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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. 

Abstract This work proposes vision-only navigation strategies for an autonomous underwater robot. This approach is a step towards solving the coverage path planning problem in a 3-D environment for surveying underwater structures. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work, we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this article is a step towards enabling the autonomous operation of lightweight robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3-D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed. 

Visual monitoring operations underwater require both observing in close-proximity the objects of interest, and tracking the few feature-rich areas necessary for state estimation. This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs), to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automatically-extracted points of interest, with low computational overhead and fast re-planning times. 

This paper addresses the problem of the coverage path planning in a 3D environment for surveying underwater structures. We propose to use the navigation strategy that a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. In contrast to the previous methods in the literature, we are aiming to perform coverage in completely unknown environment with some initial prior information. Our proposed method uses convolutional neural networks to learn the control commands based on the visual input. Preliminary results and a detailed overview of the proposed method are discussed.