UMD Theses and Dissertations
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Item Computational Methods for Natural Walking in Virtual Reality(2024) Williams, Niall; Manocha, Dinesh; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Virtual reality (VR) allow users to feel as though they are really present in a computer-generated virtual environment (VE). A key component of an immersive virtual experience is the ability to interact with the VE, which includes the ability to explore the virtual environment. Exploration of VEs is usually not straightforward since the virtual environment is usually shaped differently than the user's physical environment. This can cause users to walk on virtual routes that correspond to physical routes that are obstructed by unseen physical objects or boundaries of the tracked physical space. In this dissertation, we develop new algorithms to understand how and enable people to explore large VEs using natural walking while incurring fewer collisions physical objects in their surroundings. Our methods leverage concepts of alignment between the physical and virtual spaces, robot motion planning, and statistical models of human visual perception. Through a series of user studies and simulations, we show that our algorithms enable users to explore large VEs with fewer collisions, allow us to predict the navigability of a pair of environments without collecting any locomotion data, and deepen our understanding of how human perception functions during locomotion in VR.Item Learning Autonomous Underwater Navigation with Bearing-Only Data(2024) Robertson, James; Duraiswami, Ramani; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent applications of deep reinforcement learning in controlling maritime autonomoussurface vessels have shown promise for integration into maritime transportation. These could have the potential to reduce at-sea incidents such as collisions and groundings which are majorly attributed to human error. With this in mind the goal of this work is to evaluate how well a similar deep reinforcement learning agent could perform the same task in submarines but using passive SONAR rather than the ranging data provided by active RADAR aboard surface vessels. A simulated submarine outfitted with a passive spherical, hull-mounted SONAR sensor is placed into contact scenarios under the control of a reinforcement learning agent and directed to make its way to a navigational waypoint while avoiding interfering surface vessels. In order to see how this best translates to lower power autonomous vessels (vice warship submarines), no estimation for the range of the surface vessels is maintained in order to cut down on computing requirements. Inspired by my time aboard U.S. Navy submarines, the agent is provided with simply the simulated passive SONAR data. I show that this agent is capable of navigating to a waypoint while avoiding crossing, overtaking, and head-on surface vessels and thus could provide a recommended course to a submarine contact management team in ample time since the maneuvers made by the agent are not instantaneous in contrast to the assumptions of traditional target tracking with bearing-only data. Additionally, an in-progress plugin for Epic Games’ Unreal Engine is presented with the ability to simulate underwater acoustics inside the 3D development software. Unreal Engine is a powerful 3D game engine that is incredibly flexible and capable of being integrated into many different forms of scientific research. This plugin could provide researchers with the ability to conduct useful simulations in intuitively designed 3D environments.Item Safe Navigation of Autonomous Vehicles in Structured Mixed-Traffic Environments(2023) Tariq, Faizan Muhammad; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The primary driving force behind autonomous vehicle (AV) research is the prospect of enhancing road safety by preventing accidents caused by human errors. To that end, it seems rather improbable that AVs will replace all human-driven vehicles in the near future. The more plausible scenario is that AVs will gradually be introduced on public roads and highways in the presence of human-driven vehicles, leading to mixed-traffic scenarios. In addition to the existing challenges associated with autonomous driving stemming from various uncertainty factors associated with sensing, prediction, control, and computation, these situations pose further difficulties pertaining to the variability in human driving patterns. Therefore, to ensure widespread public acceptance of AVs, it is crucial to develop planning and decision-making algorithms, while benefiting from modern sensing, computation, and control methods, that can deliver safe, efficient, and reliable performance in mixed-traffic situations. Considering the need to cater to the behavior variability of human drivers, we address the joint decision-making and motion planning problem in structured environments with a multi-timescale navigation architecture. Specifically, we design algorithms for commonly encountered highway driving scenarios that require effective real-time decision-making, reliable motion prediction