Soft Biomimetic Finger with Tactile Sensing and Sensory Feedback Capabilities

The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine (SVM) classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over sixteen independent parameters when tested on thirteen standardized textured surfaces. The sixteen parameters were the combination of four angles of flexion of the soft finger and four speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation (TENS) to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provides sensory feedback; furthermore, texture feedback has the potential to enhance the user experience when interacting with their surroundings. Additionally, this work showed that an inexpensive, soft biomimetic finger combined with a flexible tactile sensor array can potentially help users perceive their environment better

Merging Local and Global 3D Perception for Robotic Grasping and Manipulation

This paper presents our results towards fusing RGB-D images with data from contact and proximity sensors embedded in a robotic hand for improved object perception, recognition and manipulation. Optical depth information from multiple sensors is often inaccurate and inconsistent. These problems arise from problems with sensor calibration, but also occlusion of objects by other objects or the robot arm itself. In this paper, we propose to combine global pose information from RGB-D sensing with local proximity sensing during approach. Here, we use contact information based on a novel contact sensor and additional pose information provided by the arm's pose

A Unified Visual-Haptic Fingertip Sensor For Advanced Robot Dexterity

The problem of robotic grasping and manipulation requires a system level perspective that needs to be aimed at solving the interlinked sub-problems simultaneously. These sub-problems consists of designing an appropriate robot hand, sensing technology, control, and planning strategy, that can increase the dexterity of a robot hand in complex environments. Approaches towards these lack the proper use and integration of tactile feedback that can potentially enable robot hands with far superior capabilities than found today. This thesis addresses this challenge from three aspects: hardware design, system integration, and algorithm development. On the hardware side, it traces the thorough development of a multi and cross-modal tactile sensor that can measure proximity, contact, and force (PCF). Three unique features of the PCF sensor are (i) the ability to measure visual as well as tactile object features, (ii) its low manufacturing cost and (iii) that it can be easily integrated into different type of robot hands. This is achieved by embedding infrared proximity sensing integrated chips in soft elastomer to achieve a multitude of signals. On the system integration side, the thesis manifests the individual importance of the hand design, visual and tactile sensing modalities in the context of robotic manipulation related tasks through careful real-world robotic experiments. On the algorithmic side, it shows the implementation of several algorithms concerning signal processing, computer vision, controls, probabilistic theory and machine learning for experimental evaluation.</p

Fabric-based eversion type soft actuators for robotic grasping applications

Humans have managed to simplify their lives by using robots to automate dull and repetitive tasks. Traditional robots have been very helpful in this respect, but in certain applications, the complexity of manufacturing and the requisite control strategies have rendered these systems inadequate. The concept of robots made of soft materials has increasingly been explored and a new avenue of research has opened up within the robotics community. In terms of construction, robots made of soft and flexible materials have several advantages over their rigid-bodied counterparts, among them simple design, simple control mechanisms, inexpensive constituent materials and the fact that they can be easily integrated into existing systems. Soft grippers in particular have been the subject of extensive research and we have witnessed significant development in terms of attributes like grasping, payload and sensing methodologies. Progress has been enhanced by the development of new materials used in the construction of actuators or end effectors of the grippers. The use of lightweight, non-stretch fabrics is a relatively new concept but initial studies have demonstrated their effectiveness in grasping applications. This thesis sets out a comparative study of popular gripping systems, focusing on the advantages of using fabrics in the construction of soft grippers. Multiple designs for fabric based finger like actuators, each addressing the drawbacks of the preceding design, are discussed along with the experimental evaluation of each design. A novel gripping mechanism in which the fingers of the gripper grow lengthwise from the tip (evert) to access and grasp the object is also presented. Large-scale fabric based eversion robots have been constructed to access environments with restricted access and for monitoring purposes. An experimental evaluation of the eversion capable finger is also presented, outlining important attributes such as payload, bending and force capability of the designed finger. An optical fibre based sensing methodology is also presented, capable of measuring the bending behaviour in soft actuators. The proposed sensor can be configured to sense bending angles, as well as the contact forces along different points along the length of the actuators

Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1 degree of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.Comment: Accepted to ICRA202

