While fluidic elastomer actuators (FEAs) have been widely adopted for soft roboticsapplications showing potential for capabilities not achievable by their rigid counterparts, they have limited stiffness due to their innate compliance and cannot alter stiffness independently of their configuration on their own. Existing methods of combining FEAs with other mechanisms to achieve stiffness tunability limit the flexibility of the actuators and necessitate additional control inputs, or require energy to maintain higher stiffness and need onboard electronics, and may not work in the presence of electromagnetic fields. To address these limitations, this work proposes the use of supercooled liquid (sodium acetate trihydrate solution) as the multifunctional working fluid of FEAs to control both position and stiffness; we hypothesize that such fluid would be capable of both inflating the elastomer chambers while in liquid state and rapidly solidifying and stiffening the actuators. We investigate the mechanical properties of crystallized SAT samples and ways that they can be reinforced. Additionally, we analyze the stiffness of FEAs that use the supercooled SAT solution as their working fluid, highlighting the high degree of stiffness change (increase by 6.56 times) induced by the crystallization of the solution. Furthermore, we demonstrate the multifunctional manipulation and stiffness change of an FEA with minimal control inputs using supercooled SAT solution as the working fluid. The proposed mechanism is a new way to combine the high manipulability of FEAs with stiffness tunability and opens up new possibilities in designing soft robots and manipulators.

Many platforms have been developed for moving remotely underwater; however, many of these systems are limited to traversing open water and must expend large amounts of energy to maintain a position in flow for long periods of time. Legged animals are common in nature, but often have fixed body morphologies restricting them to constant hydrodynamic profiles. This work presents an underwater legged robot with soft legs and a soft morphing body for manipulating the hydrodynamic forces in flow. Computational fluid dynamics (CFD) simulations of the morphing body in flow allow 1) prediction of the effect of morphing on lift and drag forces, and thus 2) determination of which body configuration is most favorable for specific tasks. Flow over the morphing body separates behind the trailing edge which determines where turbulence begins to form, causing additional drag. When the legged robot needs to remain stationary in flow, a flat structure offers reduced hydrodynamic forces for resisting sliding. When the legged robot needs to walk with flow, a larger inflated body is pushed along by the flow, causing that robot to walk faster than it would otherwise. A commercial force sensor can detect flow so that the robot can respond by morphing into a more advantageous shape. Experiments with the prototype robot are used to test these capabilities.

Mobile robots are commonly used to perform tasks in underwater environments that are difficult for humans to endure, such as exploration, long-duration measurements, or maintenance and repair of underwater structures. Traditional underwater robots are often bulky and disruptive to the environment around it and are often adapted from engineered systems that were designed for operation in air. However, the underwater fluid environment is significantly different from the in-air environment and motivates the development of new robot paradigms specifically to address the challenges that arise from the surrounding water. I have taken inspiration from nature, which has evolved fast and efficient mechanisms for underwater locomotion, to design soft, bioinspired walking and swimming robots.

In this work, I have explored several ways to design mobile robots specifically for the underwater fluid environment. To mitigate the negative effects of flow on an underwater walking robot, I created a soft inflatable structure that can be attached to the robot to alter the lift and drag forces on the robot and increase traction in flow. To create locomotion independent of the flow on the robot, I designed soft suction discs and soft linear actuators that enable adhesive-based locomotion. By leveraging interactions with fluid, I created a steerable, shape-changing robot that uses vectored jet propulsion to swim through open water. This work has the potential to enable more efficient locomotion in underwater environments more closely resembling the capabilities of biological systems.

Traditional robots, such as robotic arms in assembly lines, are designed for specialized, repeatable tasks. However, robotic applications are not always set in structured environments. Rather than using rigid components, soft robots made from soft materials are able to bend, extend, and deform. These movements enable soft robots to swim, walk, crawl, and grasp objects like the impressive capabilities of biological systems, such as the octopus. I hypothesized that the ability of soft legs to easily compress and bend would reduce the complexity of the control algorithms and hardware required for navigating unstructured obstacles. However, because soft walking robots move very differently than their rigid counterparts, they provide a new challenge. Soft appendages must be soft enough to passively adapt to the environment while being stiff enough to generate forces for walking.

