<p>By using flexible and stretchable materials in place of fixed components, soft robots can materially adapt or change to their environment, providing built-in safeties for robotic operation around humans or fragile, delicate objects. And yet, building a robot out of only soft and flexible materials can be a significant challenge depending on the tasks that the robot needs to perform, for example if the robot were to need to exert higher forces (even temporarily) or self-report its current state (as it deforms unexpectedly around external objects). Thus, the appeal of multifunctional materials for soft robots, wherein the materials used to build the body of the robot also provide actuation, sensing, or even simply electrical connections, all while maintaining the original vision of environmental adaptability or safe interactions. Multifunctional material systems are explored throughout the body of this dissertation in three ways: (1) Sensor integration into high strain actuators for state estimation and closed-loop control. (2) Simplified control of multifunctional material systems by enabling multiple functions through a single input stimulus (<i>i.e.</i>, only requiring one source of input power). (3) Presenting a solution for the open challenge of controlling both well established and newly developed thermally-responsive soft robotic materials through an on-body, high strain, uniform, Joule-heating energy source. Notably, these explorations are not isolated from each other as, for example, work towards creating a new material for thermal control also facilitated embedded sensory feedback. The work presented in this dissertation paves a way forward for multifunctional material integration, towards the end-goal of full-functioning soft robots, as well as (more broadly) design methodologies for other safety-forward or adaptability-forward technologies.</p>

No description available.

3D printing technology, which has its origins in rapid prototyping, is increasingly used to build functional devices. Although 3D printing technology has been well developed for thermoplastic polymers and metals, it is still in the research phase for soft polymeric materials such as silicones. Silicones are an industrially vital polymer characterized by a broad spectrum of chemical and physical properties for several smart applications, including on skin printing, smart sensors, multigradient material, and soft actuators. Extrusion-based multimaterial printing is one of the 3D printing techniques that have been adapted due to its compatibility to process silicone-based materials for constructing various functional devices. However, there are several challenges such as achieving on the fly mixing at low Reynolds numbers regime, achieving fast switching while using Newtonian/non-Newtonian inks, and achieving multimaterial printing on nonplanar surfaces. The development of suitable and robust printheads that are able to tackle those challenges can expand the application of this technology to a wide range of fields. In this thesis, several deposition-controlled printhead designs have been created for 3D printing multifunctional devices using an understanding of microfluidics. The established printhead can be controlled to formulate different multigradient structures through on the fly mixing during the material printing. Moreover, the developed printhead can be adapted to print multi viscous inks with high switching rates up to 50 Hz. Through the developed system, the printhead was able to track topologies in real-time, allowing objects to be printed over complex substrates. These new capabilities were applied to fabricate functional structures in order to demonstrate the potential of the developed printhead approaches that can be used in various applications, including smart sensors, soft robotics and multigradient objects. / Thesis / Doctor of Philosophy (PhD) / 3D printing techniques, such as extrusion-based multimaterial printing, have recently been utilized to process silicones due to their versatility in different smart applications, including multigradient material and soft actuators. Although it represents significant progress, there are still several challenges, including the proper mixing during printing with a laminar flow regime, the fast switching between different inks, and the printing over complex topographies. Therefore, various printhead designs have been developed in this thesis to tackle these challenges. In particular, a mixer printhead has been designed to allow mixing during printing for building multigradient objects. Also, a scalable printhead has been developed to allow fast switching for creating pixelated structures. Finally, a simple mechanical system has achieved multimaterial printing over various nonplanar surfaces. To the best of the author's knowledge, the developed printheads can be used in many fields, such as soft robotics and smart devices.

No description available.

