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.

Au nanoparticles are increasingly being used in biological applications. Their use is of interest based upon their unique properties that are achieved at the nanoscale, which includes strong optical absorbances that are size and aggregation state dependent. Such absorbances can be used in sensitive chemical/biological detection schemes where bioligands can be directly attached to the nanoparticle surface using facile methods. Unfortunately, a number of complications persist that prevent their wide-scale use. These limitations include minimal nanoparticle stability in biological-based media of high ionic strength, unknown surface functionalization effects using simple biomolecules, and determining the binding motifs of the ligands to the nanoparticle surface. This situation can be further complicated when employing shaped materials where crystallographic facets can alter the binding potential of the bioligands. We have attempted to address these issues using traditional nanoparticle functionalization techniques that are able to be characterized using readily available analytical methods. By exploiting the optical properties of Au nanomaterials, we have been able to determine the solution stability of Au nanorods in a buffered medium and site-specifically functionalized Au nanomaterials of two different shapes: spheres and rods. Such abilities are hypothesized to be intrinsic to the bioligand once bound to the surface of the materials. Our studies have focused mainly on simple amino acids that have demonstrated unique assembly abilities for the materials in solution, resulting in the formation of specific patterns. The applications for such capabilities can range from the use of the materials as sensitive biochemical sensors to their directed assembly for use as device components.

Cette thèse s’inscrit dans le domaine de la navigation robotique bio-inspirée en environnement réel et implique la capacité pour un robot mobile à se déplacer de manière autonome dans un monde a priori dynamique et inconnu. Les travaux décrits au cours de ce manuscrit s’attacheront à montrer comment, en partant des travaux académiques réalisés par l’équipe Neurocybernétique du laboratoire ETIS, il a été possible de concevoir le robot mobile Diya One capable de naviguer de manière autonome dans de grands environnements intérieurs. Depuis une vingtaine d’année, l’équipe Neurocybernétique élabore des modèles de navigation bio-inspirée. De précédents travaux ont montré qu’un modèle de cellules de lieu, enregistrées chez le rat, permet à un robot mobile d’apprendre des comportements de navigation robustes, tels qu’une ronde ou un retour au nid, à partir d’associations entre lieu et action. L’apprentissage et la reconnaissance d’un lieu ne reposent alors que sur des informations visuelles. Cependant, trois problèmes critiques ne permettent pas de naviguer dans de grands environnements : 1- l’ambiguïté de certaines situations visuelles (ou alias perceptif), 2- l’apprentissage sur le long terme et 3- la sensibilité aux conditions environnementales. L’ajout d’autres modalités constitue une solution efficace pour augmenter la robustesse de la localisation. L’équipe a développé plusieurs modèles basés sur la proprioception du robot afin desuppléer, dans les cas limites, les modèles purement visuels. La principale limitation des approches proprioceptives est qu’elles sont soumisesà l’accumulation d’erreurs. Il est donc nécessaire de recalibrer périodiquement les modèles. Fusionner des modalités allothétiques et idiothétiques semblent être une bonne stratégie pour obtenir une estimation fiable de la localisation du robot. Les champs de neurones dynamiques (DNF) ou continous attractor neural network (CANN) constituent un puissant candidat pour mettre en œuvre le type de mémoire requis pour la construction de cellules de lieu. Nous présenterons un premier modèle de fusion utilisant les champs de neurones dynamiques pour maintenir l’orientation du robot puis un second généralisant le principe du modèle de fusion pour la construction de cellules de lieux multimodales.