VTechWorks Repository :: Browsing by Author "Tokekar, Pratap"

Browsing by Author "Tokekar, Pratap"

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Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications
Nagabushan, Naresh (Virginia Tech, 2019-06-14)
Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability.
Autonomous Source Localization
Peterson, John Ryan (Virginia Tech, 2020-05-01)
This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments.
Cinemacraft: Exploring Fidelity Cues in Collaborative Virtual World Interactions
Narayanan, Siddharth (Virginia Tech, 2018-02-15)
The research presented in this thesis concerns the contribution of virtual human (or avatar) fidelity to social interaction in virtual environments (VEs) and how sensory fusion can improve these interactions. VEs present new possibilities for mediated communication by placing people in a shared 3D context. However, there are technical constraints in creating photo realistic and behaviorally realistic avatars capable of mimicking a person's actions or intentions in real time. At the same time, previous research findings indicate that virtual humans can elicit social responses even with minimal cues, suggesting that full realism may not be essential for effective social interaction. This research explores the impact of avatar behavioral realism on people's experience of interacting with virtual humans by varying the interaction fidelity. This is accomplished through the creation of Cinemacraft, a technology-mediated immersive platform for collaborative human-computer interaction in a virtual 3D world and the incorporation of sensory fusion to improve the fidelity of interactions and realtime collaboration. It investigates interaction techniques within the context of a multiplayer sandbox voxel game engine and proposes how interaction qualities of the shared virtual 3D space can be used to further involve a user as well as simultaneously offer a stimulating experience. The primary hypothesis of the study is that embodied interactions result in a higher degree of presence and co-presence, and that sensory fusion can improve the quality of presence and co-presence. The argument is developed through research justification, followed by a user-study to demonstrate the qualitative results and quantitative metrics.This research comprises of an experiment involving 24 participants. Experiment tasks focus on distinct but interrelated questions as higher levels of interaction fidelity are introduced.The outcome of this research is the generation of an interactive and accessible sensory fusion platform capable of delivering compelling live collaborative performances and empathetic musical storytelling that uses low fidelity avatars to successfully sidestep the 'uncanny valley'. This research contributes to the field of immersive collaborative interaction by making transparent the methodology, instruments and code. Further, it is presented in non-technical terminology making it accessible for developers aspiring to use interactive 3D media to pro-mote further experimentation and conceptual discussions, as well as team members with less technological expertise.
Coexistence of Vehicular Communication Technologies and Wi-Fi in the 5 and 6 GHz bands
Naik, Gaurang Ramesh (Virginia Tech, 2020-11-20)
The unlicensed wireless spectrum offers exciting opportunities for developing innovative wireless applications. This has been true ever since the 2.4 GHz band and parts of the 5 GHz bands were first opened for unlicensed access worldwide. In recent years, the 5 GHz unlicensed bands have been one of the most coveted for launching new wireless services and applications due to their relatively superior propagation characteristics and the abundance of spectrum therein. However, the appetite for unlicensed spectrum seems to remain unsatiated; the demand for additional unlicensed bands has been never-ending. To meet this demand, regulators in the US and Europe have been considering unlicensed operations in the 5.9 GHz bands and in large parts of the 6 GHz bands. In the last two years alone, the Federal Communications Commission in the US has added more than 1.2 GHz of spectrum in the pool of unlicensed bands. Wi-Fi networks are likely to be the biggest beneficiaries of this spectrum. Such abundance of spectrum would allow massive improvements in the peak throughput and potentially allow a considerable reduction of latency, thereby enabling support for emerging wireless applications such as augmented and virtual reality, and mobile gaming using Wi-Fi over unlicensed bands. However, access to these bands comes with its challenges. Across the globe, a wide range of incumbent wireless technologies operate in the 5 GHz and 6 GHz bands. This includes weather and military radars, and vehicular communication systems in the 5 GHz bands, and fixed-service systems, satellite systems, and television