Mems Imu Inertial Measurement Unit One Way Travel Time Inertial Measurement Unit Autonomous Underwater Vehicles
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MEMS IMU Inertial Measurement Unit One-way-travel-time Inertial Measurement Unit Autonomous Underwater Vehicles
Recent 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-way-travel- 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 [mu]AUV. 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.
MEMS IMU Navigation with Model Based Dead-reckoning and One-way-travel-time Acoustic Range Measurements for Autonomous Underwater Vehicles
Recent 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 AUV. 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.
A Method for On-line Water Current Velocity Estimation Using Lol-cost Autonomous Underwater Vehicles
Author: Christopher Raymond Dolan (Lieutenant Commander)
language: en
Publisher:
Release Date: 2020
Advances in the miniaturization of microelectronics has greatly contributed to the proliferation of small, low cost autonomous underwater vehicles (AUVs). These affordable vehicles offer organizations a flexible platform that can be adapted to support a multitude of research goals. The small size and low entry cost come with a trade off of simple navigation systems, typically dead reckoning (DR) using a speed determined via propeller counts and heading from a low cost micro-electromechanical system (MEMS) inertial measurement unit (IMU), whose error grows unbounded without the availability of a ground referenced fix source and is compounded by the bias present in the speed measurement due to the change in hydrodynamics from the addition of sensors to the hull form. Additionally, some capabilities such as water current velocity measurement traditionally requires the addition of equipment that is not only expensive, but also whose size and power consumption can adversely affect operating characteristics and deployment times. This thesis expands on previous research using one-way travel time inverted USBL (OWTT-iUSBL) to calculate the local current velocity without the addition of a Doppler velocity log (DVL) or acoustic Doppler current profiler (ADCP). A novel extended Kalman filter (EKF) is proposed that, in addition to calculating the current velocity, estimates and corrects for the bias present in the speed measurement as determined by the main vehicle computer. Using data collected on the Charles River at the Massachusetts Institute of Technology (MIT) Sailing Pavilion, it is shown that current velocities can be reasonably calculated using OWTT-iUSBL data as compared to the values calculated using long baseline (LBL) data.