Claus Brian

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Claus
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  • Article
    Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles
    (John Wiley & Sons, 2017-09-07) Claus, Brian ; Kepper, James ; Suman, Stefano ; Kinsey, James C.
    This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.
  • Preprint
    A parameterized geometric magnetic field calibration method for vehicles with moving masses with applications to underwater gliders
    ( 2016-04) Claus, Brian ; Bachmayer, Ralf
    The accuracy of magnetic measurements performed by autonomous vehicles is often limited by the presence of moving ferrous masses. This work presents a parameterized ellipsoid eld calibration method for magnetic measurements in the sensor frame. In this manner the ellipsoidal calibration coe cients are dependent on the locations of the moving masses. The parameterized calibration method is evaluated through eld trials with an autonomous underwater glider equipped with a low power precision uxgate sensor. A rst set of eld trials were performed in the East Arm of Bonne Bay, Newfoundland in December of 2013. During these trials a series of calibration pro les with the mass shifting and ballast mecha- nisms at di erent locations were performed before and after the survey portion of the trials. Further trials were performed in the Labrador Sea in July of 2014 with two reduced sets of calibration runs. The nominal ellipsoidal coe cients were extracted using the full set of measurements from a set of calibration pro les and used as the initial conditions for the polynomials which de ne each parameterized coe cient. These polynomials as well as the sensor misalignment matrix were then optimized using a gradient descent solver which minimizes both the total magnetic eld di erence and the vertical magnetic eld variance between the modeled and measured values. Including the vertical eld in this manner allows for convergence in spite of severe limitations on the platform's motion and for computation of the vehicle's magnetic heading.
  • Article
    Satellites to seafloor : toward fully autonomous ocean sampling
    (Oceanography Society, 2017-06) Thompson, Andrew F. ; Chao, Yi ; Chien, Steve ; Kinsey, James C. ; Flexas, M. Mar ; Erickson, Zachary K. ; Farrara, John ; Fratantoni, David M. ; Branch, Andrew ; Chu, Selina ; Troesch, Martina ; Claus, Brian ; Kepper, James
    Future ocean observing systems will rely heavily on autonomous vehicles to achieve the persistent and heterogeneous measurements needed to understand the ocean’s impact on the climate system. The day-to-day maintenance of these arrays will become increasingly challenging if significant human resources, such as manual piloting, are required. For this reason, techniques need to be developed that permit autonomous determination of sampling directives based on science goals and responses to in situ, remote-sensing, and model-derived information. Techniques that can accommodate large arrays of assets and permit sustained observations of rapidly evolving ocean properties are especially needed for capturing interactions between physical circulation and biogeochemical cycling. Here we document the first field program of the Satellites to Seafloor project, designed to enable a closed loop of numerical model prediction, vehicle path-planning, in situ path implementation, data collection, and data assimilation for future model predictions. We present results from the first of two field programs carried out in Monterey Bay, California, over a period of three months in 2016. While relatively modest in scope, this approach provides a step toward an observing array that makes use of multiple information streams to update and improve sampling strategies without human intervention.