A New Paradigm for Ship Hull Inspection Using a Holonomic Hover-Capable Auv

Damus, Robert; Desset, S.; Morash, J.; Polidoro, Victor; Hover, Franz S.; Chryssostomidis, Chryssostomos; Vaganay, J.; Willcox, Scott

doi:10.1007/1-4020-4543-3_23

Cited by 6 publications

(12 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Section proposed control approach, and obtained results (experiments and s ends with some concluding remarks. [7], while required to use Doppler fin HAUV [5]. Positioning this paper, however a very an be found in [5] and [7].…”

Section: Introductionmentioning

confidence: 98%

“…[7], while required to use Doppler fin HAUV [5]. Positioning this paper, however a very an be found in [5] and [7]. ce and orientation servoing ving wall following.…”

Section: Introductionmentioning

confidence: 99%

“…They h an increased level of development, V) have reached a Operated Vehicles assist the pilot. In main aspects of the ted tasks such as hull following [5] tions of underwater ction. It consists in wall (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

State feedback control of an underwater vehicle for wall following

Maalouf

Creuze

Chemori

2012

2012 20th Mediterranean Conference on Control &Amp; Automation (MED)

View full text Add to dashboard Cite

Wall following consists in naviga distance and orientation from a certain surfac This task is required in many application vehicles (e.g. ship hull or hydraulic dam propose a nonlinear state feedback, designe embedded acoustic sensing system. The prop the associated sensor can work on very underwater vehicles at high sampling rates. Se show the ability of a single beam transduce distance and orientation of a wall, while s show how this sensing system can be successf a nonlinear state feedback controller to perf wall following with an underwater veh disturbances.

show abstract

Section: Introductionmentioning

confidence: 98%

“…[7], while required to use Doppler fin HAUV [5]. Positioning this paper, however a very an be found in [5] and [7]. ce and orientation servoing ving wall following.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State feedback control of an underwater vehicle for wall following

Maalouf

Creuze

Chemori

2012

2012 20th Mediterranean Conference on Control &Amp; Automation (MED)

View full text Add to dashboard Cite

show abstract

“…The Hovering Autonomous Underwater Vehicle (HAUV) was initially designed and built by Massachusetts Institute of Technology (MIT) Sea Grant with the assistance of Bluefin Robotics as a platform for close-range underwater inspection [21,60]. The vehicle has been deployed on numerous ship hulls ranging from the USS Salem, a 215 meter heavy cruiser [132] to a 64 meter Sauro-class Italian naval submarine [132].…”

Section: Vehicle Platformmentioning

confidence: 99%

“…ABE and other underwater vehicles estimate their position based upon acoustic time-of-flight measurements to a set of beacons. Known as long baseline (LBL), this navigation strategy mimics the functionality of GPS and yields accurate 3D position data at the cost of setting up the [21] is designed to be a highly stable, highly maneuverable platform for the inspection of underwater structures, including ship hulls. The vehicle is equipped with a high resolution imaging sonar that serves as the primary sensor for surveys.…”

Section: Localizationmentioning

confidence: 99%

Sparse Bayesian information filters for localization and mapping

Walter¹

2008

View full text Add to dashboard Cite

This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment.We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning.This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior.We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly iden...

show abstract

Autonomous ship hull inspection by omnidirectional path and view

Soberi

Zakaria

2016

2016 IEEE/OES Autonomous Underwater Vehicles (AUV)

View full text Add to dashboard Cite

A New Paradigm for Ship Hull Inspection Using a Holonomic Hover-Capable Auv

Cited by 6 publications

References 1 publication

State feedback control of an underwater vehicle for wall following

State feedback control of an underwater vehicle for wall following

Sparse Bayesian information filters for localization and mapping

Autonomous ship hull inspection by omnidirectional path and view

Contact Info

Product

Resources

About