Être capable de produire et commercialiser rapidement un produit est un enjeu majeur pour la survie de Partnering. En plus des capacités de navigation et de localisation, un robot commercialisable requiert un ensemble de comportements indispensables à la mobilité, à la sécurité (loi de contrôle, évitement des obstacles et des trous) et à son autonomie (gestion d’énergie et retour à la station de recharge). Pour aboutir à cette première solution, nous avons suivit une démarche ascendante (bottom-up) défendue par la robotique comportementale. Nous avons développé progressivement la complexité du robot au travers de comportements élémentaires intégrés dans une architecture de contrôle régissant à chaque instant l’expression de ces comportements et la sélection des actions à exécuter.Ce mémoire est découpée en deux parties. Une première partie industrielle relevant d’objectifs à court terme, consistant à mettre en place, à partir des modèles existants développés par l’équipe Neurocybernétique, l’architecture comportementale de la première version du robot Diya One. Puis, une seconde partie plus fondamentale dans laquelle nous traiterons de la réalisation demodèles de fusion multimodale. Ces modèles seront ajoutés incrémentalement au robot afin d’améliorer progressivement ses capacités de navigation. / This thesis falls into the field of navigation in bio-inspired robotics in real environment and implies the ability for a mobile robot to move autonomously in a world a priori dynamic and unknown. The work described in this manuscript will show how, starting from the academic work carried out by the Neurocybernetics team of the ETIS laboratory, it was possible to design the mobile robot Diya One able to navigate autonomously in large indoor environments. For the past 20 years, the Neurocybernetics team has been developing bio-inspired navigation models. Previous work has shown that a model of place cells, recorded in the rat, allows a mobile robot to learn robust navigation behaviors, such as a round or a homing, from associations between place and action. Learning and the recognition of a place are based only on visual information. However, three critical problems do not allow to navigate in large environments: 1- the ambiguity of certain visual situations (or perceptual alia), 2- long-term learning, and 3-sensitivity to environmental conditions. The addition of other modalities is an effective solution for increasing the robustness of the location. The team has developed several models based on the proprioception of the robot in order to compensate, in limiting cases, for purely visual models. The main limitation of proprioceptive approaches is that, based on the proprioception of the robot, they are subject to the accumulation of errors. It is therefore necessary to periodically recalibrate the models. Merging allothetic and idiothetic modalities seems to be a good strategy for obtaining a reliable estimate of the robot’s location. Dynamic neural network (DNF) or continous attractor neural network (CANN) constitute a powerful candidate to implement the type of memory required for the construction of place cells. We present a first fusion model using dynamic neuron fields to maintain the orientation of the robot and then a second one generalizing the principle of fusion model for the construction of cells of multimodal places. Being able to produce and market quickly a product is a major challenge for Partnering’s survival. In addition to navigation and location capabilities, a marketable robot requires a set of behaviors that are essential to mobility, security (control law, avoidance of obstacles and holes) and its autonomy (energy management and return to the charging station). To arrive at this first solution, we followed a bottom-up approach defended by behavioral robotics. We have progressively developed the complexity of the robot through elementary behaviors integratedin a control architecture governing at each moment the expression of these behaviors and the selection of the actions to be executed. This manuscript is divided into two parts. A first industrial part with short-term objectives, consisting of implementing the behavioral architecture of the first version of the Diya One robot, based on the existing models developed by the Neurocybernetics team. Then, a second more theoretical part in which we will deal with the realization of multimodal fusion models. These models will be added incrementally to the robot in order to gradually improve its navigationcapabilities.

