Soft Robotic Hand Research Articles

Development of A Thin Soft Robotic Hand Capable of Packaging Cut Cakes

Development of A Thin Soft Robotic Hand Capable of Packaging Cut Cakes

A 3D Printed Soft Robotic Hand With Embedded Soft Sensors for Direct Transition Between Hand Gestures and Improved Grasping Quality and Diversity.

In this study, a three-dimensional (3D) printed soft robotic hand with embedded soft sensors, intended for prosthetic applications is designed and developed to efficiently operate with new-generation myoelectric control systems, e.g., pattern recognition control and simultaneous proportional control. The mechanical structure of the whole hand ('ACES-V2') is fabricated as a monolithic structure using a low-cost and open-source 3D printer. It minimizes the post-processing required for the addition of the embedded sensors in the hand. These are significant benefits for the robotic hand that features low cost, low weight (313 grams), and anthropomorphic appearance. With the soft position sensors added to the fingers, the fingers' positions can be monitored to avoid self-collision of the hand. Besides, it allows a robotic prosthetic hand to eliminate the conventional way of returning to the neutral full open position when switching from one type of gesture to another. This makes the transition between the hand gestures much faster, more efficient, and more intuitive as well. Further, initial contact detection of each finger is achieved for the preshaping of multi-finger grasps, e.g., tripod grip and power grasps, to improve the stability and quality of the grasps. Combinations of different gestures allow the hand to perform multi-stage grasps to seize and carry multiple objects simultaneously. It can potentially augment the hand's dexterity and grasping diversity. Providing direct transition between the hand gestures and improved grasping quality and diversity are the primary contributions of this study.

Multijointed Pneumatic Soft Hand with Flexible Thenar.

Soft robotic hands provide better safety and adaptability than rigid robotic hands. Furthermore, a multijointed structure that imitates the movement of a human hand represents significant progress in realizing its anthropomorphism. In this study, we present a multijointed pneumatic soft anthropomorphic hand that is capable of expressing letters through sign language and grasping different objects using three grasping modes, namely thumb grasping, precision grasping, and power grasping. This novel soft hand is composed of multijointed soft fingers, a thumb, thenar, and 3D-printed palm. Tests were performed to characterize the displacement track and force performance of the fingers, thumb, and thenar, which was made by mold casting silicone rubber. In addition, a dedicated pneumatic control system was designed and built to enable the soft hand to automatically perform the tasks set by specific programs. This new multijointed hand with a flexible thenar represents significant progress in the development of anthropomorphic bionic hands, offering the benefits of fast response, low cost, as well as ease of fabrication, assembly, and replacement.

Designing superhydrophobic robotic surfaces: Self-cleaning, high-grip impact, and bacterial repelling

Biomimetic surface has attracted a lot of interest in designing the high grip and superhydrophobic surfaces. In particular, adjusting the load on a flexible surface to increase the solid-solid contact area can effectively increase its overall solid-solid static friction. Here we utilized laser processing and hydrochloric acid etching methods on the aluminum alloy substrate surface to generate different textures as templates to produce polydimethylsiloxane (PDMS) films with flexible superhydrophobic surfaces. Four versatile PDMS films with surface morphologies were produced, which gave superhydrophobic properties at varying angles. Scanning electron microscopy revealed that a micron- and nano-scale structure is produced by the etched methods applied here. The static friction and its impact on grip using these flexible superhydrophobic surfaces were further studied under different loads and various conditions. We found that the flexible superhydrophobic surface increased grip under all conditions, using a critical threshold load. The high surface area of superhydrophobic surfaces can increase surface contact and friction. Also, the concave-convex structure can act as a suction cup, increasing the static friction. The designed surfaces were further applied to soft robotic hands, which significantly improved its gripping impact and bacteria-repelling properties after washing.

Learning to Prevent Grasp Failure with Soft Hands: From Online Prediction to Dual‐Arm Grasp Recovery

Soft hands allow to simplify the grasp planning to achieve a successful grasp, thanks to their intrinsic adaptability. At the same time, their usage poses new challenges, related to the adoption of classical sensing techniques originally developed for rigid end defectors, which provide fundamental information, such as to detect object slippage. Under this regard, model‐based approaches for the processing of the gathered information are hard to use, due to the difficulties in modeling hand–object interaction when softness is involved. To overcome these limitations, in this article, we proposed to combine distributed tactile sensing and machine learning (recurrent neural network) to detect sliding conditions for a soft robotic hand mounted on a robotic manipulator, targeting the prediction of the grasp failure event and the direction of sliding. The outcomes of these predictions allow for an online triggering of a compensatory action performed with a second robotic arm–hand system, to prevent the failure. Despite the fact that the network is trained only with spherical and cylindrical objects, we demonstrate high generalization capabilities of our framework, achieving a correct prediction of the failure direction in of cases, and a of successful regrasps, for a selection of 12 objects of common use.

