Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.

As the explorations beyond the Earth's boundaries continue to evolve, researchers and engineers strive to develop versatile technologies capable of adapting to unknown space conditions. For instance, the utilization of Screw-Propelled Vehicles (SPVs) and robotics that utilize helical screws propulsion to transverse planetary bodies is a growing area of interest. An example of such technology is the Extant Exobiology Life Surveyor (EELS), a snake-like robot currently developed by the NASA Jet Propulsion Laboratory (JPL) to explore the surface of Saturn’s moon, Enceladus. However, the utilization of such a mechanism requires a deep and thorough understanding of screw mobility in uncertain conditions. The main approach to exploring screw dynamics and optimal design involves the utilization of Discrete Element Method (DEM) simulations to assess interactions and behavior of screws when interacting with granular terrains. In this investigation, the Simplified Johnson-Kendall-Roberts (SJKR) model is implemented into the utilized simulation environment to account for cohesion effects similar to what is experienced on celestial bodies like Enceladus. The model is verified and validated through experimental and theoretical testing. Subsequently, the performance characteristics of screws are explored under varying parameters, such as thread depth, number of screw starts, and the material’s cohesion level. The study has examined significant relationships between the parameters under investigation and their influence on the screw performance.

While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling, control and evaluation of wearable soft robots. Machine learning algorithms have shown promising results for sensor fusion with wearable robots, however, they require extensive data to train models for different users and experimental conditions. Modeling soft sensors and actuators require characterizing non-linearity and hysteresis, which complicates deriving an analytical model. Experimental characterization can capture the characteristics of non-linearity and hysteresis but requires developing a synthesized model for real-time control. Controllers for wearable soft robots must be robust to compensate for unknown disturbances that arise from the soft robot and its interaction with the user. Since developing dynamic models for soft robots is complex, inaccuracies that arise from the unmodeled dynamics lead to significant disturbances that the controller needs to compensate for. In addition, obtaining a physical model of the human-robot interaction is complex due to unknown human dynamics during walking. Finally, the performance of soft robots for wearable applications requires extensive experimental evaluation to analyze the benefits for the user. To address these challenges, this dissertation focuses on the sensing, modeling, control and evaluation of soft robots for wearable applications. A model-based sensor fusion algorithm is proposed to improve the estimation of human joint kinematics, with a soft flexible robot that requires compact and lightweight sensors. To overcome limitations with rigid sensors, an inflatable soft haptic sensor is developed to enable gait sensing and haptic feedback. Through experimental characterization, a mathematical model is derived to quantify the user's ground reaction forces and the delivered haptic force. Lastly, the performance of a wearable soft exosuit in assisting human users during lifting tasks is evaluated, and the benefits obtained from the soft robot assistance are analyzed.

Acrobatic maneuvers of quadrotors present unique challenges concerning trajectorygeneration, control, and execution. Specifically, the flip maneuver requires dynamically feasible trajectories and precise control. Various factors, including rotor dynamics, thrust allocation, and control strategies, influence the successful execution of flips. This research introduces an approach for tracking optimal trajectories to execute flip maneuvers while ensuring system stability autonomously. Model Predictive Control (MPC) designs the controller, enabling the quadrotor to plan and execute optimal trajectories in real-time, accounting for dynamic constraints and environmental factors. The utilization of predictive models enables the quadrotor to anticipate and adapt to changes during aggressive maneuvers. Simulation-based evaluations were conducted in the ROS and Gazebo environments. These evaluations provide valuable insights into the quadrotor’s behavior, response time, and tracking accuracy. Additionally, real-time flight experiments utilizing state- of-the-art flight controllers, such as the PixHawk 4, and companion computers, like the Hardkernel Odroid, validate the effectiveness of the proposed control algorithms in practical scenarios. The conducted experiments also demonstrate the successful execution of the proposed approach. This research’s outcomes contribute to quadrotor technology’s advancement, particularly in acrobatic maneuverability. This opens up possibilities for executing maneuvers with precise timing, such as slingshot probe releases during flips. Moreover, this research demonstrates the efficacy of MPC controllers in achieving autonomous probe throws within no-fly zone environments while maintaining an accurate desired range. Field application of this research includes probe deployment into volcanic plumes or challenging-to-access rocky fault scarps, and imaging of sites of interest. along flight paths through rolling or pitching maneuvers of the quadrotor, to use sensorsuch as cameras or spectrometers on the quadrotor belly.

Seed awns (Erodium and Pelargonium) bury themselves into ground for germination usinghygroscopic coiling and uncoilingmovements. Similarly,wormlizards (Amphisbaenia) create tunnels for habitation by oscillating their heads along the long axis of the trunks. Inspired by these burrowing strategies, this research aims to understand these mechanisms from a soil mechanics perspective, investigate the factors influencing penetration resistance, and develop a self-burrowing technology for subterranean explorations. The rotational movements of seed awns, specifically their coiling and uncoiling movements, were initially examined using the Discrete Element Method (DEM) under shallow and dry conditions. The findings suggest that rotation reduces penetration resistance by decreasing penetrator-particle contact number and the force exerted, and by shifting the contact force away from vertical direction. The effects of rotation were illustrated through the force chain network, displacement field, and particle trajectories, supporting the "force chain breakage" hypothesis and challenging the assumptions of previous analytical models. The factors reducing penetration resistance were subsequently examined, both numerically and experimentally. The experimental results link the reduction of horizontal penetration resistance to embedment depth and penetrator geometry. Notably, both numerical and experimental results confirm that the reduction of penetration resistance is determined by the relative slip velocity, not by the absolute values. The reduction initially spikes sharply with the relative slip velocity, then increases at a slower rate, leveling off at higher relative slip velocities. Additional findings revealed a minimal impact of relative density, particle shape, and inertial number on penetration resistance reduction. Conversely, interface friction angle appeared to increase the reduction, while penetrator roundness and confining pressure decreased it. The investigation also extended to the effect of rotational modes on the reduction of penetration resistance. Reductions between cone-continuous rotation (CCR) and cone-oscillatory rotation (COR) cases were i comparable. However, whole-body-continuous rotation (WCR) yielded a higher reduction under the same relative slip velocities. Interestingly, the amplitude of oscillation movement demonstrated a negligible effect on the reduction. Lastly, a self-burrowing soft robot was constructed based on these insights. Preliminary findings indicate that the robot can move horizontally, leveraging a combination of extensioncontraction and rotational movements.

