  Monitoring and patrolling large water resources is a major challenge for
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 agents 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 maps. This work proposes a framework to obtain a collision-free
policy that addresses the patrolling task for static and dynamic scenarios.
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 usefulness
of the proposed algorithm for water resource monitoring for static and dynamic
scenarios. Simulations showed the use of noise-networks are a good choice for
enhanced exploration, with 3 times less redundancy in the paths. 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, these results indicate the
appropriateness of the proposed framework for monitoring scenarios with
autonomous vehicles.
