A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics

Jaffe, Jules S.; Franks, Peter J. S.; Roberts, Paul L. D.; Mirza, Diba; Schurgers, Curt; Kastner, Ryan; Boch, Adrien

doi:10.1038/ncomms14189

Cited by 152 publications

(92 citation statements)

References 25 publications

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“…Compact underwater agents play an important role in oceanographic research and industrial monitoring. They have been deployed in the investigation of ocean phenomena [87] and in the development of underwater sensor networks [88]. However, the design of accurate controllers for this kind of mechatronic system can become very challenging, due to their intrinsic nonlinear behavior and highly uncertain dynamics [49].…”

Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Depth Control Of a Micro Diving Agentmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…The definition of a fine-scale sampling strategy is therefore a critical task which consists of choosing which aspects to favor and which to sacrifice (Wang et al, 2019). Viable solutions (Shcherbina et al, 2015;Jaffe et al, 2017;Pascual et al, 2017;Petrenko et al, 2017;Wang et al, 2018; see also section "Some Case Studies for the SWOT Fast-Sampling Phase") include the use of an affordable number of moorings deployed on a smaller area, or along a one-dimensional array; the restriction to ship-based shallow CTD casts and to instruments in use, to minimize the occupation time at sample stations; the coordinated use of autonomous platforms; and the use of non-regular, time-dependent (i.e., adaptive) grids. All these strategies may benefit from near-real time feature detection (e.g., d 'Ovidio et al, 2015) from satellite and in situ data, in order to optimize the position and orientation of the sampling grid.…”

Section: Entanglement Of Space and Time Variabilitymentioning

confidence: 99%

Frontiers in Fine-Scale in situ Studies: Opportunities During the SWOT Fast Sampling Phase

et al. 2019

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Conceived as a major new tool for climate studies, the Surface Water and Ocean Topography (SWOT) satellite mission will launch in late 2021 and will retrieve the dynamics of the oceans upper layer at an unprecedented resolution of a few kilometers. During the calibration and validation (CalVal) phase in 2022, the satellite will be in a 1day-repeat fast sampling orbit with enhanced temporal resolution, sacrificing the spatial coverage. This is an ideal opportunity-unique for many years to come-to coordinate in situ experiments during the same period for a focused study of fine scale dynamics and their broader roles in the Earth system. Key questions to be addressed include the role of fine scales on the ocean energy budget, the connection between their surface and internal dynamics, their impact on plankton diversity, and their biophysical dynamics at the ice margin.

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“…Localization of dynamic point sets from distance measurements finds applications whenever objects move. Robot swarms, for example, often must localize autonomously [15], especially in remote situations such as extraterrestrial exploration [16] or deep-water missions [17]. Related applications exist in environmental monitoring, for example for dynamic sensor networks composed of river-borne sensing nodes [18].…”

Section: Introductionmentioning

confidence: 99%

Kinetic Euclidean Distance Matrices

Tabaghi

Vetterli

2020

IEEE Trans. Signal Process.

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Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize points from a snapshot of distances. If the objects move, one expects to do better by modeling the motion. In this paper, we introduce Kinetic Euclidean Distance Matrices (KEDMs)-a new kind of time-dependent distance matrices that incorporate motion. The entries of KEDMs become functions of time, the squared time-varying distances. We study two smooth trajectory models-polynomial and bandlimited trajectoriesand show that these trajectories can be reconstructed from incomplete, noisy distance observations, scattered over multiple time instants. Our main contribution is a semidefinite relaxation (SDR), inspired by SDRs for static EDMs. Similarly to the static case, the SDR is followed by a spectral factorization step; however, because spectral factorization of polynomial matrices is more challenging than for constant matrices, we propose a new factorization method that uses anchor measurements.Extensive numerical experiments show that KEDMs and the new semidefinite relaxation accurately reconstruct trajectories from noisy, incomplete distance data and that, in fact, motion improves rather than degrades localization if properly modeled. This makes KEDMs a promising tool for problems in geometry of dynamic points sets.

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A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics

Cited by 152 publications

References 25 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Frontiers in Fine-Scale in situ Studies: Opportunities During the SWOT Fast Sampling Phase

Kinetic Euclidean Distance Matrices

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