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

TL;DR

This paper introduces a solar-powered autonomous surface vehicle capable of collecting 72 high-resolution water samples per mission, significantly improving spatial coverage and reliability for water quality monitoring in remote environments.

Contribution

The work presents a novel syringe-based sampling system integrated with advanced autonomy and navigation technologies, enabling high-resolution, contamination-minimized water sampling beyond previous USV capabilities.

Findings

Achieved 87% waypoint accuracy in field trials.

Collected physicochemical data comparable to manual sampling.

Demonstrated reliable autonomous operation in unstructured environments.

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.