Underwater navigation

A Motion Compensated (MC) Ultra Short Baseline (USBL) Acoustic Positioning
System (APS) operable in shallow water and port environment has been implemented at
Florida Atlantic University. Multi-tones signal modulation and log-likelihood
maximization enable this APS to operate in volumes of water of less than 10 cubic
meters. Standard deviations of the acoustic source elevation and azimuth estimates were
computed to be 3 degrees in an 8 cubic meters test tank, and reduce to 0.9 degree in a 2
meters deep marina. The motion compensating system estimates the array position and
orientation while merging noisy measurements from a Magnetic, Angular Rate, and
Gravity (MARG) sensor and a Differential Global Positioning System (DGPS) using
Kalman filters. Experiments show 0.67 and 2.67 degrees of error for the array tilt and
heading estimates, and 0.74 meter for the array position estimate.

This thesis describes the development of a simulation environment for Autonomous Underwater Vehicles (AUVs) on UNIX platforms. AUV missions can therefore be carried out without going to the sea. The Yoyo controller, a component for AUVs is also described in this thesis. The main function of Yoyo is to control the vertical profile of an AUV while it is navigating underwater performing data collection missions. The development of the controller is done in the simulation environment. Several test cases have been performed, and the test results have clearly demonstrated the successful development of the controller.

This thesis presents a mapping and navigation system intended for an unmanned untethered underwater vehicle. The system utilizes range data obtained from a time of flight sonar operating at 307KHz. The range data, along with an angle measurement of the transducer, is used to generate a simple object map (detected object and its position in two dimensions). The raw range data is filtered using an edge detection algorithm. The edge detection algorithm extracts possible corners from the acoustic data of the scanned environment. The output of the edge detection algorithm is sent to a confidence program. The confidence program determines which of the possible "corners", determined by the edge detection algorithm, are "actual" corners. The output of the confidence program is then used to produce the object map. This object map may be used as the input to an annotated map-builder. The output of the confidence program is then input to the navigation system. The navigation system determines the position of the vehicle relative to a detected object without any a-priori information, which may be used as an input to a path planner and an obstacle avoidance system. The experiments were carried out in a 25 x 30 foot pool and the experimental data processed on a Sun Workstation using Matlab and C generated code for post-processing of the raw acoustical data.

Autonomous underwater vehicle (AUV) missions are generally of a multi-tasked nature, i.e., there are usually several criteria which need to be met concurrently during the course of a mission. An example is the bottom altitude tracking mission proposed by the University of South Florida. They have developed a bottom classification and albedance package (BCAP) which will be used to record data to ground-truth oceanographic satellites. Two criteria needed for this mission are vehicle safety and motion stability of the recording sensors. This thesis will respectively compare the results of three bottom altitude tracking controllers: a linear modification of an existing depth controller, a TSK fuzzy logic controller, and a behavior based decision controller. Aspects analyzed for meeting the criteria were the ability of the auv to avoid collisions with bottom, the ability of the auv to maintain a desired altitude above the sea floor, and the ability of the auv to keep the amount of blur in a picture taken by a downward looking camera under one pixel. From simulation and real world testing, final results indicate the behavioral based decision controller was proven to be the most robust and the only controller tested to be able to handle multi-criteria.

The operation of unmanned underwater vehicles requires communications with other nearby vehicles as well as accurate positioning to prevent duplication of work, collisions and other mishaps. This thesis details the integration of an ultra-short baseline positioning system with four transducers arranged as a tetrahedron for use with the FAU Dual Purpose Acoustic Modem. The source position is estimated by processing coherently a series of frequency-hopped pulses to obtain a set of bearings, optimally combined through maximum likelihood estimation of the azimuth and elevation. A simulation has been implemented and experiments have been performed in a calibration tank. Model and experiments confirm that the accuracy of this system improves with the number of pulses and the signal-to-noise ratio. A mean positional error of 5.51% can be obtained with an SNR of 20 dB and a single processed pulse, the error decreases to 2.84% using six processed pulses.