In this thesis, I have investigated methods to design, fabricate, model, and control soft-legged robots that are able to navigate over obstacles using simple control strategies. Inspired by nature, I found that a pneumatically actuated soft-legged quadruped robot with three actuated degrees-of-freedom (DoF) per leg was able to navigate over loose rocks or pebbles, squeeze into tight spaces, and walk underwater against flow when augmented with an inflatable soft body. I developed an application-driven design framework to relate the geometry and material properties of the soft legs to common metrics such as bend angle and blocked force. This design framework enables roboticists to rapidly design and fabricate soft robots to satisfy functional requirements. I also developed a lumped-parameter soft robot simulator to replicate the movement of the robot and used genetic algorithms to evolve practical, task-driven control strategies for accomplishing challenging problems, such as squeezing through a confined opening. Practical implementation of these gaits normally requires heavy and bulky pumps and valves which can be very challenging to carry onboard a robot with soft legs. To address this concern, I developed soft air-powered circuits to control the gait of the soft-legged robot without requiring any electronic components. The sequential behaviors were mechanically “programmed” in the circuit by storing information using the snap-through instability in hemispherical elastomeric membranes. The pneumatic memory elements in the circuits changed the walking direction of the robot based on physical interactions with the world. These pneumatic circuits could potentially be used to control electronics-free soft robots for navigating environments where electronics may not be suitable, such as environments sensitive to spark ignition. The contributions in this dissertation enable more versatile soft robotic systems which could potentially be used to monitor hazardous environments.

In this work, a data-efficient method is applied to learn a model of the dynamics of a self-folding robot driven by vibration. These robots can be autonomously fabricated and deployed, but complex dynamics lead to challenges in modeling. Learning from a limited set of observed experiments, a model is developed to control the locomotion of the robot along a desired trajectory. The model is fit assuming a probabilistic Gaussian model and a neural network. The two methods are benchmarked against a differential drive algorithm.

Soft sensors using a highly conductive liquid metal eGaIn(eutectic gallium-indium) are widely researched due to the increasing need for flexible sensors such as temperature sensors, haptic sensors, multi-modal sensors and force sensors. Manufacturing methods for these soft sensors have been explored including printing, injection, lithography and spraying. Among these methods, direct writing of eGaIn is the fastest and simplest method. Other methods are mostly conventional mold-based fabrication while printing does not require complicated pre-processing of elastomer. Although existing printing methods are able to pattern the liquid metal into various designs within micro scale, these methods either require high-precision feedback control over SOD, extrusion rate, low stage velocity or modification of liquid metal. As a result, the low tolerance in parameters of printing or extra modification of liquid metal make it difficult to replicate the work. Therefore, as a trade-off between printing quality and tolerance in precision, in this paper we present two open loop liquid metal printing methods with higher tolerance in precision using a low-cost open source system without the need to modify property of liquid metal. We discuss how parameters and setup of the printer will affect the printing quality. We also discuss how well the printing methods can resist variation in substrate height. As an example of applications of the proposed methods, we manufacture a strain sensor using the proposed method and demonstrate calibration result of the sensor. In general, these printing methods are alternative ways to manufacture liquid metal sensors when high accuracy is not desired.

Robots capable of generating adhesion forces that can achieve free movement in application environments while overcoming their own gravity are a subject of interest for researchers. A robot with controllable adhesion could be useful in many engineered systems. materials processing equipment, robots that climb walls, and pick-and-place machines are some examples. However, most adhesion methods either require a large energy supply system or are limited by the properties of the contact plane. For example, elector-magnetic adhesion requires a ferromagnetic surface and electrostatic adhesion requires a dielectric surface. Furthermore, nearly all existing approaches are only used to generate adhesion forces and often require additional mechanisms to remove the adhesive component from the surface. In this study, we aimed to develop a simpler method of adhering to a surface while simultaneously moving in directions parallel to the surface, using multiple vibration sources to generate normal adhesion. To test our approach, we constructed circular and elliptical models and conducted experiments with various inputs and model parameters. Our results show that this new method can achieve adhesive rotation and displacement in the plane without requiring any auxiliary operating system. By using only vibration sources, we were able to generate the necessary adhesion forces for the robot's motion. This work represents a step towards the construction of a small-sized tetherless robot that can overcome gravity and move freely in a general environment.

As robots grow increasingly prevalent in real-world environments, sensory systems capable of sensing complex deformations and environmental interactions are needed for robust control. Soft robotics has emerged as a field of study that seeks to replace rigid components in traditional robots with materials that are compliant. It has garnered interest for real-world applications due to intrinsic safety embedded at the material level, deformable materials capable of shape and behavioral changes, and conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multi-modal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This dissertation describes the design, fabrication, and data processing of soft, tactile sensor skins, with the ultimate goal of enhancing future collaborative robots that will work alongside and physically interact with people with a human-like sense of touch.

