When compared with traditional robotic manipulators that use discreterigid links and discrete joints, the sensing of soft robotic manipulators is inaccurate and their control is clumsy. This is because that the orientation of a traditional robotic arm can be known sufficiently by measuring relative rotation and translation of the links and joints subject to some initial condition. This could be done by implementing rotary encoders at every joint in a traditional rigid manipulator. Because of their continuum nature, a soft robotic arm’s orientation can only be known exactly if an infinite number were to be used. Therfore, the orientation of soft robotic manipulators can only be practically described when applying a set of simplifying assumptions such as piecewise-constant curvature. There are many ways to obtain information that can be used to describe the curvature of a soft robot; Motion capture is a prevailing technique in research but require that the robot is confined to spaces where motion capture can be used. Since this is not possible in many promising applications of soft robotics, including but not limited to minimally invasive surgery and disaster site navigation, many are looking to other ways to make robots capable of proprioception without using external sensors. One option is incorporating fiber optic sensing into soft robots. Fiber is flexible and a staggering amount of information about a system can be determined by reading changes in wavelength, phase shift, etc, which makes it an excellent solution for soft systems. The problem with incorporating fiber optics into soft robotics is that equipment and technologies such as optical spectrum analyzers, interrogators, fiber Bragg gratings are prohibitively expensive. Some work has been done showcasing that the optical intensity transmitted through a fiber can be correlated with the deformation that the fiber is experiencing (also known as macrobend loss or fiber optic intensity modulation), which can be read with much more accessible equipment. While promising, existing research only uses this technique to measure axial strain. We propose extending the method to determine arbitrary shapes of a continuum arm. We believe the power of data-driven methods will allow us to glean enough information from bending loss to achieve shape reconstruction currently only possible with high-cost optical spectrum analyzers. Specifically, this work aims to use a data-driven method to link the pose of a flexible arm with the intensity outputs of a number of fiber optic sensors attached to its body, using motion capture as ground truth. This work serves as the groundwork necessary for establishing a new low-cost optical sensing technique for soft robotics that could potentially allow important advancements in sensing and thus control.

In this work, I developed and tested designs for increasing energy efficiency in threeareas: soft robotics, buildings, and jumping.

In the area of soft robotics, researchers are interested in using low-boiling point fluids topower pneumatic soft robots, because they can produce large volumes of pressurized gas without the need for bulky pumps and tubing. While this approach is effective at doing work via pressurized vapor, it is highly inefficient. One way to reduce energy consumption from onboard sources (e.g., batteries) is to harvest it, and another way is to improve the energy conversion process directly. In my work, I developed soft robots that can detect heat and light sources and use ambient thermal energy to locomote up to 10 m. I also developed strategies to improve efficiency by nearly two orders of magnitude.

In the area of buildings, efficient thermal regulation is a major challenge; currently, halfof US building energy usage goes towards heating and cooling. One way to increase efficiency in heating and cooling is to take advantage of the Sun and deep space, respectively, via radiative heat transfer. For my work, I designed and tested a mechanical radiative switch in the form of a roofing tile. It helps save energy by passively switching between radiative heating and cooling states. Unlike previous passive designs, my design switches over a small range of temperatures (15 °C). Tests show potential energy savings of at least 2.5× for heating and cooling compared to similar non-switching devices.

In the area of jumping, engineers have built jumpers that can achieve jump heights >30m; while impressive, an estimated 50% of the energy is lost before takeoff. To reduce the energy loss, I developed and analyzed new jumper designs. Preliminary experimental results show that these new designs can improve jump height. My findings can help make future jumping robots more efficient at converting spring potential energy into vertical center of mass kinetic energy, so that they jump higher and last longer.

Soft robots use compliance to interact with and navigate complex environments. High-resolution shape sensing is required to leverage this mechanical advantage by enabling feedback control and environmental mapping. Fiber optic sensing is a promising approach for shape estimation in soft robots. However, commercial fiber-optic shape sensors are prohibitively expensive for many applications in research and development due to their extensive data acquisition apparatus. Moreover, low-cost solutions that have been developed in research settings lack the sensing range of these commercial sensors and can only estimate discrete bending angles or a single radius of curvature. We present a fiber optic sensor that enables multi-curve shape sensing with sub-percent prediction error using an off-the-shelf camera and multiple inexpensive PMMA optical fibers for sensing. This paper introduces a sensor fabrication method based on laser-engraving optical fibers and a data-processing pipeline based on conventional image processing and machine learning tools for interpreting sensor output. We furthermore provide an analytical model to inform sensor design based on the required spatial resolution. Finally, we report empirical findings from several prototypes to highlight the sensor's unique capabilities.

