A new hydrodynamics test facility for UUV dynamics and control research

Kinsey,; Smallwood,; Whitcomb, Louis L.

doi:10.1109/oceans.2003.178587

Cited by 33 publications

(17 citation statements)

References 13 publications

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“…The JHU Hydrodynamic Test Facility [11] contains an indoor fresh water tank measuring 7.75 m in diameter and 4.25 m deep, as shown in Figure 1. The facility is equipped with the JHU ROV, a fully instrumented UV designed for navigation and control research.…”

Section: Experimental Evaluation a Experimental Setupmentioning

confidence: 99%

Experimental evaluation of adaptive model-based control for underwater vehicles in the presence of unmodeled actuator dynamics

McFarland

Whitcomb

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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This paper reports a comparative experimental evaluation of proportional derivative and adaptive modelbased control for underwater vehicles. To the best of the authors' knowledge, this is the first such evaluation of modelbased adaptive tracking control for underwater vehicles during simultaneous dynamic motion in all 6 degrees-of-freedom. This experimental evaluation revealed the presence of unmodeled thruster dynamics arising during reversals of the vehicle's thrusters, and that the unmodeled thruster dynamics can destabilize parameter adaptation. The three major contributions of this paper are: an experimental analysis of how unmodeled thruster dynamics can destabilize parameter adaptation, a twostep adaptive model-based control algorithm which is robust to the thruster modeling errors present, and a comparative experimental evaluation of adaptive model-based control and proportional derivative control for fully-actuated underwater vehicles preforming simultaneous 6 degree-of-freedom trajectory tracking. I. INTRODUCTIONThis paper addresses the problem of adaptive modelbased trajectory tracking control of Underwater Vehicles (UV) for dynamic 6 degree-of-freedom (DOF) motion. The approach of adaptive model-based control (AMBC) refines estimates of plant parameters during the trajectory tracking control process. The adaptive controller estimates parameters for a rigid-body plant such as vehicle mass with added hydrodynamic mass, quadratic drag, gravitational force, and buoyancy torque parameters that arise in the dynamic models of rigid-body UV. We report a comparative experimental evaluation of AMBC and proportional derivative control (PDC) in full scale vehicle trials utilizing the Johns Hopkins University Hydrodynamic Test Facility and Johns Hopkins University Remotely Operated Vehicle (JHU ROV) (Fig. 1). This experimental evaluation shows that AMBC provides 30% better position tracking performance and 8% worse velocity tracking performance over PDC. To the best of the authors' knowledge, this is the first experimental comparison of AMBC and PDC during simultaneous motion in all DOF.

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Section: Experimental Evaluation a Experimental Setupmentioning

confidence: 99%

Experimental evaluation of adaptive model-based control for underwater vehicles in the presence of unmodeled actuator dynamics

McFarland

Whitcomb

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…It measures 1.37 m long, 0.85 m wide, and 0.61 m high. Figure 3(a) depicts the JHU ROV and its sensor suite [8]. Figure 3(b) depicts the JHU ROV's vehicle coordinate frame axes.…”

Section: B Upd: Underactuated Proportional Derivative Controllermentioning

confidence: 99%

Preliminary experiments in underactuated nonlinear model-based tracking control of underwater vehicles with three degree-of-freedom fully-coupled dynamical plant models: Theory and experimental evaluation

Martin

Whitcomb

2012

2012 Oceans

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This paper reports a comparative experimental evaluation of one non-model-based proportional derivative (PD) 3-degree-of-freedom (3-DOF) controller and one model-based 3-DOF controller designed to enable an underactuated underwater vehicle to perform exact X-Y position and velocity tracking. We report a comparative experimental evaluation of the two controllers in free motion experiments. We conclude that the model-based controller's mean absolute position and velocity tracking error is significantly smaller than the non-model-based PD controller for coupled maneuvers.

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“…The experimental setup consisted of a downward-looking digital still camera mounted to an underwater, moving, pose-instrumented ROV at the Johns Hopkins University (JHU) Hydrodynamic Test Facility [70]. Their vehicle [71] is instrumented with a typical suite of oceanographic dead-reckoning navigation sensors capable of measuring heading, attitude, XYZ bottom-referenced Doppler velocities, and a pressure sensor for depth.…”

Section: ) Experimental Setupmentioning

confidence: 99%

Exactly Sparse Delayed-State Filters for View-Based SLAM

2006

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Abstract-This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic.Index Terms-Information filters, Kalman filtering, machine vision, mobile robot motion planning, mobile robots, recursive estimation, robot vision systems, simultaneous localization and mapping (SLAM), underwater vehicles.

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A new hydrodynamics test facility for UUV dynamics and control research

Cited by 33 publications

References 13 publications

Experimental evaluation of adaptive model-based control for underwater vehicles in the presence of unmodeled actuator dynamics

Experimental evaluation of adaptive model-based control for underwater vehicles in the presence of unmodeled actuator dynamics

Preliminary experiments in underactuated nonlinear model-based tracking control of underwater vehicles with three degree-of-freedom fully-coupled dynamical plant models: Theory and experimental evaluation

Exactly Sparse Delayed-State Filters for View-Based SLAM

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