A 3D-Printed Omni-Purpose Soft Gripper

Numerous soft grippers have been developed based on smart materials, pneumatic soft actuators, and underactuated compliant structures. In this article, we present a three-dimensional (3-D) printed omni-purpose soft gripper (OPSOG) that can grasp a wide variety of objects with different weights, sizes, shapes, textures, and stiffnesses. The soft gripper has a unique design that incorporates soft fingers and a suction cup that operate either separately or simultaneously to grasp specific objects. A bundle of 3-D-printable linear soft vacuum actuators (LSOVA) that generate a linear stroke upon activation is employed to drive the tendon-driven soft fingers. The support, fingers, suction cup, and actuation unit of the gripper were printed using a low-cost and open-source fused deposition modeling 3-D printer. A single LSOVA has a blocked force of 30.35 N, a rise time of 94 ms, a bandwidth of 2.81 Hz, and a lifetime of 26 120 cycles. The blocked force and stroke of the actuators are accurately predicted using finite element and analytical models. The OPSOG can grasp at least 20 different objects. The gripper has a maximum payload-to-weight ratio of 7.06, a grip force of 31.31 N, and a tip blocked force of 3.72 N

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

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

A soft, sensorized gripper for delicate harvesting of small fruits

Harvesting fruits and vegetables is a complex task worth to be fully automated with robotic systems. It involves several precision tasks that have to be performed with accuracy and the appropriate amount of force. Classical mechanical grippers, due to the complex control and stiffness, cannot always be used to harvest fruits and vegetables. Instead, the use of soft materials could provide a visible advancement. In this work, we propose a soft, sensorized gripper for harvesting applications. The sensing is performed by tracking a set of markers integrated into the soft part of the gripper. Different machine learning-based approaches have been used to map the markers’ position and dimensions into forces in order to perform a close-loop control of the gripper. Results show that force can be measured with an error of 2.6% in a range from 0 to 4 N. The gripper was integrated into a robotic arm having an external vision system used to detect plants and fruits (strawberries in our case scenario). As a proof of concept, we evaluated the performance of the robotic system in a laboratory scenario. Plant and fruit identification reached a positive rate of 98.2% and 92.4%, respectively, while the correct picking of the fruits, by removing it from the stalk without a direct cut, achieved an 82% of successful rate

HERO Glove

Non-repetitive manipulation tasks that are easy for humans to perform are difficult for autonomous robots to execute. The Haptic Exoskeletal Robot Operator (HERO) Glove is a system designed for users to remotely control robot manipulators whilst providing sensory feedback to the user. This realistic haptic feedback is achieved through the use of toroidal air-filled actuators that stiffen up around the user’s fingers. Tactile sensor data is sent from the robot to the HERO Glove, where it is used to vary the pressure in the toroidal actuators to simulate the sense of touch. Curvature sensors and inertial measurement units are used to capture the glove’s pose to control the robot

Anthropomorphic Twisted String-Actuated Soft Robotic Gripper with Tendon-Based Stiffening

Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force output, small actuation range, and poor compactness. Despite advances in this area, realizing compact soft grippers with high dexterity and force output is still challenging. This paper explores twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by the human hand. Tunable stiffness was implemented in the fingers with antagonistic TSAs. The fingers' bending angles, actuation speed, blocked force output, and stiffness tuning were experimentally characterized. The gripper achieved a score of 6 on the Kapandji test and recreated 31 of the 33 grasps of the Feix GRASP taxonomy. It exhibited a maximum grasping force of 72 N, which was almost 13 times its own weight. A comparison study revealed that the proposed gripper exhibited equivalent or superior performance compared to other similar soft grippers.Comment: 19 pages, 15 figure

Design, Modeling and Control of a 3D Printed Monolithic Soft Robotic Finger with Embedded Pneumatic Sensing Chambers

IEEE This paper presents a directly 3D printed soft monolithic robotic finger with embedded soft pneumatic sensing chambers (PSC) as position and touch sensors. The monolithic finger was fabricated using a low-cost and open-source fused deposition modeling (FDM) 3D printer that employs an off-the-shelf soft and flexible commercially available thermoplastic polyurethane (TPU). A single soft hinge with an embedded PSC was optimized using finite element modeling (FEM) and a hyperelastic material model to obtain a linear relationship between the internal change in the volume of its PSC and the corresponding input mechanical modality, to minimize its bending stiffness and to maximize its internal volume. The soft hinges with embedded PSCs have several advantages, such as fast response to very small changes in their internal volume (~0.0026ml/°), linearity, negligible hysteresis, repeatability, reliability, long lifetime and low power consumption. Also, the flexion of the soft robotic finger was predicted using a geometric model for use in real-time control. The real-time position and pressure/force control of the soft robotic finger were achieved using feedback signals from the soft hinges and the touch PSC embedded in the tip of the finger. This study contributes to the development of seamlessly embedding optimized sensing elements in the monolithic topology of a soft robotic system and controlling the robotic system using the feedback data provided by the sensing elements to validate their performance

