Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. 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. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.

The importance of monitoring and evaluating the quality of water resources has significantly grown over time. To achieve this effectively, an option is to employ an intelligent monitoring system capable of measuring the physical and chemical parameters of water. Surface vehicles equipped with sensors for measuring water quality parameters offer a viable solution for these missions. This work presents a novel approach called AquaHet-PSO, which addresses the challenge of simultaneously monitoring multiple water quality parameters with several peaks of contamination using a heterogeneous fleet of autonomous surface vehicles. Each vehicle in the fleet possesses a different set of sensors, such as number of sensors and sensor types, which is the definition provided by the authors for a heterogeneous fleet. The AquaHet-PSO consists of three main phases. In the initial phase, the vehicles traverse the water resource to obtain preliminary models of water quality parameters. These models are then utilized in the second phase to identify potential contamination areas and assign vehicles to specific action zones. In the final phase, the vehicles focus on a comprehensive characterization of the parameters. The proposed system combines several techniques, including Particle Swarm Optimization and Gaussian Processes, with the integration of genetic algorithm to maximize the distances between the initial positions of vehicles equipped with identical sensors, and a distributed communication system in the final phase of the AquaHet-PSO. Simulation results in the Ypacarai lake demonstrate the effectiveness and efficiency of AquaHet-PSO in generating accurate water quality models and detecting contamination peaks. The proposed method demonstrated improvements compared to the lawnmower approach. It achieved a remarkable 17% improvement, on r-squared data, in generating complete models of water quality parameters throughout the lake. In addition, it achieved a 230% improvement in accurate characterization of high pollution areas and a 24% increase in pollution peak detection specifically for heterogeneous fleets equipped with four or more identical sensors.

Preserving water resources is an objective that is constantly being pursued. Monitoring the aquatic environments is an action to fulfill this objective, since the state of the water quality will be controlled. The monitoring task can be carried out with Autonomous Surface Vehicles equipped with sensors that measure water quality parameters and with a monitoring system. This paper presents a comparison between informative path planners based on PSO for autonomous surface vehicles for water resources monitoring. The case presented is the case of Ypacarai Lake. The simulations carried out allow visualizing and comparing the response of different methods. The methods evaluated are the Local Best method, the Global Best method, the Uncertainty method, the Contamination method, the Classic PSO, Enhanced GP-based PSO, and the Epsilon Greedy method. For the optimization of the Enhanced GP-based PSO coefficients, Bayesian optimization is selected. The results show that the Enhanced GP-based PSO is the algorithm with the best solutions for monitoring the lake environment.

Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process mapping. During the mission, the Gaussian Process updates a mean or contamination map and a variance or uncertainty map, from which dynamic action zones are derived and used to guide an explicit explore then exploit policy. The method is evaluated in a simulated water resource monitoring scenario inspired by Lake Ypacaraí, considering three exploration distances and two heuristic weights. In a comparison against five baseline planners, ACO-Path achieves the lowest hotspot error, $Erro{r}_{\mathrm{peak}}=0.19896\pm 0.39400$, while remaining competitive in global reconstruction, $MS{E}_{\mathrm{map}}=0.00144\pm 0.00348$, $R^2=0.96066\pm 0.09861$. In addition, a turning analysis based on the absolute heading change between consecutive segments $|\Delta \alpha |$ shows that ACO-Path produces smoother trajectories, with fewer sharp turns $|\Delta \alpha |\ge 45°$ than counterpart baselines under the same mission constraints.