pick-up stations in the 6 GHz bands. Furthermore, due to the development of several cellular-based unlicensed technologies (such as Licensed Assisted Access and New Radio Unlicensed, NR-U), the competition for channel access among unlicensed devices has also been increasing. Thus, coexistence across wireless technologies in the 5 GHz and 6 GHz bands has emerged as an extremely challenging and interesting research problem. In this dissertation, we first take a comprehensive look at the various coexistence scenarios that emerge in the 5 GHz and 6 GHz bands as a consequence of new regulatory decisions. These scenarios include coexistence between Wi-Fi and incumbent users (both in the 5 GHz and 6 GHz bands), coexistence of Wi-Fi and vehicular communication systems, coexistence across different vehicular communication technologies, and coexistence across different unlicensed systems. Since a vast majority of these technologies are fundamentally different from each other and serve diverse use-cases each coexistence problem is unique. Insights derived from an in-depth study of one coexistence problem do not help much when the coexisting technologies change. Thus, we study each scenario separately and in detail. In this process, we highlight the need for the design of novel coexistence mechanisms in several cases and outline potential research directions. Next, we shift our attention to coexistence between Wi-Fi and vehicular communication technologies designed to operate in the 5.9 GHz intelligent transportation systems (ITS) bands. Until the development of Cellular V2X (C-V2X), dedicated short range communications (DSRC) was the only major wireless technology that was designed for communication in high-speed and potentially dense vehicular settings. Since DSRC uses the IEEE 802.11p standard for its physical (PHY) and medium access control (MAC) layers, the manner in which DSRC and Wi-Fi devices try to gain access to the channel is fundamentally similar. Consequently, we show that spectrum sharing between these two technologies in the 5.9 GHz bands can be easily achieved by simple modifications to the Wi-Fi MAC layer. Since the design of C-V2X in 2017, however, the vehicular communication landscape has been fast evolving. Because DSRC systems were not widely deployed, automakers and regulators had an opportunity to look at the two technologies, consider their benefits and drawbacks and take a fresh look at the spectrum sharing scenario. Since Wi-Fi can now potentially share the spectrum with C-V2X at least in certain regions, we take an in-depth look at various Wi-Fi and C-V2X configurations and study whether C-V2X and Wi-Fi can harmoniously coexist with each other. We determine that because C-V2X is built atop cellular LTE, Wi-Fi and C-V2X systems are fundamentally incompatible with each other. If C-V2X and Wi-Fi devices are to share the spectrum, considerable modifications to the Wi-Fi MAC protocol would be required. Another equally interesting scenario arises in the 6 GHz bands, where 5G NR-U and Wi-Fi devices are likely to operate on a secondary shared basis. Since the 6 GHz bands were only recently considered for unlicensed access, these bands are free from Wi-Fi and NR-U devices. As a result, the greenfield 6 GHz bands provide a unique and rare opportunity to freshly evaluate the coexistence between Wi-Fi and cellular-based unlicensed wireless technologies. We study this coexistence problem by developing a stochastic geometry-based analytical model. We see that by disabling the listen before talk based legacy contention mechanism---which has been used by Wi-Fi devices ever since their conception---the performance of both Wi-Fi and NR-U systems can improve. This has important implications in the 6 GHz bands, where such legacy transmissions can indeed be disabled because Wi-Fi devices, for the first time since the design of IEEE 802.11a, can operate in the 6 GHz bands without any backward compatibility issues. In the course of studying the aforementioned coexistence problems, we identified several gaps in the literature on the performance analysis of C-V2X and IEEE 802.11ax---the upcoming Wi-Fi standard. We address three such gaps in this dissertation. First, we study the performance of C-V2X sidelink mode 4, which is the communication mode in C-V2X that allows direct vehicular communications (i.e., without assistance from the cellular infrastructure). Using our in-house standards-compliant network simulator-3 (ns-3) simulator, we perform simulations to evaluate the performance of C-V2X sidelink mode 4 in highway environments. In doing so, we identify that packet re-transmissions, which is a feature introduced in C-V2X to provide frequency and time diversity, thereby improving the system performance, can have the opposite effect if the vehicular density increases. In fact, packet re-transmissions are beneficial for C-V2X system performance only