Contributions to automated realtime underwater navigation

Stanway, M. Jordan

doi:10.1575/1912/5095

Cited by 15 publications

(8 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This relies on the assump-tion of horizontal homogeneity across the water current layer (Gordon (1996)). In parallel to our work (Medagoda et al (2010(Medagoda et al ( , 2011) was that of Stanway (2011Stanway ( , 2012. In Stanway's work, a least squares approach to estimate water currents and vehicle pose was applied.…”

Section: Adcp Sensor Aiding With Water Layersmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Adcp Sensor Aiding With Water Layersmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Such measurements can be obtained by using an acoustic Doppler current profiler (ADCP) or the current profiling function on some DVLs, which are nowadays becoming standard for small AUVs. With such a sensor suite, improved navigation performance, although with theoretically unbounded uncertainty, can be potentially achieved in mid-depth applications utilizing methods proposed by Stanway [34] or Medagoda et al [37] that overlap the consecutive current profiling measurements. We aim at providing long-term performance improvement to inertial navigation systems by taking advantage of the information contained in large-scale ocean current simulation.…”

Section: A Preliminaries On Probabilistic Formulationmentioning

confidence: 99%

“…Their navigation scheme focuses on gliders that perform dead reckoning using depth and attitude sensors instead of initial navigation systems and requires the vehicles to resurface at regular intervals. Medagoda et al [34], [37] focused on the navigation performance during vehicle descent by utilizing the fact that ocean currents are approximately invariant within a short period of time. Similarly, Hegrenaes and Berglund [33] only considered their DVL water-tracking technique as an intermediate approach to bridge the gap between other more reliable global localization techniques involving GPS or USBL.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Song

Mohseni

2018

IEEE J. Oceanic Eng.

View full text Add to dashboard Cite

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in longduration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller-scale, turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved, small-scale flow perturbations are present. For a 6-hour navigation with an automotive-grade inertial navigation system, the currentaided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a turbulent, meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-hour mission, positioning result with under 25% UDT for a 24-hour navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

show abstract

“…The DR position estimate,

\binom{w}{} p_{d}

, is computed by integrating the world velocities

\binom{i}{} \dot{p}

in time:

\binom{1false w}{} p_{d} (t) = \binom{1false w}{} p_{d} (t_{0}) + \int_{t_{0}}^{t} \binom{1false w}{1false v} R (τ) \binom{1false v}{1false i} R \binom{1false i}{} \dot{p} (τ) d τ,

where

\binom{w}{} p (t_{0})

is the initial position estimate. For a more detailed discussion of DR navigation in underwater vehicles, see Brokloff (); Whitcomb, Yoerger, & Singh (); Kinsey & Whitcomb (); and Stanway ().…”

Section: Application To Underwater Robot Navigationmentioning

confidence: 99%

Rotation Identification in Geometric Algebra: Theory and Application to the Navigation of Underwater Robots in the Field

Stanway

Kinsey

2015

Journal of Field Robotics

View full text Add to dashboard Cite

We report the derivation and experimental evaluation of a stable adaptive identifier to estimate rigid body rotations using rotors in Geometric Algebra (GA). This work is motivated by the need for in situ estimation of the alignment between sensors commonly used in underwater vehicle navigation. Here we derive an adaptive identifier using a geometric interpretation of the error to drive first-order rotor kinematics. We prove that it is Lyapunov stable, and we show that it is asymptotically stable in the presence of persistent excitation. We use the identifier to estimate the alignment between the Doppler velocity log sonar and the fiber optic gyrocompass used by underwater vehicles for dead reckoning (DR). We evaluate this method in the laboratory with a remotely operated vehicle (ROV), and then with an autonomous underwater vehicle (AUV) operating in the field at 1,200 m depth. Our results show that this technique reduces dead reckoning navigation errors on these platforms and provides comparable performance to previously reported SO(3) constrained Linear Algebra (LA) approaches. The rotor identifier has a number of advantages over these previously reported methods, including a more straightforward derivation, simpler gain tuning, increased computational efficiency, and reduced data manipulation.

show abstract

Contributions to automated realtime underwater navigation

Cited by 15 publications

References 82 publications

Mid-water current aided localization for autonomous underwater vehicles

Mid-water current aided localization for autonomous underwater vehicles

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Rotation Identification in Geometric Algebra: Theory and Application to the Navigation of Underwater Robots in the Field

Contact Info

Product

Resources

About