OCEANS 2019 MTS/IEEE SEATTLE最新文献

{"title":"Successful Repeated Docking under Fluctuating Current Disturbances in Real Sea*","authors":"T. Monden, Kohei Yamashita, Yoshiki Kanda, Horng Yi Hsu, Y. Toda, M. Minami","doi":"10.23919/OCEANS40490.2019.8962590","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962590","url":null,"abstract":"To extend the persistence time of an underwater operation of Autonomous Underwater Vehicles(AUVs) in the sea, many studies have been performed worldwide. The docking function takes place as an important role not only for battery recharging but also for other advanced applications. In the previous studies, the repeated sea docking using a Remotely Operated Vehicle(ROV) was succeeded. However, during the experiment in the sea, ROV sometimes failed the docking due to the ocean current disturbance since ROV is hard to correct large error of orientation around z-axis. Therefore, we develop an autonomous rotary station that defects the ocean current direction for correcting the error between the ocean current direction and the docking direction. This paper presents the details of an autonomous rotary station and the analysis of the repeated sea docking experimental result.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126558758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The NANOOS Visualization System (NVS): A Decade of Development and Progress Addressing Stakeholder Needs","authors":"C. Risien, J. Newton, T. Tanner, P. Kosro, E. Mayorga, R. Wold, J. Allan, C. Seaton","doi":"10.23919/OCEANS40490.2019.8962588","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962588","url":null,"abstract":"Over the past few decades coastal regions have experienced considerable socio-economic change. Accompanying these socio-economic shifts are unprecedented environmental changes, which include variation in magnitude and frequency of extreme weather events, marine heatwaves, increased ocean acidification, expansion of dead zones, extreme harmful algal blooms, and accelerating sea level rise. To understand these emerging environmental shifts, the past two decades have witnessed increased capacity to monitor changing environmental conditions and predict with greater accuracy such variations and events. These observation and prediction systems produce ever increasing amounts of data. Ongoing efforts to deliver this information using standard data models, metadata, data access protocols, and community accepted data server applications have helped reduce the heterogeneity of these data and improved data distribution. However, delivering critical information to stakeholders in a user-friendly and accessible manner remains a challenge. Beginning in 2009, the Northwest Association of Networked Ocean Observing Systems (NANOOS), the U.S. Integrated Ocean Observing System (IOOS) regional association for the Pacific Northwest, began to address this challenge by developing the NANOOS Visualization System (NVS), a map-based platform that aggregated a multitude of diverse data sets and forecast model fields into one system with the goal of delivering a more seamless, one-stop-shopping experience for users of coastal, ocean and atmospheric data. Here we describe the early vision and development of NVS and how it evolved into a flexible, multi-application platform where customized web applications can be developed to meet the needs of specific stakeholder groups. We focus on three applications (Seacast, Shellfish Growers, and Tsunami Evacuation Zones) that were developed using more formal design processes in close coordination with commercial crab fishermen, shellfish growers, and state and local emergency managers. In addition, we briefly describe the Tuna Fishers application, which evolved out of informal discussions with recreational tuna fishers. In highlighting these applications, we demonstrate the flexibility of NVS to quickly spin up prototype applications using pre-existing NVS framework elements. Working closely with small groups of dedicated stakeholders, we are then able to refine and extend an application before releasing it to the broader audience. Such a capability has enabled NANOOS to truly meet stakeholder needs, while increasing user capacity to understand and better respond to ongoing regional environmental changes.