The water quality monitoring of large water masses using robotic vehicles is a complex task highly developed in recent years. The main approaches utilize adaptative informative path planning of fleets of autonomous surface vehicles and computer learning methods. However, water monitoring is characterized by a highly dynamic and unknown environment. Thus, the characterization of the water quality state of a water mass becomes a challenge. This paper proposes a variational autoencoder structure, trained in a model-free manner, that aims to provide a dynamic contamination model from partial observations of a homogeneous fleet of autonomous surface vehicles. To train the proposed approach, an oil spillage simulator based on heuristics is provided for world building. The proposed variational autoencoder was tested in three different environments characterized by different oil spill movements and twp different fleets equipped with different sensors. The results show accurate future contamination distribution predictions with a mean squared error ranging from 3 to 9% of the baseline at validation, defined as the static approach. Further tests addressed the overfit of the proposed neural network, showing a high robustness against unseen scenarios, and the effects of the gathered monitoring information in the variational autoencoder performance.

The evaluation of informative path planning algorithms for autonomous vehicles is often hindered by fragmented execution pipelines and limited transferability between simulation and real-world deployment. This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control, enabling algorithms to be evaluated consistently across different abstraction levels without modification. The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution. It is an open and extensible research tool that supports discrete graph-based environments and interchangeable planning strategies, and is built upon widely adopted robotics technologies, including ROS2, MAVLink, and MQTT. Its design allows the same algorithmic logic to be deployed in fully simulated environments, software-in-the-loop configurations, and physical autonomous vehicles using an identical execution pipeline. The approach is validated through a set of experiments, including real-world deployment on an autonomous surface vehicle performing water quality monitoring with real-time sensor feedback.