Aerospace Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2737
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Item Deployment of Large Vision and Language Models for Real-Time Robotic Triage in a Mass Casualty Incident(2024) Mangel, Alexandra Paige; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the event of a mass casualty incident, such as a natural disaster or war zone, having a system of triage in place that is efficient and accurate is critical for life-saving intervention, but medical personnel and resources are often strained and struggle to provide immediate care to those in need. This thesis proposes a system of autonomous air and ground vehicles equipped with stand-off sensing equipment designed to detect and localize casualties and assess them for critical injury patterns. The goal is to assist emergency medical technicians in identifying those in need of primary care by using generative AI models to analyze casualty images and communicate with the victims. Large language models are explored for the purpose of developing a chatbot that can ask a casualty where they are experiencing pain and make an informed assessment about injury classifications, and a vision language model is prompt engineered to assess a casualty image to produce a report on designated injury classifiers.Item Applied Aerial Autonomy for Reliable Indoor Flight and 3D Mapping(2024) Shastry, Animesh Kumar; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Uncrewed Aerial Systems (UAS) are essential for safely exploring indoor environments damaged by shelling, fire, floods, and structural collapse. These systems can gather critical visual and locational data, aiding in hazard assessment and rescue planning without risking human lives. Reliable UAS deployments requires advanced sensors and robust algorithms for real-time data processing and safe navigation, even in GPS-denied and windy conditions. This dissertation details three research projects to improve UAS performance: (1) in-flight calibration to improve estimation and control, (2) system identification for wind rejection, and (3) indoor aerial 3D mapping. The dissertation begins with introducing a comprehensive nonlinear filtering framework for UAV parameter estimation, which considers factors such as external wind, drag coefficients, IMU bias, and center of pressure. Additionally, it establishes optimized flight trajectories for parameter estimation through empirical observability. Moreover, an estimation and control framework is implemented, utilizing the mean of state and parameter estimates to generate suitable control inputs for vehicle actuators. By employing a square-root unscented Kalman filter (sq-UKF), this framework can derive a 23-dimensional state vector from 9-dimensional sensor data and 4-dimensional control inputs. Numerical results demonstrate enhanced tracking performance through the integration of the estimation framework with a conventional model-based controller. The estimation of unsteady winds results in improved gust rejection capabilities of the onboard controller as well. Closely related to parameter estimation is system identification. Combining with the previous work a comprehensive system identification framework with both linear offline and nonlinear online methods is introduced. Inertial parameters are estimated using frequency-domain linear system identification, incorporating control data from motor-speed sensing and state estimates from automated frequency sweep maneuvers. Additionally, drag-force coefficients and external wind are recursively estimated during flight using a sq-UKF. A custom flight controller is developed to manage the computational demands of online estimation and control. Flight experiments demonstrate the tracking performance of the nonlinear controller and its improved capability in rejecting gust disturbances. Aside from wind rejection, aerial indoor 3D mapping is also required for indoor navigation, and therefore, the dissertation introduces a comprehensive pipeline for real-time mapping and target detection in indoor environments with limited network access. Seeking a best-in-class UAS design, it provides detailed analysis and evaluation of both hardware and software components. Experimental testing across various indoor settings demonstrates the system's efficacy in producing high-quality maps and detecting targets.Item Multi-Domain Human-Robot Interfaces(2024) Abdi, Sydrak Solomon; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As autonomous robots become more capable and integrated into daily society, it becomes crucial to consider how a user will interact with them, how a robot will perceive a user, and how a robot will comprehend a user’s intentions. This challenge increases in difficulty when the user is required to interact with and control multiple robots simultaneously. Human intervention is often required during autonomous operations, particularly in scenarios that involve complex decision-making or where safety concerns arise. Thus, the methods by which users interact with multi-agent systems is an important area of research. These interactions should be intuitive, efficient, and effective all while preserving the operator's safety. We present a novel human swarm interface (HSI) that utilizes gesture control and haptic feedback to interact with and control a swarm of quadrotors in a confined space. This human swarm interface prioritizes operator safety while reducing cognitive load during control of an aerial swarm. Human-robot interfaces (HRIs) are mechanisms designed to facilitate communication between humans and robots, enhancing the user's ability to command and collaborate with robots in an intuitive and user-friendly manner. One challenge is providing mobile robotic systems with the capability to localize and interact with a user in their environment. Localization involves estimating the pose (position and orientation) of the user relative to the robot, which is essential for tasks that require close interactions or navigation in shared spaces. We present a novel method for obtaining user pose as well as other anthropometric measurements useful for human-robot interactions. Another challenge is extending these HRI and HSI paradigms to the outdoors. Unlike controlled laboratory conditions, outdoor environments involve a variety of variables such as fluctuating weather conditions as well as a mix of static and dynamic obstacles. In this dissertation, we design a portable human swarm interface that allows an operator to interact with and control a multi-agent system outdoors. The portable HSI takes the form of smart binoculars. The user uses the smart binoculars to select an outdoor location and assign a task for the multi-agent system to complete given the targeted area. This system allows for new methods of multi-agent operation, that will leverage a user's on-the-ground knowledge while utilizing autonomous vehicles for line-of-sight operations, without compromising their situational awareness.Item Inertial Parameter Identification of a Captured Payload Attached to a Robotic Manipulator on a Free-Flying Spacecraft(2024) Limparis, Nicholas Michael; Akin, David L; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The groundwork for the dynamics of a free-flyer with a manipulator has been laid out by Yoshida, Vafa and Dubowsky, and Papadopoulos and Moosovian with the Generalized Jacobian Matrix, Virtual Manipulator, and Barycentric Vector Approach respectively. The identification of parameters for a robot manipulator has also been approached for industrial robots as well as through adaptive control theory. What is proposed is a method for inertial parameter identification and verification for a spacecraft with an attached manipulator that is an extension of the ground-fixed Inverse Direct Dynamic Model to function for a free-flying spacecraft. This method for inertial parameter identification for a spacecraft-manipulator system with an attached client spacecraft, debris, or other grappled payload is developed in this thesis and is experimentally tested using results for a servicer and an "unknown" grappled payload using three separate test beds. The results of the experiments show that the proposed method is capable of identifying the inertial parameters of the servicer and the grappled payload.Item INDOOR TARGET SEARCH, DETECTION, AND INSPECTION WITH AN AUTONOMOUS DRONE(2024) Ashry, Ahmed; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis investigates the deployment of unmanned aerial vehicles (UAVs) in indoor search and rescue (SAR) operations, focusing on enhancing autonomy through the development and integration of advanced technological solutions. The research addresses challenges related to autonomous navigation and target inspection in indoor environments. A key contribution is the development of an autonomous inspection routine that allows UAVs to navigate to and meticulously inspect targets identified by fiducial markers, replacing manual piloted inspection. To enhance the system’s target recognition, a custom-trained object detection model identifies critical markers on targets, operating in real-time on the UAV’s onboard computer. Additionally, the thesis introduces a comprehensive mission framework that manages transitions between coverage and inspection phases, experimentally validated using a quadrotor equipped with onboard sensing and computing across various scenarios. The research also explores integration and critical analysis of state-of-the-art path planning algorithms, enhancing UAV autonomy in cluttered settings. This is supported by evaluations conducted through software-in-the-loop simulations, setting the stage for future real-world applications.Item FAST FEASIBLE MOTION PLANNING WITHOUT TWO-POINT BOUNDARY VALUE SOLUTION(2023) Nayak, Sharan Harish; Otte, Michael; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autonomous robotic systems have seen extensive deployment across domains such as manufacturing, industrial inspection, transportation, and planetary surface exploration. A crucial requirement for these systems is navigating from an initial to a final position, while avoiding potential collisions with obstacles en route. This challenging task of devising collision-free trajectories, formally termed as motion planning, is of prime importance in robotics. Traditional motion planning approaches encounter scalability challenges when planning in higher-dimensional state-spaces. Moreover, they rarely consider robot dynamics during the