Within the bodies of living organisms are multitudes of sustainable design solutions that engineers have yet to master. Through the use of tailored sustainable bioinspired design (BID) tools and methodologies, engineers could access and apply this body of biological knowledge to reduce the environmental impact of engineering designs. However, the underlying theory of BID must be more thoroughly fleshed out – and a clearer understanding of the types of sustainability solutions present in biology must be achieved – before such tools and methodologies can be developed. The goal of this thesis is to tackle both issues and, consequently, lay the foundation for environmentally sustainable BID. The first section of this work critically examines thirteen of the most frequently-cited benefits of BID, using academic literature from both biology and engineering design. This analysis presents a nuanced explanation of the ways BID could improve designs and the conditions in which these improvements are expected. Hence, it provides the theoretical foundation necessary to develop tools and methodologies that capitalize on the design opportunities found in biological organisms. The second section focuses on identifying sustainability-related trends in a pool of existing, sustainable BIDs. The type of environmental impact reduction conferred by the bioinspired feature is delineated using a set of 65 green design guidelines (GDGs) to compare the impact of the BID and a functionally-equivalent comparison product. Additionally, the general design features that impart an environmental impact reduction to the sustainable BIDs are identified, analyzed, and discussed. These results provide insight into the types of sustainability solutions that can be found using biological analogies. / text

Bioinspired design uses biological systems to inspire engineering designs. One of bioinspired design’s challenges is identifying relevant information sources in biology for an engineering design task. Currently information can be retrieved by searching biology texts or journals using biology-focused keywords that map to engineering functions. However, this search technique can overwhelm designers with unusable results. This work explores the use of text classification tools to identify relevant biology passages for design. Further, this research examines the effects of using biology passages as stimuli during idea generation. Four human-subjects studies are examined in this work. Two surveys are performed in which participants evaluate sentences from a biology corpus and indicate whether each sentence prompts an idea for solving a specific design problem. The surveys are used to develop and evaluate text classification tools. Two idea generation studies are performed in which participants generate and record solutions for designing a corn shucker using either different sets of biology passages as design stimuli, or no stimuli. Based 286 sentences from the surveys, a k Nearest Neighbor classifier is developed that is able to identify helpful sentences relating to the function “separate” with a precision of 0.62 and recall of 0.48. This classifier could potentially double the number of helpful results found using a keyword search. The developed classifier is specific to the function “separate” and performs poorly when used for another function. Classifiers developed using all sentences and participant responses from the surveys are not able to reliably identify helpful sentences. From the idea generation studies, we determine that using any biology passages as design stimuli increases the quantity and variety of participant solutions. Solution quantity and variety are also significantly increased when biology passages are presented one at a time instead of all at once. Quality and variety are not significantly affected by the presence of design stimuli. Biological stimuli are also found to lead designers to types of solution that are not typically produced otherwise. This work develops a means for designers to find more useful information when searching biology and demonstrates several ways that biology passages can improve ideation.

Bioinspired design methods are a new and evolving collection of techniques used to extract biological principles from nature to solve engineering problems. The application of bioinspired design methods is typically confined to existing problems encountered in new product design or redesign. A primary goal of this research is to utilize existing bioinspired design methods to solve a complex engineering problem to examine the versatility of the method in solving new problems. Here, current bioinspired design methods are applied to seek a biologically inspired solution to geoengineering. Bioinspired solutions developed in the case study include droplet density shields, phosphorescent mineral injection, and reflective orbiting satellites. The success of the methods in the case study indicates that bioinspired design methods have the potential to solve new problems and provide a platform of innovation for old problems. A secondary goal of this research is to help engineers use bioinspired design methods more efficiently by reducing post-processing time and eliminating the need for extensive knowledge of biological terminology by applying natural language processing techniques. Using the complex problem of geoengineering, a hypothesis is developed that asserts the usefulness of nouns in creating higher quality solutions. A designation is made between the types of nouns in a sentence, primary and spatial, and the hypothesis is refined to state that primary nouns are the most influential part of speech in providing biological inspiration for high quality ideas. Through three design experiments, the author determines that engineers are more likely to develop a higher quality solution using the primary noun in a given passage of biological text. The identification of primary nouns through part of speech tagging will provide engineers an analogous biological system without extensive analysis of the results. The use of noun identification to improve the efficiency of bioinspired design method applications is a new concept and is the primary contribution of this research.

