Robotic technology can be broadly categorized into two main approaches based on the compliance of the robot's materials and structure: hard and soft. Hard, traditional robots, with mechanisms to transmit forces, provide high degrees of freedom (DoFs) and precise manipulation, making them commonly used in industry and academic research. The field of soft robotics, on the other hand, is a new trend from the past three decades of robotics that uses soft materials such as silicone or textiles as the body or material base instead of the rigid bodies used in traditional robots. Soft robots are typically pre-programmed with specific geometries, and perform well at tasks such as human-robot interaction, locomotion in complex environments, and adaptive reconfiguration to the environment, which reduces the cost of future programming and control. However, full soft robotic systems are often less mobile due to their actuation --pneumatics, high-voltage electricity or magnetics -- even if the robot itself is at a millimeter or centimeter scale. Rigid or hard robots, on the other hand, can often carry the weight of their own power, but with a higher burden of cost for control and sensing. A middle ground is thus sought, to combine soft robotics technologies with rigid robots, by implementing mechanism design principles with soft robots to embed functionalities or utilize soft robots as the actuator on a rigid robotic system towards an affordable robotic system design. This dissertation showcases five examples of this design principle with two main research branches: locomotion and wearable robotics. In the first research case, an example of how a miniature swimming robot can navigate through a granular environment using compliant plates is presented, compared to other robots that change their shape or use high DoF mechanisms. In the second pipeline, mechanism design is implemented using soft robotics concepts in a wearable robot. An origami-inspired, soft "exo-shell", that can change its stiffness on demand, is introduced. As a follow-up to this wearable origami-inspired robot, a geometry-based, ``near" self-locking modular brake is then presented. Finally, upon combining the origami-inspired wearable robot and brake design, a concept of a modular wearable robot is showcased for the purpose of answering a series of biomechanics questions.

This thesis encompasses a comprehensive research effort dedicated to overcoming the critical bottlenecks that hinder the current generation of neural networks, thereby significantly advancing their reliability and performance. Deep neural networks, with their millions of parameters, suffer from over-parameterization and lack of constraints, leading to limited generalization capabilities. In other words, the complex architecture and millions of parameters present challenges in finding the right balance between capturing useful patterns and avoiding noise in the data. To address these issues, this thesis explores novel solutions based on knowledge distillation, enabling the learning of robust representations. Leveraging the capabilities of large-scale networks, effective learning strategies are developed. Moreover, the limitations of dependency on external networks in the distillation process, which often require large-scale models, are effectively overcome by proposing a self-distillation strategy. The proposed approach empowers the model to generate high-level knowledge within a single network, pushing the boundaries of knowledge distillation. The effectiveness of the proposed method is not only demonstrated across diverse applications, including image classification, object detection, and semantic segmentation but also explored in practical considerations such as handling data scarcity and assessing the transferability of the model to other learning tasks. Another major obstacle hindering the development of reliable and robust models lies in their black-box nature, impeding clear insights into the contributions toward the final predictions and yielding uninterpretable feature representations. To address this challenge, this thesis introduces techniques that incorporate simple yet powerful deep constraints rooted in Riemannian geometry. These constraints confer geometric qualities upon the latent representation, thereby fostering a more interpretable and insightful representation. In addition to its primary focus on general tasks like image classification and activity recognition, this strategy offers significant benefits in real-world applications where data scarcity is prevalent. Moreover, its robustness in feature removal showcases its potential for edge applications. By successfully tackling these challenges, this research contributes to advancing the field of machine learning and provides a foundation for building more reliable and robust systems across various application domains.

