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

A comprehensive gaze stabilization controller based on cerebellar internal models

Gaze stabilization is essential for clear vision; it is the combined effect of two reflexes relying on vestibular inputs: the vestibulocollic reflex (VCR), which stabilizes the head in space and the vestibulo-ocular reflex (VOR), which stabilizes the visual axis to minimize retinal image motion. The VOR works in conjunction with the opto-kinetic reflex (OKR), which is a visual feedback mechanism that allows the eye to move at the same speed as the observed scene. Together they keep the image stationary on the retina. In this work, we implement on a humanoid robot a model of gaze stabilization based on the coordination of VCR, VOR and OKR. The model, inspired by neuroscientific cerebellar theories, is provided with learning and adaptation capabilities based on internal models. We present the results for the gaze stabilization model on three sets of experiments conducted on the SABIAN robot and on the iCub simulator, validating the robustness of the proposed control method. The first set of experiments focused on the controller response to a set of disturbance frequencies along the vertical plane. The second shows the performances of the system under three-dimensional disturbances. The last set of experiments was carried out to test the capability of the proposed model to stabilize the gaze in locomotion tasks. The results confirm that the proposed model is beneficial in all cases reducing the retinal slip (velocity of the image on the retina) and keeping the orientation of the head stable

Stiffening in soft robotics: A review of the state of the art

The need for building robots with soft materials emerged recently from considerations of the limitations of service robots in negotiating natural environments, from observation of the role of compliance in animals and plants [1], and even from the role attributed to the physical body in movement control and intelligence, in the so-called embodied intelligence or morphological computation paradigm [2]-[4]. The wide spread of soft robotics relies on numerous investigations of diverse materials and technologies for actuation and sensing, and on research of control techniques, all of which can serve the purpose of building robots with high deformability and compliance. But the core challenge of soft robotics research is, in fact, the variability and controllability of such deformability and compliance

Delicate yet strong: characterizing the electro-adhesion lifting force with a soft gripper

Compliant grippers are one of the most promising soft robotic devices for industrial tasks. Soft grippers dramatically simplify grasping control because the gripper automatically conforms to the object's shape. A common limitation of soft structures is that they can only generate low forces, limiting grasping ability. One approach to increase the holding force is to increase the shear force by using controlled adhesion: the lifting force is thus increased, while the clamping force can be kept low, important for manipulating delicate objects. In this work, we explore the lifting force generated with a soft gripper using electroadhesion. We show that this force is highly dependent on the holding posture, which depends on both the shape of the gripper and the shape of the object. For a 1 cm(2) electroadhesion area, we measure maximum lifting forces up to 16 N, strongly dependent on object's shape. Reliability is also an essential feature to move soft robots into industrial scenarios. The gripper survived over 100 cycles at high load with no damage, showing its high robustness. Combining electroadhesion and dielectric elastomers actuators, our soft gripper generates grasping forces so high that we reach the structural limits of the rigid plastic frame, yet it is delicate enough to gently pick up and release a cherry tomato

Comparison of Optimization Algorithms for the Indirect Encoding of a Neural Controller for a Soft Robotic Arm

Abstract With their dexterity, robustness and safe interaction with humans, soft robots bode to revolution the field of robotics. However, featuring structures undergoing nonlinear deformations and under-actuated mechanisms, traditional control techniques are usually unsuccessful. Artificial neural networks have instead shown to be a suitable solution to control soft robots in several cases. Among the different classes of algorithms to train neuro-controllers, one that recently experienced a wide spread consists of optimization with genetic algorithms (GAl through indirect encoding. Main advantages are: the ability to produce networks with functional regularities that exploit the geometry of the domain; the decoupling of problem complexity from its resolution. The predominant use of GA has several reasons, ranging from bio-inspiration to some undeniable technical advantages. However, two main issues suggest the need to explore different and possibly more efficient algorithms to train neuro-controllers for soft robots: the high computationaI cost of mathematical models to simulate soft robots and evidences of unsuccessful global convergence of GA if not carefully tuned. In this study, we compared the performance of GA with those of other optimization algorithms in training an artificial neural network to control a soft robotic arm inspired by the octopus, simulated through a non-linear dynamic mathematical mode

