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Monitoring Peak Pollution Points of Water Resources with Autonomous Surface Vehicles Using a PSO-Based Informative Path Planner

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Mobile Robot: Motion Control and Path Planning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1090))

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Abstract

The preservation of water resources is an increasingly urgent issue. Therefore, monitoring the water quality of these resources is a very important task so that appropriate actions could be taken. This chapter focuses on water resource monitoring using a fleet of Autonomous Surface Vehicles equipped with sensors capable of measuring water quality parameters. The objective is to obtain the maximum points of contamination of the water resource through the exploration and exploitation of the water surface. The proposed algorithm is based on Particle Swarm Optimization (PSO) in combination with some machine learning techniques (Gaussian Process, Bayesian Optimization, among others) to address the limitations of PSO, such as premature convergence and difficulty in setting the initial values of the coefficients. To validate the performance of the algorithm, uni-modal and multi-modal benchmark functions are used in the simulation experiments. The results show that the proposed algorithm, the Enhanced GP-based PSO, based on the epsilon greedy method has the best performance for detecting water resource pollution peaks. It was also demonstrated that this algorithm is the one that generates the most accurate water quality model. However, when it comes to finding the highest pollution peak, the algorithm with the best response is the Enhanced GP-based PSO with a focus on exploitation.

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Notes

  1. 1.

    The following video shows the ASV operating at Lago de la Vida https://www.youtube.com/watch?v=3nYzSSGeyHw.

  2. 2.

    https://scikit-learn.org/stable/.

  3. 3.

    https://deap.readthedocs.io/en/master/.

  4. 4.

    https://github.com/fmfn/BayesianOptimization.

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Acknowledgements

This work has been partially funded by the regional government Junta de Andalucía under the Projects “Despliegue Inteligente de una red de Vehículos Acuáticos no Tripulados para la monitorización de Recursos Hídricos US-1257508”, and “Despliegue y Control de una Red Inteligente de Vehículos Autónomos Acuáticos para la Monitorización de Recursos Hídricos Andaluces PY18-RE0009”.

Author information

Authors and Affiliations

  1. Loyola Andalucía University, Av. de las Universidades, s/n, 41704, Seville, Spain

    Micaela Jara Ten Kathen & Isabel Jurado Flores

  2. Liverpool John Moores University, Byrom St, Liverpool, L3 3AF, UK

    Princy Johnson

  3. University of Seville, C. Américo Vespucio, 41092, Seville, Spain

    Daniel Gutiérrez Reina

Authors
  1. Micaela Jara Ten Kathen
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  2. Princy Johnson
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  3. Isabel Jurado Flores
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  4. Daniel Gutiérrez Reina
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Corresponding author

Correspondence to Micaela Jara Ten Kathen .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Electrical Engineering Department, University of Baghdad, College of Engineering, Baghdad, Iraq

    Ibraheem Kasim Ibraheem

  3. University of Technology, Baghdad, Iraq

    Amjad Jaleel Humaidi

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Ten Kathen, M.J., Johnson, P., Flores, I.J., Gutiérrez Reina, D. (2023). Monitoring Peak Pollution Points of Water Resources with Autonomous Surface Vehicles Using a PSO-Based Informative Path Planner. In: Azar, A.T., Kasim Ibraheem, I., Jaleel Humaidi, A. (eds) Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol 1090. Springer, Cham. https://doi.org/10.1007/978-3-031-26564-8_4

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