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ThesisMEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles(Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2017-09) Kepper, JamesRecent advances in acoustic navigation methodologies are enabling the way for AUVs to extend their submerged mission time and maintain a bounded XY position error. Additionally, advances in inertial sensor technology have drastically lowered the size, power consumption, and cost of these sensors. Nonetheless, these sensors are still noisy and accrue error over time. This thesis builds on the research and recent developments in single beacon one-waytravel- time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying mostly on a consumer grade microelectromechanical system (MEMS) inertial measurement unit (IMU) and a vehicle’s dynamic model velocity. An implementation of an Extended Kalman Filter (EKF) that includes IMU bias estimation and coupled with a range filter, is obtained in the field on two OceanServer Technology, Inc. Iver2 AUVs and one Bluefin Robotics SandShark 𝜇AUV. Results from these field trials on Ashumet Pond of Falmouth, Massachusetts, the Charles River of Cambridge, Massachusetts, and Monterey Bay near Santa Cruz, California show a navigation solution accuracy comparable to current standard navigation techniques.
ArticleClosed‐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.
ArticleSatellites 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, JamesFuture 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.