El diseño molecular fundamentado en la imitación de las complejas estructuras y procesos que se encuentran en la naturaleza se conoce como Química bioinspirada o Química biomimética. Una de las aproximaciones utilizadas en esta disciplina es la preparación de compuestos seudopeptídicos macrocíclicos a partir de aminoácidos naturales y componentes abióticos. En la naturaleza existen proteínas con propiedades muy específicas y diversas. El uso de la información codificada en las cadenas laterales de los aminoácidos es un factor clave que, a su vez, puede utilizarse con ventaja para el diseño de seudopéptidos con propiedades específicas bien definidas. Por tanto, una selección apropiada de los componentes estructurales, naturales y no naturales, permite el diseño estructural adecuado para obtener la funcionalidad deseada. / The molecular design based on the imitation of the complex structures and processes found in nature is known as bioinspired Chemistry or biomimetic Chemistry. An approach used in this discipline is the preparation of macrocyclic pseudopeptidic compounds from natural amino acids and abiotic components. Proteins exist in nature with very specific properties encoded by the diverse structural and conformational information of the amino acids side chains. The use of this information is a key factor in the design of pseudopeptides with well-defined properties. Therefore, a suitable structural design to obtain the desired functionality relies on the appropriate choice of structural components, natural and abiotic.

Made available in DSpace on 2016-03-15T19:37:57Z (GMT). No. of bitstreams: 1 DAVILA PATRICIA FERREIRA CRUZ.pdf: 3761174 bytes, checksum: 5bdf7491a01f52fa9d31b6f66eca7c87 (MD5) Previous issue date: 2015-06-17 / With the popularization of Internet, the advancement of electronic devices and the ease of storage, the volume of data stored and available at companies has increased substantially. Therefore, it becomes necessary to use intelligent techniques to extract useful information and knowledge from these data. In this context, Data Mining has been the aim of several researches by providing a set of intelligent techniques to the exploration of large volumes of data. The present project aims to research and develop new algorithms inspired by the collective behavior of bee colonies for solving complex clustering and classification tasks. More specifically, this project proposes adaptations of an optimization algorithm inspired by the behavior of bees so that it can be applied to solve clustering problems and also for positioning centers of RBF neural networks. The proposed approaches were applied to several benchmark problems with promising results. / Com a popularização da Internet, o avanço dos dispositivos eletrônicos e a facilidade de armazenamento, o volume de dados armazenados e disponibilizados por empresas de diversos ramos tem aumentado rapidamente. Com isso, torna-se necessária a utilização de técnicas avançadas capazes de extrair desses dados informações úteis e conhecimentos que, na maioria das vezes, estão implícitos. Nesse contexto, a Mineração de Dados tem sido alvo de diversas pesquisas por prover um conjunto de técnicas inteligentes para a exploração de grandes volumes de dados. O presente projeto visa à investigação e desenvolvimento de novos algoritmos inspirados no comportamento coletivo das colônias de abelhas para aplicação em problemas complexos de classificação e agrupamentos de dados, que são importantes tarefas da Mineração de Dados. Mais especificamente, esse projeto propõe adaptações de um algoritmo de otimização inspirado no comportamento de abelhas, sua aplicação em problemas de agrupamento de dados e para o posicionamento de centros de redes neurais do tipo RBF. Os resultados experimentais em bases de dados da literatura mostraram a viabilidade e benefícios das propostas, tanto para problemas de agrupamento, quanto para problemas de classificação.

The combination or integration of locomotion modes, is analyzed through the design, development, and verification of a miniature integrated jumping and gliding robot, the MultiMo-Bat, which is inspired by the locomotion strategies of vampire bats, locusts, and pelicans. This robot has a mass of between 100 and 162 grams and exhibits high jumping and gliding performance, reaching heights of over 4.5 meters, to overcome obstacles in the environment. Integration results in a smaller, lighter robot with high cooperation between the modes. This thesis presents a previously unstudied robot design concept and highlights the understudied evolutionary concept within organism mobility of integration of locomotion modes. High performance locomotion modes also require high energy density actuators. To this end, a design methodology is developed for tailoring magnetic springs to the characteristics of shape memory alloy-actuated mechanisms, which allow the MultiMo-Bat to reach jumping heights of 3.5 m with active wing deployment and full controller. Through a combinations of permanent magnets, a magnetic spring can be customized to desired characteristics; theoretically any welldefined function of force vs. displacement can be created. The methodology is not limited to SMA but can be adapted to any smart actuator, joint, or situation which requires a fixed complex force-displacement relationship with extension other interactions and magnetic field design. Robotic locomotion is also much more idealized than that of their biological counter parts. This thesis serves to highlight just how non-ideal, yet robust, biological locomotion can inspire concepts for enhancing the robustness of robot locomotion. We studied the desert locust (Schistocerca gregaria), which is adapted for jumping at the extreme limits of its surface friction, as evident by its morphological adaptations for not only jumping, but slipping. Analysis of both foot morphology and jumping behavior are used to understand how the feet interact with different surfaces, including hydrophobic glass, hydrophilic glass, wood, sandstone, and mesh. The results demonstrate a complex interplay of embodied mechanical intelligence, allowing the foot to interact and adapt passively to different surfaces without burdening the organism with additional tasks. The key morphological and dynamical features are extracted to create a concept for developing multi-Surface Locust Inspired Passively-adaptable (SLIP) feet. A simple interpretation of the concepts are then used to construct a SLIP foot for the MultiMo-Bat. These feet allow the MultiMo-Bat to reach jumping heights of well over 4 m, greater than any other electrically powered robot, and this is achieved on a 45 degree angled surface while slipping. The SLIP foot concept can be directly applied to a wide range of robot size scales, thus enhancing their dynamic terrestrial locomotion on variable surfaces.