<div><div><div><div><p>Insects are the most successful animal on the planet, undergoing evolutionary adaptions in size and the development of flight that have allowed access to vast ecological niches and enabled a means by which to both prey and escape predation. Possessing some of the fastest visual systems on the planet, powerful sets of flight muscles, and mechanosensors tuned to perceive complex environments in high-fidelity, they are capable of performing acrobatic maneuvers at speeds that far exceed that of any engineered system. In turn, stable flight requires the coordinated effort of these highly specialized flight systems while performing activities ranging from evasive flight maneuvers to long-distance seasonal migrations in the presence of adverse flow conditions. As a result, the exceptional flight performance of flying insects has inspired a new class of aerial robots expressly tailored to exploit the unique aerodynamic mechanisms inherent to flapping wings. Over the course of three research studies, I explore new actuation techniques to address limitations in power and scalability of current robot platforms, develop new analytical techniques to aid in the design of insect-inspired robot flapping wings, and investigate attributes of flapping wing aerodynamics that allow insects to overcome the difficulties associated with flight in turbulent flow conditions, in an effort to advance the science of animal locomotion.</p><p>Recent advancements in the study of insect flight have resulted in bio-inspired robots uniquely suited for the confined flight environments of low Reynolds number flow regimes. Whereas insects employ powerful sets of flight muscles working in conjunction with specialized steering muscles to flap their wings at high frequencies, robot platforms rely on limited sets of mechanically amplified piezoelectric actuators and DC motors mated with gear reductions or linkage systems to generate reciprocating wing motion. As a result, these robotic systems are typically underactuated - with wing rotation induced by inertial and aerodynamic loading - and limited in scale by the efficiency of their actuation method and the electronics required for autonomous flight (e.g., boost converters, microcontrollers, batteries, etc.). Thus, the development of novel actuation techniques addressing the need for scalability and use of low-power components would yield significant advancements to the field of bio-inspired robots. As such, a scalable low-power electromagnetic actuator configurable for a range of resonant frequencies was developed. From physics-based models capturing the principles of actuation, improvements to the electromagnetic coil shape and a reconfiguration of components were made to reduce weight and increases overall efficiency. Upon completion of a proof-of-concept prototype, multiple actuators were then integrated into a full-scale robot platform and validated through a series of free flight experiments. Design concepts and modeling techniques established by this study have since been used to develop subsequent platforms utilizing similar forms of actuation, advancing the state-of-art in bio-inspired robotics.</p><p>With the ability to make instantaneous changes in mid-flight orientation through subtle adjustments in angle-of-attack, the maneuverability of flying insects far exceeds that of any man-made aircraft. Yet, studies on insect flight have concluded that the rotation of insect wings is predominately passive. Coincidentally, bio-inspired flapping wing robots almost universally rely on passive rotational mechanisms to achieve desired angles-of-attack - a compromise between actuator mass and the controllable degrees-of-freedom that results in underactuated flight systems. For many platforms, the design of passive mechanisms regulating the rotational response of the wing is determined from either simulations of the wing dynamics or empirically derived data. While these approaches are able to predict the wing kinematics with surprising accuracy, they provide little insight into the effects that wing parameters have on the response or the aerodynamic forces produced. Yet, these models establish a means by which to both study insect flight physiology and explore new design principles for the development of bio-inspired robots. Using a recent model of the passively rotating insect wing aerodynamics, a novel design principle used to tune the compliance of bio-inspired robot wings is developed. Further, through the application of nonlinear analysis methods, parameters optimizing lift production in flapping wings is identified. Results from this analysis are then validated experimentally through tests preformed on miniature flapping wings with passive compliant hinges. This work provides new insight into the role passive rotational dynamics plays in insect flight and aids in the development future flapping wing robots.</p><div>Insect flight is remarkably robust, enabling myriad species to routinely endure adverse flow environments while undergoing common foraging activities and long-distance migratory flights. In contrast to the laminar (or smooth) flow conditions of high-altitude flights by commercial aircraft, insect flight occurs within the lower atmosphere where airflows are unsteady, and often turbulent. Yet despite the substantial challenge these conditions pose to an insect's physiology, flights spanning entire continents are common for numerous migratory species. To investigate how insects sustain stable flight under fluctuating flow conditions, the aerodynamic forces and flows produced by a dynamically scaled robotic insect wing immersed in a specially devised turbulence tank were examined. Despite variation in aerodynamic forces generated between wing strokes, results show that the averaged force from flapping remains remarkably steady under turbulent conditions. Furthermore, measurements of the flows induced by the wing demonstrated that unsteady aerodynamic forces generated by flying insects actively buffer against external flow fluctuations. These results provide mechanistic evidence that insect flight is resilient to turbulent conditions, and establishes principles that aid in the development of insect-inspired robots tailored for flight in adverse flow environments.<br></div></div></div></div></div>

