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

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

This dissertation addresses path planning for an autonomous vehicle navigating in a two dimensional environment for which an a priori map is inaccurate and for which the environment is sensed in real-time. For this class of application, planning decisions must be made in real-time. This work is motivated by the need for fast autonomous vehicles that require planning algorithms to operate as quickly as possible. In this dissertation, we first study the case in which there are only static obstacles in the environment. We propose a hybrid receding horizon control path planning algorithm that is based on level-set methods. The hybrid method uses global or local level sets in the formulation of the receding horizon control problem. The decision to select a new level set is made based on certain matching conditions that guarantee the optimality of the path. We rigorously prove sufficient conditions that guarantee that the vehicle will converge to the goal as long as a path to the goal exists. We then extend the proposed receding horizon formulation to the case when the environment possesses moving obstacles. Since all of the results in this dissertation are based on level-set methods, we rigorously investigate how level sets change in response to new information locally sensed by a vehicle. The result is a dynamic fast marching algorithm that usually requires significantly less computation that would otherwise be the case. We demonstrate the proposed dynamic fast marching method in a successful field trial for which an autonomous surface vehicle navigated four kilometers through a riverine environment. / Ph. D.

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

Despite the advancements in the development of autonomous vehicles (AVs), there are still numerous complex situations in which AVs may encounter challenges. In recent years, the concept of teleoperation, which entails establishing a connection between a remote operator and the AV, has garnered substantial attention from both AV companies and governmental bodies as a viable safety backup method. However, a research gap is apparent when it comes to the remote manipulation of AVs positioned at a considerable distance. This gap involves a) AV with a temporal delay through real-time direct control within the constraints of current wireless communication technology in an unpredictable road environment, and b) enhancing the AV's inherent detection capabilities to augment its autonomous control abilities, thereby reducing the operator's workload. To address this research gap, this dissertation introduces an innovative teleoperation system. Initially, we devise a control system utilizing the wave variable approach as a communication method to alleviate the impact of signal latency. And Radial Basis Function Networks (RBFN) are employed to effectively manage the uncertain nonlinear dynamics of the vehicle. Subsequently, a saliency-based object detection (OD) algorithm, named SalienDet, is proposed to identify objects not present in the training sample set. SalienDet incorporates saliency maps generated without prior information into the neural network, enhancing image features for unfamiliar objects. This augmentation significantly aids the OD algorithm in detecting previously unknown objects, thereby empowering the AV to possess an improved perception ability. This advancement is particularly valuable when the operator imparts driving advice to the AV instead of exercising direct control. In conclusion, this dissertation makes a noteworthy contribution to AV teleoperation by furnishing a comprehensive solution that spans various aspects of AV teleoperation. / Doctor of Philosophy / This dissertation revolves around the teleoperation of autonomous vehicles (AVs), with the objective of formulating a comprehensive teleoperation system that encompasses two critical aspects: direct control and indirect control. In the initial segment of the dissertation, we introduce a real-time teleoperation direct control system based on neural networks. This framework plays a pivotal role in assisting operators in navigating AVs efficiently, especially in the face of challenges such as communication delays and complex external environments. Following this, we present a novel saliency-based object detection (OD) algorithm. This algorithm empowers the AV to recognize potential objects beyond its prior knowledge, thereby enhancing its level of autonomous control, particularly when operators opt not to exercise direct control over the remote AV. Our research findings delve into the essential facets of AV teleoperation. The developed teleoperation system serves as a valuable reference for future researchers and engineers dedicated to advancing autonomous vehicle technology.

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.