Geometry-based Direct Simulation for Multi-Material Soft Robots

Robots fabricated by soft materials can provide higher flexibility and thus better safety while interacting with natural objects with low stiffness such as food and human beings. However, as many more degrees of freedom are introduced, the motion simulation of a soft robot becomes cumbersome, especially when large deformations are presented. Moreover, when the actuation is defined by geometry variation, it is not easy to obtain the exact loads and material properties to be used in the conventional methods of deformation simulation. In this paper, we present a direct approach to take the geometric actuation as input and compute the deformed shape of soft robots by numerical optimization using a geometry-based algorithm. By a simple calibration, the properties of multiple materials can be modeled geometrically in the framework. Numerical and experimental tests have been conducted to demonstrate the performance of our approach on both cable-driven and pneumatic actuators in soft robotics

SPADA: A Toolbox of Designing Soft Pneumatic Actuators for Shape Matching based on Surrogate Modeling

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This paper addresses these challenges, offering an end-to-end shape-matching design framework for bellow-SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a GUI for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, Finite Element Method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An Artificial Neural Network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging 3D PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match 2D shapes with root-mean-square errors of 4.16, 2.70, and 2.51mm, and demonstrating its potential by designing a 3D deformable actuator

Pneumatic Hyperelastic Robotic End-Effector for Grasping Soft Curved Organic Objects

Pneumatically-driven soft robotic grippers can elastically deform to grasp delicate, curved organic objects with minimal surface damage. However, common actuators have complex geometries and are fabricated with ultra-soft hyperelastic elastomers not originally intended for scientific applications. The complexity of the actuator geometry and extreme nonlinearity of their material’s stress-strain behaviour make it difficult to predict the actuator’s deformation prior to experimentation. In this work, a compact soft pneumatic gripper made with polydimethylsiloxane (PDMS) is developed for grasping delicate organic objects, analyzed through computational modelling and experimentally validated. COMSOL Multiphysics is used to simulate the impact of geometrical parameters on the actuator’s behaviour, allowing for the refinement of the proposed geometry prior to fabrication. Optimal parameters are selected for fabrication, with experimental tests matching simulations within ± 1 mm. Gripper performance is evaluated for three actuator wall thicknesses in terms of contact area with target, contact force, and maximum payload before slippage. The comparative assessment between simulations and experiments demonstrate that the proposed soft actuators can be used in robotic grippers tailored for grasping delicate objects without damaging their surface. Furthermore, analysis of the actuators provides additional insight on how to design simple but effective soft systems

SPADA: a toolbox of designing soft pneumatic actuators for shape matching based on surrogate modeling

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however, they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters to achieve a target actuator's shape. Within SPAs, bellow SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This article addresses these challenges, offering an end-to-end shape-matching design framework for bellow SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a graphic user interface for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, finite element method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An artificial neural network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging three-dimensional (3D) PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match two-dimensional shapes with root-mean-squared-errors of 4.16, 2.70, and 2.51 mm, and demonstrating its potential by designing a 3D deformable actuator

Functional Soft Robotic Actuators Based on Dielectric Elastomers

Dielectric elastomer actuators (DEAs) are a promising soft actuator technology for robotics. Adding robotic functionalities--folding, variable stiffness, and adhesion--into their actuator design is a novel method to create functionalized robots with simplified actuator configurations. We first propose a foldable actuator that has a simple antagonistic DEA configuration allowing bidirectional actuation and passive folding. To prove the concept, a foldable elevon actuator with outline size of 70 mm × 130 mm is developed with a performance specification matched to a 400 mm wingspan micro air vehicle (MAV) of mass 130 g. The developed actuator exhibits actuation angles up to ± 26 ° and a torque of 2720 mN·mm in good agreement with a prediction model. During a flight, two of these integrated elevon actuators well controlled the MAV, as proven by a strong correlation of 0.7 between the control signal and the MAV motion. We next propose a variable stiffness actuator consisting of a pre-stretched DEA bonded on a low-melting-point alloy (LMPA) embedded silicone substrate. The phase of the LMPA changes between liquid and solid enabling variable stiffness of the structure, between soft and rigid states, while the DEA generates a bending actuation. A proof-of-concept actuator with dimension 40 mm length × 10mm width × 1mm thickness and a mass of 1 g is fabricated and characterized. Actuation is observed up to 47.5 ° angle and yielding up to 2.4 mN of force in the soft state. The stiffness in the rigid state is ~90 × larger than an actuator without LMPA. We develop a two-finger gripper in which the actuators act as the fingers. The rigid state allows picking up an object mass of 11 g (108 mN), to be picked up even though the actuated grasping force is only 2.4 mN. We finally propose an electroadhesion actuator that has a DEA design simultaneously maximizing electroadhesion and electrostatic actuation, while allowing self-sensing by employing an interdigitated electrode geometry. The concept is validated through development of a two-finger soft gripper, and experimental samples are characterized to address an optimal design. We observe that the proposed DEA design generates 10 × larger electroadhesion force compared to a conventional DEA design, equating to a gripper with a high holding force (3.5 N shear force for 1 cm^2) yet a low grasping force (1 mN). These features make the developed simple gripper to handle a wide range of challenging objects such as highly-deformable water balloons (35.6 g), flat paper (0.8 g), and a raw chicken egg (60.9 g), with its lightweight (1.5 g) and fast movement (100 ms to close fingers). The results in this thesis address the creation of the functionalized robots and expanding the use of DEAs in robotics

