Introduction to Ypacarai Lake

In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km2) using the Bayesian optimization approach with a Voronoi Partition system.

Concluding remarks indicate that the proposed method not only performs better while minimizing the Mean Squared Error (via active monitoring), but also manages to quickly identify an approximate of the black-box function, which is very useful for monitoring lakes like Ypacarai Lake ( $60\: km^{2}$ ) in Paraguay.

To evaluate the proposed approach, the Ypacarai Lake (Paraguay) serves as the test scenario, where multiple maps of water quality parameters, such as pH and dissolved oxygen, need to be obtained efficiently.

The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model.

In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km2) using the Bayesian optimization approach with a Voronoi Partition system.

Since two of the preferred approaches for path planning of autonomous vehicles in the literature are Evolutionary Algorithms and Reinforcement Learning, in this paper, We compare the performance of both techniques in a simulator of Ypacarai Lake in Asunción (Paraguay).

A developed simulator is then tailored to the case of the Ypacarai Lake and some tests were carried out to follow a set of waypoints in this environment.

The main objective is that the ASV covers the maximum area of a large mass of water such as the Ypacarai Lake while taking water samples for sensing pollution conditions.