at low vehicular densities. Thus, if vehicles are statically configured to always use/disable re-transmissions, the maximum potential of this feature is not realized. Therefore, we propose a simple and effective, distributed re-transmission control mechanism named Channel Congestion Based Re-transmission Control (C2RC), which leverages the locally available channel sensing results to allow vehicles to autonomously decide when to switch on re-transmissions and when to switch them off. Second, we present a detailed analysis of the performance of Multi User Orthogonal Frequency Division Multiple Access (MU OFDMA)---a feature newly introduced in IEEE 802.11ax---in a wide range of deployment scenarios. We consider the performance of 802.11ax networks when the network comprises of only 802.11ax as well as a combination of 802.11ax and legacy stations. The latter is a practical scenario, especially during the initial phases of 802.11ax deployments. Simulation results, obtained from our ns-3 based simulator, give encouraging signs for 802.11ax performance in many real-world scenarios. That being said, there are some scenarios where naive usage of MU OFDMA by an 802.11ax-capable Wi-Fi AP can be detrimental to the overall system performance. Our results indicate that careful consideration of network dynamics is critical in exploiting the best performance, especially in a heterogeneous Wi-Fi network. Finally, we perform a comprehensive simulation study to characterize the performance of Multi Link Aggregation (MLA) in IEEE 802.11be. MLA is a novel feature that is likely to be introduced in next-generation Wi-Fi (i.e., Wi-Fi 7) devices and is aimed at reducing the worst-case latency experienced by Wi-Fi devices in dense traffic environments. We study the impact of different traffic densities on the 90 percentile latency of Wi-Fi packets and identify that the addition of a single link is sufficient to substantially bring down the 90 percentile latency in many practical scenarios. Furthermore, we show that while the addition of subsequent links is beneficial, the largest latency gain in most scenarios is experienced when the second link (i.e., one additional) link is added. Finally, we show that even in extremely dense traffic conditions, if a sufficient number of links are available at the MLA-capable transmitter and receiver, MLA can help Wi-Fi devices to meet the latency requirements of most real-time applications.
Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization
Christie, Gordon A. (Virginia Tech, 2017-01-05)
Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air.
Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration
Kontoudis, Georgios Pantelis (Virginia Tech, 2022-01-25)
In this dissertation, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. The first challenge is to compute a spatial field that represents underwater acoustic communication performance from a set of measurements. We compare kriging to cokriging with vehicle range as a secondary variable using a simple approximate linear-log model of the communication performance. Next, we propose a model-based learning methodology for the prediction of underwater acoustic performance using a realistic propagation model. The methodology consists of two steps: i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters; and ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The second challenge is to perform predictions at unvisited locations with a team of agents and limited inter-agent information exchange. To decentralize the implementation of GP training, we employ the alternating direction method of multipliers (ADMM). A closed-form solution of the decentralized proximal ADMM is provided for the case of GP hyper-parameter training with maximum likelihood estimation. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. In addition, we propose a covariance-based nearest neighbor selection strategy that enables a subset of agents to perform predictions. Empirical evaluations illustrate the efficiency of the proposed methods
Competitive Algorithms and System for Multi-Robot Exploration of Unknown Environments
Premkumar, Aravind Preshant (Virginia Tech, 2017-09-08)
We present an algorithm to explore an orthogonal polygon using a team of p robots. This algorithm combines ideas from information-theoretic exploration algorithms and computational geometry based exploration algorithms. The algorithm is based on a single-robot polygon exploration algorithm and a tree exploration algorithm. We show that the exploration time of our algorithm is competitive (as a function of p) with respect to the offline optimal exploration algorithm. We discuss how this strategy can be adapted to real-world settings to deal with noisy sensors. In addition to theoretical analysis, we investigate the performance of our algorithm through simulations for multiple robots and experiments with a single robot.