3D printed pneumatic soft actuators and sensors: their modeling, performance quantification, control and applications in soft robotic systems

Continued technological progress in robotic systems has led to more applications where robots and humans operate in close proximity and even physical contact in some cases. Soft robots, which are primarily made of highly compliant and deformable materials, provide inherently safe features, unlike conventional robots that are made of stiff and rigid components. These robots are ideal for interacting safely with humans and operating in highly dynamic environments. Soft robotics is a rapidly developing field exploiting biomimetic design principles, novel sensor and actuation concepts, and advanced manufacturing techniques. This work presents novel soft pneumatic actuators and sensors that are directly 3D printed in one manufacturing step without requiring postprocessing and support materials using low-cost and open-source fused deposition modeling (FDM) 3D printers that employ an off-the-shelf commercially available soft thermoplastic poly(urethane) (TPU). The performance of the soft actuators and sensors developed is optimized and predicted using finite element modeling (FEM) analytical models in some cases. A hyperelastic material model is developed for the TPU based on its experimental stress-strain data for use in FEM analysis. The novel soft vacuum bending (SOVA) and linear (LSOVA) actuators reported can be used in diverse robotic applications including locomotion robots, adaptive grippers, parallel manipulators, artificial muscles, modular robots, prosthetic hands, and prosthetic fingers. Also, the novel soft pneumatic sensing chambers (SPSC) developed can be used in diverse interactive human-machine interfaces including wearable gloves for virtual reality applications and controllers for soft adaptive grippers, soft push buttons for science, technology, engineering, and mathematics (STEM) education platforms, haptic feedback devices for rehabilitation, game controllers and throttle controllers for gaming and bending sensors for soft prosthetic hands. These SPSCs are directly 3D printed and embedded in a monolithic soft robotic finger as position and touch sensors for real-time position and force control. One of the aims of soft robotics is to design and fabricate robotic systems with a monolithic topology embedded with its actuators and sensors such that they can safely interact with their immediate physical environment. The results and conclusions of this thesis have significantly contributed to the realization of this aim

Automated Sensing Methods in Soft Stretchable Sensors for Soft Robotic Gripper

A soft robot is made from deformable and flexible materials such as silicone, rubber, polymers, etc. Soft robotics is a rapidly evolving field where the human-robot-interaction and bio-inspired design align. The physical characteristics such as highly deformable material and dexterity make soft robots widely applicable. A soft robotic gripper is a robotic hand that acts like a human hand and grasps any object. The most common applications of soft robotics grippers are gripping and locomotion in sensitive applications where high dynamic and sensitivity are essential. Nowadays, soft robotics grippers are used without any sensing method and feedback as it is crucial to make the output feedback from the gripper. The major drawback of soft robotic grippers is their need for more precision sensing. In traditional robots, we can integrate any sensor to detect the force and orientation of objects. Still, soft robotic grippers rely on the deformation sensing method, where the sensor must be highly flexible and deformable. With a precise sensing method, it is easier to determine the exact position or orientation of the object being gripped, and it limits the application of the soft robotic gripper. Sometimes, soft robots are employed in harsh environments to solve problems. With the sensing feedback, automation may become more reliable and succeed altogether. So, in this research, we have designed and fabricated a soft sensor to integrate with the gripper and to observe the feedback of the gripper. We propose integrated multimodal sensing that incorporates applied pressure and resistance change. The sensor provides feedback when the grippers hold any object, and the output response is the resistance change of the sensor. The liquid metal is susceptible and can respond to low force levels. We presented the 3D design, FEM simulation, fabrication, and integration of the gripper and sensor, and by showing both simulation and experimental results, the gripper is validated for real-time application. FEM simulation simulates behavior, optimizing design and predicting performance. We have designed and fabricated a soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy and a soft robotic gripper hand. Different printing processes and characterization results are presented for the sensor and actuator. The fabrication process of the gripper and sensor is adequately described. The gripper output characteristics are tested for bending angle, load test, elongation, and object holding under various applied pressure. Additionally, the sensor was tested for stretchability, linearity and durability, and human gesture integration with the finger, and this sensor can be easily reused/ reproduced. Furthermore, the sensor exhibits good sensitivity concerning different pressure and grasping various objects. Finally, we collected data using this sensor-integrated gripper and trained the dataset using machine learning models for automation. With more data, this can be an autonomous gripper with intelligent sensing methodologies. Moreover, this proposed stretchable sensor can be integrated into any existing gripper for innovative real-time applications

Investigation and development of a flexible gripper with adaptable finger geometry