of on-road entities, behavior consideration of on-road agents, and attention to safety as well as passenger comfort. The specific problems addressed in this dissertation include bidirectional highway overtaking, highway maneuvering in traffic, and crash mitigation on highways. In the proposed framework, we first identify and exploit the different timescales involved in the navigation architecture. Then, we propose algorithmic modules while pursuing systematic complexity (data and computation) reduction at different timescales to gain immediate performance improvements in inference and action/response delay minimization. This leads to real-time situation assessment, computation, and action/control, allowing us to satisfy some of the key requirements for autonomous driving algorithms. Notably, the algorithms proposed in this dissertation ensure that the safety of the overall system is a fundamental constraint built into the system. Distinctive features of the proposed approaches include real-time operation capability, consideration for behavior variability of on-road agents, modularity in design, and optimality with respect to various metrics. The algorithms developed and implemented as part of this dissertation fundamentally rely upon the application of optimization techniques in a receding horizon fashion which allows for optimality in performance while explicitly accounting for actuation limits, vehicle dynamics, and safety. Even though the modularity of the proposed navigation framework allows for the incorporation of modern prediction and control methods, we develop various prediction modules for the trajectory prediction of on-road agents. We further benefit from risk evaluation methodologies to ensure robustness to behavior variability of human drivers on the road and handle collision-prone situations. In the spirit of emulating real-world situations, we place special emphasis on utilizing realistic driving simulations that capture the effects of communication delays between different modules, limitations in computation resources, and randomization of scenarios. For the developed algorithms, we evaluate the performance in comparative singular case studies as well as randomized Monte Carlo simulations with respect to several metrics to assess the efficacy of the developed methods.Item Phase Tracking Methods for X-ray Pulsar-Based Spacecraft Navigation(2021) Anderson, Kevin; Pines, Darryll J; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)X-ray pulsars are potential aids to spacecraft navigation due to the periodicity, uniqueness, and stability of their signals. As the load on the deep space network increases in the future, techniques to navigate with less frequent communication will become desirable. Improved methods of x-ray pulsar-based spacecraft navigation (XNAV) are developed, analyzed, and confirmed over multiple simulated scenarios. A phase-tracking algorithm modeled at the level of individual photon arrivals provides improvements over the current state of the art, and a novel phase maximum likelihood estimator (MLE) is proposed. Relaxing the constant signal frequency assumption with a second-order Taylor polynomial phase model and feedback of frequency and frequency derivative from a third-order digital phase-locked loop is shown to overcome previous phase tracking difficulties due to low flux with millisecond period pulsars (MSPs), which have the best navigation characteristics. Empirical MLE tests are performed to determine threshold observation times for convergence to the Cramer-Rao Bound. A lower limit is identified due to Poisson statistics and an upper limit due to orbit dynamic stress. For a 1 m^2 detector, one second for the Crab pulsar and 4000 seconds for the lowest flux MSPs are required. An analytical method is presented to predict the necessary threshold observation times for signals with pulse widths under 0.15 cycles. Simulations are performed for dynamic stress conditions including two heliocentric trajectories, a cislunar trajectory, and three Earth orbits. The Crab pulsar and four MSPs: B1821-24, B1937+21, J0218+4232, and J0437-4715 are investigated. Position errors of 2 to 7 km are shown for most of the MSPs along the interplanetary and cislunar trajectories. B1821-24 tracks on the Earth orbits with 1 – 2 m^2 detectors with 2.5 – 3.5 km error. B1937+21 and J0218+4232 require larger detector areas. An extended Kalman filter combines multiple pulsar phase tracking range measurements for various observation schedules. Scenarios with one and three detectors are considered. Position error under 3 km is demonstrated for an interplanetary trajectory. Phase tracking shows great promise for deep space navigation and more limited potential in scenarios with greater orbital dynamics.Item Human Gait Based Relative Foot Sensing for Personal Navigation(2010) Spiridonov, Timofey N.; Pines, Darryll J; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human gait dynamics were studied to aid the design of a robust personal navigation and tracking system for First Responders traversing a variety of GPS-denied environments. IMU packages comprised of accelerometers, gyroscopes, and magnetometer