Solar photovoltaic (PV) generation has seen significant growth in 2021, with an increase of around 22% and exceeding 1000 TWh. However, this has also led to reliability and durability issues, particularly potential induced degradation (PID), which can reduce module output by up to 30%. This study uses cell- and module-level analysis to investigate the impact of superstrate, encapsulant, and substrate on PID.The influence of different substrates and encapsulants is studied using one-cell modules, showing that substrates with poor water-blocking properties can worsen PID, and encapsulants with lower volumetric resistance can conduct easily under damp conditions, enabling PID mechanisms (results show maximum degradation of 9%). Applying an anti-soiling coating on the front glass (superstrate) reduces PID by nearly 53%. Typical superstrates have sodium which accelerates the PID process, and therefore, using such coatings can lessen the PID problem. At the module level, the study examines the influence of weakened interface adhesion strengths in traditional Glass-Backsheet (GB) and emerging Glass-Glass (GG) (primarily bifacial modules) constructions. The findings show nearly 64% more power degradation in GG modules than in GB. Moreover, the current methods for detecting PID use new modules, which can give inaccurate information instead of DH-stressed modules for PID testing, as done in this work. A comprehensive PID susceptibility analysis for multiple fresh bifacial constructions shows significant degradation from 20 to 50% in various constructions. The presence of glass as the substrate exacerbates the PID problem due to more ionic activity available from the two glass sides. Recovery experiments are also conducted to understand the extent of the PID issue. Overall, this study identifies, studies, and explains the impact of superstrate, substrate, and encapsulant on the underlying PID mechanisms. Various pre- and post-stress characterization tests, including light and dark current-voltage (I-V) tests, electroluminescence (EL) imaging, infrared (IR) imaging, and UV fluorescence (UVF) imaging, are used to evaluate the findings. This study is significant as it provides insights into the PID issues in solar PV systems, which can help improve their performance and reliability.

Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and mapping strategy which do not use vision systems but are still able to explore and map the environment. The map can further be used by first responders and cave explorers to access the environments. This thesis presents the design of a collision-resilient Unmanned Aerial Vehicle (UAV), XPLORER that utilizes a novel navigation algorithm for exploration and simultaneous mapping of the environment. The real-time navigation algorithm uses the onboard Inertial Measurement Units (IMUs) and arm bending angles for contact estimation and employs an Explore and Exploit strategy. Additionally, the quadrotor design is discussed, highlighting its improved stability over the previous design. The generated map of the environment can be utilized by autonomous vehicles to navigate the environment. The navigation algorithm is validated in multiple real-time experiments in different scenarios consisting of concave and convex corners and circular objects. Furthermore, the developed mapping framework can serve as an auxiliary input for map generation along with conventional LiDAR or vision-based mapping algorithms. Both the navigation and mapping algorithms are designed to be modular, making them compatible with conventional UAVs also. This research contributes to the development of navigation and mapping techniques for GPS-denied environments, enabling safer and more efficient exploration of challenging territories.

Children's hospitals can be a scary place for children and their parents. Patients are stressed and anxious because they are in a space that is unfamiliar to them, and being forced to be in a confined space feels like a punishment. Parents accompanying their children in hospitals are also emotionally stressed due to the overwhelming parental and financial responsibilities. There is a product opportunity gap which allows the patients to interact with the environment to make it more familiar to them and interact with the people around them to alleviate stress anxiety. This project aims to use the user-inspired engineering process to close that product opportunity gap.

Nanoparticle (NP) assembly is critical where NPs are organized into complex superstructures through direct and indirect interactions. Long-range NP orders have nanoscale locational selectivity, orientational alignment, and scalable micropatterning, which are indispensable for enabling multiple functionalities and improving the performances of different systems. Though nanoparticles can self-assemble into organized nanostructures via simple drying thermodynamics, scalability has been a primary issue. Thus, this research focuses on more scalable manufacturing for directed NP assembly. First, 3D printing was used for template fabrications with varying topology features. Next, nanoparticle engineering with colloidal and surface studies leads to desirable NP packing on template surfaces. Finally, the processed devices will also demonstrate a few applications of surface micropatterning with nanoscale particle orders. Specifically, a few manufacturing procedures involve (i) stereolithography (SLA)/layer-by-layer dip coating, (ii) continuous liquid interface projection (CLIP)/ink writing, (iii) fused deposition melting (FDM)/direct ink writing, and (iv) multiphase direct ink writing (MDIW)/wet etching. To demonstrate the applicability of hybrid manufacturing, a broad range of nanoparticles, including carbon nanofibers (CNFs), MXene nanoflakes, and boron nitride nanoplatelets (BNNPs) were studied in this research. With well-managed template physics and NP dispersion control, nanoparticle orientational alignment and positional preferences are driven by short- and long-range intermolecular interactions (e.g., convective, van der Waals, capillarity, shear, and other secondary bonding). The printed devices displayed multifunctional properties, i.e., anisotropic conductivity, piezoresistive and chemical sensitivity, mechanical durability, and heat dissipation capabilities, for microelectronic applications. This fabrication technique shows enormous potential for rapid, scalable, and low-cost manufacturing of hierarchical structures, especially for micropatterning of nanoparticles not easily accessible through conventional processing methods.