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117349357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustness Analysis of a Convolutional Neural Network Approach to Source-Range Estimation in a Simulated Arctic Environment","authors":"Rui Chen, H. Schmidt","doi":"10.23919/OCEANS40490.2019.8962829","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962829","url":null,"abstract":"This study presents a convolutional neural network (CNN) approach to underwater source-range estimation in an Arctic propagation environment and compares its performance to conventional matched field processing (MFP). The covariance matrices of simulated sources upon a vertical line array are used as input data and estimates through both classification and regression approaches are examined. The network architecture is designed to be intuitive and lightweight; regularization is implemented to prevent over-fitting. The training data consist of acoustic outputs from a near-surface monopole source placed at discrete range increments between 3-50 km away from the receiving array; the test data are generated by placing the source at random ranges within the training interval. Robustness of the CNN models to sound speed profile (SSP) variability is tested. Results show that the CNN approaches are more tolerant of SSP mismatch compared to MFP at the expense of worse range resolution when the SSP is modelled accurately. By examining the CNN models filter activations and intermediate layer outputs, we present insights into how they generate predictions and achieve their robustness.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127531249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Motion Models for Time-Optimal Risk-Aware Motion Planning for Variable-Speed AUVs","authors":"James P. Wilson, Khushboo Mittal, Shalabh Gupta","doi":"10.23919/OCEANS40490.2019.8962644","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962644","url":null,"abstract":"In this paper, we develop new motion models for curvature-constrained and variable-speed Autonomous Underwater Vehicles (AUVs) for time-optimal risk-aware motion planning. AUVs are becoming increasingly useful and cost-effective for a variety of tasks in many underwater applications including surveillance and scientific expeditions. Despite recent advances, the autonomy of AUVs is limited, especially for vehicles that operate in environments with obstacles. In particular, there is limited research for time-optimal risk-aware motion planning. In contrast, there has been significant research for finding the time-optimal paths in environments without obstacles; however, adapting these models to environments with obstacles yields sub-optimal results, since these models force the AUV to operate at extremal speeds at close proximity to obstacles. Specifically, moving at maximum speed increases the risk of collision, while moving at minimum speed dramatically increases travel time. As such, this paper presents new motion models for AUVs that enable the selection of intermediate speeds to provide a better balance between time and risk near obstacles. These models enhance the agility and maneuverability of the AUV and provide motion planners the flexibility to select appropriate speeds and therefore construct time-optimal risk-aware paths. Additionally, the models are simple to compute and are suitable for on-demand real-time computation. The performance of the proposed model is compared against existing models using our recently developed T⋆ algorithm for time-optimal risk-aware motion planning. The results show that our new model yields paths that are shorter in obstacles-rich scenarios with substantially lower risks.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WEC Array Networked Microgrid Control Design and Energy Storage System Requirements","authors":"W. Weaver, Alex Hagmuller, Max Ginsburg, D. Wilson, G. Bacelli, R. Robinett, R. Coe, B. Gunawan","doi":"10.23919/OCEANS40490.2019.8962576","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962576","url":null,"abstract":"Wave Energy Converter (WEC) technologies transform power from the waves to the electrical grid. WEC system components are investigated that support the performance, stability, and efficiency as part of a WEC array. To this end, Aquaharmonics Inc took home the $1.5 million grand prize in the 2016 U.S. Department of Energy Wave Energy Prize, an 18-month design-build-test competition to increase the energy capture potential of wave energy devices. Aquaharmonics intends to develop, build, and perform open ocean testing on a 1: 7 scale device. Preliminary wave tank testing on the mechanical system of the 1: 20 scale device has yielded a data-set of operational conditions and performance. In this paper, the Hamiltonian surface shaping and power flow control (HSSPFC) method is used in conjunction with scaled wave tank test data to explore the design space for the electrical transmission of energy to the shore-side power grid. Of primary interest is the energy storage system (ESS) that will electrically link the WEC to the shore. Initial analysis results contained in this paper provide a trade-off in storage device performance and design selection.