Robotics Commons^™

Deep Multimodal Sensor Fusion For Autonomous Driving: A Real-World Deployment On Little Blue, Raphael T. Pamie-George

Electrical & Computer Engineering Theses & Dissertations

Accurate perception and control systems are essential for autonomous vehicles to safely interpret and navigate complex environments. Individual sensing modalities such as cameras, LiDAR, and inertial measurement units (IMUs) provide valuable environmental and vehicle state information; however, each sensor exhibits inherent limitations when used independently. Camera performance can degrade under varying lighting conditions, LiDAR measurements may be sparse or affected by environmental noise, and IMU measurements are susceptible to drift over time. Little Blue, a research autonomous vehicle platform from ODU (Old Dominion University), previously relied primarily on camera-based data for neural network training, limiting the system’s ability to exploit …

Seeing What Matters: Safety-Critical Semantic Segmentation Via Transfer Learning On Construction Sites, Obiora J. Odugu

LSU Master's Theses

Autonomous robots are increasingly deployed on construction sites for tasks such as progress monitoring, inspection, and safety assessment. For these robots to operate effectively, they must perceive and interpret complex, dynamic environments populated by workers, machinery, and unstructured terrain. Achieving reliable perception depends on high performing semantic segmentation models trained on large volumes of annotated data—an expensive and logistically challenging requirement in construction due to privacy restrictions, variable site access, and slow digitalization. This research addresses the challenge of limited labeled data by investigating transfer learning as a label-efficient approach for construction-site segmentation. Specifically, it explores whether road construction imagery—abundant …

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Graph Based Deep Reinforcement Learning Aided By Transformers For Multi-Agent Cooperation, Michael S. Elrod

All Theses

Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveil- lance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particu- larly when prior information is not available. To address these challenges, I propose an innovative framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task ex- ecution. My approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient …

A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor

UNF Graduate Theses and Dissertations

Previous literature demonstrates that autonomous UAVs (unmanned aerial vehicles) have the po- tential to be utilized for wildfire surveillance. This advanced technology empowers firefighters by providing them with critical information, thereby facilitating more informed decision-making processes. This thesis applies deep Q-learning techniques to the problem of control policy design under the objective that the UAVs collectively identify the maximum number of locations that are under fire, assuming the UAVs can share their observations. The prohibitively large state space underlying the control policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from …

An In-Depth Analysis Of Domain Adaptation In Computer And Robotic Vision, Muhammad Hassan Tanveer, Zainab Fatima, Shehnila Zardari, David A. Guerra-Zubiaga

Faculty Articles

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of …

Faults Segmentation In Levee Systems Using Deep Learning Approaches, Manisha Panta

LSU New Orleans Theses and Dissertations

Levees are earthen structures constructed to mitigate flooding in low-lying areas. Although levee systems can reduce flood risks, they cannot completely eliminate them. Failures within flood control systems due to inadequate maintenance or strong water currents can lead to significant property damage and catastrophic loss of life, as was seen during Hurricane Katrina. Consequently, regular inspections are essential to identify and address any issues with the levees promptly. However, current inspection methods rely on manual techniques that are time-consuming, labor-intensive, and prone to human error. Therefore, this study proposes using deep learning models for more efficient and frequent assessment of …

Modeling And Control Strategies For A Two-Wheel Balancing Mobile Robot, John Alan Moritz

Graduate Theses and Dissertations

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan

Future Computing and Informatics Journal

In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …

Deep Learning Assisted Intelligent Visual And Vehicle Tracking Systems, Liang Xu

Theses and Dissertations

Sensor fusion and tracking is the ability to bring together measurements from multiple sensors of the current and past time to estimate the current state of a system. The resulting state estimate is more accurate compared with the direct sensor measurement because it balances between the state prediction based on the assumed motion model and the noisy sensor measurement. Systems can then use the information provided by the sensor fusion and tracking process to support more-intelligent actions and achieve autonomy in a system like an autonomous vehicle. In the past, widely used sensor data are structured, which can be directly …

Action Recognition Using The Motion Taxonomy, Maxat Alibayev

USF Tampa Graduate Theses and Dissertations

In the last years, modern action recognition frameworks with deep architectures have achieved impressive results on the large-scale activity datasets. All state-of-the-art models share one common attribute: two-stream architectures. One deep model takes RGB frames, while the other model is fed with pre-computed optical flow vectors. The outputs of both models are combined to be used as a final probability distribution for the action classes. When comparing the results of individual models with the fused model, it is common to see that that latter method is more superior. Researchers explain that phenomena with the fact that optical flow vectors serve …

Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh

Theses: Doctorates and Masters

Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.

Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …

Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali

Publications and Research

Our research focuses on building a student affordable platform for scale model self-driving cars. The goal of this project is to explore current developments of Open Source hardware and software to build a low-cost platform consisting of the car chassis/framework, sensors, and software for the autopilot. Our research will allow other students with low budget to enter into the world of Deep Learning, self-driving cars, and autonomous cars racing competitions.

A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau

Research Collection School Of Computing and Information Systems

The age of artificial intelligence is here! Artificial Intelligence, robotics, machine learning, and automation are impacting the field of marketing and sales in an unprecedented way. In this study, the qualitative research methodology will be used to better understand the revolution and evolution of marketing and sales field in the AI age. Multiple case studies will be performed in various marketing and sales units in different organizations. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the changes in marketing and sales functionalities and job markets as AI …

Review Of Deep Learning Methods In Robotic Grasp Detection, Shehan Caldera, Alexander Rassau, Douglas Chai

Research outputs 2014 to 2021

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods …