With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an easily customizable, low-cost drone design with the necessary hardware for autonomous behavior, swarm coordination, and on-board object detection capabilities. Additionally, this thesis outlines the necessary network architecture to handle the interconnection and bandwidth requirements of the drone swarm. The drone on-board system uses a PixHawk 4 flight controller to handle flight mechanics, a Raspberry Pi 4 as a companion computer for general-purpose computing power, and a NVIDIA Jetson Nano Developer Kit to perform object detection in real-time. The implemented network follows the 802.11s standard for multi-hop communications with the HWMP routing protocol. This topology allows drones to forward packets through the network, significantly extending the flight range of the swarm. Our experiments show that the selected hardware and implemented network can provide direct point-to-point communications at a range of up to 1000 feet, with extended range possible through message forwarding. The network also provides sufficient bandwidth for bandwidth intensive data such as live video streams. With an expected flight time of about 17 minutes, the proposed design offers a low-cost drone swarm solution for mid-range aerial surveillance applications.

<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>

<p dir="ltr">State estimation is a type of an <i>inverse problem</i> in which some amount of observed data needs to be processed using computer algorithms (which are designed using analytical techniques) to infer or reconstruct the underlying model that produced the data. Due to the ubiquity of data and interconnected control systems in the present day, many engineering domains have become replete with inverse problems that can be formulated as state estimation problems. The interconnectedness of these control systems imparts the associated state estimation problems with distinctive structural properties that must be taken into consideration. For instance, the observed data could be high-dimensional and have a dependency structure that is best described by a graph. Furthermore, the control systems of today interface with each other and with the internet, bringing in new possibilities for large-scale collaborative sensor fusion, while also (potentially) introducing new sources of disturbances, faults, and cyberattacks. </p><p dir="ltr">The main thesis of this document is to investigate the unique challenges related to the issues of robustness, resilience (to faults and cyberattacks), and scalability of state estimation algorithms. These correspond to research questions such as, <i>"Does the state estimation algorithm retain its performance when the measurements are perturbed by unknown disturbances or adversarial inputs?"</i> and <i>"Does the algorithm have any bottlenecks that restrict the size/dimension of the problems that it could be applied to?".</i> Most of these research questions are motivated by a singular domain of application: autonomous navigation of unmanned aerial vehicles (UAVs). Nevertheless, the mathematical methods and research philosophy employed herein are quite general, making the results of this document applicable to a variety of engineering tasks, including anomaly detection in time-series data, autonomous remote sensing, traffic monitoring, coordinated motion of dynamical systems, and fault-diagnosis of wireless sensor networks (WSNs), among others.</p>

<p dir="ltr">Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians</p><p dir="ltr">and vehicles) to make optimal decisions for navigation. The existing methods focus on</p><p dir="ltr">techniques to utilize the positions and velocities of these agents and fail to capture semantic</p><p dir="ltr">information from the scene. Moreover, to mitigate the increase in computational complexity</p><p dir="ltr">associated with the number of agents in the scene, some works leverage Euclidean distance to</p><p dir="ltr">prune far-away agents. However, distance-based metric alone is insufficient to select relevant</p><p dir="ltr">agents and accurately perform their predictions. To resolve these issues, we propose the</p><p dir="ltr">Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture</p><p dir="ltr">semantics along with spatial information and optimally select relevant agents for motion</p><p dir="ltr">prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant</p><p dir="ltr">agents from the scene and passing them through an attention mechanism to extract</p><p dir="ltr">global encodings. These encodings along with agents’ local information, are passed through</p><p dir="ltr">an encoder to obtain time-dependent latent variables for a motion policy predicting the future</p><p dir="ltr">trajectories. Our results show that the proposed approach outperforms state-of-the-art</p><p dir="ltr">baselines and provides more accurate and scene-consistent predictions. </p>