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129125225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Signals of Opportunity based Geometry Calibration of Hydrophone Arrays","authors":"I. Skog, E. Gudmundson","doi":"10.23919/OCEANS40490.2019.8962552","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962552","url":null,"abstract":"A method to calibrate the geometries of hydrophone arrays using the sound emitted from nearby ships, is presented. The calibration problem is formulated as a simultaneous localization and mapping (SLAM) estimation problem, where the locations and geometries of the arrays are viewed as unknown map states and the position of the source is viewed as the unknown dynamic state. Two models for the geometry of the arrays are presented. The first model does not impose any constraint on array geometry, whereas the second model takes into account the known maximum distance between the hydrophones. The performance of the proposed calibration method is evaluated using data from two PASS-2447 Omnitech Electronics Inc. 56-element hydrophone arrays. Tests with three data sets show that array geometries in the north-east plane can be consistently estimated. Only the second model provides consistent results in the depth direction. The calibration of the array geometries is shown to increase source localization accuracy significantly.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114929877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Power at Sea: Opportunities for Expanded Ocean Observations through Marine Renewable Energy Integration","authors":"Rebecca Green, A. Copping, Robert J. Cavagnaro, Deborah J. Rose, Dorian M. Overhus, D. Jenne","doi":"10.23919/OCEANS40490.2019.8962706","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962706","url":null,"abstract":"The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and in-person interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Hybrid-Clustering and Localized Regression for Time Synchronization of a Hierarchical Underwater Acoustic Sensor Array","authors":"T. Fu, Xinming Lin, Jason Hou, D. Deng","doi":"10.23919/OCEANS40490.2019.8962752","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962752","url":null,"abstract":"Time synchronization is a critical requirement for the application of underwater acoustic sensor network (UWSN). Although a number of time synchronization protocols have been proposed for UWSN, none of them can be directly applied to stand-alone autonomous acoustic receivers, as they lack hardware platforms permitting communication. In this paper, we propose a machine learning-based time synchronization framework for stand-alone autonomous receiver arrays, using the Juvenile Salmon Acoustic Telemetry System as a case study. The proposed framework consists of array partition and time synchronization. Using detections of receiver-attached beacons as input, this framework synchronizes all receiver clocks to a root receiver clock. The framework has been successfully used in a field study at Trevallyn Dam forebay in Tasmania, Australia.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130451770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of PoseNet for 6-DOF Underwater Pose Estimation","authors":"M. C. Nielsen, M. Leonhardsen, I. Schjølberg","doi":"10.23919/OCEANS40490.2019.8962814","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962814","url":null,"abstract":"Autonomy in underwater intervention operations requires localization systems of high accuracy. State-of-the-art methods rely on computer vision to provide the necessary localization accuracy. However, traditional computer vision solutions rely on hand-crafted features, which often exhibit low robustness to variations in the lighting conditions. Furthermore, the most common image localization method, Perspective-n-Point (PnP), relies on specific knowledge of the distances in the scene, something which is not always available. Recent advances within deep learning, in particular, convolutional neural networks (CNNs), have resulted in promising methods for pose estimation based on imagery input. This article investigates the potential of applying a specific CNN architecture, named PoseNet, to estimate the 6-DoF pose between an underwater vehicle and a fixed object, without the need for artificial markers.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116601764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Station Keeping of AUVs","authors":"Kristoffer Borgen Knudsen, M. C. Nielsen, I. Schjølberg","doi":"10.23919/OCEANS40490.2019.8962598","DOIUrl":"https://doi.org/10.23919/OCEANS40490.2019.8962598","url":null,"abstract":"Control of underwater vehicles remains an active research topic within the literature. Multiple challenges exists for controlling an underwater vehicle, including highly nonlinear effects due to hydrodynamics. Control based models seek to model the underlying dynamics but suffer from the balance between tractable computation and performance. Machine Learning (ML) control techniques show promise as an alternative to classical model-based approaches. This article investigates the application of a model-free deep reinforcement learning algorithm, Deep Deterministic Policy Gradient (DDPG), for station keeping in six degrees of freedom (DOF) for an underwater vehicle.","PeriodicalId":208102,"journal":{"name":"OCEANS 2019 MTS/IEEE SEATTLE","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}