The Buried Object Scanning Sonar (BOSS) is being developed at Florida Atlantic University to image targets buried under the seabed. Tomographic images are constructed using a sequence of sonar transmissions while the vehicle is moving. This motion causes image distortion and should be measured and removed by mapping the echoes received to an absolute coordinate system. The aim of this thesis is to develop and simulate a technique for generating BOSS images that provide an accurate representation of target shape and size, by removing vehicle motion while mapping the image pixels. Synthetic acoustic data sets are generated by convolving the auto-correlated FM transmission pulse with the impulse response of an elastic sphere. Synthetic outputs of a Doppler velocity log and a 3-axis inertial measurement unit are generated to simulate vehicle motion. Noise is added to the sensor data to show the effects of motion sensor errors on image quality.

This thesis addresses the problem of tracking a thermocline---a layer of water showing an intense vertical temperature gradient---with an Autonomous Underwater Vehicle (AUV). One of Florida Atlantic University's Ocean Explorer (OEX) AUV has been upgraded, as part of the work described here, by integration of a standard and convenient software interface, and used in several thermocline survey experiments aimed at gathering oceanographic data relevant to thermoclines. A tool that simulates the longitudinal motion of the OEX through a water slice, whose temperature map is read using a simulated temperature and depth sensor, has been developed. Using this tool and information from at-sea experiments, several control methods for the OEX to track a thermocline were analyzed. In particular, two different algorithms were implemented and tested by simulation. Overall, two control algorithms have been validated, and it will soon be possible to provide the AUV with a thermocline tracking capability.

This thesis presents a method for modeling navigation sensors used on ocean systems and particularly on Autonomous Underwater Vehicles (AUV). An extended Kalman filter was previously designed for the implementation of the Inertial Navigation System (INS) making use of Inertial Measurement Unit (IMU), a magnetic compass, a GPS/DGPS system and a Doppler Velocity Log (DVL). Emphasis is put on characterizing the static sensor error model. A "best-fit ARMA model" based on the Aikake Information Criterion (AIC), Whiteness test and graphical analyses were used for the model identification. Model orders and parameters were successfully estimated for compass heading, GPS position and IMU static measurements. Static DVL measurements could not be collected and require another approach. The variability of the models between different measurement data sets suggests online error model estimation.

This thesis describes the determination of linear and nonlinear coefficients for the Morpheus vehicle. Added mass and nonlinear damping terms were obtained by strip-theory. These added mass coefficients were compared to the ones previously computed by boundary-integral method. Open-loop simulations were conducted using both sets of added-mass coefficients along with the damping terms, which were adjusted to fit at-sea data. A previously estimation technique for hydrodynamic coefficients has been applied to the Morpheus AUV using a Kalman filter. This technique based on linearized equations of motion was tested with linear and nonlinear data generated by simulation. Steering and diving motions were considered resulting in the estimation of different sets of coefficients. Results showed that the estimated values were able to reproduce accurately the vehicle motion in the linear as well as in the nonlinear case.

Navigation of Unmanned Underwater Vehicles (UUVs) is commonly assisted in confined areas by acoustic positioning systems. The Department of Ocean Engineenng at Florida Atlantic University is developing an altemative system based on submerged modems. This thesis describes an optimal target location estimation technique using a multi-channel spatial receiver array (Millscross) used as a development tool combined with a synchronous modem and transponder network mounted on buoys and UUVs. The Millscross provides a reference to evaluate the performance of the navigation estimator. Spatial array principles are used to develop decoding and beamforming techniques to process modem messages, enabling the end user (the UUV) to estimate in real-time its own position and navigate in space. A simulation was used to compare actual results with theory and determine the processing and decoding algorithms. These algorithms were then applied to real data to estimate the target position (direction of arrival and geodetic coordinates).