Development of a novel robotic hand with soft materials and rigid structures

PurposeRigid robotic hands are generally fast, precise and capable of exerting large forces, whereas soft robotic hands are compliant, safe and adaptive to complex environments. It is valuable and challenging to develop soft-rigid robotic hands that have both types of capabilities. The paper aims to address the challenge through developing a paradigm to achieve the behaviors of soft and rigid robotic hands adaptively.Design/methodology/approachThe design principle of a two-joint finger is proposed. A kinematic model and a stiffness enhancement method are proposed and discussed. The manufacturing process for the soft-rigid finger is presented. Experiments are carried out to validate the accuracy of the kinematic model and evaluate the performance of the flexible body of the finger. Finally, a robotic hand composed of two soft-rigid fingers is fabricated to demonstrate its grasping capacities.FindingsThe kinematic model can capture the desired distal deflection and comprehensive shape accurately. The stiffness enhancement method guarantees stable grasp of the robotic hand, without sacrificing its flexibility and adaptability. The robotic hand is lightweight and practical. It can exhibit different grasping capacities.Practical implicationsIt can be applied in the field of industrial grasping, where the objects are varied in materials and geometry. The hand’s inherent characteristic removes the need to detect and react to slight variations in surface geometry and makes the control strategies simple.Originality/valueThis work proposes a novel robotic hand. It possesses three distinct characteristics, i.e. high compliance, exhibiting discrete or continuous kinematics adaptively, lightweight and practical structures.

Trigger-Based Dexterous Operation with Multimodal Sensors for Soft Robotic Hand

This paper focuses on how to improve the operation ability of a soft robotic hand (SRH). A trigger-based dexterous operation (TDO) strategy with multimodal sensors is proposed to perform autonomous choice operations. The multimodal sensors include optical-based fiber curvature sensor (OFCS), gas pressure sensor (GPS), capacitive pressure contact sensor (CPCS), and resistance pressure contact sensor (RPCS). The OFCS embedded in the soft finger and the GPS series connected in the gas channel are used to detect the curvature of the finger. The CPCS attached on the fingertip and the RPCS attached on the palm are employed to detect the touch force. The framework of TDO is divided into sensor detection and action operation. Hardware layer, information acquisition layer, and decision layer form the sensor detection module; action selection layer, actuator drive layer, and hardware layer constitute the action operation module. An autonomous choice decision unit is used to connect the sensor detecting module and action operation module. The experiment results reveal that the TDO algorithm is effective and feasible, and the actions of grasping plastic framework, pinching roller ball pen and screwdriver, and handshake are executed exactly.

Design of a Single-Material Complex Structure Anthropomorphic Robotic Hand.

In the field of robotic hand design, soft body and anthropomorphic design are two trends with a promising future. Designing soft body anthropomorphic robotic hands with human-like grasping ability, but with a simple and reliable structure, is a challenge that still has not been not fully solved. In this paper, we present an anatomically correct robotic hand 3D model that aims to realize the human hand’s functionality using a single type of 3D-printable material. Our robotic hand 3D model is combined with bones, ligaments, tendons, pulley systems, and tissue. We also describe the fabrication method to rapidly produce our robotic hand in 3D printing, wherein all parts are made by elastic 50 A (shore durometer) resin. In the experimental section, we show that our robotic hand has a similar motion range to a human hand with substantial grasping strength and compare it with the latest other designs of anthropomorphic robotic hands. Our new design greatly reduces the fabrication cost and assembly time. Compared with other robotic hand designs, we think our robotic hand may induce a new approach to the design and production of robotic hands as well as other related mechanical structures.

A Supernumerary Soft Robotic Limb for Reducing Hand-Arm Vibration Syndromes Risks.