Aprendizado de máquina é uma área de pesquisa na qual se investiga como desenvolver sistemas capazes de aprender com a experiência. Muitos algoritmos de aprendizado possuem parâmetros cujos valores devem ser especificados pelo usuário. Em geral, esses valores influenciam diretamente no processo de aquisição do conhecimento, podendo gerar diferentes modelos. Recentemente, algoritmos de otimização bioinspirados têm sido aplicados com sucesso no ajuste de parâmetros de técnicas de aprendizado de máquina. Essas técnicas podem apresentar diferentes sensibilidades em relação aos valores escolhidos para seus parâmetros e diferentes algoritmos de ajuste de parâmetros podem apresentar desempenhos singulares. Esta dissertação investiga a utilização de algoritmos bioinspirados para o ajuste de parâmetros de redes neurais artificiais e máquinas de vetores de suporte em problemas de classificação. O objetivo dessa investigação é verificar quais são as técnicas que mais se beneficiam do ajuste de parâmetros e quais são os algoritmos mais eficientes para essas técnicas. Os resultados experimentais mostram que os algoritmos bioinspirados conseguem encontrar melhores clasificadores que outras abordagens. Porém, essa melhoria é estatisticamente significativa para alguns conjuntos de dados. Foi possível verificar que o uso dos valores padrão para os parâmetros das técnicas de classificação leva a desempenhos similares aos obtidos com os algoritmos bioinspirados. Entretanto, para alguns conjuntos de dados, o ajuste de parâmetros pode melhorar significativamente o desempenho dos classificadores / Machine learning is a research area whose main goal is to design computational systems capable of learning through experience. Many machine learning techniques have free parameters whose values are generally defined by the user. Usually, these values affect the knowledge acquisition process directly, resulting in different models. Recently, bioinspired optimization algorithms have been successfully applied to the parameter tuning of machine learning techniques. These techniques may present variable sensitivity to the selection of the values of its parameters and different parameter tuning algorithms may present different behaviors. This thesis investigates the use of bioinspired algorithms for the parameter tuning of artificial neural networks and support vector machines in classification problems. The goal of this thesis is to investigate which techniques benefits most from parameter tuning and which are the most efficient algorithms to use with these techniques. Experimental results show that these bioinspired algorithms can find better classifiers when compared to other approaches. However, this improvement is statistically significant only to some datasets. It was possible to verify that the use of standard parameter values for the classification techniques leads to similar performances to those obtained with the bioinspired algorithms. However, for some datasets, the parameter tuning may significantly improve a classifier performance

abstract: The Atlantic razor clam burrows underground with effectiveness and efficiency by coordinating shape changings of its shell and foot. Inspired by the burrowing strategy of razor clams, this research is dedicated to developing a self-burrowing technology for active underground explorations by investigating the burrowing mechanism of razor clams from the perspective of soil mechanics. In this study, the razor clam was observed to burrow out of sands simply by extending and contracting its foot periodically. This upward burrowing gait is much simpler than its downward burrowing gait, which also involves opening/closing of the shell and dilation of the foot. The upward burrowing gait inspired the design of a self-burrowing-out soft robot, which drives itself out of sands naturally by extension and contraction through pneumatic inflation and deflation. A simplified analytical model was then proposed and explained the upward burrowing behavior of the robot and razor clams as the asymmetric nature of soil resistances applied on both ends due to the intrinsic stress gradient of sand deposits. To burrow downward, additional symmetry-breaking features are needed for the robot to increase the resistance in the upward burrowing direction and to decrease the resistance in the downward burrowing direction. A potential approach is by incorporating friction anisotropy, which was then experimentally demonstrated to affect the upward burrowing of the soft robot. The downward burrowing gait of razor clams provides another inspiration. By exploring the analogies between the downward burrowing gait and in-situ soil characterization methods, a clam-inspired shape-changing penetrator was designed and penetrated dry granular materials both numerically and experimentally. Results demonstrated that the shell opening not only contributes to forming a penetration anchor by compressing the surrounding particles, but also reduces the foot penetration resistance temporally by creating a stress arch above the foot; the shell closing facilitates the downward burrowing by reducing the friction resistance to the subsequent shell retraction. Findings from this research shed lights on the future design of a clam-inspired self-burrowing robot. / Dissertation/Thesis / Video for section A1 of APPENDIX A / Video for section A2 of APPENDIX A / Video for section A3 of APPENDIX A / Video for section B8 of APPENDIX B / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2020