This research undertakes the problem of vibration control of vehicular and structural systems using intelligent materials and controllable devices. Advanced modeling tools validated with experimental test data are developed to help with understanding the fundamentals as well as advanced and novel applications of smart and conventional suspension systems. The project can be divided into two major parts. The first part is focused on development of novel smart suspensions using Magneto-Rheological (MR) fluids in unique configurations in order to improve efficiency, controllability, and safety of today's vehicles. In this part of the research, attention is paid to fundamentals as well as advanced applications of MR technology. Extensive rheological studies, both theoretical and experimental, are performed to understand the basic behaviors of MR fluids as complex non-Newtonian fluids in novel applications. Using the knowledge obtained from fundamental studies of MR fluids, unique application concepts are investigated that lead to design, development, and experimental testing of two new classes of smart devices: MR Hybrid Dampers and MR Squeeze Mounts. Multiple generations of these devices are built and tested as proof of concept prototypes. Advanced physics-based mathematical models are developed for these devices. Experimental test data are used to validate the models and great agreement is obtained. The models are used as design tools at preliminary as well as detailed design stages of device development. The significant finding in this part of the research is that MR fluids can deliver a much larger window of controllable force in squeeze mode compared to shear and valve modes which can be used in various applications. The second part of the research is devoted to the development of innovative design tools for suspension design and tuning. Various components of suspension systems are studied and modeled using a new physics-based modeling approach. The component of main interest is the shim stack assembly in hydraulic dampers which is modeled using energy and variational methods. A major finding is that the shims should be modeled individually in order to represent the sliding effects properly when the shim stack is deflected. Next, the individual component models are integrated into a full suspension model. This model is then used as a tool for suspension design, synthesis, and tuning. Using this design tool, suspension engineers in manufacturing companies and other industrial sections can easily perform parametric studies without the need to carry out time consuming and expensive field and laboratory tests. / Ph. D.

This research identifies the attributes to be considered in the selection of an automated guided vehicle (AGV). A distinction is made between automated guided vehicles (AGVs) and an automated guided vehicle system (AGVS). This research is concerned only with the selection of automated guided vehicles (AGVs). A selection model is developed which forces the user to evaluate attributes. his requirements and preferences for AGV The first step of the model allows the user to enter his specifications for AGV attributes which are applicable to his production environment. The second step in the selection model is for the user to determine 8-15 attributes to use as selection criteria. In the third phase, the user inputs his preferences and priorities with respect to the attributes chosen as selection criteria in the second step. model ranks the Based on this information, the selection AGV models in the feasible set. A description of the model and a numerical example are included. Steps 1 and 2, described above, are implemented using an R:BASE™ program. The BASIC computer language was used to perform the interrogation of the user with respect to his priorities and preferences among attributes in Step 3. The IBM PC™ is the hardware chosen for running the selection model. / M.S.

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013 / Automatic Guided Vehicles (AGVs) are being used more frequently in a manufacturing environment. These AGVs are navigated in many different ways, utilising multiple types of sensors for detecting the environment like distance, obstacles, and a set route. Different algorithms or methods are then used to utilise this environmental information for navigation purposes applied onto the AGV for control purposes. Developing a platform that could be easily reconfigured in alternative route applications utilising vision was one of the aims of the research. In this research such sensors detecting the environment was replaced and/or minimised by the use of a single, omnidirectional Webcam picture stream utilising an own developed mirror and Perspex tube setup. The area of interest in each frame was extracted saving on computational recourses and time. By utilising image processing, the vehicle was navigated on a predetermined route. Different edge detection methods and segmentation methods were investigated on this vision signal for route and sign navigation. Prewitt edge detection was eventually implemented, Hough transfers used for border detection and Kalman filtering for minimising border detected noise for staying on the navigated route. Reconfigurability was added to the route layout by coloured signs incorporated in the navigation process. The result was the manipulation of a number of AGV’s, each on its own designated coloured signed route. This route could be reconfigured by the operator with no programming alteration or intervention. The YCbCr colour space signal was implemented in detecting specific control signs for alternative colour route navigation. The result was used generating commands to control the AGV through serial commands sent on a laptop’s Universal Serial Bus (USB) port with a PIC microcontroller interface board controlling the motors by means of pulse width modulation (PWM). A total MATLAB® software development platform was utilised by implementing written M-files, Simulink® models, masked function blocks and .mat files for sourcing the workspace variables and generating executable files. This continuous development system lends itself to speedy evaluation and implementation of image processing options on the AGV. All the work done in the thesis was validated by simulations using actual data and by physical experimentation.

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013 / In the industry, automation is used to optimize production, improve product quality and increase profitability. By properly implementing automation systems, the risk of injury to workers can be minimized. Robots are used in many low-level tasks to perform repetitive, undesirable or dangerous work. Robots can perform a task with higher precision and accuracy to lower errors and waste of material. Machine Vision makes use of cameras, lighting and software to do visual inspections that a human would normally do. Machine Vision is useful in application where repeatability, high speed and accuracy are important. This study concentrates on the development of a dedicated robot vision system to automatically place components exiting from a conveyor system onto Automatic Guided Vehicles (AGV). A personal computer (PC) controls the automated system. Software modules were developed to do image processing for the Machine Vision system as well as software to control a Cartesian robot. These modules were integrated to work in a real-time system. The vision system is used to determine the parts‟ position and orientation. The orientation data are used to rotate a gripper and the position data are used by the Cartesian robot to position the gripper over the part. Hardware for the control of the gripper, pneumatics and safety systems were developed. The automated system‟s hardware was integrated by the use of the different communication protocols, namely DeviceNet (Cartesian robot), RS-232 (gripper) and Firewire (camera).

