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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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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