In this study, the teeth of the Carcharodon carcharias (Great White) and the Galeocerdo cuvier (Tiger) sharks were analyzed to examine their optimized structure-property relationships and edge serrations with regards to shearing. Structure-property analysis was conducted using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy, X-ray diffraction, and optical microscopy to study the teeth using parametric optimization. Quantifying the structural properties also focused on the tooth serrations, which were captured in SEM and micrographs and were analyzed for geometric parameters using ImageJ software. Nanoindentation was performed to determine the material's mechanical properties. Further, finite element analysis (FEA) of the sharks' teeth serrations were carried out to quantify the optimum shearing performance of each serration type – zeroth (no serrations), first (a single array of serrations), and second (a secondary array of serrations upon the first array) order serration. Here, serration order, bite velocity, and angle-of-impact for ascertaining sharks' teeth shearing performance were analyzed. FEA results showed that serrated edges reduced the energy required to pierce and shear materials as the angle of penetration moved away from perpendicular to the surface. These bioinspired findings will help advance the design and optimization of engineered cutting tools.

<p>Structures with inherent shape change capabilities enable adaptive, efficient designs without the weight and complexity of external actuators and sensors. Morphing structures are found in nature: plants are able to achieve fast motion without muscular or nervous systems. For example, the Venus flytrap snaps to a closed state with spatially distributed curvatures in less than one second. In contrast, synthetic shape change has been limited by a trade-off between complexity and speed. Shape memory polymers (SMPs) can remember complex shapes, but morphing is slow and one-way. Multistability due to mechanical buckling is fast and reversible, but it has been limited to simple shapes. Furthermore, many examples of biological shape change follow logical patterns with mechanisms that selectively respond to environmental stimuli. This suggests that synthetic morphing structures may also lend themselves to alternative forms of sensing, memory, and logic.</p> <p><br></p> <p>In this research, we introduce a new method of using SMPs in combination with the hierarchical architectures of pre-strained multistable laminates to create switchable multistable structures (SMS). An SMS can remember multiple permanent shapes and reversibly snap between them. We use extrusion-based 3D printing to encode contrasting shape memory-based pre-strain fields in a bilayer. Above the SMP’s glass transition temperature, the SMS becomes compliant and remembers multiple encoded permanent shapes with fast snap-through between them. Below the transition temperature, the SMS regains its stiffness and is fixed in a single state. The geometric freedom of 3D printing enables the design and manufacture of bioinspired structures with complex pre-strain fields and deflections. The developed printing method is applied in multiple subsequent studies, including mechanical pixels, self-folding spring origami structures, and multistable structures printed with thermoset composite inks. </p> <p><br></p> <p>The highly nonlinear behavior of bistable, pre-strained structures makes their design difficult and nonintuitive. Generally, these structures are designed using a slow, iterative process with finite element analysis (FEA). We aim to solve the inverse optimization problem: start with target stable states and solve for the necessary pre-strain distributions. To this end, we develop and implement the switching tunneling method (STM) to design pre-strained,</p> <p>multistable structures. Instead of FEA, we leverage analytical solutions for gradient-based optimization. Tunneling allows for the efficient search of a design space which may contain multiple local and global minima. Switching enables us to take advantage of two different function transformations, depending on if the search is far from or close to a minimum. The STM is validated through FEA and experiments for both conventional and variable</p> <p>pre-strain bistable structures.</p> <p><br></p> <p>Structures designed to react to external conditions or events offer the opportunity to directly integrate sensing, memory, and computation into a structure. This concept is explored using metasheets composed of locally bistable unit cells, which display spatiotemporal mechanical sensing (mechanosensing) and memory. A unit cell consists of a bistable dome with a piezoresistive strip at the base; the resistance indicates the state of the dome. The mechanics of bistability offer inherent filtering and nonlinear signal amplification capabilities, tunable via geometric parameters. Metasheet arrays of these unit cells display distributed sensing capabilities, as well as hierarchical multistability.</p> <p><br></p> <p>We explore the use of time-dependent material properties combined with the mechanics of multistability to encode many unique values within a single mechanosensor unit cell, beyond binary memory. When the piezoresistive material is viscoelastic, cyclic loading causes cumulative changes in both the ground and inverted state resistances. Effectively, the metamaterial is able to count how many times an external force has been applied; this count is stored in the metamaterial’s intrinsic, measurable properties.</p> <p><br></p> <p>This work demonstrates the importance of incorporating memory concepts into structural design, which enables multistability with complex stable shapes, as well as spatiotemporal sensing and memory capabilities. Engineered systems require increasingly adaptive and responsive structures to improve efficiency. The incorporation of inherent memory and sensing enables the complex behaviors needed to interact with unstructured environments</p> <p>and biological features, a pressing issue for aerospace, soft robotics and biomedical devices. The methodology developed here to manufacture, design, and analyze multistable structures advances the state of the art and makes their implementation more practical.</p>

