Society has an every increasing thirst for electrical energy; this is only set to increase as the 21st Century progresses. In order to sustain this increasing demand, the power industry needs to consider a number of factors; adding generation capacity and maintaining the transmission and distribution networks that connect the producers to the consumers. This work focuses on the development of systems to aid maintenance operations. Parts of the transmission network in the UK date back to the 1950's and 60's, consisting of over 22,500 circuit km of overhead lines. The monitoring of this network is a significant ongoing task and needs to locate potential problems prior to failure. Numerous assessment techniques are presented in literature which discuss the examination of line components from the air or ground using the visual, infra-red or ultra-violet spectrums. Of particular interest in this work is the live-line inspection of composite insulators; thereby aligning with other ongoing work at The University of Manchester. While existing techniques have proved adequate to date, not all insulator surfaces can be appropriately seen. The ideal solution would be a device capable of photographing all insulator surfaces from a camera mounted on the insulator itself. While a number of live-line robotic systems are both in development and use around the world, operation and performance information is lacking; possibly due to commercial sensitivity issues. This work aims to clarify this situation, in particular focusing on the nature of broadband communication from, and survivability of complex electronics in areas of intense electric field strength and partial discharges. These areas are explored through the development of a technology demonstrator, a robot capable of imaging composite insulator surfaces in real-time and transmitting them to a ground station. Knowledge gained can then be adapted to create systems for other high-voltage monitoring situations. A systems level approach is taken whereby the technology demonstrator is divided into its constituent functional components. The requirements of each are assessed and research and development needs are detailed. Literature is reviewed to collate existing knowledge and enable comparison with the envisaged requirements. Prototype systems are developed to test the selected communication mechanism under high voltage conditions, while designs are created and fabricated for imaging and mechanical needs. The separate systems are then combined into the technology demonstrator and examined as a single unit under energised conditions. The author presents extensive results on the capability and nature of broadband radio frequency communication from areas of high electric field strength and partial discharges. They show that high data rates from such environments is possible up to a certain point at which high enhanced shield and antenna protection needs to be considered. They additionally demonstrate the transmission of live video from an energised composite insulator. This knowledge can be used to both improve the current system and as a basis to create additional monitoring solutions for high voltage situations. As such a new method of electric field distribution assessment is proposed.

Autonomous firefighting platforms are being developed to support firefighters.  One aspect of this is location of a fire inside a structure.  A multi-spectral sensor platform and fire location algorithm was developed in this research to locate a fire indoors autonomously. The multi-spectral sensor platform used a long wavelength infrared (LWIR) camera and ultraviolet (UV) sensor.  The LWIR camera was chosen for its ability to see through smoke, while the UV sensor was selected for its ability to discriminate between fires and non-fire hot objects.  The fire location algorithm by radiation emission (FLARE) developed in this research used the multi-spectral sensor data to provide the robot heading angle toward the fire. The system was tested in a large-scale structural fire facility.  A series of 20 different scenarios were used to evaluate the robustness of the system including different fuel types, structural features, non-fire hot objects, and potential robot positions within the enclosure.  This demonstrated that FLARE could direct a robot towards the fire regardless of these variables. Directional fire discrimination was added to the platform by limiting the field of view of the UV sensor to that of the LWIR cameras.  Three methods were evaluated to limit the field of view of a UV sensor. These included angled plate housing, bulb cover, and slit opening housing methods. The slit opening housing method was recommended for ease of implementation and size required to limit the field of view of the sensor to the desired value. / Master of Science

Unmanned ground vehicles (UGVs) have been widely used in many areas such as agriculture, mining, construction and military applications. This results from the fact that UGVs can not only be easily built and controlled, but also be featured with high mobility and handling hazardous situations in complex environments. Among the competences of UGVs, autonomous navigation is one of the most challenging problems. This is because that the success in achieving autonomous navigation depends on four factors: Perception, localization, cognition, and proper motion controller. In this thesis, we introduce the realization of autonomous navigation for a two-wheeled differential ground robot under the robot operating system (ROS) environment from both the simulation and experimental perspectives. In Chapter 2, the simulation work is discussed. Firstly, the robot model is described in the unified robot description format (URDF)-based form and the working environment for the robot is simulated. Then we use the \textit{gmapping} package which is one of the packages integrating simultaneous localization and mapping (SLAM) algorithm to build the map of the working environment. In addition, ROS packages including \textit{tf}, \textit{move\_base}, \textit{amcl}, etc., are used to realize the autonomous navigation. Finally, simulation results show the feasibility and effectiveness of the autonomous navigation system for the two-wheeled UGV with the ability to avoid collisions with obstacles. In Chapter 3, we introduce the experimental studies of implementing autonomous navigation for a two-wheeled UGV. The necessary hardware peripherals on the UGV to achieve autonomous navigation are given. The process of implementation in the experiment is similar to that in simulation, however, calibration of several devices is necessary to adapt the scenario in a practical environment. Additionally, a proportional-integral-derivative (PID) controller for the robot base is used to handle the external noise during the experiment. The experimental results demonstrate the success in the implementation of autonomous navigation for the UGV in practice. / Graduate

No description available.