Le thème central de cette thèse est la conception d’actionneurs doux à partir de matériaux intelligents et d’une prothèse de main robotique souple. Notre approche prends en compte les différents points qui peuvent influer sur le développement d’une stratégie d’actionnement ou d’un muscle artificiel : i) Les mécanismes et la fonctionnalité de la main humaine afin d’identifier les exigences fonctionnelles pour une prothèse de main robotique en matière de préhension. ii) L’analyse et l’amélioration des mécanismes de la main robotique pour intégrer un comportement souple dans la prothèse. iii) L’évaluation expérimentale de la prothèse de main robotique afin d’identifier les spécifications du système d’actionnement nécessaire au fonctionnement cinématique et dynamique du robot. iv) Le développement et la modélisation d’une stratégie d’actionnement utilisant des matériaux intelligents.Ces points sont abordés successivement dans les 4 chapitres de cette thèse1. Analyse du mouvement de la main humaine pour l’identification des exigences technologiques pour la prothèse de main robotique.2. Conception et modélisation de la prothèse de main robotique à comportement souple.3. Evaluation mécatronique de la prothèse de main.4. Conception d’un muscle artificiel basé sur des matériaux intelligents. / In the field of robotic hand prosthesis, the use of smart and soft materials is helpful in improving flexibility, usability, and adaptability of the robots, which simplify daily living activities of prosthesis users. However, regarding the smart materials for artificial muscles, technologies are considered to be far from implementation in anthropomorphic robotic hands. Therefore, the target of this thesis dissertation is to reduce the gap between smart material technologies and robotic hand prosthesis. Five central axes address the problem: i)identification of useful grasping gestures and reformulation of the robotic hand mechanism, ii) analysis of human muscle behavior to mimic human grasping capabilities, iii) modeling robot using the hybrid model DHKK-SRQ for the kinematics and the virtual works principle for dynamics, iv) definition of actuation requirements considering the synergy between prehension conditions and robot mechanism, and v) development of a smart material based actuation system.This topics are addressed in four chapters:1. Human hand movement analysis toward the hand prosthesis requirements2. Design and modeling of the soft robotic hand ProMain-I3. Mechatronic assessment of Prosthetic hand4. Development of an artificial muscle based on smart materials

Cette thèse s’intéresse à la modélisation et à la commande de robots déformables, c’est à dire de robots dont le mouvement se fait par déformation. Nous nous intéressons à la conception de lois de contrôle en boucle fermée répondant aux besoins spécifiques du contrôle dynamique de robots déformables, sans restrictions fortes sur leur géométrie. La résolution de ce défi soulève des questions théoriques qui nous amènent au deuxième objectif de cette thèse: développer de nouvelles stratégies pour étudier les systèmes de grandes dimensions. Ce manuscrit couvre l’ensemble du développement des lois de commandes, de l’étape de modélisation à la validation expérimentale. Outre les études théoriques, différentes plateformes expérimentales sont utilisées pour valider les résultats. Des robots déformables actionnés par câble et par pression sont utilisés pour tester les algorithmes de contrôle. A travers ces différentes plateformes, nous montrons que la méthode peut gérer différents types d’actionnement, différentes géométries et propriétés mécaniques. Cela souligne l’un des intérêts de la méthode, sa généricité. D’un point de vue théorique, les systèmes dynamiques à grande dimensions ainsi que les algorithmes de réduction de modèle sont étudiés. En effet, modéliser des structures déformables implique de résoudre des équations issues de la mécanique des milieux continus, qui sont résolues à l’aide de la méthode des éléments finis (FEM). Ceci fournit un modèle précis des robots mais nécessite de discrétiser la structure en un maillage composé de milliers d’éléments, donnant lieu à des systèmes dynamiques de grandes dimensions. Cela conduit à travailler avec des modèles de grandes dimensions, qui ne conviennent pas à la conception d’algorithmes de contrôle. Une première partie est consacrée à l’étude du modèle dynamique à grande dimension et de son contrôle, sans recourir à la réduction de modèle. Nous présentons un moyen de contrôler le système à grande dimension en utilisant la connaissance d’une fonction de Lyapunov en boucle ouverte. Ensuite, nous présentons des algorithmes de réduction de modèle afin de concevoir des contrôleurs de dimension réduite et des observateurs capables de piloter ces robots déformables. Les lois de contrôle validées sont basées sur des modèles linéaires, il s’agit d’une limitation connue de ce travail car elle contraint l’espace de travail du robot. Ce manuscrit se termine par une discussion qui offre un moyen d’étendre les résultats aux modèles non linéaires. L’idée est de linéariser le modèle non linéaire à grande échelle autour de plusieurs points de fonctionnement et d’interpoler ces points pour couvrir un espace de travail plus large. / This thesis focuses on the design of closed-loop control laws for the specific needs of dynamic control of soft robots, without being too restrictive regarding the robots geometry. It covers the entire development of the controller, from the modeling step to the practical experimental validation. In addition to the theoretical studies, different experimental setups are used to illustrate the results. A cable-driven soft robot and a pressurized soft arm are used to test the control algorithms. Through these different setups, we show that the method can handle different types of actuation, different geometries and mechanical properties. This emphasizes one of the interests of the method, its genericity. From a theoretical point a view, large-scale dynamical systems along with model reduction algorithms are studied. Indeed, modeling soft structures implies solving equations coming from continuum mechanics using the Finite Element Method (FEM). This provides an accurate model of the robots but it requires to discretize the structure into a mesh composed of thousands of elements, yielding to large-scale dynamical systems. This leads to work with models of large dimensions, that are not suitable to design control algorithms. A first part is dedicated to the study of the large-scale dynamic model and its control, without using model reduction. We present a way to control the large-scale system using the knowledge of an open-loop Lyapunov function. Then, this work investigates model reduction algorithms to design low order controllers and observers to drive soft robots. The validated control laws are based on linear models. This is a known limitation of this work as it constrains the guaranteed domain of the controller. This manuscript ends with a discussion that offers a way to extend the results towards nonlinear models. The idea is to linearize the large-scale nonlinear model around several operating points and interpolate between these points to cover a wider workspace.

