This thesis describes the current state of development of the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at the Virginia Tech Transportation Institute (VTTI). The FLASH lab and the scale model cars contained therein provide a testbed for the small scale development stage of intelligent transportation systems (ITS). In addition, the FLASH lab serves as a home to the prototype display being developed for an educational museum exhibit. This thesis also gives details of the path following lateral controller implemented on the FLASH car. The controller was developed using the kinematic model for a wheeled robot. The global model is converted into the path coordinate model so that only local variables are needed. then the path coordinate model is converted into chained form and a controller is given to perform path following. The path coordinate model introduces a new parameter to the system: the curvature of the path. Thus, it is necessary to provide the path's curvature value to the controller. Because of the environment in which the car is operating, the curvature values are known a priori. Several online methods for determining the curvature are developed. A MATLAB simulation environment was created with which to test the above algorithms. The simulation uses the kinematic model to show the car's behavior and implements the sensors and controller as closely as possible to the actual system. The implementation of the lateral controller in hardware is discussed. The vehicle platform is described and the harware and software architecture detailed. The car described is capable of operating manually and autonomously. In autonomous mode, several sensors are utilized including: infrared, magnetic, ultrasound, and image based technology. The operation of each sensor type is described and the information received by the processor from each is discussed. / Master of Science

The navigation and control of an autonomous vehicle is a highly complex task. Making a vehicle intelligent and able to operate "unmanned" requires extensive theoretical as well as practical knowledge. An autonomous vehicle must be able to make decisions and respond to situations completely on its own. Navigation and control serves as the major limitation of the overall performance, accuracy and robustness of an autonomous vehicle. This thesis will address this problem and propose a unique navigation and control scheme for an autonomous lawn mower (ALM). Navigation is a key aspect when designing an autonomous vehicle. An autonomous vehicle must be able to sense its location, navigate its way toward its destination, and avoid obstacles it encounters. Since this thesis attempts to automate the lawn mowing process, it will present a navigational algorithm that covers a bounded region in a systematic way, while avoiding obstacles. This algorithm has many applications including search and rescue, floor cleaning, and lawn mowing. Furthermore, the robustness and utility of this algorithm is demonstrated in a 3D simulation. This thesis will specifically study the dynamics of a two-wheeled differential drive vehicle. Using this dynamic model, various control techniques can then be applied to control the movement of the vehicle. This thesis will consider both open loop and closed loop control schemes. Optimal control, path following, and trajectory tracking are all considered, simulated, and evaluated as practical solutions for control of an ALM. To design and build an autonomous vehicle requires the integration of many sensors, actuators, and controllers. Software serves as the glue to fuse all these devices together. This thesis will suggest various sensors and actuators that could be used to physically implement an ALM. This thesis will also describe the operation of each sensor and actuator, present the software used to control the system, and discuss physical limitations and constraints that might be encountered while building an ALM. / Master of Science

As technology advances in all areas of society and industry, the technology used to produce one of life's essentials - food - is also improving. The majority of agriculture production in developed countries has gone from family farms to industrial operations. With the advent of large-scale farming, the automation of basic farming operations has increasingly made practical and economic sense. Broccoli, which is still harvested by hand, is one of the most expensive crops to produce. Investing in sensing technology that can provide detailed information about the location, maturity and viability of broccoli heads has the potential to produce great commercial benefits. This technology is also a prerequisite for developing an autonomous harvester that could select and harvest mature heads of broccoli. This thesis details the work done to develop a computer vision algorithm that has the ability to locate the broccoli head within an image of an entire broccoli plant and to distinguish between mature and immature broccoli heads. Locating the head involves the use of a Hough transform to find the leaf stems and, once the stems are found, the location and extent of the broccoli head can be ascertained with the use of contrast texture analysis at the intersection of the stems. A co-occurrence matrix is then produced of the head and statistical texture analysis is performed to determine the maturity of the broccoli head. The conceptual design of a selective autonomous broccoli harvester, as well as suggestions for further research, is also presented. / Master of Science

