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.

Abstract-- This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. The proposed approach is implemented based on Robot Operating System (ROS), processing real-time sensor data to update probabilistic source location estimates. Experiments conducted in simulated environments with varying source locations, wave conditions, and starting positions demonstrate the framework's ability to localise pollution sources with high accuracy. Results show that the proposed approach achieves reliable source localisation efficiently and outperforms the existing baseline. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents. Pollution discharged into the marine environment causes severe consequences to ecosystems [1], [2] and human health [3].

Collaborating teams of robots show promise due in their ability to complete missions more efficiently and with improved robustness, attributes that are particularly useful for systems operating in marine environments. A key issue is how to model, analyze, and design these multi-robot systems to realize the full benefits of collaboration, a challenging task since the domain of multi-robot autonomy encompasses both collective and individual behaviors. This paper introduces a layered model of multi-robot autonomy that uses the principle of census, or a weighted count of the inputs from neighbors, for collective decision-making about teaming, coupled with multi-objective behavior optimization for individual decision-making about actions. The census component is expressed as a nonlinear opinion dynamics model and the multi-objective behavior optimization is accomplished using interval programming. This model can be reduced to recover foundational algorithms in distributed optimization and control, while the full model enables new types of collective behaviors that are useful in real-world scenarios. To illustrate these points, a new method for distributed optimization of subgroup allocation is introduced where robots use a gradient descent algorithm to minimize portions of the cost functions that are locally known, while being influenced by the opinion states from neighbors to account for the unobserved costs. With this method the group can collectively use the information contained in the Hessian matrix of the total global cost. The utility of this model is experimentally validated in three categorically different experiments with fleets of autonomous surface vehicles: an adaptive sampling scenario, a high value unit protection scenario, and a competitive game of capture the flag.

Scientists have a lot of questions about our planet's most important carbon sink--and a new project could help answer them. NEW YORK, NEW YORK - APRIL 16: Eric Schmidt, former chairman and CEO at GOOGLE visits Fox Business Network Studios on April 16, 2019 in New York City. A foundation created by Eric Schmidt, the former CEO of Google, will fund a project to send drone boats out into the rough ocean around Antarctica to collect data that could help solve a crucial climate puzzle. The project is part of a suite of funding announced today from Schmidt Sciences, which Schmidt and his wife Wendy created to focus on projects tackling research into the global carbon cycle. It will spend $45 million over the next five years to fund these projects, which includes the Antarctic research.

Typical marine environments are highly complex with spatio-temporally varying currents and dynamic obstacles, presenting significant challenges to Unmanned Surface Vehicles (USVs) for safe and efficient navigation. Thus, the USVs need to continuously adapt their paths with real-time information to avoid collisions and follow the path of least resistance to the goal via exploiting ocean currents. In this regard, we introduce a novel algorithm, called Self-Morphing Adaptive Replanning Tree for dynamic Obstacles and Currents (SMART-OC), that facilitates real-time time-risk optimal replanning in dynamic environments. SMART-OC integrates the obstacle risks along a path with the time cost to reach the goal to find the time-risk optimal path. The effectiveness of SMART-OC is validated by simulation experiments, which demonstrate that the USV performs fast replannings to avoid dynamic obstacles and exploit ocean currents to successfully reach the goal.

This paper presents the design, development, and experimental validation of MARVEL, an autonomous unmanned surface vehicle built for real-world testing of sensor fusion-based navigation algorithms in GNSS-denied environments. MARVEL was developed under strict constraints of cost-efficiency, portability, and seaworthiness, with the goal of creating a modular, accessible platform for high-frequency data acquisition and experimental learning. It integrates electromagnetic logs, Doppler velocity logs, inertial sensors, and real-time kinematic GNSS positioning. MARVEL enables real-time, in-situ validation of advanced navigation and AI-driven algorithms using redundant, synchronized sensors. Field experiments demonstrate the system's stability, maneuverability, and adaptability in challenging sea conditions. The platform offers a novel, scalable approach for researchers seeking affordable, open-ended tools to evaluate sensor fusion techniques under real-world maritime constraints.