The most common causes of the risk of work-related musculoskeletal disorders (WMSD) have been identified as joint overloading, bad postures, and vibrations. In the last two decades, various solutions ranging from human-robot collaborative systems to robotic exoskeletons have been proposed to mitigate them. More recently, a new approach has been proposed with a high potential in this direction: the supernumerary robotic limbs SRLs are additional robotic body parts (e.g., fingers, legs, and arms) that can be worn by the workers, augmenting their natural ability and reducing the risks of injuries. These systems are generally proposed in the literature for their potentiality of augmenting the user’s ability, but here we would like to explore this kind of technology as a new generation of (personal) protective equipment. A supernumerary robotic upper limb, for example, allows for indirectly interacting with hazardous objects like chemical products or vibrating tools. In particular, in this work, we present a supernumerary robotic limbs system to reduce the vibration transmitted along the arms and minimize the load on the upper limb joints. For this purpose, an off-the-shelf wearable gravity compensation system is integrated with a soft robotic hand and a custom damping wrist, designed starting from theoretical considerations on a mass-spring-damper model. The real efficacy of the system was experimentally tested within a simulated industrial work environment, where seven subjects performed a drilling task on two different materials. Experimental analysis was conducted according to the ISO-5349. Results showed a reduction from 40 to 60% of vibration transmission with respect to the traditional hand drilling using the presented SRL system without compromising the time performance.

A pneumatic random-access memory for controlling soft robots.

Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed microfluidic valves as transistor-like elements; this enables our circuits to support more complex computational functions than those built from normally-open valves. We demonstrate an eight-bit nonvolatile random-access pneumatic memory (RAM) that can maintain the states of multiple actuators, control both individual actuators and multiple actuators simultaneously using a pneumatic version of time division multiplexing (TDM), and set actuators to any intermediate position using a pneumatic version of analog-to-digital conversion. We perform proof-of-concept experimental testing of our pneumatic RAM by using it to control soft robotic hands playing individual notes, chords, and songs on a piano keyboard. By dramatically reducing the amount of hardware required to control multiple independent actuators in pneumatic soft robots, our pneumatic RAM can accelerate the spread of soft robotic technologies to a wide range of important application areas.

Soft Robotic Hands and Tactile Sensors for Underwater Robotics

Research in the field of underwater (UW) robotic applications is rapidly developing. The emergence of coupling the newest technologies on submersibles, different types of telecommunication devices, sensors, and soft robots is transforming the rigid approach to robotic design by providing solutions that bridge the gap between accuracy and adaptability in an environment where there is so much fluctuation in object targeting and environmental conditions. In this paper, we represent a review of the history, development, recent research endeavors, and projected outlook for the area of soft robotics technology pertaining to its use with tactile sensing in the UW environment.

Effects of a Soft Robotic Hand for Hand Rehabilitation in Chronic Stroke Survivors

ObjectivesSoft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated the clinical efficacy of a soft robotic hand that could actively flex and extend the fingers in chronic stroke subjects with different levels of spasticity. MethodsSixteen chronic stroke subjects were recruited into this single-group study. Subjects underwent 20 sessions of 1-hour EMG-driven soft robotic hand training. Training effect was evaluated by the pre-training and post-training assessments with the clinical scores: Action Research Arm Test(ARAT), Fugl-Meyer Assessment for Upper Extremity(FMA-UE), Box-and-Block test(BBT), Modified Ashworth Scale(MAS), and maximum voluntary grip strength. ResultsFor all the recruited subjects (n = 16), significant improvement of upper limb function was generally observed in ARAT (increased mean=2.44, P = 0.032), FMA-UE (increased mean=3.31, P = 0.003), BBT (increased mean=1.81, P = 0.024), and maximum voluntary grip strength (increased mean=2.14 kg, P < 0.001). No significant change was observed in terms of spasticity with the MAS (decreased mean=0.11, P = 0.423). Further analysis showed subjects with mild or no finger flexor spasticity (MAS<2, n = 9) at pre-training had significant improvement of upper limb function after 20 sessions of training. However, for subjects with moderate and severe finger flexor spasticity (MAS=2,3, n = 7) at pre-training, no significant change in clinical scores was shown and only maximum voluntary grip strength had significant increase. ConclusionEMG-driven rehabilitation training using the soft robotic hand with flexion and extension could be effective for the functional recovery of upper limb in chronic stroke subjects with mild or no spasticity.

