Using Field Subspaces for On-Line Survey Guidance (original) (raw)
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Design of Field Experiments for Adaptive Sampling of the Ocean with Autonomous Vehicles
2010
Due to the highly non-linear and dynamical nature of oceanic phenomena, the predictive capability of various ocean models depends on the availability of operational data. A practical method to improve the accuracy of the ocean forecast is to use a data assimilation methodology to combine in-situ measured and remotely acquired data with numerical forecast models of the physical environment. Autonomous surface and underwater vehicles with various sensors are economic and efficient tools for exploring and sampling the ocean for data assimilation; however there is an energy limitation to such vehicles, and thus effective resource allocation for adaptive sampling is required to optimize the efficiency of exploration. In this paper, we use physical oceanography forecasts of the coastal zone of Singapore for the design of a set of field experiments to acquire useful data for model calibration and data assimilation. The design process of our experiments relied on the oceanography forecast including the current speed, its gradient, and vorticity in a given region of interest for which permits for field experiments could be obtained and for time intervals that correspond to strong tidal currents. Based on these maps, resources available to our experimental team, including Autonomous Surface Craft (ASC) are allocated so as to capture the oceanic features that result from jets and vortices behind bluff bodies (e.g., islands) in the tidal current. Results are summarized from this resource allocation process and field experiments conducted in January 2009.
Performance metrics for oceanographic surveys with autonomous underwater vehicles
IEEE Journal of Oceanic Engineering, 2001
The focus of this paper is the development of tools to facilitate the effective use of AUVs to survey small-scale oceanographic processes. A fundamental difficulty in making oceanographic surveys with autonomous underwater vehicles (AUVs) is the coupling of space and time through the AUV survey trajectory. Combined with the finite velocity and battery life of an AUV, this imposes serious constraints on the extent of the survey domain and on the spatial and temporal survey resolutions. In this paper, we develop a quantitative survey error metric which accounts for errors due to both spatial undersampling and temporal evolution of the sample field. The accuracy of the survey error metric is established through surveys of a simulated oceanographic process. Using the physical constraints of the platform, we also develop the "survey envelope" which delineates a region of survey parameter space within which an AUV can sucessfully complete a mission. By combining the survey error metric with the survey envelope, we create a graphical survey analysis tool which can be used to gain insight into the AUV survey design problem. We demonstrate the application of the survey analysis tool with an examination of the impact of certain survey design and parameters on surveys of a simple oceanographic process.
IFAC Proceedings Volumes, 2000
AUV technology provides a valuable addition to the traditional oceanographic platforms by providing a sensor suite which is rapidly and adaptively relocatable complementing the traditional Eulerian and Lagrangian measurements provided by fixed moorings and floats and drifters , respectively. However , the full potential provided by this new technology is just beginning to emerge. For example, AUV-s may be used as cost-effective mobile source platforms for ocean acoustic tomography networks. Provided the tomographic inversion can be performed in real time, the oceanographic field estimates can subsequently be used to relocate the AUV resources to areas with high variability or uncertainty to perform direct point measurements. This new Acoustically Focused Oceanographic Sampling (AFOS) concept optimally combines the synoptic volumetric coverage of the acoustic tomography and the spatial and temporal resolution provided by the AUVs. The basic components of AFOS , including new real-time tomographic inversion techniques have recently been demonstrated in connection with the Haro Strait Frontal Mapping experiment. However, the full potential of the combined use of A UV s and acoustic tomography is currently being explored within the context of general coastal ocean forecasting systems where the observational capabilities are directly coupled with oceanographic modeling through data assimilation. This paper describes the progress made so far and discusses the future potential of the combined use of underwater vehicle technology and acoustical oceanography. Copyright©2000 IFAC
Multi-Platforms and Multi-Sensors Integrated Survey for the Submerged and Emerged Areas
Journal of Marine Science and Engineering
In this paper, the state-of-the-art concerning new methodologies for surveying in coastal areas in order to obtain an efficient quantification of submerged and emerged environments is described and evaluated. This work integrates an interdisciplinary approach involving both geomatics and robotics and focuses on definition, implementation, and development of a methodology to execute integrated aerial and underwater survey campaigns in shallow water areas. A preliminary test was performed at Gorzente Lakes near Genoa (Italy), to develop and integrate different survey techniques, enabling working in a smarter way, reducing costs and increasing safety for the operators. In this context, Remote Sensing techniques were integrated with a UAV (Unmanned Aerial Vehicle) carrying an aerial optical sensor for photogrammetry and with an ASV (Autonomous Surface Vehicle) expressly addressed to work in extremely shallow water with underwater acoustic sensors (single echo sounder). The obtained cont...
