8 research outputs found
Persistent ocean monitoring with underwater gliders: Adapting sampling resolution
Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high-value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long-term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions.United States. National Oceanic and Atmospheric Administration. Monitoring and Event Response for Harmful Algal Blooms (NA05NOS4781228)National Science Foundation (U.S.). Center for Embedded Networked Sensing (CCR-0120778)National Science Foundation (U.S.). (Grant number CNS-0520305)National Science Foundation (U.S.). (Grant number CNS-0540420)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-09-1-1031)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-08-1-0693)United States. Office of Naval Research. Service-Oriented Architectur
Computing Energy Optimal Paths in Time-Varying Flows
Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration space spanned by the spatio-temporal coordinates. This enables a more efficient traversal through the search space, as opposed to a full search of the spatio-temporal configuration space. Furthermore, the proposed strategy results in solutions that are closer to the global optimal when compared to greedy searches through the spatial coordinates alone. We demonstrate the proposed algorithms by computing optimal energy paths around the Channel Islands in the Santa Barbara bay using time-varying flow field forecasts generated by the Regional Ocean Model System. We verify the accuracy of the computed paths by comparing them with paths computed via an optimal control formulation
Desenvolvimento de uma embarcação de baixo custo controlada a distância para monitoramento ambiental em corpos d'água rasos
Lakes and lagoons are a network of ecosystems in different geographical and climatic regions.
Many of the non-studied hydrographical systems are subject to negative effects of degradation
with destruction of river sides and pollution of rivers affluents, many of which are not monitored.
One of the major shortcomes for establishing a monitor routine of a water body is cost. Since
monitoring a water body or hydrographical system requires advanced equipment and specialized
personnel, the acquisition and implementation is prohibitive in many cases, especially for
developing countries. Another difficulty is that many of the ecosystems are shallow, making the
use of manned vessels impossible. Thus, this study aimed at developing and evaluate a remotely
controlled vessel, which is able to measure significant variables, such as temperature, pH and to
collect water samples. A global positioning system (GPS) obtains the location of the sample
collection in relation to the earth globe (geographic coordinates), these data are stored in a nonvolatile memory card. Additionally, the vessel is moved through solar energy, the navigation is
controlled by Arduino® electronic prototyping boards, based on an open source code. The
prototype was developed using low cost technologies and does not require specialized personnel
to assemble and operate. The robot vessel was tested in field, showing satisfactory results for pH
measurements, temperature and successfully collected water samples. The prototype is an
advance in terms of low-cost (estimated cost R 2.000,00. Este
dispositivo tem potencial para contribuir com o monitoramento ambiental em corpos d’águas
rasos, esta capacidade de navegação não foi observada em outros projetos de barcos robôs
Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments
Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature.
Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map.
Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes).
Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening