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Monitoring and patrolling large water resources is a major challenge forconservation. The problem of acquiring data of an underlying environment thatusually changes within time involves a proper formulation of the information.The use of Autonomous Surface Vehicles equipped with water quality sensormodules can serve as an early-warning system agents for contaminationpeak-detection, algae blooms monitoring, or oil-spill scenarios. In addition toinformation gathering, the vehicle must plan routes that are free of obstacleson non-convex maps. This work proposes a framework to obtain a collision-freepolicy that addresses the patrolling task for static and dynamic scenarios.Using information gain as a measure of the uncertainty reduction over data, itis proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism formodel-based obstacle avoidance. The obtained results demonstrate the usefulnessof the proposed algorithm for water resource monitoring for static and dynamicscenarios. Simulations showed the use of noise-networks are a good choice forenhanced exploration, with 3 times less redundancy in the paths. Previouscoverage strategies are also outperformed both in the accuracy of the obtainedcontamination model by a 13% on average and by a 37% in the detection ofdangerous contamination peaks. Finally, these results indicate theappropriateness of the proposed framework for monitoring scenarios withautonomous vehicles.

Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles