Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay

Ramp, Steven R.; Davis, Russ E.; Leonard, Naomi Ehrich; Shulman, Igor; Chao, Yi; Robinson, Allan R.; Marsden, J.; Lermusiaux, Pierre F. J.; Fratantoni, David M.; Paduan, Jeffrey D.; Chávez, Francisco P.; Bahr, Fred; Liang, Shuxiu; Leslie, Wayne G.; Li, Z.

doi:10.1016/j.dsr2.2008.08.013

Cited by 116 publications

(89 citation statements)

References 58 publications

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Section: Introductionmentioning

confidence: 99%

“…Distributed environmental sampling is an active field of research [2], [14] due to its many applications, including contaminant plume localization [23], biological monitoring [18], and data assimilation in atmospheric and ocean sciences [5], [13]. Significant hardware and sensor improvements [19] as well as algorithmic performance guarantees [9], [10] have further encouraged interest.…”

Section: Introductionmentioning

confidence: 99%

“…For example, small-scale (15 km) eddies appeared at the mouth of Monterey Bay during field experiments of the second Autonomous Ocean Sampling Network (AOSN-II) and contributed to the overall transport of cold water away from the southern part of the bay [18]. However, the movement of these eddies could not be correlated with local wind shear stress [18]. Understanding transport within these flow structures is possible but requires in situ observations over a large spatiotemporal volume collected by fleets of autonomous vehicles [3], [6], [8], [17].…”

Section: Introductionmentioning

confidence: 99%

“…Understanding transport within these flow structures is possible but requires in situ observations over a large spatiotemporal volume collected by fleets of autonomous vehicles [3], [6], [8], [17]. Underwater gliders, which are steered, buoyancy-driven, long-endurance vehicles, and drifters, which are depth-controlled underwater platforms that passively drift, are deployed to reduce uncertainties in estimates of ocean processes [14], [16], [20] and to sample and track oceanographic features [2], [18]. A steered sampling platform such as a glider that travels within the flowfield of an eddy taking targeted observations may be even more beneficial than passive drifter, even if the drifter has longer endurance.…”

Section: Introductionmentioning

confidence: 99%

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Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Lagor

Paley

2015

Dynamic Data-Driven Environmental Systems Science

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Abstract. This paper presents a path-planning paradigm for distributed control of multiple sensor platforms in a geophysical flow well-approximated by a point-vortex model. We utilize Hamiltonian dynamics to generate control vector fields for vehicle motion in N -vortex flows using the concept of an active singularity whose strength is a tunable control input. We introduce active singularities that are virtual point vortices possibly collocated with virtual point sources or sinks. We provide a principled method to stabilize relative equilibria of these virtual vortices in the presence of the actual point vortices, which represent the underlying geophysical flow. We illustrate how these relative equilibria may be useful for vehicle path planning and sampling in a geophysical flow. Preliminary results presented here are based on an adaptive control design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Lagor

Paley

2015

Dynamic Data-Driven Environmental Systems Science

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“…The first of these was the Autonomous Ocean Sampling Network (AOSN) II project [1,2] with its field experiment in Monterey Bay, CA, over the month of August 2003 and the second was the Adaptive Sampling and Prediction (ASAP) project with its field experiment in Monterey Bay, CA, over the month of August 2006 [3].…”

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confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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Can we do better than the grid survey: Optimal synoptic surveys in presence of variable uncertainty and decorrelation scales

Фролов

Garau²,

Bellingham

2014

JGR Oceans

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Regular grid (''lawnmower'') survey is a classical strategy for synoptic sampling of the ocean. Is it possible to achieve a more effective use of available resources if one takes into account a priori knowledge about variability in magnitudes of uncertainty and decorrelation scales? In this article, we develop and compare the performance of several path-planning algorithms: optimized ''lawnmower,'' a graph-search algorithm (A*), and a fully nonlinear genetic algorithm. We use the machinery of the best linear unbiased estimator (BLUE) to quantify the ability of a vehicle fleet to synoptically map distribution of phytoplankton off the central California coast. We used satellite and in situ data to specify covariance information required by the BLUE estimator. Computational experiments showed that two types of sampling strategies are possible: a suboptimal space-filling design (produced by the ''lawnmower'' and the A* algorithms) and an optimal uncertainty-aware design (produced by the genetic algorithm). Unlike the space-filling designs that attempted to cover the entire survey area, the optimal design focused on revisiting areas of high uncertainty. Results of the multivehicle experiments showed that fleet performance predictors, such as cumulative speed or the weight of the fleet, predicted the performance of a homogeneous fleet well; however, these were poor predictors for comparing the performance of different platforms.

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Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay

Cited by 116 publications

References 58 publications

Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Cooperative Vehicle Environmental Monitoring

Can we do better than the grid survey: Optimal synoptic surveys in presence of variable uncertainty and decorrelation scales

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