planning process. To address these limitations, a class of probabilistic planning methods called Sampling-Based Motion Planning (SBMP) has gained prominence. SBMP strategies exploit probabilistic techniques to construct motion planning solutions. In this dissertation, our focus turns towards feasible SBMP algorithms that prioritize rapidly discovering solutions while considering robot kinematics and dynamics. These algorithms find utility in quickly solving complex problems (e.g., Alpha puzzle) where obtaining any feasible solution is considered as an achievement. Furthermore, they find practical use in computationally constrained systems and in seeding time-consuming optimal solutions. However, many existing feasible SBMP approaches assume the ability to find precise trajectories that exactly connect two states in a robot's state space. This challenge is framed as the Two-Point Boundary Value Problem (TPBVP). But finding closed-form solutions for the TPBVP is difficult, and numerical approaches are computationally expensive and prone to precision and stability issues. Given these complexities, this dissertation's primary focus resides in the development of SBMP algorithms for different scenarios where solving the TPBVP is challenging. Our work addresses four distinct scenarios -- two for single-agent systems and two for multi-agent systems. The first single-agent scenario involves quickly finding a feasible path from the start to goal state, using bidirectional search strategies for fast solution discovery. The second scenario focuses on performing prompt motion replanning when a vehicle's dynamical constraints change mid-mission. We leverage the heuristic information from the original search tree constructed using the vehicle's nominal dynamics to speed up the replanning process. Both these two scenarios unfold in static environments with pre-known obstacles. Transitioning to multi-agent systems, we address the feasible multi-robot motion planning problem where a robot team is guided towards predefined targets in a partially-known environment. We employ a dynamic roadmap updated from the current known environment to accelerate agent planning. Lastly, we explore the problem of multi-robot exploration in a completely unknown environment applied to the CADRE mission. We demonstrate how our proposed bidirectional search strategies can facilitate efficient exploration for robots with significant dynamics. The effectiveness of our algorithms is validated through extensive simulation and real-world experiments.Item Examining the Passive Stiffness Workspace Using Variable Stiffness Robots(2023) Feinberg, Evan Harris; Akin, David L; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Passive stiffness control is a method for managing contact forces and dynamics between a robotic manipulator and its environment. Compliance control is typically implemented in redundant serial manipulators using a force torque sensor and active software algorithms. However, time delays in these algorithms can cause large impulse forces between the manipulator and its environment. For applications with limited computation power, large time delays, and low damping, such as In-space Servicing Assembly and Manufacturing (ISAM) and Active Debris Removal (ADR), these effects can cause a manipulator to push away or tip off the target, preventing successful capture. This thesis examines the implementation of passive stiffness control in a redundant serial manipulator using Variable Stiffness Actuators (VSA). Unlike traditional robot actuators, VSAs have an adjustable stiffness element in series with the primary joint position/control motor to generate varying end-effector position and stiffness. These adjustable springs act as low pass force filters to increase the actuator robustness against external loads at the cost of positioning accuracy. Different optimization algorithms are used to vary the VSA joint stiffness to achieve a desired Cartesian stiffness matrix. However, there are severe limitations with the passive joint to Cartesian stiffness mapping performance over the whole robotic workspace due to the significant kinematic configuration dependence. Given this limitation, this work attempts to answer for a single, well-defined task, are there regions of the workspace where a prescribed level of passive stiffness realization can be achieved? Or for a mobile robot, can we plan trajectories within a region of the workspace to improve realization performance? This is done by first examining the implementation of three passive stiffness realization methods, each with increasing performance. Next, the idea of Successful Task/Stiffness Trajectories and Success Task/Stiffness Regions are introduced as a way to examine the workspace dependency of the passive stiffness realization. Finally, the application of passive stiffness control for ISAM and ADR applications is studied, and unique design objectives for the manipulator are proposed.Item Dynamics and Control of Bioinspired Swimming, Schooling, and Pursuit(2023) Thompson, Anthony Allan; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding the benefits of the behaviors of aquatic animals can improve the capabilities of robotic systems. Aquatic species such as the zebrafish swim with