Automated Sensor Network to Advance Ocean Science

Schofield, Oscar; Glenn, Scott; Orcutt, John A.; Arrott, M.; Meisinger, Michael; Gangopadhyay, Avijit; Brown, W.; Signell, Richard P.; Moline, Mark A.; Chao, Yi; Chien, Steve; Thompson, David R.; Balasuriya, A.; Lermusiaux, Pierre F. J.; Oliver, Matthew J.

doi:10.1029/2010eo390001

Cited by 38 publications

(17 citation statements)

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“…Schofield et al [104] describe a number of field tests including a test of remote coordination of an array of acoustically networked AUVs and the coordinated sampling of underwater gliders and the space-based Hyperion imager flying on the Earth Observing-1 spacecraft. Using decentralized data fusion and control, two unmanned aerial vehicles (UAVs) were used to demonstrate cooperative localization of ground-based features by Cole et al [105].…”

Section: Recent Developments and Future Directionsmentioning

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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Section: Recent Developments and Future Directionsmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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“…Since the early days of ocean science, fixed platforms or expeditions with vessels have been used to collect varied observations (e.g., Stommel, 1989;Dickey, 2003;Schofield et al, 2010). In the past decade, adventurous research expeditions have been augmented with varied robotics-based platforms and sensors (e.g., Rudnick and Perry, 2003;Bellingham and Rajan, 2007;Nicholson and Healey, 2008).…”

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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“…This feature-oriented methodology is also model-independent and can be applied in lieu of either satellite or in situ observations, or both, especially in coastal regions. It has been applied in many real-time simulations, including the recent IOOS efforts (Schofield et al, 2010).…”

Section: Introductionmentioning

confidence: 99%