Recent advances in technology have enabled the development of truly autonomous ground vehicles capable of performing complex navigation tasks. As a result, the demand for practical unmanned ground vehicle (UGV) systems has increased dramatically in recent years. Central to these developments is maturation of emerging mobile robotic intelligent and autonomous capability. While the progress UGV technology has been substantial, there are many challenges that still face unmanned vehicle system developers. Foremost is the improvement of perception hardware and intelligent software that supports the evolution of UGV capability. The development of a Next-generation Experimentation Robotic Vehicle (NERV) serves to provide a small UGV baseline platform supporting experimentation focused on progression of the state-of-the-art in unmanned systems. Supporting research and user feedback highlight the needs that provide justification for an advanced small UGV research platform. Primarily, such a vehicle must be based upon open and technology independent system architecture while exhibiting improved mobility over relatively structured terrain. To this end, a theoretical kinematic model is presented for a novel two-body multi degree-of-freedom, four-wheel drive, small UGV platform. The efficacy of the theoretical kinematic model was validated through computer simulation and experimentation on a full-scale proof-of-concept mobile robotic platform. The kinematic model provides the foundation for autonomous multi-body control. Further, a modular system level design based upon the concepts of the Joint Architecture for Unmanned Systems (JAUS) is offered as an open architecture model providing a scalable system integration solution. Together these elements provide a blueprint for the development of a small UGV capable of supporting the needs of a wide range of leading-edge intelligent system research initiatives. / Master of Science

The wireless technology of today provides combat systems with the potential to communicate mission critical data to every asset involved in the operation. In such a dynamic environment, the network must be able maintain communication by adapting to subsystems moving relative to each other. A theoretical and experimental foundation is developed that allows an autonomous ground vehicle to serve as an adaptive communication node in a larger network. The vehicle may perform other functions, but its primary role is to constantly reposition itself to maintain optimal link quality for network communication. Experimentation with existing wireless network hardware and software led to the development, implementation, and analysis of two main concepts that provided a signal optimization solution. The first attracts the communication ground vehicle to the network subsystems with weaker links using a vector summation of the signal-to-noise ratio and network subsystem position. This concept continuously generates a desired waypoint for repositioning the ground vehicle. The second concept uses a-priori GIS data to evaluate the desired vehicle waypoint determined by the vector sum. The GIS data is used primarily for evaluating the viewshed, or line-of-sight, between two network subsystems using elevation data. However, infrastructure and ground cover data are also considered in navigation planning. Both concepts prove to be powerful tools for effective autonomous repositioning for maximizing the communication link quality. / Master of Science

This thesis describes the design of an inexpensive autonomous vehicle system using a small scaled model vehicle. The system is capable of operating in two different modes: telerobotic manual mode and automated driving mode. In telerobotic manual mode, the model vehicle is controlled by a human driver at a stationary remote control station with full-scale steering wheel and gas pedal. The vehicle can either be an unmodified toy remote-control car or a vehicle equipped with wireless radio modem for communication and microcontroller for speed control. In both cases the vehicle also carries a video camera capable of transmitting video images back to the remote control station where they are displayed on a monitor. In automated driving mode, the vehicle's lateral movement is controlled by a lateral control algorithm. The objective of this algorithm is to keep the vehicle in the center of a road. Position and orientation of the vehicle are determined by an image processing algorithm identifying a white middle marker on the road. Two different algorithm for image processing have been designed: one based on the pixel intensity profile and the other on vanishing points in the image plane. For the control algorithm itself, two designs are introduced as well: a simple classical P-control and a control scheme based on H-Infinity. The design and testing of this autonomous vehicle system are performed in the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at Virginia Tech. / Master of Science

