Water contamination in extensive aquatic resources is a pressing issue, especially during current drought conditions across the world. To adress this, a novel approach involving a heterogeneous sensing capabilities fleet of four autonomous surface vehicles is introduced for efficient contamination mapping. To reduce costs, vehicles may be equipped with low quality sensors meaning measurements reliability differs between vehicles and affects model accuracy. The diverse sensing capabilities are characterized by a wide range of sensor standard deviations, addressing the applicability of the framework in real-world scenarios with commercial sensors. This research leverages Gaussian Processes to accurately model spatial distribution of contamination, integrating measurements from the vehicles to understand contamination patterns comprehensively. Additionally, an informative path planning strategy is introduced based on a centralized neural network which implements a Double Deep Q-Learning algorithm, driving the decision-making process of all agents. Effective learning hinges on accurately defining the observation and reward functions, for which several proposals will be compared. These tailored definitions are essential for guiding the learning process, and minimizing the error towards the main goal: to obtain the best possible contamination model. Remarkably, the proposed system demonstrates superior performance in Ypacaraí Lake scenario, surpassing traditional heuristics like lawn mower or particle swarm optimization by up to 82% in reducing mean squared error in highly contaminated regions for several combinations of agents.

This paper presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep reinforcement approach, allowing agents to learn strategies to maximize cleaning efficiency. The primary objective is for the scout team to provide an up-to-date contamination model, while the cleaner team collects as much waste as possible following this model. This strategy leads to heterogeneous teams that optimize fleet efficiency through inter-team cooperation supported by a tailored reward function. Different trainings of the proposed algorithm are compared with other state-of-the-art algorithms in three distinct scenarios, one with moderate convexity, another with narrow corridors and challenging access, and the last one larger, more complex and with more difficult to access shape. According to the obtained results, it is demonstrated that deep reinforcement learning based algorithms outperform baselines, exhibiting superior adaptability. In addition, training with examples of actions from other algorithms further improves performance, especially in scenarios where the search space is larger.

The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.

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.