Artículo
Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles
Autor/es | Yanes Luis, Samuel
Gutiérrez Reina, Daniel Toral, S. L. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería Electrónica |
Fecha de publicación | 2023-01 |
Fecha de depósito | 2023-02-13 |
Publicado en |
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Resumen | Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the ... Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles. |
Agencias financiadoras | Ministerio de Ciencia, Innovación y Universidades RTI2018-098964-B-I00 Junta de Andalucía US-1257508 Junta de Andalucía PY18-RE0009 |
Identificador del proyecto | RTI2018-098964-B-I00
US-1257508 PY18-RE0009 |
Cita | Yanes Luis, S., Gutiérrez Reina, D. y Toral, S.L. (2023). Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles. Applied Soft Computing, 132, 109874. https://doi.org/10.1016/j.asoc.2022.109874. |
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ASC_2023_Yanes_Censored_OA.pdf | 2.632Mb | [PDF] | Ver/ | |