Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators

Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation.

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“…Interested readers may refer to recent more comprehensive underactuated and soft grippers reviews such as [34,38,45].…”

Section: Underactuated and Soft Grippersmentioning

confidence: 99%

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

et al. 2021

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“…Soft robots, inspired by nature and living organisms, [1][2][3] are robots made of flexible, soft materials, that are motor-less, unlike hard-bodied robust robots. Soft robotics is based mainly on material science and can be executed through a variety of mechanisms such as pneumatic, [4][5][6] electrostatic, [7,8] heat activation, [9,10] and magnetic actuations. [11,12] Due to their flexible nature, these soft robots can perform delicate, precise, and continuous movements, such as grasping a fragile object [13][14][15] or moving forward on various substrates at different environments.…”

Section: Introductionmentioning

confidence: 99%

3D Printing Materials for Soft Robotics

et al. 2020

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that uses the sensor-generated information to accurately regulate and enhance the dynamic performance.Soft robots (and more in general soft material-based systems) can be built with a plethora of materials and composites having very different mechanical properties, spanning from fluids (i.e., liquid metals, etc.) and hydrogels, to stiff solids (i.e., acrylonitrile butadiene styrene (ABS), epoxy resins, etc.). Also, in most cases, the constituent materials have many degrees of freedom and are highly non-linear, with relevant hysteresis and viscoelastic behavior. Hence, modeling soft systems is a very challenging task, and it still misses a comprehensive theoretical foundation. [19] As a result, the modeling phase is often overlooked in the design process, which in most cases relies on trial and error experimentation. The most commonly used materials in the design of soft robotics are silicone-based elastomers, such as polydimethylsiloxane (PDMS). They are used as the flexible material in pneumatic actuators, [4,5,14] in magnetic actuators, [11] and in electrically driven actuators as well, usually along with a conductive layer, commonly a carbon-based material. [15,16] Besides, other flexible polymers such as polyimides, [8] polyurethanes (PU), [12,20] or hydrogels [9] are used. So far, most devices are fabricated by casting or molding of liquid monomers, followed by hardening by UV light or heat.3D printing is an additive manufacturing technique, in which a required material is deposited, layer by layer, to form a 3D object, that is, it is a sequential 2D printing. The main advantages of 3D printing over subtractive manufacturing methods, are the simplicity of the process and the ability to form complex and hollow structures. There is a variety of 3D printing technologies that are commercially available, as described in a diversity of review reports. [21][22][23] Therefore, we will present here only a concise description of the printing technologies that are most relevant to soft robotics, all of them are based on ink compositions which are liquid during the printing processes.The most widely used methods are based on extrusion of materials, called fused deposition modeling (FDM) [24,25] and direct ink writing (DIW). [26,27] Both methods rely on a material that is extruded through a nozzle onto a substrate, and differ by the materials being used, the nozzle's nature, and the fixation mechanism. In FDM, a thermoplastic filament is extruded through a heated nozzle, followed by solidification upon cooling on the substrate, to form a solid 2D layer. This technique is simple and not expensive, but is limited to thermoplastic polymers and has relatively low resolution. In DIW, a viscous Soft robotics is a growing field of research, focusing on constructing motorless robots from highly compliant materials, some are similar to those found in living organisms. Soft robotics has a high potential for applications in various fields such as soft grippers, actuators, and biomedical devices. 3D printing of soft robotics present...

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“…A personal computer with a Visual Basic program was used to control the glove to practice grasp-and-release motions. Therefore, soft exoskeletons have greater market prospects than rigid ones [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG

Chen

Yang

Wen

2021

Sensors

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Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

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Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators

Cited by 262 publications

References 152 publications

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

3D Printing Materials for Soft Robotics

A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG

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