Robotic manipulator inspired by human fingers based on tendon-driven soft grasping

Die menschliche Hand ist in der Lage, verschiedene Greif- und Manipulationsaufgaben auszuführen und kann als einer der geschicktesten und vielseitigsten Effektoren angesehen werden. In dieser Arbeit wurde ein Soft Robotic-Greifer entwickelt, der auf den Erkenntnissen aus der Literatur zur Taxonomie der menschlichen Greiffähigkeiten und den biomechanischen Synergien der menschlichen Hand basiert. Im Bereich der Roboterhände sind sehnengetriebene, unteraktuierte Strukturen weit verbreitet. Inspiriert von der Anatomie der menschlichen Hand, bieten sie durch ihre Flexibilität passive Adaptivität und Robustheit. Es wurde ein Sensorsystem implementiert, bestehend aus Force Sensing Resistors (FSRs), Biegungssensoren und einem Stromsensor, wodurch das System charakterisiert werden kann. Die Kraftsensoren wurden in die Fingerkuppen integriert. In Anlehnung an die menschliche Haut wurden Abgüsse aus Silikonkautschuk an den Fingerballen verwendet. Diese versprechen eine erhöhte Reibung und bessere Adaptivität zum gegriffenen Objekt. Um den entwickelten Greifer zu evaluieren, wurden erste Tests durchgeführt. Zunächst wurde die Funktionalität der Sensoren, wie z.B. der als FSRs ausgewählten Kraftsensoren, getestet. Im weiteren Verlauf wurden die Greiffähigkeiten des Greifers durch Manipulation verschiedener Objekte getestet. Basierend auf den Erkenntnissen aus den praktischen Versuchen kann festgestellt werden, dass der entwickelte Greifer ein hohes Maß an Geschicklichkeit aufweist. Auch die Adaptivität ist dank der verwendeten mechanischen Komponenten gewährleistet. Mittels der Sensorik ist es möglich, den Greifprozess zu kontrollieren. Die Ergebnisse zeigen aber auch, dass z. B. die interne Systemreibung die Verlustleistung des Systems stark beeinflusst.The human hand is able to perform various grasping and manipulation tasks, and can be seen as one of the most dexterous and versatile effectors known. The prehensile capabilities of the hand have already been analyzed, categorized and summarized in a taxonomy in numerous studies. In addition to the taxonomies, research on the biomechanical synergies of the human hand led to the following conceptions: The adduction/abduction movement is independent of the flexion/extension movement. Furthermore, the thumb is rather independent in its mobility from the other fingers, while those move synchronously within their corresponding joints. Lastly, the consideration of the synergies provides that the proximal and distal interphalangeal joints of a human finger are more intensely coordinated than those of the metacarpal joints. In this work, a soft robotic gripper was developed based on the knowledge from the literature on the taxonomy of human gripping abilities and the biomechanical synergies of the human hand. In the domain of robotic hands, tendon-driven underactuated structures are widely used. Inspired by the tensegrity structure of the human hand, they offer passive adaptivity and robustness through their flexibility. A sensor system was implemented, consisting of FSRs, flex sensors and a current sensor, thus the system parameters can be characterized continously. The force sensors were integrated into the fingertips. Molds of silicone rubber were used as finger pads to provide higher friction and better adaptivity to the grasped object on the contact areas of the finger, to mimic human skin. Initial tests were carried out to evaluate the gripper. First, the functionality of the sensors, such as the force sensors selected as FSRs, was tested. In the further course, the gripping capabilities of the gripper were tested by manipulation of various different objects. Based on the findings from the practical experiments, it may be stated that the gripper has a high degree of dexterity. Thanks to the mechanical components used, adaptivity is guaranteed as well. By means of the sensor system it is possible to control the gripping processes. However, the results also showed that, for example, the internal system friction dominates the system’s power dissipation