O desenvolvimento tecnológico e o elevado investimento em tecnologias de veículos \"inteligentes\" vão, provavelmente, transformar veículos autônomos em realidade em alguns anos. A inserção de inteligência em veículos rodoviários visa obter uma redução nos acidentes de trânsito devido à mitigação de erros cometidos por motoristas humanos, graças à sua substituição por máquinas. Além disso, os veículos autônomos devem ser capazes de mitigar os perigos existentes nos sistemas de transporte rodoviário, sem criar novos riscos. Assim, é importante a pesquisa de como garantir a segurança crítica (safety) nesse novo cenário. Algumas pesquisas nesta área já vêm sendo desenvolvidas, porém elas não mostram como projetar um sistema veicular autônomo no qual se possa aplicar métodos já existentes para analisar e garantir níveis de segurança adequados em tais veículos. Frente a isso, este trabalho de mestrado desenvolve uma proposta que visa facilitar o desenvolvimento e a análise dessa nova classe de veículos, além de assegurar níveis de segurança crítica adequados aos veículos autônomos. A proposta é representada por um sistema denominado Controle Veicular Autônomo (CVA), o qual foi desenvolvido sob o conceito de Sistemas de Transporte Inteligentes (STI). O sistema CVA é formado por duas camadas, uma de operação (Operação Veicular Autônoma - OVA), responsável pela condução do veículo e outra de proteção (Proteção Veicular Autônoma - PVA). A ideia principal é que se utilize a camada PVA para a prevenção de acidentes. A camada PVA foi desenvolvida e testada em um ambiente de simulação, considerando um Estudo de Caso. Observou-se que, conforme previsto, o sistema CVA, por possuir uma camada voltada para a proteção veicular, conseguiu evitar diversas situações de colisões entre veículos. / Technological development and the massive investment in \'intelligent\' vehicle technologies are likely to turn autonomous vehicles into reality in a few years. The insertion intelligence in road vehicles aims to obtain a reduction in traffic accidents due to the mitigation of errors committed by human drivers, thanks to their replacement by machines. In addition, autonomous vehicles should be able to mitigate hazards in road transportation systems without creating new risks. Thus, It is important to study how to ensure safety in this new scenario. Some research in this area has already been developed, but they do not show how to design properly an autonomous vehicle system in which existing methods can be applied to analyze and guarantee adequate levels of safety in such vehicles. As a result, this master\'s work develops a proposal that aims to facilitate the development and analysis of this new class of vehicles, in addition to ensuring levels of critical safety appropriate to autonomous vehicles. The proposal is represented by a system called Autonomous Vehicle Control (CVA), which was developed under the concept of Intelligent Transport Systems (STI). The CVA system is formed by two layers, one of operation (Autonomous Vehicle Operation - OVA), responsible for the driving of the vehicle and another one of protection (Autonomous Vehicle Protection - PVA). The main idea is to use the PVA layer for the prevention of accidents. The PVA layer was developed and tested in a simulation environment, considering a Case Study. It was observed that, as predicted, the CVA system, because it has a layer aimed at vehicular protection, was able to avoid several collision situations between vehicles.