Computer vision has been developed rapidly in the last few decades and it has been used in a variety of fields such as robotics, autonomous vehicles, traffic surveillance camera etc. nowadays. However, when we process these high-resolution raw materials from the cameras, it brings a heavy burden to the processors. Because of the physical architecture of the CPU, the pixels of the input image should be processed sequentially. So even if the computation capability of modern CPUs is increasing, it is still unable to make a decent performance in repeating one single work millions of times. The objective of this thesis is to give an alternative solution to speed up the execution time of processing images through integrating popular image recognition algorithms (SURF and FREAK) on GPUs with the help of CUDA platform developed by NVIDIA, to speed up the recognition time. The experiments were made to compare the performances between traditional CPU-only program and CUDA program, and the result show the algorithms running on CUDA platform have a significant speedup. / Thesis / Master of Applied Science (MASc)

The automotive industry is seen to be making a monumental paradigm shift from manual to semi-autonomous to fully Autonomous Vehicles. An Advanced Driver Assistance System (ADAS) forms a major building block for realizing these next generation of highly Autonomous-capable Vehicles. Although the general ADAS architecture is widely discussed, limited details are available about the functionality of the modules and their interactions, backed up by scientific justification. This limits the utilization of such an architecture for pragmatic implementation. A Cognitive ADAS Architecture for level 4 Autonomous-capable Electrified Vehicles (EV) is proposed in this thesis. Variations for levels 3 and 3.5 (combination of levels 3 and 4, with the primary fallback through a human driver and the secondary through an Automated Driving System) are also presented. A validated simulation framework is built for highway driving based on the proposed level 4 architecture for an enhanced Tesla Model S. It was concluded that the autonomous control provided a 28% energy economy increase, on average, compared to human driver control. Through a quantitative sensitivity analysis, the optimal Mission/Motion Planning and energy management are seen in addition to a positive impact on the EV battery, motor, and dynamics, realized from the minimized instantaneous fluctuations. These factors are considered to contribute to this significant increase in the energy economy of an autonomous-controlled EV. Furthermore, this impact was seen to be relatively higher for autonomous longitudinal vehicle control compared to lateral. This difference in the improved operation of the Autonomous-capable EV components between the Automated Driving System and the human driver control was seen to be the highest for the battery current. In overall, an increase in vehicle autonomy, resulted in an improvement in the EV performance, dynamics and operation of the battery and motor, compared to a human driver control. / Thesis / Doctor of Philosophy (PhD)

<p dir="ltr">The growth of electric vehicles (EVs) offers several benefits for air quality improvement and emissions reduction. Nonetheless, EVs also pose several challenges in the area of highway transportation. These barriers are related to the limitations of EV technology, particularly the charge duration and speed of battery recharging, which translate to vehicle range anxiety for EV users. A promising solution to these concerns is V2V DWC technology (Vehicle to Vehicle Dynamic Wireless Charging), particularly mobile energy disseminators (MEDs). The MED is mounted on a large vehicle or truck that charges all participating EVs within a specified locus from the MED. However, current research on MEDs offers solutions that are widely considered impractical for deployment, particularly in urban environments where range anxiety is common. Acknowledging such gap in the literature, this thesis proposes a comprehensive methodological framework for optimal MED deployment decisions. In the first component of the framework, a practical system, termed “ChargingEnv” is developed using reinforcement learning (RL). ChargingEnv simulates the highway environment, which consists of streams of EVs and an MED. The simulation accounts for a possible misalignment of the charging panel and incorporates a realistic EV battery model. The second component of the framework uses multiple deep RL benchmark models that are trained in “ChargingEnv” to maximize EV service quality within limited charging resource constraints. In this study, numerical experiments were conducted to demonstrate the MED deployment decision framework’s efficacy. The findings indicate that the framework’s trained model can substantially improve EV travel range and alleviate battery depletion concerns. This could serve as a vital tool that allows public-sector road agencies or private-sector commercial entities to efficiently orchestrate MED deployments to maximize service cost-effectiveness.</p>

