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

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.