JARA TEN KATHEN, MICAELA, JURADO FLORES, ISABEL, GUTIÉRREZ REINA, DANIEL, TAPIA CÓRDOBA, ALEJANDRO, IEEE
No
2021 Ieee Congress On Evolutionary Computation (cec 2021)
Conference Paper
Científica
01/01/2021
000703866100224
2-s2.0-85124599404
Monitoring water resources represents a crucial task not just for nature preservation worldwide but also for human survival. Traditional methods, based on manual sampling routines, are inefficient in the context of large bodies of water. For this reason the interest in new monitoring approaches based on autonomous vehicles has increased during the last few years. Among these, strategies based on fleets or swarms of vehicles have been proven especially efficient. One of the main components of these systems is the global mission planner, which is responsible for determining the optimal movements. In this paper a novel Particle Swarm Optimization (PSO), based on a surrogate model like a Gaussian Process, is proposed to guide a fleet of Autonomous Surface Vehicles (ASV). The proposed approach takes advantage of the uncertainty provided by the Bayesian model to guide the movements of the swarm towards unexplored areas of the search space. The proposed system has been validated using a benchmark function that models a water quality parameter, achieving better results than the original PSO algorithm.
Particle Swarm Optimization; Gaussian Process; Water Monitoring; Autonomous Surface Vehicles; Machine Learning