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.

This dissertation introduces and examines Soft Curved Reconfigurable Anisotropic Mechanisms (SCRAMs) as a solution to address actuation, manufacturing, and modeling challenges in the field of soft robotics, with the aim of facilitating the broader implementation of soft robots in various industries. SCRAM systems utilize the curved geometry of thin elastic structures to tackle these challenges in soft robots. SCRAM devices can modify their dynamic behavior by incorporating reconfigurable anisotropic stiffness, thereby enabling tailored locomotion patterns for specific tasks. This approach simplifies the actuation of robots, resulting in lighter, more flexible, cost-effective, and safer soft robotic systems. This dissertation demonstrates the potential of SCRAM devices through several case studies. These studies investigate virtual joints and shape change propagation in tubes, as well as anisotropic dynamic behavior in vibrational soft twisted beams, effectively demonstrating interesting locomotion patterns that are achievable using simple actuation mechanisms. The dissertation also addresses modeling and simulation challenges by introducing a reduced-order modeling approach. This approach enables fast and accurate simulations of soft robots and is compatible with existing rigid body simulators. Additionally, this dissertation investigates the prototyping processes of SCRAM devices and offers a comprehensive framework for the development of these devices. Overall, this dissertation demonstrates the potential of SCRAM devices to overcome actuation, modeling, and manufacturing challenges in soft robotics. The innovative concepts and approaches presented have implications for various industries that require cost-effective, adaptable, and safe robotic systems. SCRAM devices pave the way for the widespread application of soft robots in diverse domains.

In this work, I propose to bridge the gap between human users and adaptive control of robotic systems. The goal is to enable robots to consider user feedback and adjust their behaviors. A critical challenge with designing such systems is that users are often non-experts, with limited knowledge about the robot's hardware and dynamics. In the domain of human-robot interaction, there exist different modalities of conveying information regarding the desired behavior of the robot, most commonly used are demonstrations, and preferences. While it is challenging for non-experts to provide demonstrations of robot behavior, works that consider preferences expressed as trajectory rankings lead to users providing noisy and possibly conflicting information, leading to slow adaptation or system failures. The end user can be expected to be familiar with the dynamics and how they relate to their desired objectives through repeated interactions with the system. However, due to inadequate knowledge about the system dynamics, it is expected that the user would find it challenging to provide feedback on all dimension's of the system's behavior at all times. Thus, the key innovation of this work is to enable users to provide partial instead of completely specified preferences as with traditional methods that learn from user preferences. In particular, I consider partial preferences in the form of preferences over plant dynamic parameters, for which I propose Adaptive User Control (AUC) of robotic systems. I leverage the correlations between the observed and hidden parameter preferences to deal with incompleteness. I use a sparse Gaussian Process Latent Variable Model formulation to learn hidden variables that represent the relationships between the observed and hidden preferences over the system parameters. This model is trained using Stochastic Variational Inference with a distributed loss formulation. I evaluate AUC in a custom drone-swarm environment and several domains from DeepMind control suite. I compare AUC with the state-of-the-art preference-based reinforcement learning methods that are utilized with user preferences. Results show that AUC outperforms the baselines substantially in terms of sample and feedback complexity.

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.

A swarm of unmanned aerial vehicles (UAVs) has many potential applications including disaster relief, search and rescue, and area surveillance. A critical factor to a UAV swarm’s success is its ability to collectively locate and pursue targets determined to be of high quality with minimal and decentralized communication. Prior work has investigated nature-based solutions to this problem, in particular the behavior of honeybees when making decisions on future nest sites. A UAV swarm may mimic this behavior for similar ends, taking advantage of widespread sensor coverage induced by a large population. To determine whether the proven success of honeybee strategies may still be found in UAV swarms in more complex and difficult conditions, a series of simulations were created in Python using a behavior modeled after the work of Cooke et al. UAV and environmental properties were varied to determine the importance of each to the success of the swarm and to find emergent behaviors caused by combinations of variables. From the simulation work done, it was found that agent population and lifespan were the two most important factors to swarm success, with preference towards small teams with long-lasting UAVs.

This thesis presents a study on the user adaptive variable impedance control of a wearable ankle robot for robot-aided rehabilitation with a primary focus on enhancing accuracy and speed. The controller adjusts the impedance parameters based on the user's kinematic data to provide personalized assistance. Bayesian optimization is employed to minimize an objective function formulated from the user's kinematic data to adapt the impedance parameters per user, thereby enhancing speed and accuracy. Gaussian process is used as a surrogate model for optimization to account for uncertainties and outliers inherent to human experiments. Student-t process based outlier detection is utilized to enhance optimization robustness and accuracy. The efficacy of the optimization is evaluated based on measures of speed, accuracy, and effort, and compared with an untuned variable impedance controller during 2D curved trajectory following tasks. User effort was measured based on muscle activation data from the tibialis anterior, peroneus longus, soleus, and gastrocnemius muscles. The optimized controller was evaluated on 15 healthy subjects and demonstrated an average increase in speed of 9.85% and a decrease in deviation from the ideal trajectory of 7.57%, compared to an unoptimized variable impedance controller. The strategy also reduced the time to complete tasks by 6.57%, while maintaining a similar level of user effort.

