Progress toward autonomous ocean sampling networks

Curtin, Tom; Bellingham, James

doi:10.1016/j.dsr2.2008.09.005

Cited by 55 publications

(30 citation statements)

References 26 publications

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“…For reviews on marine path planning and adaptive sampling, we refer readers to Leonard et al (2007), Lermusiaux (2007), Roy et al, (2007), Paley et al (2008), Lermusiaux et al (2016, and in press), and , and the references cited therein. Other results that are useful for marine autonomy but are not covered here include nested autonomy (e.g., Schmidt et al, 2016); underwater navigation, communication, and motion control (e.g., Leonard and Bahr, 2016); and ocean sensing and underwater networks (e.g., Curtin and Bellingham, 2009;Venkatesan et al, 2017).…”

Section: Planning Fundamentals For Expert Alps Systemsmentioning

confidence: 99%

“…To best account for environmental effects, ALPS systems should become "expert systems" (e.g., Jackson, 1998) that plan and optimize their motions, using and integrating predictive models, control algorithms, uncertainty estimates, and data assimilation. Such integration of disciplines was initiated by the Autonomous Ocean Sampling Network (e.g., Curtin et al, 1993;Rudnick et al, 2004;Leonard et al, 2007Leonard et al, , 2010Lermusiaux, 2007;Ramp et al, 2009;Curtin and Bellingham, 2009), and it remains an area of active research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Planning and Sampling Predictions for Autonomous and Lagrangian Platforms and Sensors in the Northern Arabian Sea

Lermusiaux¹,

Haley²,

Jana³

et al. 2017

Oceanog.

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Section: Planning Fundamentals For Expert Alps Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Planning and Sampling Predictions for Autonomous and Lagrangian Platforms and Sensors in the Northern Arabian Sea

Lermusiaux¹,

Haley²,

Jana³

et al. 2017

Oceanog.

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“…A central focus was on reliable, efficient and adaptive coordinated control strategies for mobile sensor platforms to collect data of high value. Both the AOSN II and ASAP experiments were designed to bring together new techniques in sensing, forecasting and coordinated control; see [88] for a summary of goals and progress. The 2003 AOSN II experiment brought these techniques together for the first time, yielding an unprecedented data set.…”

Section: Advances In Cooperative Vehicle Ocean Monitoringmentioning

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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“…This requirement does not hold for many traditional platforms of oceanographic sampling because of their physical, economic, and/or operational limits. For this reason, ocean-observing technology is transforming from platform-based capabilities to networking of sensor nodes [Curtin et al, 1993;Curtin and Bellingham, 2009]. Underlying this transformation is the idea that the most efficient and economic way to sample the ocean is through networks of small, intelligent, and cheap oceanobserving platforms [Kunzig, 1996].…”

Section: Introductionmentioning

confidence: 99%

Volumetric estimation of thermal fields inferred from glider‐like and remote‐sensing measurements in undersampled coastal regions

Álvarez

Reyes

2010

J. Geophys. Res.

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[1] A method to estimate volumetric oceanographic conditions in a given coastal area merging information from remote sensing and gliders is investigated. The method relies on the application of a linear model that makes use of a degenerated 3-D covariance model inferred from in situ and remote-sensing measurements and takes into account the functional nature of glider data. The optimum weighting function results from a Fredholm integral equation of the first kind. A centroid collocation method with piecewise constant bases and a specific class of stabilizing functional are used to numerically solve the problem. The investigated methodology was validated during the oceanographic cruise TSS08 conducted by the NATO Undersea Research Center in September 2008 in a region of the Black Sea, offshore the Bosphorus Strait. Data sampled with a towed vehicle emulating a glider were merged with remote-sensing information to estimate the 3-D temperature field at unobserved locations. A set of the observations was only considered for validation. Estimations from the functional model are confronted with those obtained from a discrete approach. Results indicate a better performance of the functional model and raise the question of whether functional techniques are more appropriate than algebraic ones to analyze high-resolution data obtained from new robotic platforms.Citation: Alvarez, A., and E. Reyes (2010), Volumetric estimation of thermal fields inferred from glider-like and remotesensing measurements in undersampled coastal regions,

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Progress toward autonomous ocean sampling networks

Cited by 55 publications

References 26 publications

Optimal Planning and Sampling Predictions for Autonomous and Lagrangian Platforms and Sensors in the Northern Arabian Sea

Optimal Planning and Sampling Predictions for Autonomous and Lagrangian Platforms and Sensors in the Northern Arabian Sea

Cooperative Vehicle Environmental Monitoring

Volumetric estimation of thermal fields inferred from glider‐like and remote‐sensing measurements in undersampled coastal regions

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