Das zuverlässige und schonende Greifen ist ein Hauptanliegen bei der Entwicklung von neuartigen Greifvorrichtungen. Je größer die Kontaktfläche zwischen dem Greifer und dem Greifobjekt ist, desto schonender und zuverlässiger ist der Greifvorgang. Um dieses Ziel zu erreichen wurden in den letzten Jahrzehnten zahlreiche Untersuchungen zu adaptiven passiven Greifern durchgeführt. Ein neuer Forschungszweig im Bereich selbstadaptiver Greifer sind Greifer mit nachgiebigen blattfederartigen Greifelementen (Greiferfinger) Die Funktionsweise basiert auf dem elastischen Ausknicken der Greifelemente infolge einer translatorische Antriebsbewegung Die vorliegende Arbeit konzentriert sich auf die Verbesserung des Greifvorgangs, indem die Kontaktlänge zwischen den blattfederartigen Greiferfingern und dem zu greifenden Objekt deutlich erhöht wird. Um diese Aufgabenstellung zu lösen, muss eine geeignete Greifergeometrie für ein gegebenes Greifobjekt berechnet werden. Die gezielte Berechnung der erfoderlichen Greifergeometrie für ein bekanntes Greifobjekt ist nicht möglich. Daher wurde als Lösungsansatz die umkehrte Richtung gewählt. Für eine definierte Greifgeometrie wird die Gestalt des dazu passenden “idealen” Greifobjektes ermittelt und anschließend mit der Gestalt zu greifenden Objektes verglichen. Bei Gestaltabweichungen wird die Greifergeometrie iterative verändert, bis seine geeignete Greifergeometrie gefunden wurde. Im Rahmen der vorliegenden Arbeit wird zunächst die Ermittlung des “idealen” Greifobjektes behandelt. Es wurde ein Algorithmus entwickelt, der für eine vorgegebene Greifergeometrie die Gestalt eines runden bzw. elliptischen Objektes ermittelt. Der Algorithmus verwendet als Eingabedaten die Biegelinien der elastisch ausgeknickten Greiffinger unter Berücksichtigung unterschiedlicher Randbedingungen. Als Ausgabedaten liefert der Algorithmus die Gestalt des passenden Greifobjektes zurück. Für quadratische bzw. rechteckige sowie für dreieckige Objekte wurden unterschiedliche Greifgeometrien untersucht. Außerdem wird für quadratische und rechteckige Objekte das Lösungskonzept für die Entwicklung eines weiteren Algorithmus beschrieben. In Kapitel 1 wird eine Klassifizierung von Greifern basierend auf der Anpassungsfähigkeit vorgestellt. In Kapitel 2 werden Lösungskonzepte, Modelle und Theorien vorgestellt. In Kapitel 3 werden Ablaufdiagramme der Algorithmen dargestellt. In Kapitel 4 wird die Entwicklung des Algorithmus für elliptische Objekte und deren Betriebsmodi beschrieben. In Kapitel 5 werden Greifgeometrien für quadratische bzw. Rechteckige sowie für dreieckige Objekte analysiert und die Ideen eines Algorithmus für quadratisch bzw. rechteckige Objekte beschrieben. In Kapitel 6 wird ein kurzer Überblick über die zukünftige Arbeiten.Reliable and gentle gripping is a major concern in the development of new gripping devices. The larger contact surface between the gripper and the gripping object, the gentler and more reliable the gripping process. In order to achieve this goal, further investigations on adaptive passive grippers have been carried out in the recent decades. A new branch of research in the field of self-adaptive grippers are compliant leaf-spring-like gripping elements (gripper fingers). Its mode of operation is based on the elastic buckling of the gripping elements as a result of a translatory drive movement. The present work focuses on improving the gripping process by increasing significantly the contact length between the compliant leaf-spring-like gripper fingers and the object to be gripped. In order to solve this task, a suitable gripper geometry for a given gripping object should be calculated The specific calculation of the required gripper geometry for a known gripping object is not possible; therefore, this work aims in the opposite direction. For a defined gripping geometry, the shape of the matching “ideal” gripping object is determined and then compared with the desired object to be gripped. In case of a deviation in the size, the gripper geometry is iteratively changed until its suitable gripper geometry has been found. In the present work, the determination of the “ideal” gripping object is the first task to deal with. An algorithm has been developed to determine the shape of a round-elliptical object for a given gripper geometry. The algorithm uses as data input the bend lines of the compliant twogripper finger under different boundary conditions. As data output, the algorithm returns the shape of the matching gripping object. For square-rectangular and triangular objects, different gripping geometries have been investigated. Furthermore, for square-rectangular objects, solution concepts for the development of an algorithm is described. In chapter 1, a classification based on adaptability is presented. In chapter 2, solution concepts, models and theories involved are introduced. In chapter 3, process flow diagrams of the algorithms are presented. In chapter 4, the development of the algorithm for elliptical objects and its operation modes are described. In chapter 5, gripping geometries for square-rectangular and triangular objects are analysed and the ideas of an algorithm for square-rectangular objects are described. In chapter 6, a brief overview of the futur work is commented.Tesi