abstract: As experiencing hot months and thermal stresses is becoming more common, chemically protective fabrics must adapt and provide protections while reducing the heat stress to the body. These concerns affect first responders, warfighters, and workers regularly surrounded by hazardous chemical agents. While adapting traditional garments with cooling devices provides one route to mitigate this issue, these cooling methods add bulk, are time limited, and may not be applicable in locations without logistical support. Here I take inspiration from nature to guide the development of smart fabrics that have high breathability, but self-seal on exposure to target chemical(s), providing a better balance between cooling and protection. Natural barrier materials were explored as a guide, focusing specifically on prickly pear cacti. These cacti have a natural waxy barrier that provides protection from dehydration and physically changes shape to modify surface wettability and water vapor transport. The results of this study provided a basis for a shape changing polymer to be used to respond directly to hazardous chemicals, swelling to contain the agent. To create a stimuli responsive material, a novel superabsorbent polymer was synthesized, based on acrylamide chemistry. The polymer was tested for swelling properties in a wide range of organic liquids and found to highly swell in moderately polar organic liquids. To help predict swelling in untested liquids, the swelling of multiple test liquids were compared with their thermodynamic properties to observe trends. As the smart fabric needs to remain breathable to allow evaporative cooling, while retaining functionality when soaked with sweat, absorption of water, as well as that of an absorbing liquid in the presence of water were tested. Micron sized particles of the developed polymer were deposited on a plastic mesh with pore size and open area similar to common clothing fabric to establish the proof of concept of using a breathable barrier to provide chemical protection. The polymer coated mesh showed minimal additional resistance to water vapor transport, relative to the mesh alone, but blocked more than 99% of a xylene aerosol from penetrating the barrier. / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2020

Nature is an excellent design that inspires scientists to develop smart systems. In the realm of separation technology, biological membranes have been an ideal model for synthetic membranes due to their ultrahigh permeability, sharp selectivity, and stimuliresponse. In this research, fabrications of bioinspired membranes from block copolymers were studied. Membranes with isoporous morphology were mainly prepared using selfassembly and non-solvent induced phase separation (SNIPS). An effective method that can dramatically shorten the path for designing new isoporous membranes from block copolymers via SNIPS was first proposed by predetermining a trend line computed from the solvent properties, interactions and copolymer block sizes of previously-obtained successful systems. Application of the method to new copolymer systems and fundamental studies on the block copolymer self-assembly were performed. Furthermore, the manufacture of bioinspired membranes was explored using (1) poly(styrene-b-4-hydroxystyrene-b-styrene) (PS-b-PHS-b-PS), (2) poly(styrene-bbutadiene- b-styrene) (PS-b-PB-b-PS) and (3) poly(styrene-b-γ-benzyl-L-glutamate) (PSb- PBLG) copolymers via SNIPS. The structure formation was investigated using smallangle X-ray scattering (SAXS) and time-resolved grazing-Incidence SAXS. The PS-b- PHS-b-PS membranes showed preferential transport for proteins, presumably due to the hydrogen bond interactions within the channels, electrostatic attraction, and suitable pore dimension. Well-defined nanochannels with pore sizes of around 4 nm based on PS-b- PB-b-PS copolymers could serve as an excellent platform to fabricate bioinspired channels due to the modifiable butadiene blocks. Photolytic addition of thioglycolic acid was demonstrated without sacrificing the self-assembled morphology, which led to a five-fold increase in water permeance compared to that of the unmodified. Membranes with a unique feather-like structure and a lamellar morphology for dialysis and nanofiltration applications were obtained from PS-b-PBLG copolymers, which exhibited a hierarchical self-assembled morphology with confined α-helical polypeptide domains. Our results suggest that bioinspired nanochannels can be designed via block copolymer self-assembly using classical methods of membrane preparation. Investigation of the membrane formation mechanism leads us to a better understanding of the design strategies for the development of self-assembled nanochannels from block copolymers. In further outlook, our research could give a contribution to the discovery of future generation materials for water purification and desalination, as well as biological separation.