Precision autonomous underwater navigation
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Deep-sea archaeology, an emerging application of autonomous underwater vehicle (AUV) technology, requires precise navigation and guidance. As science requirements and engineering capabilities converge, navigating in the sensor-limited ocean remains a fundamental challenge. Despite the logistical cost, the standards of archaeological survey necessitate using fixed acoustic transpondersan instrumented navigation environment. This thesis focuses on the problems particular to operating precisely within such an environment by developing a design method and a navigation algorithm. Responsible documentation, through remote sensing images, distinguishes archaeology from salvage, and fine-resolution imaging demands precision navigation. This thesis presents a design process for making component and algorithm level tradeoffs to achieve system-level performance satisfying the archaeological standard. A specification connects the functional requirements of archaeological survey with the design parameters of precision navigation. Tools based on estimation fundamentalsthe Cram6r-Rao lower bound and the extended Kalman filterpredict the systemlevel precision of candidate designs. Non-dimensional performance metrics generalize the analysis results. Analyzing a variety of factors and levels articulates the key tradeoffs: sensor selection, acoustic beacon configuration, algorithm selection, etc. The abstract analysis is made concrete by designing a survey and navigation system for an expedition to image the USS Monitor. Hypothesis grid (Hgrid) is both a representation of the sensed environment and an algorithm for building the representation. Range observations measuring the line-of-sight distance between two acoustic transducers are subject to multipath errors and spurious returns. The quality of this measurement is dependent on the location of the estimator. Hgrids characterize the measurement quality by generating a priori association probabilitiesthe belief that subsequent measurements will correspond to the direct-path, a multipath, or an outlieras a function of the estimated location. The algorithm has three main components: the mixed-density sensor model using Gaussian and uniform probability distributions, the measurement classification and multipath model identification using expectation-maximization (EM), and the grid-based spatial representation. Application to data from an autonomous benthic explorer (ABE) dive illustrates the algorithm and shows the feasibility of the approach.
Path Planning Methods for Adaptive Sampling of Environmental and Acoustical Ocean Fields
Proceedings of IEEE/ …
Adaptive sampling aims to predict the types and locations of additional observations that are most useful for specific objectives, under the constraints of the available observing network. Path planning refers to the computation of the routes of the assets that are part of the adaptive component of the observing network. In this paper, we present two path planning methods based on Mixed Integer Linear Programming (MILP). The methods are illustrated with some examples based on environmental ocean fields and compared to highlight their strengths and weaknesses. The stronger method is further demonstrated on a number of examples covering multi-vehicle and multi-day path planning, based on simulations for the Monterey Bay region. The framework presented is powerful and flexible enough to accommodate changes in scenarios. To demonstrate this feature, acoustical path planning is also discussed.
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Ocean scientists have dreamed of and recently started to realize an ocean observing revolution with autonomous observing platforms and sensors. Critical questions to be answered by such autonomous systems are where, when, and what to sample for optimal information, and how to optimally reach the sampling locations. Definitions, concepts, and progress towards answering these questions using quantitative predictions and fundamental principles are presented. Results in reachability and path planning, adaptive sampling, machine learning, and teaming machines with scientists are overviewed. The integrated use of differential equations and theory from varied disciplines is emphasized. The results provide an inference engine and knowledge base for expert autonomous observing systems. They are showcased using a set of recent at-sea campaigns and realistic simulations. Real-time experiments with identical autonomous underwater vehicles (AUVs) in the Buzzards Bay and Vineyard Sound region first show that our predicted time-optimal paths were faster than shortest distance paths. Deterministic and probabilistic reachability and path forecasts issued and validated for gliders and floats in the northern Arabian Sea are then presented. Novel Bayesian adaptive sampling for hypothesis testing and optimal learning are finally shown to forecast the observations most informative to estimate the accuracy of model formulations, the values of ecosystem parameters and dynamic fields, and the presence of Lagrangian Coherent Structures.