Computational Approaches to Predict Effect of Epigenetic Modifications on Transcriptional Regulation of Gene Expression
Banerjee, Sharmi (Virginia Tech, 2019-10-07)
This dissertation presents applications of machine learning and statistical approaches to infer protein-DNA bindings in the presence of epigenetic modifications. Epigenetic modifications are alterations to the DNA resulting in gene expression regulation where the structure of the DNA remains unaltered. It is a heritable and reversible modification and often involves addition or deletion of certain chemical compounds to the DNA. Histone modification is an epigenetic change that involves alteration of the histone proteins – thus changing the chromatin (DNA wound around histone proteins) structure – or addition of methyl-groups to the Cytosine base adjacent to a Guanine base. Epigenetic factors often interfere in gene expression regulation by promoting or inhibiting protein-DNA bindings. Such proteins are known as transcription factors. Transcription is the first step of gene expression where a particular segment of DNA is copied into the messenger-RNA (mRNA). Transcription factors orchestrate gene activity and are crucial for normal cell function in any organism. For example, deletion/mutation of certain transcription factors such as MEF2 have been associated with neurological disorders such as autism and schizophrenia. In this dissertation, different computational pipelines are described that use mathematical models to explain how the protein-DNA bindings are mediated by histone modifications and DNA-methylation affecting different regions of the brain at different stages of development. Multi-layer Markov models, Inhomogeneous Poisson analyses are used on data from brain to show the impact of epigenetic factors on protein-DNA bindings. Such data driven approaches reinforce the importance of epigenetic factors in governing brain cell differentiation into different neuron types, regulation of memory and promotion of normal brain development at the early stages of life.
Computationally Efficient Methods for Detection and Localization of a Chirp Signal
Kashyap, Aditya (Virginia Tech, 2019-02-12)
In this thesis, a computationally efficient method for detecting a whistle and capturing it using a 4 microphone array is proposed. Furthermore, methods are developed to efficiently process the data captured from all the microphones to estimate the direction of the sound source. The accuracy, the shortcoming and the constraints of the method proposed are also discussed. There is an emphasis placed on being computationally efficient so that the methods may be implemented on a low cost microcontroller and be used to provide a heading to an Unmanned Ground Vehicle.
Control-Oriented Thermal Model for a Hybrid Vehicle Battery
Modi, Rishit Bipinkumar (Virginia Tech, 2020-06-01)
In a bid to reduce vehicular emissions, automobile manufacturers are moving towards elec- tric and hybrid vehicles. Most hybrid vehicles use Lithium-ion batteries as energy storage systems. Lithium-ion batteries have a narrow range of temperature within which they can be operated efficiently. Operation of Lithium-ion batteries outside this range decreases the life of batteries and reduces performance of the vehicle. Due to this limitation, it is important to prevent overheating of Lithium-ion batteries. Battery pack studied in this work has a fan system for air-cooling the cells. The battery management system (BMS) in the battery pack functions to keep the temperature of the cells within allowable limits by either regulating the fan speed or communicating with the vehicle controller to adjust magnitude of applied current. BMS used in the work is equipped with limited number of temperature sensors that can measure surface temperature of few cells in the battery pack. Additional temper- ature information can be used for better thermal control of the cells in the battery pack. Lithium-ion cells are known to have a measurable temperature gradient when operating un- der extreme conditions. As a result, the surface temperature of cells as measured by the temperature sensors in BMS is not always representative of the maximum cell temperature. To overcome these limitations, a simplified transient thermal model predicting core and sur- face temperature of cell is presented in this work. This model can be implemented in a BMS for real-time control of cell temperature. The thermal model is validated against data avail- able from testing the battery pack. Different current profiles, representative of real-world driving scenarios, are applied to the thermal model and the temperature rise of cells under those conditions is studied. For an array of cells, the thermal model predicts significant temperature rise along the airflow direction, suggesting the use of last cell temperature for thermal control. For short duration, high magnitude of current pulses, temperature rise is shown to be similar for same thermal energy deposited by different current pulses. The maximum thermal energy that can be deposited in the battery by a current pulse can be determined for given conditions of airflow rate, continuous current and air inlet temperature. The maximum magnitude of thermal energy that can be deposited by a peak current pulse to limit cell temperature is shown to be a function of current magnitude squared and the pulse duration time. For multiple current pulses applied to the battery pack, the model can evaluate the minimum time interval between current pulses to keep the temperature of cells within prescribed limits. The minimum time required between two current pulses is shown to decrease by increasing the airflow rate through the battery pack. By increasing the airflow rate, the battery pack is able to operate at a higher continuous current without exceeding the temperature limit.