In recent years, the design, fabrication, and control of robotic systems inspired by biology has gained renewed attention due to the potential improvements in efficiency, maneuverability, and adaptability with which animals interact with their environments. Motion studies of biological systems such as humans, fish, insects, birds and bats are often used as a basis for robotic system design. Often, these studies are conducted by recording natural motions of the system of interest using a few high-resolution, high-speed cameras. Such equipment enables the use of standard methods for corresponding features and producing three-dimensional reconstructions of motion. These studies are then interpreted by a designer for kinematic, dynamic, and control systems design of a robotic system. This methodology generates impressive robotic systems which imitate their biological counter parts. However, the equipment used to study motion is expensive and designer interpretation of kinematics data requires substantial time and talent, can be difficult to identify correctly, and often yields kinematic inconsistencies between the robot and biology. To remedy these issues, this dissertation leverages the use of low-cost, low-speed, low-resolution cameras for tracking bat flight and presents a methodology for automatically learning physical geometry which restricts robotic joints to a motion submanifold identified from motion capture data. To this end, we present a spatially recursive state estimator which incorporates inboard state correction for producing accurate state estimates of bat flight. Using these state estimates, we construct a Gaussian process dynamic model (GPDM) of bat flight which is the first nonlinear dimensionality reduction of flapping flight in bats. Additionally, we formulate a novel method for learning robotic joint geometry directly from the experimental observations. To do this, we leverage recent developments in learning theory which derive analytical-empirical potential energy fields for identifying an underlying motion submanifold. We use these energy fields to optimize a compliant structure around a single degree-of-freedom elbow joint and to design rigid structures around spherical joints for an entire bat wing. Validation experiments show that the learned joint geometry restricts the motion of the joints to those observed during experiment. / Ph. D. / In recent years, robots modeled after biological systems have become increasingly prevalent. Such robots are often designed based on motion capture experiments of the animal they aim to imitate. The motion studies are typically conducted using commercial motion capture systems such as ViconTM or OptiTrackTM or a few high-speed, high-resolution cameras such as those marketed by PhotronTM or PhantomTM. These systems allow for automated processing of video sequences into three-dimensional reconstructions of the biological motion using standard image processing and state estimation techniques. The motion data is then used to drive robotic system designs such as the SonyTM AiboTM dog and the Boston Dynamics Atlas humanoid robot. While the motion capture data forms a basis for these impressive robots, the progression from data to robotic system is neither algorithmic nor rigorous and requires substantial interpretation by a human. In contrast, this dissertation presents a novel experimental and computational framework which uses low-speed, low-resolution cameras for capturing the complex motion of bats in flight and introduces a methodology which uses the motion capture data to directly design geometry which restricts the motion of joints to the motions observed in experiment. The advantage of our method is that the designer only needs to specify a general joint geometry such as a ball or pin joint, and geometry which restricts the motion is automatically identified. To do this, we learn an energy field over the set of kinematic configurations observed during experiment. This energy field “pushes” system trajectories towards those experimentally observed trajectories. We then learn compliant or rigid geometry which approximates this energy field to physically restrict the range of motion of the joint. We validate our method by fabricating joint geometry designed using both these approaches and present experiments which confirm that the reachable set of the joint is approximately the same as the set of configurations observed during experiments.

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