Ferrofluidic microrobots have emerged as promising tools for minimally invasive medical procedures, leveraging their unique properties to navigate through complex fluids and reach otherwise inaccessible regions of the human body, thereby enabling new applications in areas such as targeted drug delivery, tissue engineering, and diagnostics. This dissertation develops a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots. Several experiments are performed to illustrate the adaptability and generalizability of the control algorithm to changes in system parameters, including the three-dimensional reference trajectory, the velocity of the workspace fluid, and the size, orientation, deformation, and velocity of the microrobotic droplet. A linear time-invariant control system governing the dynamics of locomotion is derived and used as the constraints of a least squares optimal control algorithm to minimize the projected error between the actual trajectory and the desired trajectory of the microrobot. The optimal control problem is implemented after time discretization using quadratic programming. In addition to demonstrating generalizability and adaptability, the accuracy of the control algorithm is analyzed for several different types of experiments. The experiments are performed in a workspace with a static surrounding fluid and extended to a workspace with fluid flowing through it. The results suggest that the proposed control algorithm could enable new capabilities for ferrofluidic microrobots, opening up new opportunities for applications in minimally invasive medical procedures, lab-on-a-chip, and microfluidics.

Chronic ankle instability (CAI) is caused by the failure to seek treatment and rehabilitation after an acute ankle sprain. Typically, clinical assessment of ankle sprains is done under unloaded conditions, despite the fact that ankle sprains occur during weight loading. Characterization of ankle stiffness, a representation of ankle stability during weight loading, is crucial to quantify ankle stability. Patients with CAI suffer from gait asymmetry, and the descriptions of the asymmetry ratio vary widely throughout the research community. Bilateral ankle stiffness could be a systematic metric to describe the gait asymmetry of CAI patients. Additionally, women generally have higher ankle joint and ligamentous laxity than men, and lower ankle stiffness, which has been thoroughly investigated in previous literature. However, differences in bilateral ankle stiffness between sexes still need to be investigated. Using twin dual-axis robotic platforms, this study investigated the weight loading effect on ankle stiffness in the frontal plane during standing, the bilateral difference in stiffness between the dominant and non-dominant ankle, and the sex difference in bilateral ankle stiffness during standing for varying weight distribution. The group average results of 20 healthy subjects showed that ankle stiffness increased with increasing weight loading on the ankle, which is speculated to be caused by active muscle contraction and changes in passive structure due to weight loading. For the bilateral difference of the group, the statistical analysis showed that there was no significant difference between dominant and non-dominant ankle stiffness for all the weight distributions considered. Although the group average result of the difference in bilateral ankle stiffness was statistically insignificant, individual analysis confirmed the importance of subject-specific investigation of bilateral ankle stiffness, as there were more cases of dominant ankle stiffness being larger than non-dominant ankle stiffness, and the bilateral difference was subject-specific. Investigations into sex differences in bilateral ankle stiffness showed that ankle stiffness in males is significantly greater than in females, even after normalizing the stiffness by weight, which is speculated to be caused by higher joint and ligamentous laxity in females regardless of laterality.

The introduction of assistive/autonomous features in cyber-physical systems, e.g., self-driving vehicles, have paved the way to a relatively new field of system analysis for safety-critical applications, along with the topic of controlling systems with performance and safety guarantees. The different works in this thesis explore and design methodologies that focus on the analysis of nonlinear dynamical systems via set-membership approximations, as well as the development of controllers and estimators that can give worst-case performance guarantees, especially when the sensor data containing information on system outputs is prone to data drops and delays. For analyzing the distinguishability of nonlinear systems, building upon the idea of set membership over-approximation of the nonlinear systems, a novel optimization-based method for multi-model affine abstraction (i.e., simultaneous set-membership over-approximation of multiple models) is designed. This work solves for the existence of set-membership over-approximations of a pair of different nonlinear models such that the different systems can be distinguished/discriminated within a guaranteed detection time under worst-case uncertainties and approximation errors. Specifically, by combining mesh-based affine abstraction methods with T-distinguishability analysis in the literature yields a bilevel bilinear optimization problem, whereby leveraging robust optimization techniques and a suitable change of variables result in a sufficient linear program that can obtain a tractable solution with T-distinguishability guarantees. Moreover, the thesis studied the designs of controllers and estimators with performance guarantees, and specifically, path-dependent feedback controllers and bounded-error estimators for time-varying affine systems are proposed that are subject to delayed observations or missing data. To model the delayed/missing data, two approaches are explored; a fixed-length language and an automaton-based model. Furthermore, controllers/estimators that satisfy the equalized recovery property (a weaker form of invariance with time-varying finite bounds) are synthesized whose feedback gains can be adapted based on the observed path, i.e., the history of observed data patterns up to the latest available time step. Finally, a robust kinodynamic motion planning algorithm is also developed with collision avoidance and probabilistic completeness guarantees. In particular, methods based on fixed and flexible invariant tubes are designed such that the planned motion/trajectories can reject bounded disturbances using noisy observations.

In this thesis work, a novel learning approach to solving the problem of controllinga quadcopter (drone) swarm is explored. To deal with large sizes, swarm control is often achieved in a distributed fashion by combining different behaviors such that each behavior implements some desired swarm characteristics, such as avoiding ob- stacles and staying close to neighbors. One common approach in distributed swarm control uses potential fields. A limitation of this approach is that the potential fields often depend statically on a set of control parameters that are manually specified a priori. This paper introduces Dynamic Potential Fields for flexible swarm control. These potential fields are modulated by a set of dynamic control parameters (DCPs) that can change under different environment situations. Since the focus is only on these DCPs, it simplifies the learning problem and makes it feasible for practical use. This approach uses soft actor critic (SAC) where the actor only determines how to modify DCPs in the current situation, resulting in more flexible swarm control. In the results, this work will show that the DCP approach allows for the drones to bet- ter traverse environments with obstacles compared to several state-of-the-art swarm control methods with a fixed set of control parameters. This approach also obtained a higher safety score commonly used to assess swarm behavior. A basic reinforce- ment learning approach is compared to demonstrate faster convergence. Finally, an ablation study is conducted to validate the design of this approach.