High-Resolution Water Sampling via a Solar-Powered Autonomous Surface Vehicle

TL;DR

This work tackles the challenge of obtaining high-resolution water-quality data in remote environments by delivering a solar-powered, fully autonomous USV with a syringe-based sampler capable of 72 discrete samples per mission. The system integrates a ROS 2 autonomy stack (Nav2, perception, LoRa) with a modular 6×12 syringe array and shore-based web data management, and is validated through field trials in Achocalla Lagoon, demonstrating reliable autonomy and measurement fidelity comparable to manual sampling. Key contributions include the modular syringe architecture enabling dense spatial sampling, a ROS 2–based multi-layer autonomy framework, and a public data-management workflow for offline and online data synchronization. The results highlight the platform’s potential for scalable, resource-efficient aquatic monitoring in remote or hazardous environments, while outlining avenues for speed, depth profiling, and sampling integrity improvements in future work.

Abstract

Accurate water quality assessment requires spatially resolved sampling, yet most unmanned surface vehicles (USVs) can collect only a limited number of samples or rely on single-point sensors with poor representativeness. This work presents a solar-powered, fully autonomous USV featuring a novel syringe-based sampling architecture capable of acquiring 72 discrete, contamination-minimized water samples per mission. The vehicle incorporates a ROS 2 autonomy stack with GPS-RTK navigation, LiDAR and stereo-vision obstacle detection, Nav2-based mission planning, and long-range LoRa supervision, enabling dependable execution of sampling routes in unstructured environments. The platform integrates a behavior-tree autonomy architecture adapted from Nav2, enabling mission-level reasoning and perception-aware navigation. A modular 6x12 sampling system, controlled by distributed micro-ROS nodes, provides deterministic actuation, fault isolation, and rapid module replacement, achieving spatial coverage beyond previously reported USV-based samplers. Field trials in Achocalla Lagoon (La Paz, Bolivia) demonstrated 87% waypoint accuracy, stable autonomous navigation, and accurate physicochemical measurements (temperature, pH, conductivity, total dissolved solids) comparable to manually collected references. These results demonstrate that the platform enables reliable high-resolution sampling and autonomous mission execution, providing a scalable solution for aquatic monitoring in remote environments.

Figures (15)

• Figure 1: Overview of the proposed solar-powered autonomous USV and multi-syringe sampling system.
• Figure 2: (A) CAD rendering of the USV for water sampling. (B) Physical prototype of the USV operating in Achocalla, La Paz, Bolivia. (C) CFD analysis using Rhino and Orca3D, showing an immersion depth of approximately 25 cm measured from the sampling modules. (D) Flow analysis using SolidWorks Simulation Tools representing hydrodynamic drag as a function of water velocity.
• Figure 3: Block diagram of the USV's electronic system, showing interconnections, power lines, and communication buses, and highlighting the integration of control, perception, communication, and power management subsystems.
• Figure 4: ROS 2 topics and data published by perception sensors.
• Figure 5: (A) and (B) show 2D and 3D maps of Achocalla Lagoon, respectively. (C) Visualization of the global and local navigation layers in RViz during simulation in Gazebo using the Smac Hybrid A* algorithm. The cyan area represents the global costmap (static map from Gazebo), while the purple area corresponds to the local costmap (dynamic layer updated with sensor data). The red line indicates the optimal trajectory computed by the Smac Hybrid A* planner.