Robotics Commons^™

Navigating The Future Advancing Autonomous Vehicles Through Robust Target Recognition And Real-Time Avoidance, Mohammed Ahmed Mohammed Hussein

Theses and Dissertations

The problem being tackled by this thesis is a very important one and very relevant to our days and times: it is about making improved target recognition and enhanced real-time response skills in AVs under simulated conditions. Our plan is to put some enhanced sensory capabilities into these vehicles and see if that makes them safer and more reliable. We are using as our base a particular object recognition algorithm (YOLOv7) and a particular simulation environment (CARLA). We utilized the CARLA 0.9.14 simulator on Ubuntu 20.04 as a more stable option than the initially used CARLA 0.9.15 on Ubuntu 22.04, …

Computer Vision In A Robotic Arm, Jack Maxwell

College of Engineering Summer Undergraduate Research Program

We used a machine learning-based object detection algorithm to give a robotic arm the ability to "see" with its camera.

Advanced Grasping Sensor Technologies For Autonomous Robotic Apple Harvesting Using Tactile Data And Cnns, Chris Bae

College of Engineering Summer Undergraduate Research Program

This research investigates how to achieve an optimal grasp of an apple using a four-finger soft robotic grasper equipped with force-resistive sensors. Specifically, we sought to determine whether a convolutional neural network (CNN) could accurately classify the grasper's state and recommend adjustments ("in," "out," or "good" grasp) based on tactile data from the sensors. Spatiotemporal tactile images were developed from the sensors and fed into our CNN, achieving near 100% accuracy on unseen test data. This work suggests that CNN-based processing of tactile images can be a powerful tool for real-time control of soft robotic grippers.

Design And Implementation Of An Inverted Short Baseline Acoustic Positioning System, Jakob Frabosilio

Master's Theses

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time.

A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards …

Dynamic Maze Puzzle Navigation Using Deep Reinforcement Learning, Luisa Shu Yi Chiu

Master's Theses

The implementation of deep reinforcement learning in mobile robotics offers a great solution for the development of autonomous mobile robots to efficiently complete tasks and transport objects. Reinforcement learning continues to show impressive potential in robotics applications through self-learning and biological plausibility. Despite its advancements, challenges remain in applying these machine learning techniques in dynamic environments. This thesis explores the performance of Deep Q-Networks (DQN), using images as an input, for mobile robot navigation in dynamic maze puzzles and aims to contribute to advancements in deep reinforcement learning applications for simulated and real-life robotic systems. This project is a step …

Uav-Based Tracking And Following Of Railroad Lines, Keith Michael Lewandowski

Theses and Dissertations

Given the pivotal role of the railroad industry in modern transportation and the potential risks associated with track malfunctions, the inspection and maintenance of railroad tracks emerges as a critical concern. While existing solutions excel in performing accurate measurements and detection, they often rely on large, expensive, and time-consuming platforms for inspections. This project, however, seeks to solve the same problem with the use of an unmanned aerial vehicle (UAV), significantly reducing time and cost while maintaining detection capabilities. In particular, this solution is ideal for large-scale, high-level inspections following major events such as floods [6], hurricanes [7] or earthquakes …

Vision-Based Autonomy Stacks For Farm Tractors And Intelligent Spraying Systems In Orchards, Shengli Xu

All Dissertations

Autonomous tractors equipped with intelligent sprayers have become a pivotal aspect of smart farming (SF), marking a transformative shift in traditional agricultural practices and holding the potential to revolutionize the farming industry. With 2,453,620 fruit-bearing acres in the United States as of 2022, there is a pressing need for the implementation of autonomous systems for farm tractors and intelligent spraying systems in orchards. These advancements can significantly reduce labor costs, address labor shortages, and minimize spray loss. Furthermore, to enhance profitability and productivity, it is essential to develop low-cost yet effective vision-based autonomy systems that can operate efficiently across various …

Training Uav Teams With Multi-Agent Reinforcement Learning Towards Fully 3d Autonomous Wildfire Response, Bryce Hopkins

All Theses

As climate-exacerbated wildfires increasingly threaten landscapes and communities, there is an urgent and pressing need for sophisticated fire management technologies. Coordinated teams of Unmanned Aerial Vehicles (UAVs) present a promising solution for detection, assessment, and even incipient-stage suppression – especially when integrated into a multi-layered approach with other recent wildfire management technologies such as geostationary/polar-orbiting satellites and CCTV detection networks. However, there remains significant challenges in developing the necessary sensing, navigation, coordination, and communication subsystems that enable intelligent UAV teams. Further, federal regulations governing UAV deployment and autonomy pose constraints on real-world aerial testing, creating a disconnect between theoretical research …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

All Dissertations

This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

The Effects Of Using Artificial Intelligence Techniques In The Security Work System, Abdalla Alnaqbi

Journal of Police and Legal Sciences

The goal of the research is to shed light on the techniques of artificial intelligence in the work of experts and groups, and to achieve this goal by seeking to search for the concept of artificial intelligence, as well as the classification of artificial intelligence, and clarifying the role of artificial intelligence in organizations and bodies, and it also reached the definition of the positive and cooperative effects of artificial intelligence in... the job. And the. The researcher followed the descriptive model of analysis in order to achieve the objectives of the study, and the study reached one of the …

Criminal Confrontation Of The Crime Committed Via An Automated Robot In Libyan And Emirati Law, . Mashaallah Alzwae