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

Soft Robotic Grippers

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research

Artificial skin with sense of touch for robotic hand

This Master of Science thesis proposes a method to fabricate a soft robotic hand (SRH) with a sense of touch. Electronic skin (e-skin) – flexible and/or stretchable electronics that mimic the functions of human skin – is actively researched and developed for robotic applications (especially humanoid robots), owing to the high demand of robots that can safely interact with humans in the different industrial sectors. E-skin is also in demand for high-quality prosthetics that leverage the advances in brain-machine interfaces. The emphasis in this thesis is on the fabrication and characterization of an e-skin. The objective of this skin is to give an estimation of the amount of force exerted on it, which is beneficial for the SRH to feedback information about the manipulated object. We are aiming in this thesis to use fabrication approach of rapid prototyping to fulfill the following characteristics in SRH: actuation, soft touch, and sensation capabilities. Accordingly, we propose using 3D printing to fabricate both hand skeleton and molds to be used for artificial skin casting. Fingers are actuated by driving cables which are extended through inner channels embedded inside the hand skeleton. The specific goal of this thesis is to compare two different types of touch sensors for e-skin, one piezoresistive and one capacitive. The selected technologies are discussed in detail, and sensors based on these technologies are fabricated, characterized and analyzed comparatively. The results showed the potential of disclosing tactile information by implanting sensors in SRH. With comparing the piezoresistive sensor to the capacitive sensor, the latter exhibited a simpler approach for integration with the artificial skin to develop e-skin because it was feasible to fabricate the e-skin in one step instead of fabricating the artificial skin and the sensor separately. From the perspective of performance, capacitive sensor demonstrated higher efficiency in general compared to the piezoresistive sensor. As an example, the response in the piezore-sistive and capacitive sensor, showed linearity of 5.3% (on a logarithmic scale) 1.8% for both sensors, respectively. Moreover, the signal hysteresis in the capacitive sensor was better with a deviation of 2.7%, compared to 18.2% for the piezoresistive sensor. Finally, a SRH with integrated touch sensors is demonstrated. This paves the way for further research on utilizing the developed e-skin for objects recognition during hand gripping or designing a closed control loop system for dexterous control over the force of gripping. Moreover, an efficient artificial limb with sensation capabilities can be developed to feedback sensory information to the brain of the patient after being processed by a brain-machine interface

Design of a 3D-printed soft robotic hand with distributed tactile sensing for multi-grasp object identification

Tactile object identification is essential in environments where vision is occluded or when intrinsic object properties such as weight or stiffness need to be discriminated between. The robotic approach to this task has traditionally been to use rigid-bodied robots equipped with complex control schemes to explore different objects. However, whilst varying degrees of success have been demonstrated, these approaches are limited in their generalisability due to the complexity of the control schemes required to facilitate safe interactions with diverse objects. In this regard, Soft Robotics has garnered increased attention in the past decade due to the ability to exploit Morphological Computation through the agent's body to simplify the task by conforming naturally to the geometry of objects being explored. This exists as a paradigm shift in the design of robots since Soft Robotics seeks to take inspiration from biological solutions and embody adaptability in order to interact with the environment rather than relying on centralised computation. In this thesis, we formulate, simplify, and solve an object identification task using Soft Robotic principles. We design an anthropomorphic hand that has human-like range of motion and compliance in the actuation and sensing. The range of motion is validated through the Feix GRASP taxonomy and the Kapandji Thumb Opposition test. The hand is monolithically fabricated using multi-material 3D printing to enable the exploitation of different material properties within the same body and limit variability between samples. The hand's compliance facilitates adaptable grasping of a wide range of objects and features integrated distributed tactile sensing. We emulate the human approach of integrating information from multiple contacts and grasps of objects to discriminate between them. Two bespoke neural networks are designed to extract patterns from both the tactile data and the relationships between grasps to facilitate high classification accuracy

Soft pneumatic actuators: a review of design, fabrication, modeling, sensing, control and applications

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable materials and usually follow a bioinspired design. Their high dexterity and safety make them ideal for applications such as gripping, locomotion, and biomedical devices, where the environment is highly dynamic and sensitive to physical interaction. Pneumatic actuation remains the dominant technology in soft robotics due to its low cost and mass, fast response time, and easy implementation. Given the significant number of publications in soft robotics over recent years, newcomers and even established researchers may have difficulty assessing the state of the art. To address this issue, this article summarizes the development of soft pneumatic actuators and robots up until the date of publication. The scope of this article includes the design, modeling, fabrication, actuation, characterization, sensing, control, and applications of soft robotic devices. In addition to a historical overview, there is a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards, and nonlinear estimation and control techniques. Finally, the capabilities and limitations of soft pneumatic actuators and robots are discussed and directions for future research are identified