O desenvolvimento tecnológico e o elevado investimento em tecnologias de veículos \"inteligentes\" vão, provavelmente, transformar veículos autônomos em realidade em alguns anos. A inserção de inteligência em veículos rodoviários visa obter uma redução nos acidentes de trânsito devido à mitigação de erros cometidos por motoristas humanos, graças à sua substituição por máquinas. Além disso, os veículos autônomos devem ser capazes de mitigar os perigos existentes nos sistemas de transporte rodoviário, sem criar novos riscos. Assim, é importante a pesquisa de como garantir a segurança crítica (safety) nesse novo cenário. Algumas pesquisas nesta área já vêm sendo desenvolvidas, porém elas não mostram como projetar um sistema veicular autônomo no qual se possa aplicar métodos já existentes para analisar e garantir níveis de segurança adequados em tais veículos. Frente a isso, este trabalho de mestrado desenvolve uma proposta que visa facilitar o desenvolvimento e a análise dessa nova classe de veículos, além de assegurar níveis de segurança crítica adequados aos veículos autônomos. A proposta é representada por um sistema denominado Controle Veicular Autônomo (CVA), o qual foi desenvolvido sob o conceito de Sistemas de Transporte Inteligentes (STI). O sistema CVA é formado por duas camadas, uma de operação (Operação Veicular Autônoma - OVA), responsável pela condução do veículo e outra de proteção (Proteção Veicular Autônoma - PVA). A ideia principal é que se utilize a camada PVA para a prevenção de acidentes. A camada PVA foi desenvolvida e testada em um ambiente de simulação, considerando um Estudo de Caso. Observou-se que, conforme previsto, o sistema CVA, por possuir uma camada voltada para a proteção veicular, conseguiu evitar diversas situações de colisões entre veículos. / Technological development and the massive investment in \'intelligent\' vehicle technologies are likely to turn autonomous vehicles into reality in a few years. The insertion intelligence in road vehicles aims to obtain a reduction in traffic accidents due to the mitigation of errors committed by human drivers, thanks to their replacement by machines. In addition, autonomous vehicles should be able to mitigate hazards in road transportation systems without creating new risks. Thus, It is important to study how to ensure safety in this new scenario. Some research in this area has already been developed, but they do not show how to design properly an autonomous vehicle system in which existing methods can be applied to analyze and guarantee adequate levels of safety in such vehicles. As a result, this master\'s work develops a proposal that aims to facilitate the development and analysis of this new class of vehicles, in addition to ensuring levels of critical safety appropriate to autonomous vehicles. The proposal is represented by a system called Autonomous Vehicle Control (CVA), which was developed under the concept of Intelligent Transport Systems (STI). The CVA system is formed by two layers, one of operation (Autonomous Vehicle Operation - OVA), responsible for the driving of the vehicle and another one of protection (Autonomous Vehicle Protection - PVA). The main idea is to use the PVA layer for the prevention of accidents. The PVA layer was developed and tested in a simulation environment, considering a Case Study. It was observed that, as predicted, the CVA system, because it has a layer aimed at vehicular protection, was able to avoid several collision situations between vehicles.

abstract: Lidar has demonstrated its utility in meteorological studies, wind resource assessment, and wind farm control. More recently, lidar has gained widespread attention for autonomous vehicles. The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted. The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018). / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2019

Dans le cadre de cette thèse, nous nous intéressons à la coordination et l'optimisation du trafic aux intersections des réseaux routiers, avec la particularité de considérer des véhicules autonomes intelligents. Cette thèse est organisée en deux grandes parties. La première se concentre sur le problème du partage d'un espace de voie par deux files de véhicules évoluant en sens opposés. L'état de l'art montre le peu de travaux abordant cette question. Nous explorons deux approches par coordination réactive, en relation avec un critère de minimisation des retards. Les performances de ces approches ont été mesurées statistiquement en simulation. La deuxième partie de la thèse s'attaque au problème générique de la gestion du trafic au sein d'un réseau routier. Nous développons une approche originale à deux égards: d'une part elle explore un principe de passage en alternance des flux permettant de ne pas arrêter les véhicules aux intersections, et d'autre part, elle propose des algorithmes d'optimisationdistribuée de ce passage alterné au niveau de chaque intersection et au niveau du réseau global. La thèse présente successivement les choix de modélisation, les algorithmes et l'étude en simulation de leurs performances comparées à desapproches existantes / In this thesis, we focus on traffic coordination and optimization in road intersections, while accounting for intelligent autonomous vehicles. This thesis is organized in two parts. The first part focuses on the problem of sharing a one-lane road between two opposite flows of vehicles. The state of the art shows few studies addressing this issue. We propose two reactive coordination approaches that minimize vehicle delays and measure their performances statistically through simulations. The second part of the thesis addresses the problem of generic traffic management in a traffic network. We develop a stop-free approach that explores a principle alternating vehicles between flows at intersections, and it provides distributed algorithms optimizing this alternation at each intersection and in the overall network. We present the modeling choices, the algorithms and the simulation study of our approach and we compare its performances with existing approaches