More Like this

1. Multi-Modal Lake Sampling for Detecting Harmful Algal Blooms

   https://doi.org/10.1109/OCEANS47191.2022.9977137  

   Salman, Ibrahim ; Karapetyan, Nare ; Venkatachari, Archana ; Quattrini Li, Alberto ; Bourbonnais, Annie ; Rekleitis, Ioannis ( October 2022 , OCEANS 2022, Hampton Roads)

   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. 

   more » « less
2. Collaborative Sampling Using Heterogeneous Marine Robots Driven by Visual Cues

   https://doi.org/10.1109/CRV.2017.49  

   Manjanna, Sandeep ; Hansen, Johanna ; Li, Alberto Quattrini ; Rekleitis, Ioannis ; Dudek, Gregory ( May 2017 , Conference on Computer and Robot Vision (CRV))

   This paper addresses distributed data sampling in marine environments using robotic devices. We present a method to strategically sample locally observable features using two classes of sensor platforms. Our system consists of a sophisticated autonomous surface vehicle (ASV) which strategically samples based on information provided by a team of inexpensive sensor nodes. The sensor nodes effectively extend the observational capabilities of the vehicle by capturing georeferenced samples from disparate and moving points across the region. The ASV uses this information, along with its own observations, to plan a path so as to sample points which it expects to be particularly informative. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a region with less energy expenditure. We validate our approach through simulations and test the system on real robots in field. 

   more » « less
3. Dynamic Autonomous Surface Vehicle Controls Under Changing Environmental Forces

   Jason Moulton, Nare Karapetyan ( July 2019 , 12th Conference on Field and Service Robotics (FSR))

   The ability to navigate, search, and monitor dynamic marine environments such as ports, deltas, tributaries, and rivers presents several challenges to both human operated and autonomously operated surface vehicles. Human data collection and monitoring is overly taxing and inconsistent when faced with large coverage areas, disturbed environments, and potentially uninhabitable situations. In contrast, the same missions become achievable with autonomous surface vehicles (ASV) configured and capable of accurately maneuvering in such environments. The two dynamic factors that present formidable challenges to completing precise maneuvers in coastal and moving waters are currents and winds. In this work, we present novel and inexpensive methods for sensing these external forces, together with methods for accurately controlling an ASV in the presence of such external forces. The resulting platform is capable of deploying bathymetric and water quality monitoring sensors. Experimental results in the local lakes and rivers demonstrate the feasibility of the proposed approach. 

   more » « less
4. Towards Understanding Underwater Weather Events in Rivers Using Autonomous Surface Vehicles

   https://doi.org/10.1109/OCEANS47191.2022.9977236  

   Li, Alice K. ; Mao, Yue ; Manjanna, Sandeep ; Liu, Sixuan ; Dhanoa, Jasleen ; Mehta, Bharg ; Edwards, Victoria M. ; Ojeda, Fernando Cladera ; Hsieh, M. Ani ; Men, Mael Le ; et al ( December 2022 , OCEANS 2022, Hampton Roads)

   Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards – including erosion and the undermining of urban infrastructure – consequently impacting the health of our rivers and ecosystems. Observations of these underwater phenomena are rare, because satellites and sensors mounted on aerial vehicles cannot penetrate the murky waters. Autonomous Surface Vehicles (ASVs) provides a means to track and map these complex and dynamic underwater phenomena. This work highlights preliminary results of high-resolution data gathering with ASVs, equipped with a suite of sensors capable of measuring physical and chemical parameters of the river. Measurements were acquired along the lower Schuylkill River in the Philadelphia area at high-tide and low-tide conditions. The data will be leveraged to improve our understanding of changes in bathymetry due to floods; the dynamics of mixing and stagnation zones and their impact on water quality; and the dynamics of suspension and resuspension of fine sediment. The data will also provide insight into the development of adaptive sampling strategies for ASVs that can maximize the information gain for future field experiments. 

   more » « less
5. Combining Remote and In-situ Sensing for Autonomous Underwater Vehicle Localization and Navigation

   https://doi.org/10.1109/OCEANS47191.2022.9977208  

   Rojas, Cesar A. ; Padrao, Paulo V. ; Fuentes, Jose E. ; Albayrak, Arif R. ; Osmanoglu, Batuhan ; Bobadilla, Leonardo ( December 2022 , OCEANS 2022)

   Scientists continue to study the red tide and fish-kill events happening in Florida. Machine learning applications using remote sensing data on coastal waters to monitor water quality parameters and detect harmful algal blooms are also being studied. Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) are often deployed on data collection and disaster response missions. To enhance study and mitigation efforts, robots must be able to use available data to navigate these underwater environments. In this study, we compute a satellite-derived underwater environment (SDUE) model by implementing a supervised machine learning model where remote sensing reflectance (Rrs) indices are labeled with in-situ data they correlate with. The models predict bathymetry and water quality parameters given a recent remote sensing image. In our experiment, we use Sentine1-2 (S2) images and in-situ data of the Biscayne Bay to create an SDUE that can be used as a Chlorophyll-a map. The SDUE is then used in an Extended Kalman Filter (EKF) application that solves an underwater vehicle localization and navigation problem. 

   more » « less