Thomas Bean

Measurement of magnetic field using collaborative AUVs

This paper describes an effort to adapt a fleet of autonomous underwater vehicles (AUVs) for the measurement of the magnetic signature of surface ships. Currently, two such vehicles have been upgraded with the necessary navigation and measurement hardware to perform magnetic measurements. Initial testing has been performed at the Naval Acoustic Research Detachment at Lake Pend Oreille, Idaho. Two vehicles have leveraged formation control algorithms originally developed for mine countermeasure missions to operate collaboratively. In the testing area, changes in ambient field were measured to be ∼1,000–2,000 nT. Over one square meter area segments, the standard deviation of total field measurements was below 25 nT. When a steel barge was located in the testing area, localized fields of ∼50,000 nT to ∼62,000 nT were observed.

A New Procedure for Simultaneous Navigation of Multiple AUV’s

Navigation by Underwater Autonomous Vehicles (AUV’s) is a challenging problem because radio waves do not penetrate water, and acoustic waves must be used instead for determination of position. Current systems utilize round-trip time-of-flight between single vehicles and fixed transponders to determine position. While reliable, the drawback of this method is that there is an upper limit on the number of vehicles that can navigate at the same time. We describe a new procedure that allows simultaneous navigation of multiple vehicles using the acoustic navigation signals from only one vehicle in the group. One vehicle in the group is assigned to navigate conventionally with an acoustic Long BaseLine (LBL) system. The other vehicles in the group are equipped with a sensor that can determine the relative angular heading to the source of an intercepted acoustic signal, and a separate sensor that can determine angular inertial heading. As the chosen vehicle navigates conventionally, the other vehicles in the group intercept the return pings from the fixed transponders. From these signals, and the inertial heading, each vehicle is able to determine their inertial position. The sensor used to determine relative angular heading to the source of an intercepted signal consists of two hydrophones separated by an approximate distance of one meter. Relative angular heading is extracted from the difference-in-arrival time using correlation between the hydrophone signals. An error propagation analysis is performed that quantifies the accuracy of the inertial position fix as it depends upon vehicle-transponder geometry, and sensor precision.

Calibration and localization of AUV-acquired magnetic survey data

A system is being developed to enable the measurement of the magnetic signature of a surface vessel at forward locations. The system consists of a fleet of AUVs (Autonomous Underwater Vehicles) each equipped with a magnetometer. A magnetic signature measurement would be performed in a coordinated relative movement between the fleet and the surface vessel. The precision of the magnetometer system and the precision of each position associated with magnetic measurements in a survey are crucial to the value of this measurement. Results of experiments on these two issues are reported. Calibrations of the AUV/magnetometer systems are performed in-situ and on land and are characterized by a noise floor of less than 10 nT. In experiments performed at a deepwater facility free from extraneous ambient magnetic sources, an AUV passed near a known magnetic source at a fixed location and orientation. Measurements of magnetic field and position estimates based upon navigation data acquired by the AUV were associated. An independent, high-accuracy acoustic tracking system was used during the experiments to determine a ground truth position for the AUV. The predicted magnetic field at the AUV estimated position was compared to the magnetic field acquired by the AUV near the source and found to have an error of 15 nT.

Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment

Through simulation and field testing of autonomous underwater vehicles (AUVs) it has been identified that when an extended Kalman filter is used with a two transponder long baseline (LBL) positioning system instabilities can occur when range updates are introduced to the filter. This paper describes two possible algorithms to prevent the instability. The instability is dependent on the location of the vehicle relative to the transponders during the measurement. The algorithms compensate for the instability by adjusting the range measurement standard deviation parameter R in the Kalman filter. The first algorithm uses the perpendicular distance from the line between transponders and the estimated position as an input to a fuzzy logic algorithm. The second algorithm uses the cosine of the angle between vectors drawn from the estimated position of the vehicle to each transponder, β, as an input. Monte Carlo results show that both methods were successful at eliminating the instability; however, the β algorithm produced better overall results.

Toward a Neurocognitive Agent Architecture for AUVs

We present a neurocognitive architecture for autonomous underwater vehicles that takes its inspiration from current knowledge of the neurobiological basis of human cognition and is based upon modeling the large-scale functionality of the human brain. We build on the tradition of taking inspiration from the human cognitive architecture, making explicit attempts at modeling aspects of human cognition and in addition to modeling human cognition, we attempt to model the neurobiological basis of some aspects of human cognition. We describe the functional organization of the human brain, which can be roughly ordered in a three-tiered architecture including the sensorimotor functions, cognitive functions, and executive functions. In the architecture proposed, each of these functional systems is implemented as a subsystem of the whole agent architecture consisting of a set of psychologically and conceptually inspired modules.

Survey of the magnetic signature of a moving surface vessel by multiple AUVs

A formation of autonomous underwater vehicles (AUVs) equipped with magnetometers has been used to perform a magnetic field survey of a moving surface vessel. The vessel had a nonmagnetic fiberglass hull. A permanent magnet with a known dipole moment was placed at the bow and a small intrinsic field was measured near the stern of the surface vessel. Measurements were taken at various surface vessel orientations, AUV orientations, and AUV depths. The measured field survey was compared to the fields predicted by a dipole model of the permanent magnet with an RMS error between measured and predicted fields of 3.04 nT in the vicinity of the surface vessel. The measured fields were also used to reconstruct a two-dipole model for the surface vessel accounting for the permanent magnet and the intrinsic field with an RMS error between the measured field and the model field of 2.13 nT in the vicinity of the surface vessel. The two-dipole reconstruction solution contained a dipole at a position very close to the permanent magnet on the surface vessel bow and an additional dipole at a position reasonably close to the stern of the boat.

A NONLINEAR FUZZY LOGIC CONTROLLER DEVELOPED FOR AN AUTONOMOUS SURFACE BOAT

This paper presents an optimized nonlinear fuzzy logic controller designed for an autonomous surface craft and describes the process by which it was found. The nonlinear fuzzy logic controller described herein was developed to maintain the linear feedback control of an optimal set of controller gains when the state is near the operating point. The simplex optimization method was utilized to find the optimal fuzzy logic parameters that define the shape of the control law away from the normal operating point. The resultant controller showed approximately a 20% improvement over the optimal linear controller.