Undulatory locomotion is a unique form of swimming that generates thrust through the propagation of a wave through a fish’s body. The proposed device utilizes a constrained compliant material with a single actuator to generate an undulatory motion. This paper draws inspiration from Anguilliformes and discusses the kinematics and dynamics of wave propagation of an underwater robot. A variety of parameters are explored through modeling and are optimized for thrust generation to better understand the device. This paper validates the theoretical spine behavior through experimentation and provides a path forward for future development in device optimization for various applications. Previous work developed devices that utilized either paired soft actuators or multiple redundant classical actuators that resulted in a complex prototype with intricate controls. The work of this paper contrasts with prior work in that it aims to achieve undulatory motion through passive actuation from a single actively driven point which simplifies the control. Through this work, the goal is to further explore low-cost soft robotics via bistable mechanisms, continuum material properties, and simplified modeling practices.

National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.

Humans possess the ability to entrain their walking to external pulses occurring atperiods similar to their natural walking cadence. Expanding the basin of entrainment has become a promising option for gait rehabilitation for those affected by hemiparesis. Efforts to expand the basin have utilized either conventional fixed-speed treadmill setups, which require significant alteration to natural walking biomechanics; or overground walking tracks, which are largely impractical. In this study, overground walking was simulated using an actively self-pacing variable speed treadmill, and periodic hip flexion perturbations (≈ 12 Nm) were applied about a subject using a Soft Robotic Hip Exoskeleton. This study investigated the effectiveness of conducting gait entrainment rehabilitation with simulated overground walking to improve the success rate of entrainment at high frequency conditions. This study also investigated whether simulated overground walking can preserve natural biomechanics by examining stride length and normalized propulsive impulse at various conditions. Participants in this study were subjected to four perturbation frequencies, ranging from their naturally preferred gait frequency up to 30% faster. Each subject participated in two days of testing: one day subjects walked on a conventional fixed-speed treadmill, and another day on a variable speed treadmill. Results showed that subjects were more frequently able to entrain to the fastest perturbation frequency on the variable speed treadmill. Results also showed that natural biomechanics were preserved significantly better on the variable speed treadmill across all accelerated perturbation frequencies. This study showed that simulated overground walking can aid in extending the basin of entrainment while preserving natural biomechanics during gait entrainment, which is a promising development for gait rehabilitation. However, a comparative study on neurologically disordered individuals is necessary to quantify the clinical relevance of these findings.

The unparalleled motion and manipulation abilities of an octopus have intrigued engineers and biologists for many years. How can an octopus having no bones transform its arms from a soft state to a one stiff enough to catch and even kill prey? The octopus arm is a muscular hydrostat that enables these manipulations in and through its arm. The arm is a tightly packed array of muscle groups namely longitudinal, transverse and oblique. The orientation of these muscle fibers aids the octopus in achieving core movements like shortening, bending, twisting and elongation as hypothesized previously. Through localized electromyography (EMG) recordings of the longitudinal and transverse muscles of Octopus bimaculoides quantitatively the roles of these muscle layers will be confirmed. Five EMG electrode probes were inserted into the longitudinal and transverse muscle layers of an amputated octopus arm. One into the axial nerve cord to electrically stimulate the arm for movements. The experiments were conducted with the amputated arm submerged in sea water with surrounded cameras to record the movement, all housed in a Faraday cage. The findings of this research could possibly lead to the development of soft actuators built out of soft materials for applications in minimally invasive surgery, search-and-rescue operations, and wearable prosthetics.

The construction industry holds great promise for improvement through the use of robotic technologies in its workflow. Although this industry was an early adopter of such technologies, growth in construction robotics research and its integration into current construction projects is progressing slowly. Some significant factors that have contributed to the slow pace are high capital costs, low return on investments, and decreasing public infrastructure budgets. Consequently, there is a clear need to reduce the overall costs associated with new construction robotics technologies, which would enable greater dissemination. One solution is to use a swarm robotics approach, in which a large group of relatively low-cost agents are employed to produce a target collective behavior. Given the development of deep learning algorithms for object detection and depth estimation, and novel technologies such as edge computing and augmented reality, it is becoming feasible to engineer low-cost swarm robotic systems that use a vision-only control approach. Toward this end, this thesis develops a vision-based controller for a mobile manipulator robot that relies only on visual feedback from a monocular camera and does not require prior information about the environment. The controller uses deep-learning based methods for object detection and depth estimation to accomplish material retrieval and deposition tasks. The controller is demonstrated in the Gazebo robot simulator for scenarios in which a mobile manipulator must autonomously identify, pick up, transport, and deposit individual blocks with specific colors and shapes. The thesis concludes with a discussion of possible future extensions to the proposed solution, including its scalability to swarm robotic systems.