Title A Comparison of PSO-Based Informative Path Planners for Autonomous Surface Vehicles for Water Resource Monitoring
Authors JARA TEN KATHEN, MICAELA, JURADO FLORES, ISABEL, GUTIÉRREZ REINA, DANIEL
External publication No
Means Acm International Conference Proceeding Series
Scope Conference Paper
Nature Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132391290&doi=10.1145%2f3529399.3529442&partnerID=40&md5=09fb71c6f774c83d03cb2443174f553a
Publication date 01/01/2022
Scopus Id 2-s2.0-85132391290
DOI 10.1145/3529399.3529442
Abstract Preserving water resources is an objective that is constantly being pursued. Monitoring the aquatic environments is an action to fulfill this objective, since the state of the water quality will be controlled. The monitoring task can be carried out with Autonomous Surface Vehicles equipped with sensors that measure water quality parameters and with a monitoring system. This paper presents a comparison between informative path planners based on PSO for autonomous surface vehicles for water resources monitoring. The case presented is the case of Ypacarai Lake. The simulations carried out allow visualizing and comparing the response of different methods. The methods evaluated are the Local Best method, the Global Best method, the Uncertainty method, the Contamination method, the Classic PSO, Enhanced GP-based PSO, and the Epsilon Greedy method. For the optimization of the Enhanced GP-based PSO coefficients, Bayesian optimization is selected. The results show that the Enhanced GP-based PSO is the algorithm with the best solutions for monitoring the lake environment. © 2022 ACM.
Keywords autonomous surface vehicles; Bayesian optimization; Gaussian process; particle swarm optimization; water monitoring; Ypacarai Lake
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