Computer Science
Permanent URI for this communityhttp://hdl.handle.net/1903/2224
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Item Outdoor Localization and Path Planning for Repositioning a Self-Driving Electric Scooter(2023) Poojari, Srijal Shekhar; Paley, Derek; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The long-term goal of this research is to develop self-driving e-scooter technology to increase sustainability, accessibility, and equity in urban mobility. Toward this goal, in this work, we design and implement a self-driving e-scooter with the ability to safely travel along a pre-planned route using automated, onboard control without a rider. We also construct an autonomous driving framework by synthesizing open-source robotics software libraries with custom-designed modules specific to an e-scooter, including path planning and state estimation. The hardware and software development steps along with design choices and pitfalls are documented. Results of real-world autonomous navigation of our retrofitted e-scooter, along with the effectiveness of our localization methods are presented.Item Experimental Design using Bayesian Network Simulation-based Assurance cases(2023) Gattani, Vishal; Herrmann, Jeffrey W; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Experimental design plays a critical role in ensuring the safety and reliability of systems in various domains. Bayesian belief networks (BBNs) have been widely used as a decision-making tool for probabilistic modeling and analysis of complex systems. This thesis presents an approach for using a BBN to model an assurance case and predict the likelihood of its claims. This can be used to evaluate changes to the experiments that will generate the evidence needed for the assurance case. We present two examples as case studies in the software engineering domain to demonstrate the effectiveness of our approach. The results show that our framework can effectively capture the changes in the degree of belief in a claim under uncertainties and risks associated with the experimental design and provide decision-makers with a more comprehensive understanding of the system under investigation.Item Generating Feasible Spawn Locations for Autonomous Robot Simulations in Complex Environments(2022) Ropelato, Rafael Florian; Herrmann, Jeffrey W; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Simulations have become one of the main methods in the development of autonomous robots. With the application of physical simulations that closely represent real-world environments, the behavior of a robot in a variety of situations can be tested in a more efficient manner than performing experiments in reality. With the implementation of ROS (Robot Operating System), the software of an autonomous system can be simulated separately without an existing robot. In order to simulate the physical environment surrounding the robot, a physics simulation has to be created through which the robot navigates and performs tasks. A commonly used platform for such simulations is Unity which provides a wide range of simulation capabilities as well as an interface for ROS. In order to perform multi-agent simulations or simulations with varying initial locations for the robot, it is crucial to find unobstructed spawn locations to avoid undesirable situations with collisions upon start of the simulation. For this purpose, multiple methods were implemented with this research, in order to generate feasible spawn locations within complex environments. Each of the three applied methods generates a set of valid spawn positions, which can be used to design simulations with varying initial locations for the agents. To assess the performance and functionality of these approaches, the algorithms were applied to several environments varying in complexity and scale. Overall, the implemented approaches performed very well in the applied environments, and generated mainly correctly classified locations which are suitable to spawn a robot. All approaches were tested for performance and compared in respect to their fitness to be applied to environments of varying complexity and scale. The resulting algorithms can be considered a efficient solutions to prepare simulations with multiple initial locations for robots and other test objects.Item APPLICATION OF A BAYESIAN NETWORK BASED FAILURE DETECTION AND DIAGNOSIS FRAMEWORK ON MARITIME DIESEL ENGINES(2022) Reynolds, Steven; Groth, Katrina; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Diesel engine propulsion has been the largest driver of maritime trade and transportation since its development in the early 20th century and the technology surrounding the operation and maintenance of these systems has grown in complexity leading to rapid advancement in amount and variety of data being collected. This increase in reliability data provides a fantastic opportunity to improve upon the existing tools troubleshooting and decision support tool used within the maritime engine community to enable a more robust understanding of engine reliability. This work leverages this opportunity and applies it to the Coast Guard and its acquisition of the Fast Response Cutter (FRC) fleet powered by two MTU20V4000M93 engines integrated with top of line monitoring and control equipment.The purpose of this research is to create procedures for creating a Failure Detection and Diagnosis (FDD) model of a maritime diesel engine that updates existing Probabilistic Risk Analysis (PRA) data with input from the engine monitoring and control system using Bayesian inference. A literature review of existing work within the PRA and Prognostics and Health Management (PHM) fields was conducted with specific focus on the advancement and gaps in the field specific to their use in maritime engine applications. Following this, a hierarchal ruleset was created that outlines procedures for integrating existing PRA data and PHM metrics into a Bayesian Network structure. This methodology was then used to build a Bayesian Network based FDD model of the FRC engine. This model was then validated by Coast Guard Engineers and run through a diagnostic use case scenario demonstrating the model’s suitability in the diagnostic space.Item MULTI-FLIGHT ALGORITHMS FOR MULTI-UAV ARC ROUTING PROBLEM(2021) Sathyamurthy, Eashwar; Herrmann, Jeffrey J.H; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Icy roads can cause many accidents due to their slippery nature. Thus, providingpeople the information about icy roads can help them avoid taking these roads and prevent accidents. The problem of routing UAVs optimally to collect visual information regarding icy roads is called Icy Road Vehicle Routing Problem. Vehicle Routing Problem is essentially a path planning problem, where optimal paths need to be determined for one or more vehicles. Arc Routing Problems is a subclass of vehicle routing problems where the goal is to make vehicle(s) traverse specific arcs or edges optimally. Icy road vehicle routing problem resembles arc routing problem when icy roads are considered as arcs. Usually, in arc routing problems, vehicle(s) are located in one