Abstract
Manual monitoring large water reservoirs is a complex and high-cost task that requires many human resources. By using Autonomous Surface Vehicles, informative missions for modeling and supervising can be performed efficiently. Given a model of the uncertainty of the measurements, the minimization of entropy is proven to be a suitable criterion to find a surrogate model of the contamination map, also with complete coverage pathplanning. This work uses Proximal Policy Optimization, a Deep Reinforcement Learning algorithm, to find a suitable policy that solves this maximum information coverage path planning, whereas the obstacles are avoided. The results show that the proposed framework outperforms other methods in the literature by 32% in entropy minimization and by 63% in model accuracy.
S. Y. Luis—Participation financed by the Ministry of Universities under the FPU-2020 grant of Samuel Yanes Luis and by the Regional Govt. of Andalusia under PAIDI 2020 funds - P18-TP-1520.
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References
United Nations Organization: United Nations Organization. Objective 6 (2021). https://www.un.org/sustainabledevelopment/es/water-and-sanitation/. Accessed 12 Nov 2021
Velez, F.J., et al.: Wireless sensor and networking technologies for swarms of aquatic surface drones. In: 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), pp. 1–2 (2015). https://doi.org/10.1109/VTCFall.2015.7391193
Ferreira, H., et al.: Autonomous bathymetry for risk assessment with ROAZ robotic surface vehicle. OCEANS 2009-EUROPE, pp. 1–6 (2009). https://doi.org/10.1109/OCEANSE.2009.5278235
Peralt Samaniego, F., Gutierrez-Reina, D., Toral Marin, S.L., Arzamendia, M., Gregor, D.O.: A Bayesian optimization approach for water resources monitoring through an autonomous surface vehicle: the Ypacarai lake case study. IEEE Access 9, 9163–9179 (2021). https://doi.org/10.1109/ACCESS.2021.3050934
Samaniego, F.P., Reina, D.G., Marin, S.L.T., Arzamendia, M., Gregor, D.O.: A Bayesian optimization approach for multi-function estimation for environmental monitoring using an autonomous surface vehicle: Ypacarai lake case study. Electron. (Switz.) 10(963), 9163–9179 (2021). https://doi.org/10.1109/ACCESS.2021.3050934
Yanes Luis, S., Reina, D.G., Marin, S.L.T.: A deep reinforcement learning approach for the patrolling problem of water resources through autonomous surface vehicles: the Ypacarai lake case. IEEE Access 8, 204076–204093 (2020). https://doi.org/10.1109/access.2020.3036938
Yanes Luis, S., Reina, D.G., Marin, S.L.T.: A multiagent deep reinforcement learning approach for path planning in autonomous surface vehicles: the Ypacaraí lake patrolling case. IEEE Access 9, 17084–17099 (2021). https://doi.org/10.1109/access.2021.3053348
Arzamendia, M., Gregor, D., Reina, D.G., Toral, S.L.: An evolutionary approach to constrained path planning of an autonomous surface vehicle for maximizing the covered area of Ypacarai lake. Soft Comput. 23(5), 1723–1734 (2019). https://doi.org/10.1007/s00500-017-2895-x
Arzamendia, M., Gutierrez, D., Toral, S., et al.: Intelligent online learning strategy for an autonomous surface vehicle in lake environments using evolutionary computation. IEEE Intell. Transp. Syst. Mag. 11(4), 110–125 (2019). https://doi.org/10.1109/MITS.2019.2939109
Rückin, J., Jin, L., Popović, M.: Adaptive informative path planning using deep reinforcement learning for UAV-based active sensing, pp. 1–7 (2021). https://arxiv.org/abs/2109.13570
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning, pp. 202–210. The MIT Press (2006). ISBN026218253X
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Luis, S.Y., Reina, D.G., Toral, S. (2022). Maximum Information Coverage and Monitoring Path Planning with Unmanned Surface Vehicles Using Deep Reinforcement Learning. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_2
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