UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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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 Sampling Based Motion PLanning for Minimizing Position Uncertainty with Stewart Platforms(2021) Ernandis, Ryan; Otte, Michael; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The work described in this dissertation provides a unique approach to error based motion planning. Originally designed specifically for use on a parallel robot,these methods can be extended to a more general case of any well-defined robotic platforms. Requirements for application of these methods are a known method of kinematics for defining the system as well as a means of calculating noise based on the system. Two methods of error tracking and two motion planning algorithms are tested here as approaches to this problem. Shown within are the results of the motion planning methods used. One combination of motion planning algorithm and error tracking works best as a general solution to this problem and is designed to work on a parallel robot; specifically, a Stewart platform. The motivation for use of a Stewart platform comes from research done at NASA Langley Research Center in the field of In-Space Assembly.Item Efficient Algorithms for Coordinated Motion in Shared Spaces(2020) Dasler, Philip; Mount, David M; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The steady development of autonomous systems motivates a number of interesting algorithmic problems. These systems, such as self-driving cars, must contend with far more complex and dynamic environments than factory floor robots of the past. This dissertation seeks to identify optimization problems that are simple enough to analyze formally, yet realistic enough to contribute to the eventual design of systems extant in real-world, physical spaces. With that in mind, this work examines three problems in particular: automated vehicles and unregulated traffic crossings, a smart network for city-wide traffic flow, and an online organizational scheme for automated warehouses. First, the Traffic Crossing Problem is introduced, in which a set of n axis-aligned vehicles moving monotonically in the plane must reach their goal positions within a time limit and subject to a maximum speed limit. It is shown that this problem is NP-complete and efficient algorithms for two special cases are given. Next, motivated by a problem of computing a periodic schedule for traffic lights in an urban transportation network, the problem of Circulation with Modular Demands is introduced. A novel variant of the well-known minimum-cost circulation problem in directed networks, in this problem vertex demand values are taken from the integers modulo λ, for some integer λ≥2. This modular circulation problem is solvable in polynomial time when λ=2, but the problem is NP-hard when λ≥3. For this case, a polynomial time approximation algorithm is provided. Finally, a theoretical model for organizing portable storage units in a warehouse subject to an online sequence of access requests is proposed. Complicated by the unknown request frequencies of stored products, the warehouse must be arranged with care. Analogous to virtual-memory systems, the more popular and oft-requested an item is, the more efficient its retrieval should be. Two formulations are considered, dependent on the number of access points to which storage units must be brought, and algorithms that are O(1)-competitive with respect to an optimal algorithm are given. Additionally, in the case of a single access point, the solution herein is asymptotically optimal with respect to density.Item COMBINED ROBUST OPTIMAL DESIGN, PATH AND MOTION PLANNING FOR UNMANNED AERIAL VEHICLE SYSTEMS SUBJECT TO UNCERTAINTY(2019) Rudnick-Cohen, Eliot; Azarm, Shapour; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Unmanned system performance depends heavily on both how the system is planned to be operated and the design of the unmanned system, both of which can be heavily impacted by uncertainty. This dissertation presents methods for simultaneously optimizing both of these aspects of an unmanned system when subject to uncertainty. This simultaneous optimization under uncertainty of unmanned system design and planning is demonstrated in the context of optimizing the design and flight path of an unmanned aerial vehicle (UAV) subject to an unknown set of wind conditions. This dissertation explores optimizing the path of the UAV down to the level of determining flight trajectories accounting for the UAVs dynamics (motion planning) while simultaneously optimizing design. Uncertainty is considered from the robust (no probability distribution known) standpoint, with the capability to account for a general set of uncertain parameters that affects the UAVs performance. New methods are investigated for solving motion planning problems for UAVs, which are applied to the problem of mitigating the risk posed by UAVs flying over inhabited areas. A new approach to solving robust optimization problems is developed, which uses a combination of random sampling and worst case analysis. The new robust optimization approach is shown to efficiently solve robust optimization problems, even when existing robust optimization methods would fail. A new approach for robust optimal motion planning that considers a “black-box” uncertainty model is developed based off the new robust optimization approach. The new robust motion planning approach is shown to perform better under uncertainty than methods which do not use a “black-box” uncertainty model. A new method is developed for solving design and path planning optimization problems for unmanned systems with discrete (graph-based) path representations, which is then extended to work on motion planning problems. This design and motion planning approach is used within the new robust optimization approach to solve a robust design and motion planning optimization problem for a UAV. Results are presented comparing these methods against a design study using a DOE, which show that the proposed methods can be less computationally expensive than existing methods for design and motion planning problems.Item Planning for Autonomous Operation of Unmanned Surface Vehicles(2016) Shah, Brual; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14.Item Motion Planning and Controls with Safety and Temporal Constraints(2016) Zhou, Yuchen; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Motion planning, or trajectory planning, commonly refers to a process of converting high-level task specifications into low-level control commands that can be executed on the system of interest. For different applications, the system will be different. It can be an autonomous vehicle, an Unmanned Aerial Vehicle(UAV), a humanoid robot, or an industrial robotic arm. As human machine interaction is essential in many of these systems, safety is fundamental and crucial. Many of the applications also involve performing a task in an optimal manner within a given time constraint. Therefore, in this thesis, we focus on two aspects of the motion planning problem. One is the verification and synthesis of the safe controls for autonomous ground and air vehicles in collision avoidance scenarios. The other part focuses on the high-level planning for the autonomous vehicles with the timed temporal constraints. In the first aspect of our work, we first propose a verification method to prove the safety and robustness of a path planner and the path following controls based on reachable sets. We demonstrate the method on quadrotor and automobile applications. Secondly, we propose a reachable set based collision avoidance algorithm for UAVs. Instead of the traditional approaches of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking control set design for the aircraft to avoid collision. We apply our approach to collision avoidance scenarios of quadrotors and fixed-wing aircraft. In the second aspect of our work, we address the high level planning problems with timed temporal logic constraints. Firstly, we present an optimization based method for path planning of a mobile robot subject to timed temporal constraints, in a dynamic environment. Temporal logic (TL) can address very complex task specifications such as safety, coverage, motion sequencing etc. We use metric temporal logic (MTL) to encode the task specifications with timing constraints. We then translate the MTL formulae into mixed integer linear constraints and solve the associated optimization problem using a mixed integer linear program solver. We have applied our approach on several case studies in complex dynamical environments subjected to timed temporal specifications. Secondly, we also present a timed automaton based method for planning under the given timed temporal logic specifications. We use metric interval temporal logic (MITL), a member of the MTL family, to represent the task specification, and provide a constructive way to generate a timed automaton and methods to look for accepting runs on the automaton to find an optimal motion (or path) sequence for the robot to complete the task.