Journal of Police and Legal Sciences

Today's world is witnessing an important development in telecommunications, information technology and computers that has resulted in what are known as automated robots as one of the most important applications of artificial intelligence and has increased reliance on them in various areas of life for the importance of the services they provide to humanity. However, such robots may be used to commit an offence and the study therefore aims to determine the effectiveness of legal texts in the face of the offence from which they may occur. The study required an analytical and comparative approach by analysing and comparing the …

Authenticated Diagnosing Of Covid-19 Using Deep Learning-Based Ct Image Encryption Approach, Mohamed Attia Abdelgwad, Amira Hassan Abed, Mahmoud Bahloul

Future Computing and Informatics Journal

Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional …

Deep Learning For Multiple Unmanned Aerial Vehicle Coordination In Air Corridors, Liangkun Yu

Electrical and Computer Engineering ETDs

In the future, city skies will be filled with Unmanned Aerial Vehicles (UAVs) for rapid urban transport, including parcel deliveries and air taxis. NASA's Urban Air Mobility (UAM) envisions UAVs navigating air corridors. These virtual pathways ensure safety and compliance with regulations. However, current research on UAM practical applications is limited. This dissertation focuses on designing air corridors, developing UAV control systems, and ensuring the robustness of control algorithms against disturbances in real-world environments.

Our design features an air corridor system with horizontal lanes and on-off ramps, conceptualized as cylindrical spaces and tori, respectively. To enable each UAV to locally …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

Studying The Performance Of Object Recognition With Fusion Of Visible Light And Infrared Images With Neural Networks, Plamen Petkov

Doctoral Dissertations and Master's Theses

Neural networks have been used for object detection and recognition in both color and intensity camera images. As the use of infrared cameras, colloquially termed thermal cameras, has increased and costs have decreased, object detection and recognition in infrared camera images have been increasingly studied. An infrared image is treated as an intensity image, just like a grayscale camera image, except the intensity corresponds to infrared radiation instead of visible light. The information provided by these two types of images are different, especially in different lighting and environmental situations, and some types of objects are more easily recognized in visible …

Safe And Efficient Operation Of Mobile Robots In Indoor Environments: A User-Centric Shared Control System With High-Level Navigation Capabilities, Ahmet Saglam

Electrical & Computer Engineering Theses & Dissertations

Hospitalization and isolation can be a traumatic experience for immunocompromised children, especially because they are separated from their families and friends. Social robots have been proposed as a way to improve the quality of care for children hospitalized in isolation by providing alternative means of social interaction and support. Remote control of such robots in a hospital setting, particularly where safety is a major concern, can be a daunting task for young patients.

This dissertation introduces a multilevel shared control system for mobile robots, specifically companion robots in hospital-like indoor spaces. The system integrates user inputs with algorithmic semi-autonomous control …

Authentic Impediments: The Influence Of Identity Threat, Cultivated Perceptions, And Personality On Robophobia, Kate K. Mays

Human-Machine Communication

Considering possible impediments to authentic interactions with machines, this study explores contributors to robophobia from the potential dual influence of technological features and individual traits. Through a 2 x 2 x 3 online experiment, a robot’s physical human-likeness, gender, and status were manipulated and individual differences in robot beliefs and personality traits were measured. The effects of robot traits on phobia were non-significant. Overall, subjective beliefs about what robots are, cultivated by media portrayals, whether they threaten human identity, are moral, and have agency were the strongest predictors of robophobia. Those with higher internal locus of control and neuroticism, and …

What’S In A Name And/Or A Frame? Ontological Framing And Naming Of Social Actors And Social Responses, David Westerman, Michael Vosburg, Xinyue Liu, Patric R. Spence

Human-Machine Communication

Artificial intelligence (AI) is fundamentally a communication field. Thus, the study of how AI interacts with us is likely to be heavily driven by communication. The current study examined two things that may impact people’s perceptions of socialness of a social actor: one nonverbal (ontological frame) and one verbal (providing a name) with a 2 (human vs. robot) x 2 (named or not) experiment. Participants saw one of four videos of a study “host” crossing these conditions and responded to various perceptual measures about the socialness and task ability of that host. Overall, data were consistent with hypotheses that whether …

Navigating The Rules: Integrating Td3 And Sensor Fusion For Traffic-Aware Autonomous Vehicle Path Planning, Mahmoud Ayman Mohamed Elsayed

Theses and Dissertations

This work presents a novel algorithm for local path planning for autonomous vehicles (AVs) which prioritizes both safety and adherence to traffic regulations, addressing critical functions for AV navigation, such as navigating complex environments, avoiding obstacles, and ensuring passenger and road users safety. The algorithm integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) with sensor fusion based on Nvidia Convolutional Neural Network (NCNN). The study utilizes the CARLA simulator, and real-world datasets, including KITTI and WAYMO, to train and evaluate the proposed algorithm. The proposed algorithm leverages the complementary strengths of Imitation Learning (IL) and Deep Reinforcement Learning (DRL) …

Smart Robot Design And Implementation To Assist Pedestrian Road Crossing, Hovannes Kulhandjian

Mineta Transportation Institute

This research focuses on designing and developing a smart robot to assist pedestrians with road crossings. Pedestrian safety is a major concern, as highlighted by the high annual rates of fatalities and injuries. In 2020, the United States recorded 6,516 pedestrian fatalities and approximately 55,000 injuries, with children under 16 being especially vulnerable. This project aims to address this need by offering an innovative solution that prioritizes real-time detection and intelligent decision-making at intersections. Unlike existing studies that rely on traffic light infrastructure, our approach accurately identifies both vehicles and pedestrians at intersections, creating a comprehensive safety system. Our strategy …