Navigation with Artificial Neural Networks

The objective of this dissertation is to explore the applications for Artificial Neural Networks (ANNs) in the field of Navigation. The state of the art for ANNs has improved significantly so now they can rival and even surpass humans in problems once thought impossible. We present different methods to augment, combine, or replace existing Navigation filters with ANN. The main focus of these methods is to use as much existing knowledge as possible then use ANNs to extend the current knowledge base. Next, improvements are made for a class of Artificial Neural Network (ANN)s which provide covariance called Mixture Density Network (MDN)s. MDNs are necessary since covariance is required for navigation problems. Finally the improvements and framework are demonstrated in a Very Low Frequency (VLF) signals navigation problem. Without ANNs, our VLF signals navigation problem would be very difficult. We conduct two VLF navigation experiments with an indoor and outdoor environment. The ANNs used for these problems provide confidence with probabilistic estimates of position either through class probabilities or probability distributions parameterized by the output of MDNs. ANNs need a measure of confidence in their estimates to work with the filters since navigation filters require a confidence of their estimates. In our problems we achieve an indoor localization accuracy of 86.7% for 50 discrete locations, and a 2D RMS error of 63m for a 1km2 area of navigation

Automated Aerial Refueling Position Estimation Using a Scanning LiDAR

This research examines the application of using a scanning Light Detection and Ranging(LiDAR) to perform Automated Aerial Refueling(AAR). Specifically this thesis presents two algorithms to determine the relative position between the tanker and receiver aircraft. These two algorithms require a model of the tanker aircraft and the relative attitude between the aircraft. The first algorithm fits the measurements to the model of the aircraft using a modified Iterative Closest Point (ICP) algorithm. The second algorithm uses the model to predict LiDAR scans and compare them to actual measurements while perturbing the estimated location of the tanker. Each algorithm was tested with simulated LiDAR data before real data became available from test flights. The data collected from this test ight was used to determine the accuracy of the two algorithms with real LiDAR data. After correcting for modeling errors the accuracy of each algorithm is about a Mean Radial Spherical Error of 40cm

Signal Enhancement for Magnetic Navigation Challenge Problem

Harnessing the magnetic field of the earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field data from the magnetometer on the navigation system encompass the magnetic field from not just the earth, but also from the vehicle on which it is mounted. It is difficult to separate the earth magnetic anomaly field magnitude, which is crucial for navigation, from the total magnetic field magnitude reading from the sensor. The purpose of this challenge problem is to decouple the earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset shows that the earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained neural network. These challenges offer an opportunity to construct an effective neural network for removing the aircraft magnetic field from the dataset, using an ML algorithm integrated with physics of magnetic navigation.Comment: 21 pages, 4 figures. See https://github.com/MIT-AI-Accelerator/MagNav.jl for accompanying data and cod

Effects of analgesic intervention on behavioural responses to Low Atmospheric Pressure Stunning

Worldwide, more than 50 billion chickens are killed annually for food production so their welfare at slaughter is an important concern. Low Atmospheric Pressure Stunning (LAPS) is a novel approach to pre-slaughter stunning of poultry in which birds are rendered unconscious by gradually reducing oxygen tension in the atmosphere to achieve a progressive anoxia (hypobaric hypoxia). Advantages of this approach over electrical stunning are that birds are not shackled while conscious and all birds are reliably and irreversibly stunned. However, concerns remain that birds undergoing LAPS could experience discomfort or pain. Here we investigated whether subjecting birds to LAPS with and without administration of an opioid analgesic (butorphanol) affected behavioural responses. A blocking design was used in which pairs of birds receiving either analgesic or sham treatment were allocated to three types (analgesic/analgesic, analgesic/sham, or sham/sham). In line with previous studies, birds showed a consistent sequence of behaviours during LAPS: ataxia, loss of posture, clonic/tonic convulsions, leg paddling and motionless. Overall, administration of butorphanol had no effect on the range and patterning of behavioural responses during LAPS, but there were some differences in behaviour latencies, counts and durations. For example, latencies to ataxia, mandibulation and deep inhalation were delayed by analgesic treatment, however the duration of ataxia and other behaviours related to loss of consciousness were unaffected. Fewer birds receiving analgesia showed jumping and slow wing flapping behaviour compared to controls, which suggests these may be pain related. These behaviours after the onset of ataxia and the results may reflect a smoother induction to unconsciousness in analgised birds. Collectively, the results do not provide convincing evidence that birds undergoing LAPS are experiencing pain. While there were effects of analgesia on some aspects of behaviour, these could be explained by potential sedative, dysphoric and physiological side effects of butorphanol. The behavioural responses to LAPS appear to be primarily related to exposure to anoxia rather than hypobaric conditions, and thus in terms of welfare, this stunning method may be equivalent to controlled atmosphere stunning with inert gases

U.S. Waterways Experiment Station Reports

Report regarding refraction seismic and vibratory tests conducted on a railroad test embankment near Aikman, Kansas as well as attenuation and dynamic laboratory tests. The refraction seismic tests indicated the existence of two distinct velocity zones, one for the embankment and one for the underlying limestone bedrock

U.S. Waterways Experiment Station Report S-70-12

Abstract: Geologic field reconnaissance and geophysical evaluation of eight areas in the western part of the United States were made to locate a site for conducting tests of a prototype hard rock silo (HRS)

Cavity detection and delineation research : Report 2 : Seismic methodology : Medford Cave Site, Florida /

"Prepared for Office, Chief of Engineers.""June 1983."Cover title.Bibliography: page 35.Final report.This investigation was conducted by the U.S. Army Engineer Waterways Experiment Station (WES) for the Office, Chief of Engineers (OCE), U.S. Army, under theMode of access: Internet