Uncrewed aerial systems (UAS) have steadily become more prevalent in both defense and industrial applications. Nuclear detection and deterrence is one such field that has given rise to many new opportunities for UAS operations. There is a need to research and develop methods to integrate existing radiation detection technology with UAS capable of flying low-altitude missions. This low-altitude scanning can be achieved by combining small and lightweight radiation detectors and state-of-the-art aircraft and avionics. High resolution mapping can then be conducted using the results of these scans. Significant work has been conducted in this field by both private industry and academic institutions, including the Uncrewed Systems Lab (USL) at Virginia Tech. This work seeks to expand this body of knowledge and provide practical experimental information to showcase and validate the efficacy of radiation detection via UAS. Multiple missions were conducted using samples of 137Cs and 60Co as a radioactive source. Various filtering methods were applied to the results of these missions to produce visual maps that aid in the localization of an unknown source to compare various flight parameters. In addition, significant work was conducted to characterize two radiation detectors available to the USL to provide metrics to assist in the UAS design and flight planning. Finally, the detectors were taken to Savannah River National Laboratories to conduct experiments to provide information to aid future designs and missions that wish to detect a wider variety of radioactive sources. / Master of Science / Drones are becoming more common in many applications for both industry and defense. One of these applications is in the field of nuclear detection which seeks to both regulate the shipping of radioactive material as well as aid response to nuclear disasters. Methods need to be researched to combine existing radiation detectors with new drone technology. These new state-of-the-art drones are capable of flying at very low altitudes which can allow for the use of small and lightweight radiation detectors. Past work in this area, including at the Uncrewed Systems Lab (USL) at Virginia Tech, has explored larger scale aircraft as well as simulated radioactive sources. This work expands the existing knowledge of this field by providing scan results from real radioactive sources and drone flights. Multiple search flights were conducted using small quantities of radioactive cesium and cobalt. Maps were then produced using the information from these flights to showcase the system's ability to quickly locate the areas of high radioactivity. Flights were flown with different altitudes and speeds to determine the effects on mapping accuracy. Finally, experiments were conducted at Savannah River National Laboratories on a variety of more controlled nuclear materials to help inform future drone designs and mission planning.

A future limitation of autonomous ground vehicle technology is the inability of current algorithmic techniques to successfully predict the allowable dynamic operating ranges of unmanned ground vehicles. A further difficulty presented by real vehicles is that the payloads may and probably will change with unpredictably time as will the terrain on which it is expected to operate. To address this limitation, a methodology has been developed to generate real-time estimations of a vehicle's instantaneous Maneuvering Manifold. This approach uses force-moment method techniques to create an adaptive, parameterized vehicle model. A technique is developed for estimation of vehicle load state using internal sensors combined with low-magnitude maneuvers. An unscented Kalman filter based estimator is then used to estimate tire forces for use in determining the ground/tire coefficient of friction. Probabilistic techniques are then combined with a combined-slip pneumatic trail based estimator to estimate the coefficient of friction in real-time. This data is then combined to map out the instantaneous maneuvering manifold while applying techniques to account for dynamic rollover and stability limitations. The algorithms are implemented in MATLAB, simulated against TruckSim models, and results are shown to demonstrate the validity of the techniques. The developed methodology is shown to be a novel approach that is capable of addressing the problem of successfully estimating the available maneuvering manifold for autonomous ground vehicles. / Ph. D.

Many applications for unmanned vehicles involve autonomous interaction between two or more craft, and therefore, relative navigation is a key issue to explore. Several high fidelity hardware simulations exist to produce accurate dynamics. However, these simulations are restricted by size, weight, and power needed to operate them. The use of a small Unmanned Ground Vehicle (UGV) for the relative navigation problem is investigated. The UGV has the ability to traverse large ranges over uneven terrain and into varying lighting conditions which has interesting applications to relative navigation. The basic problem of a vehicle following another is researched and a possible solution explored. Statistical pressure snakes are used to gather relative position data at a specified frequency. A cubic spline is then fit to the relative position data using a least squares algorithm. The spline represents the path on which the lead vehicle has already traversed. Controlling the UGV onto this relative path using a sliding mode control, allows the follow vehicle to avoid the same stationary obstacles the lead vehicle avoided without any other sensor information. The algorithm is run on the UGV hardware with good results. It was able to follow the lead vehicle around a curved course with only centimeter-level position errors. This sets up a firm foundation on which to build a more versatile relative motion platform. / Master of Science