In nature, some animals have an exoskeleton that provides protection, strength, and stability to the organism, but in engineering, an exoskeleton refers to a device that augments or aids human ability. However, the method of controlling these devices has been a challenge historically. Depending on the objective, control systems for exoskeletons have ranged from devices as simple spring-loaded systems to using sensors such as electromyography (EMG). Despite EMGs being very common, force sensing resistors (FSRs) can be used instead. There are multiple types of exoskeletons that target different areas of the human body, and the targeted area depends on the need of the device. Usually, the devices are developed for either medical or military usage; for this project, the focus is on medical development of an automated elbow joint to assist in rehabilitation. This thesis is a continuation of my ASU Barrett honors thesis, Upper-Extremity Exoskeleton. While working on my honors thesis, I helped develop a design for an upper extremity exoskeleton based on the Wilmer orthosis design for Mayo Clinic. Building upon the design of an orthosis, for the master’s thesis, I developed an FSR control system that is designed using a Wheatstone bridge circuit that can provide a clean reliable signal as compared to the current EMG setup.

In higher education, teacher empathy is a term that refers to the empathetic skills of teachers and has been researched since the 1980s. Multiple studies in fields such as medicine, nursing and psychology have shown that teacher empathy has reduced teacher burnout, improved teacher satisfaction and student performance. Within engineering education, there is increased research on empathy in recent years, but primarily aimed at introducing and improving empathetic skills of engineering students. There is little research on teacher empathy within engineering education. In my current study, I explored the potential longitudinal impact in perception of teacher empathy among three engineering faculty members as they utilized empathetic actions while teaching a second-year engineering course. I also explored the motivations and challenges that could arise in teacher empathy implementation. I used the Model of Empathy Framework developed by Walther and colleagues to define the complex attributes of empathy in an engineering context. I chose Teacher Action Research (TAR) methodology to provide agency to my three participants and research with them instead of on them. TAR allowed the participants to choose the empathetic actions they want to implement and to iterate when they feel appropriate. I found that all three participants had positive outcomes in their classrooms. Reduced teacher burnout, improved teacher satisfaction, and better student performance were some of the major benefits of teacher empathy that aligned with prior research. Improved confidence in their empathetic skills was observed for two participants as they showed positive evolution of their perception about teacher empathy. The other participant did not have any significant longitudinal impact in perception but was able to increase the number of empathetic approaches he could use in his classroom. External situations such as classroom technology malfunctions, having meetings or classes immediately before a class and balancing between being empathetic and being tough were some of the major challenges. Findings indicate that similar positive benefits as found in other disciplines can be realized within engineering education. The outcome of this study could be used by Learning and Teaching Centers Department Heads and University Deans to expand the implementation of teacher empathy within a college or university setting.

Urban search and rescue (USAR) teams may use Artificial Social Intelligence (ASI) agents to aid teams in adapting to dynamic environments, minimize risk, and increase mission assurance and task performance. This thesis underlines the relationship between vocal pitch, stress, and team performance from a recent experiment conducted in a simulated USAR synthetic task environment (STE). The simulated USAR-STE is a platform to use ASI as an advisor to intervene in the human team members’ cognitive processes, which aims to reduce risk to task execution and to maintain team performance. Three heterogeneous and interdependent roles interact via voice communication to search and rescue the victims: (1) medic -rescues victims and identifies the severity of injuries; (2) transporter -moves victims to their designated zone based on injury severity; (3) engineer -removes hazardous material such as rubble from a room or hallway that is blocking passage. Different speeds are associated with each role, such as medic, transporter, and engineer. Medic has a default speed; the transporter has times two over the default speed; the engineer has the slowest speed. In a total of 45 teams, three ASI conditions, manipulated based on ASI intervention communication length and frequency, were analyzed. Each team participated in two 15-min missions. The results indicate a U-shaped relationship between the transporter’s pitch and a change in team performance. A possible explanation for this significance is the task and role design. The transporter may have the most central role in voice communication because when the transporter is under varying levels of workload and stress, and thus voice pitch has a complex relationship with performance for that role.

In-Band Full-Duplex (IBFD) can maximize the spectral resources and enable new types of technology, but generates self-interference (SI) that must be mitigated to enable practical applications. Analog domain SI cancellation (SIC), usually implemented as a digitally controlled adaptive filter, is one technique that is necessary to mitigate the interference below the noise floor. To maximize the efficiency and performance of the adaptive filter this thesis studies how key design choices impact the performance so that device designers can make better tradeoff decisions. Additionally, algorithms are introduced to maximize the SIC that incorporate the hardware constraints. The provided simulations show up to 45dB SIC with 7 bits of precision at 100MHz bandwidth.