Coverage Planning for Unmanned Aerial Vehicles
Yu, Kevin Li (Virginia Tech, 2021-06-08)
This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV) for the task of covering an environment. Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground plane by flying on a fixed altitude plane parallel to the ground. The UAV has limited battery capacity which may make it infeasible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station is proposed. A practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver is presented. Then the algorithm is extended to a coverage problem that covers 2D regions on the ground with a UAV that can operate in fixed-wing or multirotor mode. The algorithm is demonstrated through proof-of-concept experiments. Then this algorithm is applied to covering 2D regions, not all of which lie on the same plane. This is motivated by bridge inspection application, where the UAV is tasked with visually inspecting planar regions on the bridge. Finally, a general version of the problem where the UAV is allowed to fly in complete 3D space and the environment to be covered is in 3D as well is presented. An algorithm that clusters viewpoints on the surface of a 3D structure and has an UAV autonomously plan online paths to visit all viewpoints is presented. These online paths are re-planned in real time as the UAV obtains new information on the structure and strives to obtain an optimal 3D coverage path.
A Data Analytics Framework for Regional Voltage Control
Yang, Duotong (Virginia Tech, 2017-08-16)
Modern power grids are some of the largest and most complex engineered systems. Due to economic competition and deregulation, the power systems are operated closer their security limit. When the system is operating under a heavy loading condition, the unstable voltage condition may cause a cascading outage. The voltage fluctuations are presently being further aggravated by the increasing integration of utility-scale renewable energy sources. In this regards, a fast response and reliable voltage control approach is indispensable. The continuing success of synchrophasor has ushered in new subdomains of power system applications for real-time situational awareness, online decision support, and offline system diagnostics. The primary objective of this dissertation is to develop a data analytic based framework for regional voltage control utilizing high-speed data streams delivered from synchronized phasor measurement units. The dissertation focuses on the following three studies: The first one is centered on the development of decision-tree based voltage security assessment and control. The second one proposes an adaptive decision tree scheme using online ensemble learning to update decision model in real time. A system network partition approach is introduced in the last study. The aim of this approach is to reduce the size of training sample database and the number of control candidates for each regional voltage controller. The methodologies proposed in this dissertation are evaluated based on an open source software framework.
Deep Learning Models for Context-Aware Object Detection
Arefiyan Khalilabad, Seyyed Mostafa (Virginia Tech, 2017-09-15)
In this thesis, we present ContextNet, a novel general object detection framework for incorporating context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to predefined classes and regressing a bounding-box around it without using any information about the corresponding scene. ContextNet is based on an intuitive idea of having cues about the general scene (e.g., kitchen and library), and changes the priors about presence/absence of some object classes. We provide a general means for integrating this notion in the decision process about the given ROI by using a pretrained network on the scene recognition datasets in parallel to a pretrained network for extracting object-level features for the corresponding ROI. Using comprehensive experiments on the PASCAL VOC 2007, we demonstrate the effectiveness of our design choices, the resulting system outperforms the baseline in most object classes, and reaches 57.5 mAP (mean Average Precision) on the PASCAL VOC 2007 test set in comparison with 55.6 mAP for the baseline.
DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets
Rangwala, Murtaza; Liu, Jun; Ahluwalia, Kulbir Singh; Ghajar, Shayan; Dhami, Harnaik Singh; Tracy, Benjamin F.; Tokekar, Pratap; Williams, Ryan K. (MDPI, 2021-11-05)
Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.
Design and Development of a Novel Reconfigurable Wheeled Robot for Off-Road Applications
Attia, Tamer Said Abdelzaher (Virginia Tech, 2018-11-14)
Autonomous navigation with high speed in rough terrain is one of the most challenging tasks for wheeled robots. To achieve mobility over this terrain, a high agility wheeled robot should adapt and react fast to optimally traverse this challenging environment. Therefore, this dissertation is geared towards the design and development of a novel reconfigurable wheeled robot paradigm for rough terrain applications. This research focuses on the design, modeling, analysis and control of the reconfigurable wheeled robot, TIGER, with an elastic actuated mechanism for improving the robot's dynamic stability on rough terrain by controlling the robot's ground clearance, body roll and pitch angles. The elastic actuated mechanism mainly consists of a linear actuator connected in series with a shock absorber. Four sets of the elastic actuated mechanism are used to create different robot configurations to adapt to the terrain. Three main aspects were considered in this research in order to extend the ability of the robot to effectively navigate in rough terrain. The first aspect focuses on designing an agile reconfigurable wheeled robot by including an elastic actuated mechanism for improving maneuverability, longitudinal/lateral stability, and rollover prevention. Robot agility, stability, and high speed have been considered during the design process. The new design provides different configuration modes. These configurations allow for controlling the robot's Center Of Mass (COM) height and optimally distribute the vertical force on each tire for enhancing the tractive efficiency, mobility and dynamic stability. The second aspect presents the robot kinematic and dynamic modeling and analysis. The robot dynamics model is represented with fourteen degrees of freedom (DOF), where the dynamic behaviors of the robot body, suspension system, forces and moments on the tires are included. The dynamic behavior is controlled using the linear actuators' position and speed as inputs to determine the resulting ground clearance, body roll, and pitch angles. Sensors are integrated onboard the robot to calculate the robot's states in real time for use in feedback control. The third aspect focuses on introducing a technique for estimating the robot state-space dynamic model and control the Elastic Actuated Mechanism (EAM) using only a noisy Inertial Measurement Unit (IMU) with COM position uncertainty. The simulation results show that the observer estimates the actual behavior of the robot with 95% accuracy and up to 20% COM uncertainty. The Root Mean Square (RMS) has been reduced by 21% for bounce, 51% for pitch and 50% for roll acceleration.
Design and Implementation of a Scalable Real-Time Motor Controller Architecture for Humanoid Robots and Exoskeletons
Shah, Shriya (Virginia Tech, 2017-08-24)
Embedded systems for humanoid robots are required to be reliable, low in cost, scalable and robust. Most of the applications related to humanoid robots require efficient force control of Series Elastic Actuators (SEA). These control loops often introduce precise timing requirements due to the safety critical nature of the underlying hardware. Also the motor controller needs to run fast and interface with several sensors. The commercially available motor controllers generally do not satisfy all the requirements of speed, reliability, ease of use and small size. This work presents a custom motor controller, which can be used for real time force control of SEA on humanoid robots and exoskeletons. Emphasis has been laid on designing a system which is scalable, easy to use and robust. The hardware and software architecture for control has been presented along with the results obtained on a novel Series Elastic Actuator based humanoid robot THOR.
