<p dir="ltr">Teletaction, the transmission of tactile feedback or touch, is a crucial aspect in the</p><p dir="ltr">field of teleoperation. High-quality teletaction feedback allows users to remotely manipulate</p><p dir="ltr">objects and increase the quality of the human-machine interface between the operator and</p><p dir="ltr">the robot, making complex manipulation tasks possible. Advances in the field of teletaction</p><p dir="ltr">for teleoperation however, have yet to make full use of the high-resolution 3D data provided</p><p dir="ltr">by modern vision-based tactile sensors. Existing solutions for teletaction lack in one or more</p><p dir="ltr">areas of form or function, such as fidelity or hardware footprint. In this thesis, we showcase</p><p dir="ltr">our research into a low-cost teletaction device for teleoperation that can utilize the real-time</p><p dir="ltr">high-resolution tactile information from vision-based tactile sensors, through both physical</p><p dir="ltr">3D surface reconstruction and shear displacement. We present our device, the Feelit, which</p><p dir="ltr">uses a combination of a pin-based shape display and compliant mechanisms to accomplish</p><p dir="ltr">this task. The pin-based shape display utilizes an array of 24 servomotors with miniature</p><p dir="ltr">Bowden cables, giving the device a resolution of 6x4 pins in a 15x10 mm display footprint.</p><p dir="ltr">Each pin can actuate up to 3 mm in 200 ms, while providing 80 N of force and 3 um of</p><p dir="ltr">depth resolution. Shear displacement and rotation is achieved using a compliant mechanism</p><p dir="ltr">design, allowing a minimum of 1 mm displacement laterally and 10 degrees of rotation. This</p><p dir="ltr">real-time 3D tactile reconstruction is achieved with the use of a vision-based tactile sensor,</p><p dir="ltr">the GelSight, along with an algorithm that samples the depth data and marker tracking to</p><p dir="ltr">generate actuator commands. With our device we perform a series of experiments including</p><p dir="ltr">shape recognition and relative weight identification, showing that our device has the potential</p><p dir="ltr">to expand teletaction capabilities in the teleoperation space.</p>

<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>

<p dir="ltr">Unmanned surface vehicles (USVs) offer a versatile platform for various maritime applications, including research, surveillance, and search-and-rescue operations. A critical capability for USVs is maintaining position (station keeping) in dynamic environments and coordinating movement with other USVs (formation control) for collaborative missions. This thesis investigates control strategies for USVs operating in challenging conditions. </p><p dir="ltr">The initial focus is on evaluating traditional control methods like Backstepping and Sliding Mode controllers for station keeping in simulated environments with disturbances. The results from these tests pointed towards the need for a more robust control technique, like deep-learning based control for enhanced performance. </p><p dir="ltr">The thesis then explores formation control, a crucial aspect of cooperative USV missions. A vision-based passive control strategy utilizing a virtual leader concept is proposed. This approach leverages onboard cameras to detect markers on other USVs, eliminating the need for direct communication and potentially improving scalability and resilience. </p><p dir="ltr">Then the thesis presents vision-based formation control architecture and the station keeping controller evaluations. Simulation results are presented, analyzed, and used to draw conclusions about the effectiveness of the proposed approaches. Finally, the thesis discusses the implications of the findings and proposes potential future research directions</p>

<p dir="ltr">This article-based research traces the evolution and advancements of vehicular aerodynamic concepts and emphasizes on the significance of vehicle aerodynamics for high-performance vehicles. The thesis further explores the scope of integrating advanced vehicle aerodynamic concepts into consumer vehicles. The thesis aims to point out the significant improvements achieved with the integration of active aerodynamic concepts in terms of both vehicle performance as well as efficiency figures for consumer vehicles. Additionally, exploring the scope for the development of these advanced active aerodynamic systems as third-party modifications is the secondary objective of the presented research. The thesis also highlights the development and integration of unique active aerodynamic systems featured in performance vehicles and analyzes the performance gains achieved using MATLAB program-based simulations supported by a graphical representation of analyzed output data. The study of Active aerodynamic systems for both performance/track-oriented and consumer vehicles remains to be the primary emphasis for the presented thesis.</p>