Soft materials have gained increasing prominence in science and technology over the last few decades. This shift from traditional rigid materials to soft, compliant materials have led to the emergence of a new class of devices which can interact with humans safely, as well as reduce the disparity in mechanical compliance at the interface of soft human tissue and rigid devices.<br><br>One of the largest application of soft materials has been in the field of flexible electronics, especially in wearable sensors. While wearable sensors for physical attributes such as strain, temperature, etc. have been popular, they lack applications and significance from a healthcare perspective. Point-of-care (POC) devices, on the other hand, provide exceptional healthcare value, bringing useful diagnostic tests to the bedside of the patient. POC devices, however, have been developed for only a limited number of health attributes. In this dissertation I propose and demonstrate wireless, wearable POC devices to measure and communicate the level of various analytes in and the properties of multiple biofluids: blood, urine, wound exudate, and sweat.<br><br>Along with sensors, another prominent area of soft materials application has been in actuators and robots which mimic biological systems not only in their action but also in their soft structure and actuation mechanisms. In this dissertation I develop design strategies to improve upon current soft robots by programming the storage of elastic strain energy. This strategy enables us to fabricate soft actuators capable of programmable and low energy consuming, yet high speed motion. Collectively, this dissertation demonstrates the use of soft compliant materials as the foundation for developing new sensors and actuators for human use and interaction.

<p>Soft pneumatic actuators allow compliant force application and movement for a variety of tasks. While most soft actuators have compliance in directions perpendicular to their direction of force application, they are most often analyzed only in their direction of actuation. In this work, we show a characterization of a soft 3D printed bellows actuator that considers shear and axial deformations, modeling both active and passive degrees of freedom. We build a model based on actuator geometry and a parallel linear and torsional spring system which we fit to experimental data in order to obtain the model constants. We demonstrate this model on two complex parallel networks, a delta mechanism and a floating actuator mechanism, and show how this single actuator model can be used to better predict movements in parallel structures of actuators. These results verify that the presented model and modeling approach can be used to speed up the design and simulation of more complex soft robot models by characterizing both active and passive forces of their one degree-of-freedom soft actuators.<br> </p>

No description available.

