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A Comparison of PSO-Based Informative Path Planners for Autonomous Surface Vehicles for Water Resource Monitoring

Authors:

Micaela Carolina Jara Ten Kathen,

Isabel Jurado Flores,

Daniel Gutierrez ReinaAuthors Info & Claims

ICMLT '22: Proceedings of the 2022 7th International Conference on Machine Learning Technologies

Pages 271 - 276

https://doi.org/10.1145/3529399.3529442

Published: 10 June 2022 Publication History

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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.

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Diop DLuis SEsteve MMarín SReina D(2024)Decoupling Patrolling Tasks for Water Quality Monitoring: A Multi-Agent Deep Reinforcement Learning ApproachIEEE Access10.1109/ACCESS.2024.340379012(75559-75576)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3403790
Jara Ten Kathen MPeralta FJohnson PJurado Flores IGutiérrez Reina D(2024)AquaFeL-PSO: An informative path planning for water resources monitoring using autonomous surface vehicles based on multi-modal PSO and federated learningOcean Engineering10.1016/j.oceaneng.2024.118787311(118787)Online publication date: Nov-2024
https://doi.org/10.1016/j.oceaneng.2024.118787
Ten Kathen MJohnson PFlores IGutiérrez Reina D(2023)Monitoring Peak Pollution Points of Water Resources with Autonomous Surface Vehicles Using a PSO-Based Informative Path PlannerMobile Robot: Motion Control and Path Planning10.1007/978-3-031-26564-8_4(93-125)Online publication date: 1-Jul-2023
https://doi.org/10.1007/978-3-031-26564-8_4
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A Comparison of PSO-Based Informative Path Planners for Autonomous Surface Vehicles for Water Resource Monitoring

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ICMLT '22: Proceedings of the 2022 7th International Conference on Machine Learning Technologies

March 2022

291 pages

ISBN:9781450395748

DOI:10.1145/3529399

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 June 2022

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ICMLT 2022: 2022 7th International Conference on Machine Learning Technologies

March 11 - 13, 2022

Rome, Italy

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Diop DLuis SEsteve MMarín SReina D(2024)Decoupling Patrolling Tasks for Water Quality Monitoring: A Multi-Agent Deep Reinforcement Learning ApproachIEEE Access10.1109/ACCESS.2024.340379012(75559-75576)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3403790
Jara Ten Kathen MPeralta FJohnson PJurado Flores IGutiérrez Reina D(2024)AquaFeL-PSO: An informative path planning for water resources monitoring using autonomous surface vehicles based on multi-modal PSO and federated learningOcean Engineering10.1016/j.oceaneng.2024.118787311(118787)Online publication date: Nov-2024
https://doi.org/10.1016/j.oceaneng.2024.118787
Ten Kathen MJohnson PFlores IGutiérrez Reina D(2023)Monitoring Peak Pollution Points of Water Resources with Autonomous Surface Vehicles Using a PSO-Based Informative Path PlannerMobile Robot: Motion Control and Path Planning10.1007/978-3-031-26564-8_4(93-125)Online publication date: 1-Jul-2023
https://doi.org/10.1007/978-3-031-26564-8_4
Kathen MFlores IReina D(2022)Performance Comparison of PSO-based Informative Path Planners for Water Monitoring under Dynamic Scenarios2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)10.1109/RAAI56146.2022.10093003(157-163)Online publication date: 9-Dec-2022
https://doi.org/10.1109/RAAI56146.2022.10093003

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A cautious PSO with conditional random

A multiple surrogates based PSO algorithm

Nearest neighbor interaction PSO based on small-world model

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