<p dir="ltr">Robotic software design and implementation have traditionally relied on human engineers to fine-tune parameters, optimize hardware utilization, and mitigate unprecedented situations. As we face more demanding and complex applications, such as distributed robotic fleets and autonomous driving, explicit fine-tuning of autonomous systems yields diminishing returns. To make autonomous systems smarter, a design-time and run-time framework is required to extract constraints from high-level human decisions, and self-adapt on-the-fly to maintain desired specifications. Specifically, for controllers that govern cyber-physical interactions, making them self-adaptive involves two challenges. Firstly, controller design methods have historically neglected computing hardware constraints that realize real-time execution. Hence, intensive manual tuning is required to materialize a controller prototype with balanced control performance and computing resource consumption. Secondly, precisely modeling the physical system dynamics at edge cases is difficult and costly. However, with modeling discrepancies, controllers fine-tuned at design time may fail at run time, causing safety concerns. While humans are inherently adept at reacting and getting used to unknown system dynamics, how to transfer this knowledge to robots is still unresolved.</p><p dir="ltr">To address the two challenges, we propose a designer-augmenting framework for self-adaptive control systems. Our framework includes a resource/performance co-design tool and a model-free controller self-adaptation method for real-time control systems. Our resource/performance co-design tool automatically exploits the Pareto front of controllers, between real-time computing resource utilization and achievable control performance. The co-design tool simplifies the iterative partitioning and verification of controller performance and distributed resource budget, enabling human engineers to directly interface with high-level design decisions between quality and cost. Our controller self-adaptation method extracts objectives and tolerances from human demonstrations and applies them to real-time controller switching, allowing human experts to design fault mitigation behaviors directly through coaching. The objective extraction and real-time adaptation do not rely on prior knowledge of the plant, making them inherently robust against mismatch between the design reference model and the physical system.</p><p dir="ltr">Only with the prerequisite of real-time schedulability under Worst-Case Execution Time (WCET), will the digital controller deliver the designed dynamics. To determine the real-time schedulability of controllers during the design-time iteration and run-time self-adaptation, we propose a novel estimate of WCET based on the Mixed Weibull distribution of profiling statistics and a linear composition model. Our hybrid approach applies to design-time estimation of arbitrary-scaled controllers, yielding results as accurate as a state-of-the-art method while being more robust under small profiling sample sizes. Finally, we propose a resource consolidator that accounts for real-time schedulable bounds to utilize available computing resources while preventing deadline misses efficiently. Our consolidator, formulated as a vector packing problem, exploits different parallelization techniques on a CPU/FPGA hybrid architecture to obtain the most compact allocation plan for a given controller complexity and throughput. </p><p dir="ltr">By jointly considering all four aspects, our framework automates the co-optimization of controller performance and computing hardware requirements throughout the life cycle of a control system. As a result, the engineering time required to design and deploy a controller is significantly reduced, while the adaptivity of human engineers is extended to fault mitigation at run-time.</p>

<p dir="ltr">Distracted driving is at the forefront of the leading causes of road accidents. Therefore, research advancements in Driver Monitoring Systems (DMS) are vital in facilitating prevention techniques. These systems must be able to detect anomalous driving behavior by evaluating deviations from some predefined normal driving behavior. This thesis proposes an improved contrastive learning approach that introduces a hybrid loss function combining triplet loss and supervised contrastive loss, as well as improvements to the projection head of the framework. It progresses the architecture by performing a multi-threshold severity calculation and data processing using an exponential moving average technique. Due to the unbounded possibilities of anomalous driving behaviors, the proposed framework was tested on the Driver Anomaly Detection (DAD) dataset that incorporates multi-modal and multi-view inputs in an open set recognition setting. The test set of the DAD dataset has anomalous actions that are unseen by the trained model; therefore, high precision on such a dataset demonstrates success on any other closed-set recognition task. The proposed framework achieved an impressive accuracy, reaching 94.14\%, AUC-ROC at 0.9787, and AUC-PR at 0.9781 on the test set. These findings contribute to in-vehicle monitoring by providing a scalable and adaptable framework suitable for real-world conditions.</p>