Quadruped robots offer distinct advantages in navigating challenging terrains due to their flexible and shock-absorbing characteristics. This flexibility allows them to adapt to uneven surfaces, enhancing their maneuverability. In contrast, rigid robots excel in tasks that require speed and precision but are limited in their ability to navigate complex terrains due to their restricted motion range. Another category of robots, known as soft robots, has gained attention for their unique attributes. Soft robots are characterized by their lightweight and cost-effective design, making them appealing for various applications. Recent advancements have made significant strides in practical control strategies for soft quadruped robots, particularly in diverse and unpredictable environments. An emerging approach in enhancing the autonomy of robots is through reinforcement learning. While this approach shows promise in enabling robots to learn and adapt to their surroundings, it necessitates rigorous training and must exhibit robustness in real-world scenarios. Moreover, a significant hurdle lies in bridging the gap between simulations and reality, as models trained in idealized virtual environments often struggle to perform as expected when deployed in the physical world. This thesis aims to address these challenges by optimizing the control of soft quadruped robots using a model-based reinforcement learning approach. The primary goal is to refine the gait control of these robots, taking into account the complexities encountered in real-world environments. The report covers the implementation of model-based reinforcement learning, including simulation setup, reward design, and policy refinement. Results show improved training efficiency and autonomous behavior, confirming the method’s effectiveness in enhancing soft quadruped robot capabilities.It is important to note that this report provides a concise summary of the thesis results due to the word limit imposed by the Department of Machine Design. For a comprehensive understanding of the research and its implications, the complete version is attached separately here. / Fyrbenta robotar är tack vare deras flexibla och stötdämpande egenskaper är väl lämpade att navigera utmanande terräng. Deras struktur möjliggör anpassning till ojämnheter i underlaget och bidrar till att öka deras rörelseförmåga. I kontrast utmärker sig stela robotar som det bästa valet för uppgifter som kräver snabbhet och precision, men deras förmåga att navigera komplex terräng är begränsad av deras rörelseomfång. En annan typ av robot, så kallade mjuka robotar, har nyligen uppmärksammats för sina unika egenskaper. Dessa robotar kännetecknas av en kostnadseffektiv lättviktsdesign, vilket gör dem attraktiva för många olika användningsområden. Nyligen har betydelsefulla framsteg gjorts inom kontroll av mjuka fyrbenta robotar, framför allt vad gäller kontroll i varierade miljöer. En av de huvudsakliga utmaningarna för att öka robotars autonomi är förstärkningsinlärning. Även om denna teknik är lovande för att ge robotar förmågan att lära sig och anpassa sig efter sin omgivning, kräver den omfattande träning samt måste uppvisa robusthet i verkliga scenarion. Ett större hinder är dessutom klyftan mellan simulation och verklighet, då modeller som tränats i ideella simuleringar ofta presterar sämre än väntat i den fysiska världen. Detta examensarbete behandlar dessa utmaningar genom att implementera en modellbaserad förstärkningsinlärningsmetod för kontroll av fyrbenta robotar, med det primära målet att förfina gångkontrollen för dessa robotar med hänsyn till de komplexa beteenden som uppstår i verkliga miljöer. Denna rapport behandlar implementeringen av modellbaserad förstärkningsin lärning samt simulering, belöningsdesign och policyförfining. Resultat visar på en förbättrad inlärningsförmåga och bättre autonomt beteende, vilket gör metoden lämplig för att förbättra prestandan av mjuka fyrbenta robotar. Var god att notera att denna rapport endast ger en nedkortad sammanfattning av forskningen och dess resultat på grund av krav från institutionen för maskinkonstruktion. En fullständig version innehållande mer detaljer kring studien och dess konsekvenser bifogas här.