This thesis focuses on how the integration of soft sensor skins and machine learning enables soft robots to perceive physical interaction for complex haptic tasks. Part 1 on Soft Sensors (Chapters 2, 3, and 4) presents the design and fabrication of various types of soft strain sensors, using compliant materials such as silicones and polymers. Fabrication methods include soft lithography and 3D printing. Performance of the sensors are characterized and modeled. Part 2 on Soft Robot Perception (Chapters 5 and 6) describes how machine learning can be used to augment the performance of soft sensors and actuators. The method demonstrates how recurrent neural networks can be used for graceful degradation and learned perception of external contacts and forces despite not having a priori information about the individual sensors. Part 3 on Social Touch for Physical Human-Robot Interaction (Chapters 7 and 8) analyzes the use of the liquid metal sensors as robotic skins for the classification of affective (social) touch and builds towards the development of a framework for representing physical contact, for use at the human-robot interaction layer of abstraction.

In recent years, the concept of soft-bodied robots has rapidly grown in popularity. Researchers have developed many interesting forms of actuation that more closely mimic the functionality and capabilities found in nature. The next step for the field is to develop biologically-inspired tactile sensing for soft-bodied robots that can safely interact with and explore their environments. In the short term, the field can focus on deployable, high-resolution sensor skins, algorithms for processing the dense sensor information, and reliable feedback control for soft robots. Building upon the fundamental work presented in this dissertation, the future consists of robots that can touch and feel with the sensitivity and perception of natural systems.

In this work, I explore two facets of manipulation of delicate objects. First I describe the development of a new closed system for differential pressure control of 3D printed soft fluidic actuators. I further explore the quantitative advancements it promises for soft robotics towards a robotics manipulator capable of safely and efficiently manipulating infant’s fingers. Secondly, we present the development of a biometrics system for vaccinations which requires manipulation and imaging of infants’ fingers. The fingerprinting process could highly benefit from automation solutions for infants’ fingerprint platen induced deformation due to contact. The next aspect of my thesis is the experimental approach for iterative testing in technology design. Starting with the volumetric control platform developed to enable accurate iterative testing in laboratory settings for experimental characterization of soft actuators with differential pressure control. In this work, I demonstrated a substantial improvement in achievable blocked force, and a significant increase in actuator workspace when using differential pressure actuation as compared to the use of only pressure or vacuum. The increased workspace allowed the robot to achieve complex tasks towards manipulation of fragile objects. Furthermore, I demonstrate a self-healing capability of the combined system for improved soft robotics robustness. Then I follow with an approach for human-centered design with iterative prototyping where experiments can only be performed in situ with live infant subjects. This separation between the design and experiments yields a very challenging progress evaluation and required a unique design iteration methodology. With the resulted fingerprints images from the two leading devices, I demonstrated a higher reliability for high quality infants’ fingerprints using non-contact imaging over contact in the goal of developing a reliable biometrics identification system of infants for vaccination.

It is often impractical or dangerous to send people to explore underwater environments. In these situations, it is preferable to send robots such as autonomous underwater vehicles or remotely operated vehicles instead. Unfortunately, robots impart their own risks: they are typically made of rigid materials that can become lodged in confined spaces or harm underwater creatures and structures. Additionally, propellers or jet thrusters are typically used for propulsion, which are power intensive, have low efficiency, and impose additional concerns of entanglement and damage to their environment. Finally, they generate considerable noise and vibration, thus adding to the ambient noise pollution and disturbing sea life, preventing researchers from being able to study more timid animals. In this dissertation, I describe physical mechanisms to develop bioinspired, soft, swimming robots with an emphasis on actuation. First, I present an approach to use an arrangement of six artificial muscles based on dielectric elastomer actuators (DEAs) to actuate a tethered robot capable of anguilliform-inspired locomotion. Next, I demonstrate pulsatile, jellyfish-inspired locomotion using DEAs with a simpler actuation and control strategy, enabling an untethered, soft, swimming robot. Finally, I explore an alternative actuation approach to achieve more robust locomotion in a cephalopod-inspired robot based on slowly storing elastic energy and then quickly releasing it to eject a pulsed jet for propulsion. The first two robots are silent and use actuators that have a high energy density and efficiency, but provide low output power and swim at low speeds. In the cephalopod-inspired robot, we trade silence and efficiency for power and speed. These results demonstrate actuation strategies for realizing bioinspired locomotion in soft, swimming robots that could be useful for structural diagnostics, environmental monitoring, or search and rescue.