discrete motions that alternate between perception and action while avoiding predators and swimming in schools, and other species such as the lionfish use their pectoral fins to herd and trap prey. This work seeks to model these bioinspired behaviors (i.e., schooling, swimming with intermittent sensing and actuation, and pursuit and evasion in a structured environment) and enhance our understanding of their benefits. A hybrid dynamic model is derived with two phases; namely a burst phase during which each particle applies a control input and a coast phase during which each particle performs state estimation. This model provides a way to investigate how having non-overlapping sensing and control affects a multi-agent system's ability to achieve collective behavior such as steering to some desired direction. By evaluating the stability properties of the equilibrium points for the collective behavior, investigators can determine parameter values that exhibit exponentially stable behavior. Aside from swimming intermittently, fish also need to avoid predators. Inspired by observations of predation attempts by lionfish (Pterois sp.), a pursuit-evasion game is derived in a bounded environment to study the interaction of an advanced predator and an intermittently steering prey. The predator tracks the prey with a pure-pursuit strategy while using a bioinspired tactic to minimize the evader's escape routes, i.e, to trap the prey. Specifically, the predator employs symmetric appendages inspired by the large pectoral fins of lionfish, but this expansion increases its drag. The prey employs a bioinspired randomly-directed escape strategy to avoid capture and collisions with the boundary known as the protean strategy. This game investigates the predator's trade-off of minimizing the work to capture the prey and minimizing the prey's escape routes. Using the predator's expected work to capture as a cost function determines when the predator should expand its appendages as a function of the relative distance to the evader and the evader's proximity to the boundary. Prey fish also swim in schools to protect themselves from predators. To drive a school of fish robots into a parallel formation, a nonlinear steering controller is derived and implemented on a robotic fish platform. These robotic fish are actuated with an internal reaction wheel driven by a DC motor. Implementation of the proposed parallel formation control law on an actual school of soft robotic fish is described, including system identification experiments to identify motor dynamics and the design of a motor torque-tracking controller to follow the formation torque control. Experimental results demonstrate a school of four robotic fish achieving parallel formations starting from random initial conditions.Item Extensile Fluidic Artificial Muscles in Payload-Carrying Continuum Soft Robots(2023) Garbulinski, Jacek; Wereley, Norman M.; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Intrinsically actuated continuum soft robots merge the features of hyper-redundant and soft robots. The soft structure and redundancy allow the robots to conduct tasks in confined or unstructured environments. Extensile fluidic artificial muscles (EFAMs) can be used to construct soft actuated structures that feature large deformation and enable the robots to access large reachable workspaces. However, the soft robots’ low structural stiffness limits their ability to exert force or carry payloads. This dissertation aims to improve the continuum soft robot's spatial and payload-carrying performance. The work seeks to accomplish the following: 1. Compare multi-segment continuum robots to understand how the number of segments and robot geometry affect their spatial performance.2. Experimentally characterize and model EFAMs to close existing knowledge gaps in their axial and bending behaviors. 3. Investigate the impact of radial reinforcement on the payload-carrying ability of an EFAM robot. 4. Propose a modeling approach that captures the deformation of the robot under payloads.Item AUTONOMOUS ESTIMATION AND GUIDANCE OF AN AMPHIBIOUS QUADROTOR FOR BISTATIC UNDERWATER LASER IMAGING(2022) Toombs, Nathan; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Underwater object classification by unmanned underwater vehicles (UUVs) is a critical task that is made difficult in shallow waters with concentrated particulate matter. Bistatic laser imaging is a current area of research that is more effective than traditional optical methods, but it requires separation of the laser receiver from the UUV-mounted laser emitter. This work explores the prospect of performing bistatic laser imaging with the receiver mounted to a quadrotor unmanned aerial vehicle (UAV). To facilitate the imaging application, estimation and guidance algorithms are developed to autonomously locate and track a UUV-mounted laser with an amphibious UAV. The UAV is equipped to carry a receiver payload in safe above-water flight and water landings. To represent the received laser measurements, laser intensity models are developed based on the distributions of the decollimated lasers used in the imaging application. The UAV autonomy is validated both in a reduced-order simulation environment and with the hardware testbed.