Control of Underwater Vehicles in Full Unsteady Flow

Levedahl, Blaine; Silverberg, Larry

doi:10.1109/joe.2009.2027798

Cited by 16 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For such images, compensating the red attenuation appears to be insufficient. However, interestingly, we observe that extending the color compensation to the blue component, as defined in Equation (5), significantly improves the enhanced images.…”

Section: B Underwater Dehazing Evaluationmentioning

confidence: 81%

Color Balance and Fusion for Underwater Image Enhancement

Ancuti

Vleeschouwer

Bekaert

2018

IEEE Trans. on Image Process.

812

446

View full text Add to dashboard Cite

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.

show abstract

Section: B Underwater Dehazing Evaluationmentioning

confidence: 81%

Color Balance and Fusion for Underwater Image Enhancement

Ancuti

Vleeschouwer

Bekaert

2018

IEEE Trans. on Image Process.

812

446

View full text Add to dashboard Cite

show abstract

“…To this end, for the derivation of the control law, a simplification in the product term B 01 cos u is made in equation (12). Because u will be regulated so that it remains small during the maneuver, the coefficient ................ ... B cos u of u c is replaced by B 01 .…”

Section: Adaptive Autopilot Designmentioning

confidence: 99%

“…11 Based on fluid compensation approach, a controller that compensates for the hydrodynamic loads has been synthesized. 12 Researchers have developed a variety of adaptive control systems for the control of AUVs. Do et al 13 have designed an adaptive path following control system for underactuated AUVs.…”

Section: Introductionmentioning

confidence: 99%

Multi-input submarine control via ℒ₁ adaptive feedback despite uncertainties

Lee

Singh

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

The development of a novel adaptive autopilot for the dive-plane control of multi-input multi-output submarines with unmodeled dynamics, based on the ℒ1 adaptive control theory, is the subject of this article. An ℒ1 adaptive autopilot is designed for the trajectory control of the depth and pitch angle using bow and stern hydroplanes. Interestingly, the structure of the adaptive controller remains fixed, regardless of the nonlinearities and external disturbance inputs, retained in the model of the submarine. Unlike the traditional adaptive control laws, the ℒ1 adaptive control input is generated by filtering the estimated control signal. A nice feature of the control law is that it is possible to achieve fast adaptation and desirable performance bounds in the closed-loop system by the choice of large adaptation gains. Simulation results are presented, which show that the autopilot accomplishes precise trajectory control in the dive plane, despite parametric uncertainties, unmodeled nonlinearities, and random disturbance inputs.

show abstract

“…is received while following the actual state where K Gk is the guidance operator, K Rk is the regulator operator, and where the control input is the sum of a guidance input u Gk , a regulator input u Rk , and a disturbance input u Dk . This is called the modern control form [15]. Note that the operator notation above does not specify the realization, that is, whether the system is represented by differential equations, difference equations, algebraic equations, or a hybrid.…”

Section: Autonomous Flight Controlsmentioning

confidence: 99%

Central Command Architecture for High-Order Autonomous Unmanned Aerial Systems

Bieber

2014

IIM

View full text Add to dashboard Cite

This paper is the first in a two-part series that introduces an easy-to-implement central command architecture for high-order autonomous unmanned aerial systems. This paper discusses the development and the second paper presents the flight test results. As shown in this paper, the central command architecture consists of a central command block, an autonomous planning block, and an autonomous flight controls block. The central command block includes a staging process that converts an objective into tasks independent of the vehicle (agent). The autonomous planning block contains a non-iterative sequence of algorithms that govern routing, vehicle assignment, and deconfliction. The autonomous flight controls block employs modern controls principles, dividing the control input into a guidance part and a regulation part. A novel feature of high-order central command, as this paper shows, is the elimination of operator-directed vehicle tasking and the manner in which deconfliction is treated. A detailed example illustrates different features of the architecture.

show abstract

Control of Underwater Vehicles in Full Unsteady Flow

Cited by 16 publications

References 16 publications

Color Balance and Fusion for Underwater Image Enhancement

Color Balance and Fusion for Underwater Image Enhancement

Multi-input submarine control via ℒ₁ adaptive feedback despite uncertainties

Central Command Architecture for High-Order Autonomous Unmanned Aerial Systems

Contact Info

Product

Resources

About

Control of Underwater Vehicles in Full Unsteady Flow

Cited by 16 publications

References 16 publications

Color Balance and Fusion for Underwater Image Enhancement

Color Balance and Fusion for Underwater Image Enhancement

Multi-input submarine control via ℒ1 adaptive feedback despite uncertainties

Central Command Architecture for High-Order Autonomous Unmanned Aerial Systems

Contact Info

Product

Resources

About

Multi-input submarine control via ℒ₁ adaptive feedback despite uncertainties