Unmanned Surface Vessels (USVs) are essential for various maritime operations. USV mission planning approach offers autonomous solutions for monitoring, surveillance, and logistics. Existing approaches, which are based on static methods, struggle to adapt to dynamic environments, leading to suboptimal performance, higher costs, and increased risk of failure. This paper introduces a novel mission planning framework that uses Large Language Models (LLMs), such as GPT-4, to address these challenges. LLMs are proficient at understanding natural language commands, executing symbolic reasoning, and flexibly adjusting to changing situations. Our approach integrates LLMs into maritime mission planning to bridge the gap between high-level human instructions and executable plans, allowing real-time adaptation to environmental changes and unforeseen obstacles. In addition, feedback from low-level controllers is utilized to refine symbolic mission plans, ensuring robustness and adaptability. This framework improves the robustness and effectiveness of USV operations by integrating the power of symbolic planning with the reasoning abilities of LLMs. In addition, it simplifies the mission specification, allowing operators to focus on high-level objectives without requiring complex programming. The simulation results validate the proposed approach, demonstrating its ability to optimize mission execution while seamlessly adapting to dynamic maritime conditions.

With the development and application of artificial intelligence and machine learning, more and more studies focus on unmanned vehicles and their applications (Zhou, Z., 2016). For example, Unmanned Ground Vehicle (UGV) or wheeled robot is widely used in field of industrial automation (automatic forklift), warehouse management, planet exploring (lunar rover), disaster rescue, intelligent transportation (automatic drive) and military operation (de-mining robot) (Arai et al., 2002; Farinelli et al., 2004; Kui et al., 2007). The application of Unmanned Aerial Vehicle (UAV) is also increasingly changed from military domain to civil use, such as remote sensing photographing, agricultural spraying, communications relay, environmental monitoring and express service (Jayoung et al., 2013; George et al., 2012; Mingzhu et al., 2016). The development of UGV and UAV has already been updated to a new level. Another unmanned vehicle should also be paid attention to, which is the Unmanned Surface Vehicle (USV). The application scenarios are not widely applied for civil use and the studies of a USV are relatively fewer and commence a bit late.

Unmanned Surface Vehicles (USVs) are increasingly applied to water operations such as environmental monitoring and river-map modeling. It faces a significant challenge in achieving precise autonomous docking at ports or stations, still relying on remote human control or external positioning systems for accuracy and safety which limits the full potential of human-out-of-loop deployment for USVs.This paper introduces a novel supervised learning pipeline with the auto-labeling technique for USVs autonomous visual docking. Firstly, we designed an auto-labeling data collection pipeline that appends relative pose and image pair to the dataset. This step does not require conventional manual labeling for supervised learning. Secondly, the Neural Dock Pose Estimator (NDPE) is proposed to achieve relative dock pose prediction without the need for hand-crafted feature engineering, camera calibration, and peripheral markers. Moreover, The NDPE can accurately predict the relative dock pose in real-world water environments, facilitating the implementation of Position-Based Visual Servo (PBVS) and low-level motion controllers for efficient and autonomous docking.Experiments show that the NDPE is robust to the disturbance of the distance and the USV velocity. The effectiveness of our proposed solution is tested and validated in real-world water environments, reflecting its capability to handle real-world autonomous docking tasks.

GUPT Aet al.: CURVITRACK: CURVILINEAR TRAJECTORY TRACKING FOR HIGH-SPEED CHASE OF A USV 3MPC Solver Fast Fourier Transform USV Motion Prediction Setpoint Generator UA V Model Reference Tracker Position/Attitude Controller Vision-based Detector Attitude rate Controller IMU UA V Actuators Onboard Sensors State Estimator Odometry & Localisation ˆ x [ b w] n = 1 ..M p r d, η d ˆ r d, ˆ η d χ d 100 Hz ω d T d 100 Hz a d τ d 1 kHz x 100 Hz initialisation only x, R, ω 100 Hz R, ω b UA V plant Pixhawk autopilot MPC Architecture USV Prediction Model UA V Prediction ModelFigure 1: The entire UA V control architecture; the MPC landing controller (red block) is integrated into the MRS system [20] (grey blocks) and supplies the desired reference (velocity r d = null x y z null T and heading rate η d). In the MRS system, the first layer containing a Reference tracker processes the desired reference and gives a full-state reference χ to the attitude controller. The feedback Position/Attitude controller produces the desired thrust and angular velocities ( T d, ω d) for the Pixhawk flight controller (Attitude rate controller). The State estimator fuses data from Odometry & localisation methods to create an estimate of the UA V translation and rotation ( x, R). The Vision-based Detector obtains the visual data from the camera and sends the pose information b of the USV to the MPC. The individual states are sent to their respective prediction models, and using these predictions, the MPC generates the desired control reference according to the cost function.