Freshwater resources including lakes and reservoirs are increasingly threatened by harmful cyanobacterial blooms (HCBs). The scarcity of high spatial and temporal resolution data presents challenges for monitoring, predicting, and managing these blooms. Autonomous surface vehicles (ASVs) equipped with water quality sensors represent a powerful tool to obtain high-resolution spatial data at Lake Wateree (LW), South Carolina (SC). LW is a hydroelectric reservoir commonly covered with extensive blooms of the benthic cyanobacteria Microseira (Lyngbya) wollei and Phormium sp., with the thickest mats in shallow coves. The main objective of this study was to determine the best speed and duration of loiter (i.e., pauses) required to collect accurate quantitative data on a mobile platform. We present a low-cost motorized kayak (USD) designed to run autonomously equipped with a YSI EXO2 sonde measuring depth, temperature, conductivity, dissolved oxygen, pH, turbidity, and phycocyanin. The sonde was positioned horizontally on a rigid mount at 0.5 m below the surface to efficiently reduce the effect of turbulence. The data were compared to another YSI EXO2 sonde installed on the same ASV design, maintained stationary midway along the moving ASV’s path to assess the data accuracy obtained at different speeds and loiter periods. No statistically significant differences were observed for measurements collected on the stationary and moving ASVs for all water-quality sensors at a speed of up to 2.7 m/s (6 mph). Differences observed between the moving and stationary sondes for phycocyanin and turbidity sensors were within the reported factory accuracy at speeds up to 1.8 m/s (4 mph) and outside the expected factory accuracy at higher speed (2.7 m/s), showing the effects of motion and mixing on the collected data. Dissolved oxygen was outside of the reported factory accuracy for all tests. It is recommended to loiter periodically when moving at a faster speed to obtain more accurate data, as the differences between the sondes were alleviated during the loiter period. Overall, our ASV design has the potential to be employed to obtain robust spatial data at LW when deployed at optimal operating conditions.

In this paper we present an integrated system for observation of transient freshwater phenomena, mainly Harmful Cyanobacteria Blooms, using a fleet of Autonomous Surface Vehicles (ASVs) fitted with onboard in-situ water quality sensors. Automated water quality sampling is often done following a predetermined trajectory, aiming to achieve the most cost effective representative coverage. We build on our previous work - the skeleton-of-skeleton technique, which selects sampling points representative of the body of water. Given a shared depot, the goal of the proposed algorithm is to produce trajectories for each ASV such that each sampling point is visited only once, thereby minimizing the traversal time for each robot and optimizing the operational timeline of the entire fleet. We formulate this NP-hard problem within the framework of the Multiple Traveling Salesperson Problem (mTSP), and use heuristics to address it. Water quality data was collected through multiple field deployments. Our experiments highlight the scalability of the automated system and are foundational in developing water quality sampling strategies.

In freshwater lakes, harmful cyanobacterial blooms often thrive due to increased dissolved nitrogen inputs, primarily from nitrate. To tackle the challenge of detecting dissolved nitrogen inputs, we present a novel autonomous system specifically designed to monitor nitrate influx from tributaries in lacustrine environments continuously. We deployed an Autonomous Surface Vehicle (ASV) with a state-of-the-art Ultraviolet (UV) nitrate sensor. Further, we enhanced its monitoring capability with conventional water quality sensors for temperature, pH, dissolved oxygen, turbidity, and total algae. The ASV systematically navigated the intake regions, capturing a detailed spatio-temporal map of nitrate concentration as it dispersed into the lake ecosystem. Our field deployments confirm the system's effectiveness, highlighting its potential to improve our understanding of nutrient dynamics in freshwater environments significantly.

This paper presents a novel algorithm for monitoring marine environments utilizing a resource-constrained robot. Collecting water quality data from large bodies of water is paramount for monitoring the ecosystem's health, particularly for predicting harmful cyanobacteria blooms. The large spatial dimensions of such bodies of water and the slow varying of water quality parameters make exhaustive, complete coverage impractical and unnecessary. This work explores a new strategy for efficiently measuring water quality quantities with an autonomous surface vehicle (ASV). The method utilizes the medial axis of the water body producing a guideline for the ASV trajectory that visits representative areas of the environment. The proposed method ensures data collection in the narrower parts of the lake, where researchers have historically observed harmful blooms while also visiting open water areas. It also presents an analysis of the Spatio-temporal sensitivity of the target sensor. A comparison with the traditional lawnmower algorithm demonstrates that the conventional BCD-based complete coverage method cannot sample the small coves of a lake. As such, we show that the proposed method captures more diverse regions of the area with a partial coverage technique. Offline analysis of several lakes and reservoirs and results from field deployments at Lake Murray, SC, USA, demonstrate the proposed method's effectiveness.

In this paper, we present a system for measuring water quality, with a focus on detecting and predicting Harmful Cyanobacterial Blooms (HCBs). The proposed approach includes stationary multi-sensor stations, Autonomous Surface Vehicles (ASVs) collecting water quality data, and manual deployments of vertical water sampling together with vertical water quality sensor data collection, in order to monitor the health of the lake and the progress of different types of algal blooms. Traditional water monitoring is performed by manual sampling, which is limited both in the spatial and the temporal domain. The proposed method will expand the range of measurements while reducing the cost. Human sampling is still included in order to provide a base of comparison and ground truth for the automated measurements. In addition, the collected data, over multiple years, will be analyzed to infer correlations between the different measured parameters and the presence of blooms. A detailed description of the proposed system is presented together with data collected during our first sampling season.