specific location, and the solution routes start and end at the same location. This thesis defines the Icy Road Vehicle Routing Problem, a new type of arc routing problem, where the goal of traversing arcs or icy roads remains the same but vehicle(s) or UAV(s) are located at one or multiple location(s). The thesis also presents new heuristic- based algorithms called multi-flight algorithms to solve the Icy Road Vehicle Routing Problem. By performing a set of experiments, the proposed algorithms are compared against several heuristic-based approaches from the literature. These experimental results show that the proposed multi-flight algorithms produced quicker and better quality solution routes to the UAVs for the Icy Road Vehicle Routing Problem than previous heuristics.Item PROOF OF CONCEPT FOR PAIR BASED APPROACH FOR SWARM ROBOTICS(2021) Singh, Anshuman; Xu, Mumu Dr.; Otte, Michael Dr.; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many cases exist where teams of agents in a multi-agent system perform better than individual agents acting alone. In swarm robots, a large number of low-cost robots with limited functionality interact with each other and the environment to result in a more complex emergent behavior capable of performing tasks collaboratively. There exist many robotic swarms composed of single agents however, the study of swarms composed of modular robots and/or smaller teams, each acting as an independent unit, is a relatively new area of study. This thesis provides a proof of concept for a pair-based approach for swarm robots where two individual robots act as a single unit in the swarm called “duos,” and the emergent behavior of the swarm consisting of these duos is studied by making concentric circles pattern using duos. For small swarm sizes, the duo swarm converged 31.6 % faster than the single-agent swarm.Item EFFECTIVENESS OF AUGMENTED REALITY INTERFACES FOR REMOTE HUMAN SWARM INTERACTION(2021) Oradiambalam Sachidanandam, Sarjana; Diaz-Mercado, Yancy; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human Swarm Interaction (HSI) is a fast-growing research area in swarm robotics. One challenging aspect of HSI is facilitating how humans can effectively handle the many degrees-of-freedom present in a swarm of robots. One emergent option is the use of Augmented Reality (AR) systems. AR based interfaces are attractive as they can help provide a human operator with visual cues about the swarm’s states and control to facilitate decision-making. In research settings, AR systems can address issues such as limited availability of lab spaces, limited access to robotics resources, and the need for the ability to simulate dynamic environments with which robots and humans can interact. Further, to make swarm robotics more accessible and ubiquitous, HSI systems that support remote interaction would al- low humans to interact with robot swarms and multi-robot systems regardless of the geographical distance between humans and swarms. Taking these into consid- eration, this thesis aims to investigate the effectiveness of AR based interfaces as tools for remote interaction in HSI systems. We develop a simple AR based in- terface and evaluate its effectiveness against an unaugmented interface, by means of remote human user studies. The results of these studies help demonstrate the effectiveness of AR based interfaces for remote HSI.Item A Method For Improving Decentralized Task Allocation For Multiagent Systems in Low-Communication Environments.(2021) Akoroda, Oghenetekevwe; Herrmann, Jeffrey W; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication is an important aspect of task allocation, but it has a cost and low communication restricts the information exchange needed for task allocation. As a result, a lot of decentralized task allocation algorithms perform worse as communication worsens. The contribution of this thesis is a method to improve the performance of a task allocation algorithm in low-communication environments and reduce the cost of communication by restricting communication. This method, applied to the Consensus Based Auction Algorithm (CBAA), determines when an agent should communicate and estimates the information that will be received from other agents. This method is compared to other decentralized task allocation algorithms at different levels of communication in a ship protection scenario. Results show that this method when applied to CBAA performs comparably to CBAA while reducing communication.Item DECENTRALIZED MULTIAGENT METAREASONING APPLICATIONS IN TASK ALLOCATION AND PATH FINDING(2021) Langlois, Samuel; Herrmann, Jeffrey W.; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Decentralized task allocation and path finding are two problems for multiagent systems where no single fixed algorithm provides the best solution in all environments. Past research has considered metareasoning approaches to these problems that take in map, multiagent system, or communication information. None of these papers address the application of metareasoning about individual agent state features which could decrease communication and increase performance for decentralized systems. This thesis presents the application of a meta-level policy that is conducted offline using supervised learning through extreme gradient boosting. The multiagent system used here operates under full communication, and the system uses an independent multiagent metareasoning structure. This thesis describes research that developed and evaluated metareasoning approaches for the multiagent task allocation problem and the multiagent path finding problem. For task allocation, the metareasoning policy determines when to run a task allocation algorithm. For multiagent path finding, the metareasoning policy determines which algorithm an agent should use. The results of this comparative research suggest that this metareasoning approach can reduce communication and computational overhead without sacrificing performance.Item Model-Based Systems Engineering Applied to the Detection and Correction of Object Slippage Within a Dexterous Robotic Hand from the Laboratory to Simulation(2020) Meehan, Charles Anthony; Baras, John S; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Now more than ever, it is important to have the ability to replicate robotic tasks in simulation and be able to validate the simulation against stakeholder requirements and verify the simulation against simulation requirements. In a previous study, a five-fingered robotic hand, the Shadow Dexterous Hand, with haptic BioTac SP sensors attached was used to detect the moment of slip of an object from the robotic hand while weight was continuously being added and stop the object from falling from the grasp while not overcorrecting. This work was accomplished by Dr. Zhenyu Lin, Dr. John S. Baras, and the author in the Autonomy Robotics Cognition Laboratory at the University of Maryland. This thesis will present the use of Model-Based System Engineering techniques to replicate the detection and correction of object slippage by a five-fingered robotic hand using force feedback control in simulation.