Dexterous textile manipulation using electroadhesive fingers

Handling of fabric is a crucial step in the manufacturing of garments. This task is typically performed by trained workers who manipulate one sheet at a time, thus introducing a bottleneck in the automation of the textile industry. This paper seeks to address the challenge of picking fabric up by proposing a new method of achieving ply-separation. Our approach relies on a finger-tip sized (2 cm ) electroadhesive skin to lift fabric up. A pinch-type grasp is then used to securely hold the separated sheet of fabric, enabling easy manipulation thereafter. The ability to successfully pick up and manipulate a variety of commercial fabrics with diverse materials, shapes, sizes and textures is demonstrated. The ability to handle fabrics 100s of times larger than the electroadhesive skin is unique to our approach. Additionally, we demonstrate the manipulation of non-flat fabrics, a challenge that has not been previously addressed by electroadhesive approaches. We believe that this method introduces a smarter way of handling flexible and limp materials, showing great potential towards automation of garment manufacturing.</p

Evolving Optimal Swimming in Different Fluids: A Study Inspired by batoid Fishes

For their efficient and elegant locomotion, batoid fishes (e.g. the manta ray) have been widely studied in biology, and also taken as a source of inspiration by engineers and roboticists willing to replicate their propulsion mechanism in order to build efficient swimming machines. In this work, a new model of an under-actuated compliant wing is proposed, exhibiting both the oscillatory and undulatory behaviors underlying batoid propulsion mechanism. The proposed model allowed an investigation of the co-evolution of morphology and control, exploiting dynamics emergent from the interaction between the environment and the mechanical properties of the soft materials. Having condensed such aspects in a mathematical model, we studied the adaptability of a batoid-like morphology to different environments. As for biology, our main contribution is an exploration of the parameters linking swimming mechanics, morphology and environment. This can contribute to a deeper understanding of the factors that led various species of the batoid group to phylogenetically adapt to different environments. From a robotics standpoint, this work offers an additional example remarking the importance of morphological computation and embodied intelligence. A direct application can be an under-water soft robot capable of adapting morphology and control to reach the maximum swimming efficiency

Soft Biomimetic Fish Robot Made of Dielectric Elastomer Actuators

This article presents the design, fabrication, and characterization of a soft biomimetic robotic fish based on dielectric elastomer actuators (DEAs) that swims by body and/or caudal fin (BCF) propulsion. BCF is a promising locomotion mechanism that potentially offers swimming at higher speeds and acceleration rates, and efficient locomotion. The robot consists of laminated silicone layers wherein two DEAs are used in an antagonistic configuration, generating undulating fish-like motion. The design of the robot is guided by a mathematical model based on the Euler–Bernoulli beam theory and takes account of the nonuniform geometry of the robot and of the hydrodynamic effect of water. The modeling results were compared with the experimental results obtained from the fish robot with a total length of 150 mm, a thickness of 0.75 mm, and weight of 4.4 g. We observed that the frequency peaks in the measured thrust force produced by the robot are similar to the natural frequencies computed by the model. The peak swimming speed of the robot was 37.2 mm/s (0.25 body length/s) at 0.75 Hz. We also observed that the modal shape of the robot at this frequency corresponds to the first natural mode. The swimming of the robot resembles real fish and displays a Strouhal number very close to those of living fish. These results suggest the high potential of DEA-based underwater robots relying on BCF propulsion, and applicability of our design and fabrication methods

Electrically-Driven Soft Fluidic Actuators Combining Stretchable Pumps With Thin McKibben Muscles

Soft wearable robots could provide support for lower and upper limbs, increase weight lifting ability, decrease energy required for walking and running, and even provide haptic feedback. However, to date most of wearable robots are based on electromagnetic motors or fluidic actuators, the former being rigid and bulky, the latter requiring external pumps or compressors, greatly limiting integration and portability. Here we describe a new class of electrically-driven soft fluidic muscles combining thin, fiber-like McKibben actuators with fully Stretchable Pumps. These pumps rely on ElectroHydroDynamics, a solid-state pumping mechanism that directly accelerates liquid molecules by means of an electric field. Requiring no moving parts, these pumps are silent and can be bent and stretched while operating. Each electrically-driven fluidic muscle consists of one Stretchable Pump and one thin McKibben actuator, resulting in a slender soft device weighing 2 g. We characterized the response of these devices, obtaining a blocked force of 0.84 N and a maximum stroke of 4 mm. Future work will focus on decreasing the response time and increasing the energy efficiency. Modular and straightforward to integrate in textiles, these electrically-driven fluidic muscles will enable soft smart clothing with multi-functional capabilities for human assistance and augmentation