are positioned on each ankle. Difficulties in eliminating drift over time make inertial systems inaccurate. A novel concept for measuring relative foot distance via a network of RF Phase Modulation sensors is introduced to augment the accuracy of inertial systems. The relative foot sensor should be capable of accurately measuring distances between each node, allowing for the geometric derivation of a drift-free heading and distance. A simulation to design and verify the algorithms was developed for five subjects in different gait modes using gait data from a VICON motion capture system as input. These algorithms were used to predict the distance traveled up to 75 feet, with resulting errors on the order of one percent.Item Gravity Gradiometer Aided Inertial Navigation Within Non-GNSS Environments(2008-01-25) Richeson, Justin Arthur; Pines, Darryll J; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Gravity gradiometer aiding of a strapdown inertial navigation system (INS) in the event of Global Navigation Satellite System (GNSS) signal loss, or as a complement to an INS/GNSS system, is proposed. Gravity gradiometry is ideal for covert military applications where a self contained, passive, spoof-free aid is desirable, and for space navigation near planetary bodies and moons where GNSS is unavailable. This dissertation provides the first comprehensive discussion on gravity gradiometry fundamentals, map modeling, and regional and altitude effects on the gravitational gradient signal for use as a navigation aid. A thorough methodology to implement strapdown and stabilized gravity gradiometer instruments (GGIs) into an autonomous extended Kalman filter is also presented in the open literature for the first time. Lastly, a brief discussion on extraterrestrial navigation using gravity gradiometry is given. To quantify the potential performance for future gravity gradiometer instruments as an INS aid, extensive Monte Carlo simulations of a hypersonic scramjet cruise missile were performed. The results for the 1000 km range mission indicate that GGI updates significantly improve the navigation accuracy of the autonomous INS. The sensitivities of the system to variations in inertial measurement unit (IMU) quality, gravity field variation, GGI noise, update rate, and type are also investigated along with a baseline INS/Global Positioning System (GPS). Given emerging technologies that have the potential to drastically decrease gradiometer noise levels, a hypothetical future grade gravity gradiometer aided INS is shown to bound root-mean-square (RMS) position errors at 0.336 m, velocity errors at 0.0069 m/s, and attitude errors at 0.00977 degrees, which is comparable to the nominal INS/GPS system with 10 sec updates. The performance of two subsonic cases is also investigated and produced impressive passive navigation accuracy. A commercial aircraft simulation using a future grade GGI provided RMS errors of 0.288 m in position, 0.0050 m/s in velocity, and 0.0135 degrees in attitude. A low altitude and velocity gravity gradiometer based survey simulation similarly showed sub-meter RMS position errors of 0.539 m, velocity errors of 0.0094 m/s, and attitude errors of 0.0198 degrees.Item Use of Multiple Cues for Navigation by the Leaf-cutter Ant Atta cephalotes.(2005-04-13) Vick, Kyle; Jeka, John; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the first chapter, there is a brief introduction to ant navigation and a review of previous literature as well as a summary chapters 2-7. In chapter 2, I examine orientation of Atta cephalotes workers in the laboratory. Laden nest-bound foragers were moved from a "bridge" with or without trail pheromone present and placed on a parallel bridge with or without pheromone. In chapter 3, I continue to examine orientation of A. cephalotes foragers in the laboratory. Foragers walked on a single bridge and I altered various cues and contexts and recorded which manipulations caused the ants to reverse course. In chapter 4, I put orientation cues into direct conflict by letting the ants forage on a Y-maze. Foragers that were returning to a food source preferred visual cues to odor cues while recruited foragers consistently used odor cues. In chapter 5, I use a vertical T-maze to investigate the role that gravity plays in A. cephalotes navigation. The gravitational cue was put in direct conflict with odor cues and light cues. There was an asymmetry to the ants' response to the gravity cue in that ants returning to a food source had a tendency to go up regardless of the previous position of the food source or the position of the odor trail. Introducing a light cue changed the angle required to make the ants respond to the gravitational cue. In chapter 6, I investigate the anatomy of A. cephalotes eyes and brains. Based on tissue sections, I measured the angles between adjacent ommatidia in the eyes, and the volumes of sub-compartments of the brain. In chapter 7, I use the results from the other chapters to inform my speculations about the nature and neural basis of A. cephalotes navigation. I develop an hypothesis of navigation in the wild and a simple model of its neural underpinnings.