paper is the use of Unmanned Aerial Vehicles (UAVs) for searching multiple targets
under uncertain conditions in the minimal possible time. The problem, known as Minimum Time Search
(MTS), belongs to the Probabilistic Search (PS) field and addresses critical missions, such as search & rescue,
and military surveillance. These operations, characterized by complex and uncertain environments, demand
efficient UAV trajectory optimization. The multi-target version of PS introduces additional challenges, due to
their higher complexity and the need to wisely distribute the UAV?s efforts among multiple targets. In order
to tackle the under-explored multi-target aspect of MTS, we optimize the time to find all targets with new
Ant Colony Optimization (ACO)-based planner. This novel optimization criterion is formulated using Bayes?
theory, considering probability models of the targets (initial belief and motion model) and the sensor likelihood.
Our work contributes significantly by (i) developing an objective function tailored for multi-target MTS, (ii)
proposing an ACO-based planner designed to effectively handle the complexities of multiple moving targets,
and (iii) introducing a novel constructive heuristic that is used by the ACO-based planner, specifically designed
for the multi-target MTS problem. The efficacy of our approach is demonstrated through comprehensive
analysis and validation across various scenarios, showing superior performance over existing methods in
complex multi-target MTS problems.
Authorship: Pérez-Carabaza S., Besada-Portas E., López-Orozco J.A.,
Fuente: Applied Soft Computing, 2024, 155, 111471
Publisher: Elsevier
Publication date: 01/04/2024
No. of pages: 20
Publication type: Article
DOI: 10.1016/j.asoc.2024.111471
ISSN: 1872-9681,1568-4946
Spanish project: PID2021-127073OB-I00
Publication Url: https://doi.org/10.1016/j.asoc.2024.111471