In recent years, interest has been shown in jump-gliding devices. A few devices have been made that can successfully outperform a ballistic projectile using low Reynolds Number glides.

This thesis presents the attempts to develop a high Reynolds Number jump-glider, using both prototyping means and modeling means. The descriptions of these processes include discussions on testing methods, computational trajectory models, and optimization fundamentals. This research has culminated in the framework of a tool that can be used to design optimal jump-gliders.

Robots are traditionally composed of rigid components for their strength and predictability while performing precise, powerful, controllable movements. However, the lack of deformability limits a robot’s capacity to passively adapt to changing tasks and environments. Conversely, soft robots intentionally incorporate material and geometric compliance to passively adapt to challenges. Soft robot performance can be further advanced by leveraging anisotropy; physical properties that are different in different directions.

In my work we use soft robotics with anisotropic properties to address a diverse array of robotic challenges. 1) We used gecko-inspired adhesives, an anisotropic compliant micro-structure which can apply high shear force with near-zero normal force, on a flexible belt in place of rigid gear teeth to create a low-cost, continuously variable transmission with high gear ratios. 2) We made a soft pneumatic gripper with fingers whose stiffnesses vary with direction and joint position to passively adopt a pinch or wrap grasp depending on the shape of the object it is to grasp. 3) We leveraged a woven fabric’s ability to stretch when pulled at 45° to its inelastic warp and weft directions to create a fabric pneumatic artificial muscle. 4) We advanced the design of a tip-extending vine robots, a device that is stiff radially but lengthens from its tip with little resistance when inflated, by creating a camera mount, retraction device, and steering method. 5) With a modified version of the isotropic vine robot concept, we used biologically inspired principles including tip extension and asymmetric, directional air fluidization to manipulate granular forces to enable robotic burrowing in sand. 6) And finally, we are exploring several planetary applications of self-anchoring by creating an anisotropic root-like anchor that requires more force to pull out of the ground than to grow deeper. I believe my work will have wide- ranging impacts on a variety of fields from medical devices and agricultural monitoring to industrial inspection and planetary exploration.

In this work, I developed four robotic systems with unconventional geometries: two multi-robot systems, and two single-robot systems. Unconventional robot geometries can provide simple solutions to perform target tasks. Two multi-robot systems leverage their geometry to easily connect together into tight-packed formations and exert forces upon each other. The first is a set of three robots that can each lift 30 kg and attach together to locomote. The second multi-robot system is a twenty unit collective that is strong enough to form sturdy structures capable of supporting a person 70 kg yet can flow into new flexible/elongated forms under its own weight. This development is a critical step toward the robotic materials of science fiction, capable of changing their material properties dynamically to suit any situation. The two single robot designs use their geometry to readily traverse unstructured terrain. The first strategy is to inflate a soft, shape changing skin to drive over rocks or up stairs. The next design is for a jumping robot that stores energy in a spinning flywheel, and releases that stored energy to jump. This jumping robot uses a deconstructed twisted string transmission, and preliminary results suggest a jumping efficiency nearly double that of previous elastic energy storage jumping robots. Careful consideration of robot interaction forces is the key to the success of these robots, and I will explore these forces in detail for each project.

Soft robotics represents a shift in engineering design methodology: one that draws inspiration from biology and strives to move away from complex, rigid assemblies of numerous discrete parts and towards soft assemblies comprised of relatively few parts. Proponents of this shift advocate that soft robots will enable safe operation around humans, increased resistance to mechanical damage, operation in hazardous environments, and other functions that are presently inaccessible via traditional (i.e., rigid) robotics. However, bringing these visions to life requires efficient energy systems, soft controllers, and new material systems. This dissertation is an exploration of the myriad ways that light-material interactions can be leveraged to offer innovations in each of these areas. Light serves as the common thread among each of these investigations as it is an accessible form of energy that can be modulated (e.g., by wavelength or intensity) and transmitted (e.g., lasers) or harvested (e.g., sunlight) over long distances with highly tunable spatial and temporal precision. First, we examine the capabilities of soft heat engines, identify their major sources of losses and offer three key insights that soft roboticists can leverage to minimize them (Chapter II). Second, we investigate the photokinetics of a novel class of photoswitches, which constitute the basis for a thermofluidic switch (Chapter III). Finally, we investigate the mechanical properties of bio-inspired stiff-soft multi-material composites that exhibit impressive toughness (Chapter IV). These endeavors at the intersection of materials, mechanics, chemistry, and thermodynamics put forth foundational knowledge that advances the field of soft robotics and also inspires new avenues for meeting the growing demand for adaptive and autonomous materials and devices.