<p>The miniaturization and thinning of wearable, soft robotics and medical devices are soon to require higher performance modeling as the physical flexibility causes direct impacts on the electrical characteristics of the circuit – changing its behavior. As a representative flexible electronic component, the organic field effect transistor (OFET) has attracted much attention in its manufacturing as well as applications. However, as the strain and stress effects are integrated into multiphysics modelers with deeper interactions, the computational complexity and accuracy of OFET modeling is resurfacing as a limiting bottleneck.</p><p>The dissertation was organized into three interrelated studies. In the first study, the Mass-Spring-Damper (MSD) model for an inverted staggered thin film transistor (TFT) was proposed to investigate the TFT’s internal stress/strain fields, and the strain effects on the overall characteristics of the TFT. A comparison study with the finite element analysis (FEA) model shows that the MSD model can reduce memory usage and raises the computational convergence speed for rendering the same results as the FEA. The second study developed the generalized solid-state model by incorporating the density of trap states in the band structure of organic semiconductors (OSCs). The introduction of trap states allows the generalized solid-state model to describe the electrical characteristics of both inorganic TFTs and organic field-effect transistors (OFETs). It is revealed through experimental verification that the generalized solid-state model can accurately characterize the bending induced electrical properties of an OFET in the linear and saturation regimes. The third study aims to model the transient and steady-state dynamics of an arbitrary organic semiconductor device under mechanical strain. In this study, the fractional drift-diffusion (Fr-DD) model and its computational scheme with high accuracy and high convergence rate were proposed. Based on simulation and experimental validation, the transconductance and output characteristics of a bendable OFET were found to be well determined by the Fr-DD model not only in the linear and saturation regimes, but also in the subthreshold regime.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Instrument assisted soft tissue mobilization (IASTM) is a form of massage using rigid manufactured or cast devices. The delivered force, which is a critical parameter in massage during IASTM, has not been measured or standardized for most clinical practices. In addition to the force, the angle of treatment and frequency play an important role during IASTM. As a result, there is a strong need to characterize the delivered force to a patient, angle of treatment, and stroke frequency. This thesis proposes two novel mechatronic designs for a specific instrument from Graston Technique(Model GT3), which is a frequently used tool to clinically deliver localize pressure to the soft tissue. The first design is based on compression load cells, where 4-load cells are used to measure the force components in three-dimensional space. The second design uses a 3D load cell, which can measure all three force components force simultaneously. Both designs are implemented with IMUduino microcontroller chips which can also measure tool orientation angles and provide computed stroke frequency. Both designs, which were created using Creo CAD platform, were also analyzed thorough strength and integrity using the finite element analysis package ANSYS. Once the static analysis was completed, a dynamic model was created for the first design to simulate IASTM practice using the GT-3 tool. The deformation and stress on skin were measured after applying force with the GT-3 tool. Additionally, the relationship between skin stress and the load cell measurements has been investigated. The second design of the mechatronic IASTM tool was validated for force measurements using an electronic plate scale that provided the baseline force values to compare with the applied force values measured by the tool. The load cell measurements and the scale readings were found to be in agreement within the expected degree of accuracy. The stroke frequency was computed using the force data and determining the peaks during force application. The orientation angles were obtained from the built-in sensors in the microchip.

An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts' Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models.

This thesis dissertation mainly compares and investigates laboratory study of different implementation methodologies of applied control systems and how they can be adopted in industrial, as well as commercial, automation applications. Namely the research paper aims to assess or evaluate eventual feedback control loops' performance and robustness over multiple conventional or state-of-the-art technologies in the field of applied industrial automation and instrumentation by implementing a laboratory case study setup: the ball on beam system. Hence, the paper tries to close the gap between industry and academia by: first, conducting a historical study and background information of main evolutional and technological eras in the field of industrial process control automation and instrumentation. Then, some related basic theoretical as well as practical concepts are reviewed in Chapter 2 of the report before displaying the detailed design. After that, the next Chapter, analyses the ball on beam control system problem as the case studied in the context of this research through reviewing previous literature, modeling and simulation. The following Chapter details the proposed design and implementation of the ball on beam case study as if it is under the introduced distributed industrial automation architecture. Finally, Chapter 5 concludes this work by listing several points leaned, remarks, and observations, and stating possible development and the future vision of this research.