Nonlinear Dynamic Model-Based State Estimators for Underwater Navigation of Remotely Operated Vehicles

Kinsey, James C.; Yang, Qingjun; Howland, Jonathan C.

doi:10.1109/tcst.2013.2293958

Cited by 28 publications

(14 citation statements)

References 27 publications

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“…A variety of frameworks are employed to fuse navigation sensor data; such as the Extended Kalman Filter (EKF) framework, although other probabilistic estimation techniques can be utilized (Paull et al (2014)). This allows for near-optimal estimation, although optimality can be traded off for stability in alternative implementations (Kinsey et al (2014)). GPS and DVL observations are unavailable in the mid-water column and thus other solutions are required for localization.…”

Section: Mid-water Localizationmentioning

confidence: 99%

“…A variety of methods exist in deep water (Kinsey et al (2006); Paull et al (2014) provide surveys of the state of the art) with Doppler Velocity Log (DVL) navigation (e.g., Brokloff (1994); Kinsey and Whitcomb (2004)) being the predominant method for AUVs operating within 200-300m of the seafloor. Precision navigation in the mid-water column (i.e., below the sea surface and more than a few hundred meters from the seafloor) is more difficult (Kinsey et al (2006(Kinsey et al ( , 2014) and presents challenges for AUVs operating in this region. This implies that few methods are available for deep-diving AUVs during descent from the sea surface to the ocean floor.…”

Section: Introductionmentioning

confidence: 99%

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Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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Survey-class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the midwater column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

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Section: Mid-water Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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“…Here, five transponders with low update rate, i.e. 0.1 kHz [22], are deployed at coordinates 1 = 0 0 0 T m, 2 = 0 0 100 T m, T h e AU V w a s s e t t o s t a r t i n i t i a l l y a t (1) = 340 300 5…”

Section: Setup and Numerical Valuesmentioning

confidence: 99%

Compensation of asynchronous time of flight measurements in a long baseline navigation

2019

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In a long baseline (LBL) acoustic positioning system, a navigation subject estimates its own position at a time based on the ranges between itself and the LBL transponders. To obtain the ranges, each transponder sends acoustic wave to the subjects receiver. Here, a range equals to time required by the wave to travel between these two points multiplied by the wave propagation speed. This method is known as the time of flight (ToF) measurement. One of the ideals in carrying out a ToF is that the navigation subject would remain still throughout the measurement. Corollary, position estimation based on the ToF holds on the same ideal. However, an acoustic wave propagates in a very low speed. Due to this characteristic, the displacement of a moving navigation subject like an autonomous underwater vehicle (AUV) may not be negligible and becomes a source of bias for the range estimations. The AUV motion would also lead to asynchronous ToFs, i.e. acoustic waves sent by transponders arrive at the AUV in different time epochs. In this paper, we present LBL navigation for an AUV that deals with uncertainty due to motion of the AUV. Here, state estimator is modeled while considering the bias for each ToF. Once we obtain the state space representation, we apply Kalman filter to estimate the AUV position and speed. By simulation, we demonstrate that the estimator follows the actual states with good accordance.

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“…Let us use Schur complement lemma again; finally, we obtain (20), which guarantees ‖ ( )‖ 2 ≤ 1 ‖ ( )‖ 2 . So if (20) holds, then (19) is exponentially stable with a ∞ performance index ‖ ( )‖ 2 ≤ 1 ‖ ( )‖ 2 .…”

Section: Nroo Algorithm Design Consider Thatmentioning

confidence: 99%

“…In contrast, most practical systems are nonlinear and therefore nonlinear models are required. Kinsey et al [20] proposed a nonlinear observer based on dynamic model of AUV, which is used to estimate the vehicle's velocity. However, in their model, the coupling terms are neglected and there was only one degree of freedom.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Reduced-Order Observer-Based Predictive Control for Diving of an Autonomous Underwater Vehicle

Yao

Yang

2017

Discrete Dynamics in Nature and Society

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The attitude control and depth tracking issue of autonomous underwater vehicle (AUV) are addressed in this paper. By introducing a nonsingular coordinate transformation, a novel nonlinear reduced-order observer (NROO) is presented to achieve an accurate estimation of AUV’s state variables. A discrete-time model predictive control with nonlinear model online linearization (MPC-NMOL) is applied to enhance the attitude control and depth tracking performance of AUV considering the wave disturbance near surface. In AUV longitudinal control simulation, the comparisons have been presented between NROO and full-order observer (FOO) and also between MPC-NMOL and traditional NMPC. Simulation results show the effectiveness of the proposed method.

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Nonlinear Dynamic Model-Based State Estimators for Underwater Navigation of Remotely Operated Vehicles

Cited by 28 publications

References 27 publications

Mid-water current aided localization for autonomous underwater vehicles

Mid-water current aided localization for autonomous underwater vehicles

Compensation of asynchronous time of flight measurements in a long baseline navigation

Nonlinear Reduced-Order Observer-Based Predictive Control for Diving of an Autonomous Underwater Vehicle

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