Development of Ground-Level Hyperspectral Image Datasets and Analysis Tools, and their use towards a Feature Selection based Sensor Design Method for Material Classification
Brown, Ryan Charles (Virginia Tech, 2018-08-31)
Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in the image. This hardware has not been studied in the context of autonomy as the sensors are large, expensive, and have non-trivial image capture times. This work presents three novel contributions: a Labeled Hyperspectral Image Dataset (LHID) of ground-level, outdoor objects based on typical scenes that a vehicle or pedestrian may encounter, an open-source hyperspectral interface software package (HSImage), and a feature selection based sensor design algorithm for object detection sensors (DLSD). These three contributions are novel and useful in the fields of hyperspectral data analysis, visual sensor design, and hyperspectral machine learning. The hyperspectral dataset and hyperspectral interface software were used in the design and testing of the sensor design algorithm. The LHID is shown to be useful for machine learning tasks through experimentation and provides a unique data source for hyperspectral machine learning. HSImage is shown to be useful for manipulating, labeling and interacting with hyperspectral data, and allows wavelength and classification based data retrieval, storage of labeling information and ambient light data. DLSD is shown to be useful for creating wavelength bands for a sensor design that increase the accuracy of classifiers trained on data from the LHID. DLSD shows accuracy near that of the full spectrum hyperspectral data, with a reduction in features on the order of 100 times. It compared favorably to other state-of-the-art wavelength feature selection techniques and exceeded the accuracy of an RGB sensor by 10%.
Directional Airflow for HVAC Systems
Abedi, Milad (Virginia Tech, 2019)
Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.
Distributed, Stable Topology Control of Multi-Robot Systems with Asymmetric Interactions
Mukherjee, Pratik (Virginia Tech, 2021-06-17)
Multi-robot systems have recently witnessed a swell in interest in the past few years because of their various applications such as agricultural autonomy, medical robotics, industrial and commercial automation and, search and rescue. In this thesis, we particularly investigate the behavior of multi-robot systems with respect to stable topology control in asymmetric interaction settings. From theoretical perspective, we first classify stable topologies, and identify the conditions under which we can determine whether a topology is stable or not. Then, we design a limited fields-of-view (FOV) controller for robots that use sensors like cameras for coordination which induce asymmetric robot to robot interactions. Finally, we conduct a rigorous theoretical analysis to qualitatively determine which interactions are suitable for stable directed topology control of multi-robot systems with asymmetric interactions. In this regard, we solve an optimal topology selection problem to determine the topology with the best interactions based on a suitable metric that represents the quality of interaction. Further, we solve this optimal problem distributively and validate the distributed optimization formulation with extensive simulations. For experimental purposes, we developed a portable multi-robot testbed which enables us to conduct multi-robot topology control experiments in both indoor and outdoor settings and validate our theoretical findings. Therefore, the contribution of this thesis is two fold: i) We provide rigorous theoretical analysis of stable coordination of multi-robot systems with directed graphs, demonstrating the graph structures that induce stability for a broad class of coordination objectives; ii) We develop a testbed that enables validating multi-robot topology control in both indoor and outdoor settings.
Empirical Analysis of Algorithms for the k-Server and Online Bipartite Matching Problems
Mahajan, Rutvij Sanjay (Virginia Tech, 2018-08-14)
The k–server problem is of significant importance to the theoretical computer science and the operations research community. In this problem, we are given k servers, their initial locations and a sequence of n requests that arrive one at a time. All these locations are points from some metric space and the cost of serving a request is given by the distance between the location of the request and the current location of the server selected to process the request. We must immediately process the request by moving a server to the request location. The objective in this problem is to minimize the total distance traveled by the servers to process all the requests. In this thesis, we present an empirical analysis of a new online algorithm for k-server problem. This algorithm maintains two solutions, online solution, and an approximately optimal offline solution. When a request arrives we update the offline solution and use this update to inform the online assignment. This algorithm is motivated by the Robust-Matching Algorithm [RMAlgorithm, Raghvendra, APPROX 2016] for the closely related online bipartite matching problem. We then give a comprehensive experimental analysis of this algorithm and also provide a graphical user interface which can be used to visualize execution instances of the algorithm. We also consider these problems under stochastic setting and implement a lookahead strategy on top of the new online algorithm.

Browsing by Author "Tokekar, Pratap"

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