Yi Chao

Planning and Implementing Trajectories for Autonomous Underwater Vehicles to Track Evolving Ocean Processes Based on Predictions from a Regional Ocean Model

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend preliminary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Autonomous Underwater Vehicle trajectory design coupled with predictive ocean models: A case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceanographic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to maneuver through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Blending Sea Surface Temperatures from Multiple Satellites and In Situ Observations for Coastal Oceans

Abstract A two-dimensional variational data assimilation (2DVAR) method for blending sea surface temperature (SST) data from multiple observing platforms is presented. This method produces continuous fields and has the capability of blending multiple satellite and in situ observations. In addition, it allows specification of inhomogeneous and anisotropic background correlations, which are common features of coastal ocean flows. High-resolution (6 km in space and 6 h in time) blended SST fields for August 2003 are produced for a region off the California coast to demonstrate and evaluate the methodology. A comparison of these fields with independent observations showed root-mean-square errors of less than 1°C, comparable to the errors in conventional SST observations. The blended SST fields also clearly reveal the finescale spatial and temporal structures associated with coastal upwelling, demonstrating their utility in the analysis of finescale flows. With the high temporal resolution, the blended SST fie...

Spatiotemporal path planning in strong, dynamic, uncertain currents

This work addresses mission planning for autonomous underwater gliders based on predictions of an uncertain, time-varying current field. Glider submersibles are highly sensitive to prevailing currents so mission planners must account for ocean tides and eddies. Previous work in variable-current path planning assumes that current predictions are perfect, but in practice these forecasts may be inaccurate. Here we evaluate plan fragility using empirical tests on historical ocean forecasts for which followup data is available. We present methods for glider path planning and control in a time-varying current field. A case study scenario in the Southern California Bight uses current predictions drawn from the Regional Ocean Monitoring System (ROMS).

Modeling responses of diatom productivity and biogenic silica export to iron enrichment in the equatorial Pacific Ocean

[1]xa0Using a three-dimensional physical-biogeochemical model, we have investigated the modeled responses of diatom productivity and biogenic silica export to iron enrichment in the equatorial Pacific, and compared the model simulation with in situ (IronEx II) iron fertilization results. In the eastern equatorial Pacific, an area of 540,000 km2 was enhanced with iron by changing the photosynthetic efficiency and silicate and nitrogen uptake kinetics of phytoplankton in the model for a period of 20 days. The vertically integrated Chl a and primary production increased by about threefold 5 days after the start of the experiment, similar to that observed in the IronEx II experiment. Diatoms contribute to the initial increase of the total phytoplankton biomass, but decrease sharply after 10 days because of mesozooplankton grazing. The modeled surface nutrients (silicate and nitrate) and TCO2 anomaly fields, obtained from the difference between the “iron addition” and “ambient” (without iron) concentrations, also agreed well with the IronEx II observations. The enriched patch is tracked with an inert tracer similar to the SF6 used in the IronEx II. The modeled depth-time distribution of sinking biogenic silica (BSi) indicates that it would take more than 30 days after iron injection to detect any significant BSi export out of the euphotic zone. Sensitivity studies were performed to establish the importance of fertilized patch size, duration of fertilization, and the role of mesozooplankton grazing. A larger size of the iron patch tends to produce a broader extent and longer-lasting phytoplankton blooms. Longer duration prolongs phytoplankton growth, but higher zooplankton grazing pressure prevents significant phytoplankton biomass accumulation. With the same treatment of iron fertilization in the model, lowering mesozooplankton grazing rate generates much stronger diatom bloom, but it is terminated by Si(OH)4 limitation after the initial rapid increase. Increasing mesozooplankton grazing rate, the diatom increase due to iron addition stays at minimum level, but small phytoplankton tend to increase. The numerical model experiments demonstrate the value of ecosystem modeling for evaluating the detailed interaction between biogeochemical cycle and iron fertilization in the equatorial Pacific.

Towards the Improvement of Autonomous Glider Navigational Accuracy Through the Use of Regional Ocean Models

Autonomous underwater gliders are robust and widelyused ocean sampling platforms that are characterized by their endurance, and are one of the best approaches to gather subsurface data at the appropriate spatial resolution to advance our knowledge of the ocean environment. Gliders generally do not employ sophisticated sensors for underwater localization, but instead dead-reckon between set waypoints. Thus, these vehicles are subject to large positional errors between prescribed and actual surfacing locations. Here, we investigate the implementation of a large-scale, regional ocean model into the trajectory design for autonomous gliders to improve their navigational accuracy. We compute the dead-reckoning error for our Slocum gliders, and compare this to the average positional

Fine-scale temporal variation in marine extracellular enzymes of coastal southern California

Extracellular enzymes are functional components of marine microbial communities that contribute to nutrient remineralization by catalyzing the degradation of organic substrates. Particularly in coastal environments, the magnitude of variation in enzyme activities across timescales is not well characterized. Therefore, we established the MICRO time series at Newport Pier, California, to assess enzyme activities and other ocean parameters at high temporal resolution in a coastal environment. We hypothesized that enzyme activities would vary most on daily to weekly timescales, but would also show repeatable seasonal patterns. In addition, we expected that activities would correlate with nutrient and chlorophyll concentrations, and that most enzyme activity would be bound to particles. We found that 34–48% of the variation in enzyme activity occurred at timescales <30u2009days. About 28–56% of the variance in seawater nutrient concentrations, chlorophyll concentrations, and ocean currents also occurred on this timescale. Only the enzyme β-glucosidase showed evidence of a repeatable seasonal pattern, with elevated activities in the spring months that correlated with spring phytoplankton blooms in the Southern California Bight. Most enzyme activities were weakly but positively correlated with nutrient concentrations (ru2009=u20090.24–0.31) and upwelling (ru2009=u20090.29–0.35). For the enzymes β-glucosidase and leucine aminopeptidase, most activity was bound to particles. However, 81.2% of alkaline phosphatase and 42.8% of N-acetyl-glucosaminidase activity was freely dissolved. These results suggest that enzyme-producing bacterial communities and nutrient dynamics in coastal environments vary substantially on short timescales (<30u2009days). Furthermore, the enzymes that degrade carbohydrates and proteins likely depend on microbial communities attached to particles, whereas phosphorus release may occur throughout the water column.

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection.We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to themodel to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks.We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Automated Sensor Network to Advance Ocean Science

Oceanography is evolving from a shipbased expeditionary science to a distributed, observatory-based approach in which scientists continuously interact with instruments in the field. These new capabilities will facilitate the collection of long-term time series while also providing an interactive capability to conduct experiments using data streaming in real time. n nThe U.S. National Science Foundation has funded the Ocean Observatories Initiative (OOI), which over the next 5 years will deploy infrastructure to expand scientists ability to remotely study the ocean. The OOI is deploying infrastructure that spans global, regional, and coastal scales. A global component will address planetary-scale problems using a new network of moored buoys linked to shore via satellite telecommunications. A regional cabled observatory will “wire” a single region in the northeastern Pacific Ocean with a high-speed optical and power grid. The coastal component will expand existing coastal observing assets to study the importance of high-frequency forcing on the coastal environment.

Current-sensitive path planning for an underactuated free-floating ocean Sensorweb

This work investigates multiagent path planning in strong, dynamic currents using thousands of highly underactuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.