Markov processes have been widely studied and used for modeling problems. A Markov process has two main components (i.e., an evolution law and an initial distribution). Markov processes are not suitable for modeling some problems, for example, the problem of predicting a trajectory with a known destination. Such a problem has three main components: an origin, an evolution law, and a destination. The conditionally Markov (CM) process is a powerful mathematical tool for generalizing the Markov process. One class of CM processes, called $CM_L$, fits the above components of trajectories with a destination. The CM process combines the Markov property and conditioning. The CM process has various classes that are more general and powerful than the Markov process, are useful for modeling various problems, and possess many Markov-like attractive properties. Reciprocal processes were introduced in connection to a problem in quantum mechanics and have been studied for years. But the existing viewpoint for studying reciprocal processes is not revealing and may lead to complicated results which are not necessarily easy to apply. We define and study various classes of Gaussian CM sequences, obtain their models and characterizations, study their relationships, demonstrate their applications, and provide general guidelines for applying Gaussian CM sequences. We develop various results about Gaussian CM sequences to provide a foundation and tools for general application of Gaussian CM sequences including trajectory modeling and prediction. We initiate the CM viewpoint to study reciprocal processes, demonstrate its significance, obtain simple and easy to apply results for Gaussian reciprocal sequences, and recommend studying reciprocal processes from the CM viewpoint. For example, we present a relationship between CM and reciprocal processes that provides a foundation for studying reciprocal processes from the CM viewpoint. Then, we obtain a model for nonsingular Gaussian reciprocal sequences with white dynamic noise, which is easy to apply. Also, this model is extended to the case of singular sequences and its application is demonstrated. A model for singular sequences has not been possible for years based on the existing viewpoint for studying reciprocal processes. This demonstrates the significance of studying reciprocal processes from the CM viewpoint.

Human-in-the-loop simulators have long been used in the research community as well as in industry. The aviation field has been the pioneers in the use of simulators for design purposes. In contrast, corresponding activities in the automotive area have been less widespread. Published reports on experimental activities based on human-in-the-loop simulations have focused on methods used in the study, but nobody seems to have taken a step back and looked at the wider methodological picture of Simulator-Based Design. The purpose of this thesis is to fill this gap by drawing, in part, upon the author’s long experience in this field. In aircraft and lately also in ground vehicles there has been a technology shift from pure mechanics to computer-based systems. The physical interface has turned into screen-based solutions. This trend towards glass has just begun for ground vehicles. This development in vehicle technology has opened the door for new design approaches, not only for design itself, but also for the development process. Simulator-Based Design (SBD) is very compatible with this trend. The first part of this thesis proposes a structure for the process of SBD and links it to the corresponding methodology for software design. In the second part of the thesis the focus changes from methodology to application and specifically to the design of three-dimensional situation displays. Such displays are supposed to support the human operator with a view of a situation beyond the more or less limited visual range. In the aircraft application interest focuses on the surrounding air traffic in the light of the evolving free-flight concept, where responsibility for separation between aircraft will be (partly) transferred from ground-based flight controllers to air crews. This new responsibility must be supported by new technology and the situational view must be displayed from the perspective of the aircraft. Some basic design questions for such 3D displays were investigated resulting in an adaptive interface approach, where the current situation and task govern the details of information presentation. The thesis also discusses work on situation displays for ground vehicles. The most prominent example may be the Night Vision system, where the road situation ahead is depicted on a screen in the cab. The existing systems are based on continuous presentation, an approach that we have questioned, since there is strong evidence for negative behavioral adaptation. This means, for example, that the driver will drive faster, since vision has been enhanced, and thereby consume the safety margins that the system was supposed to deliver. Our investigation supports a situation-dependant approach and no continuous presentation. In conclusion, the results from our simulator-based studies showed advantages for adaptive interface solutions. Such design concepts are much more complicated than traditional static interfaces. This finding emphasizes the need for more dynamic design resources in order to have a complete understanding of the situation-related interface changes. The use of human-in-the-loop simulators and deployment of Simulator-Based Design will satisfy this need.