A Novel Soft Robotic Hand Design With Human-Inspired Soft Palm: Achieving a Great Diversity of Grasps

Soft robotic hands and grippers are increasingly attracting attention as robotic end effectors. Compared with their rigid counterparts, they are safer for human-robot and environment-robot interactions, easier to control, and more compliant, and they cost and weigh less. Design studies of soft robotic hands have focused mostly on the soft fingers and bending actuators. However, the palm is also an essential part in grasping. In this work, we propose a novel design for an inexpensive soft humanoid hand with pneumatic soft fingers and a soft palm. The configuration of the soft palm is based on a modular design that can be applied to actuate different kinds of existing soft fingers. The splaying of the fingers, bending of the whole palm, and abduction and adduction of the thumb are implemented by the palm. Moreover, we present a new design of soft finger, called the hybrid-bending soft finger (HBSF), that can both bend in the grasping axis and deflect in the side-to-side axis, achieving human-like motion (Figure 1). The functions of the HBSF and soft palm are evaluated both in simulation, using the Simulation Open Framework Architecture (SOFA) framework, and experimentally. Six finger designs with 1-11 longitudinal segments are analyzed. The versatility of the soft hand is evaluated and demonstrated experimentally by its grasping of objects according to Feix's taxonomy. The results demonstrate a great diversity of grasps, with 31 of the 33 grasp types of the taxonomy performed successfully with the proposed design, showing promise for grasping a large variety of objects with different shapes and weights.

A Dual-Mode Actuator for Soft Robotic Hand

Fluidic and tendon driven actuators are widely applied in soft robotic hand design. Fluidic actuated soft robotic hands are highly compliant and can adapt to various objects. Tendon driven soft robotic hands are easier to control and have high dexterity and large force output, however, they are less compliant than fluidic actuated ones. Most robotic hands are designed with only one actuation method. Combining both actuation methods can tap the advantages and make up each other's limitations but at the expense of more complicated design and control. In this research, we propose a simple dual-mode actuator that provides both fluidic and tendon actuation. The two actuation modes are achieved simultaneously by twisting an elastic tube filled with gas, liquid, or even a combination. The soft robotic finger, designed as a fluidic elastomer actuator (FEA), is actuated by the fluid displaced from tube twisting, as well as by the tendon due to tube contraction in twisting. The tendon drives a compliant metacarpophalangeal (MCP) joint of the finger in order to provide a large bending angle and bending force to finger. Compared to an FEA soft finger, the proposed design can effectively increase the bending angle up to 150% (from 170 degrees to 260 degrees), and the blocking force up to 134% (from 3.2N to 4.3N). A soft robotic hand prototype with dual-mode actuators design is made for various grasping demonstrations.

Development and Grasp Stability Estimation of Sensorized Soft Robotic Hand.

This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.

Design of tendon mechanism for soft wearable robotic hand and its fuzzy control using electromyogram sensor

This study proposes a wearable robotic hand using an improved tendon mechanism enhanced compared to the existing link one with heavy and hard-to-ware. The enhanced wearable robot hand is controlled using the electromyogram (EMG) sensor. In previous tendon mechanism of wearable robotic hand, it only implemented the flexor digitorum profundus (FDP) tendon. Therefore, the fingertip is flexed much more, which means that the radius of curvature is large, so the motion of gripping an object became unstable. Prototype of the first proposed tendon-driven wearable robotic hand is implemented to both FDP and flexor digitorum superficialis tendon, so that the middle phalanx can flex with the distal phalanx. This leads the motion of gripping objects become stable. However, it is difficult to move the distal phalanx and middle phalanx together to grab the object completely. The second improved tendon mechanism of wearable robotic hand is implemented to only FDP, but it sets different friction force to each guide; thus, it can grip the object completely. The EMG sensor is used to detect the motion of the finger, and the fuzzy controller is used to control the finger of the wearable robotic hand according to the amount of muscle used.

A Soft 3D-Printed Robotic Hand Actuated by Coiled SMA

Robotic hands with unique designs, capabilities and applications have been presented in the literature focusing on sensing, actuation, control, powering and manufacturing, most of which are created by manual assembly process. However, due to advancements in additive manufacturing, new capabilities have replaced traditional methods of manufacturing. In this paper, we present a soft 3D-printed robotic hand actuated by custom-made coiled shape memory alloy (SMA) actuators. The hand uses additive manufacturing of flexible thermoplastic polyurethane (TPU) material, which allows flexing at the joint and hence eliminates the need for additional assembly. Here, we present the full characteristics of the robotic hand such as object grasping categorized by size and weight from the ARAT kit and others. The robotic hand is 425 mm in length, weighs 235 g and is able to operate at a frequency of 0.125 Hz without active cooling. It can grasp an object of 55–81 mm widths, weighing up to 133 g, while consuming an average power of 7.82 W. We also show the time domain response of our custom-made coiled SMA to different current inputs, and its corresponding force and displacement. The current design yields a lightweight and low cost artificial hand with significantly simplified manufacturing for applications in robotics and prosthetics.

Dynamic manipulation of pneumatically controlled soft finger for home automation

Soft robots have the advantage of inherent flexibility, adaptability, compliance, and safety in human interaction, and therefore attracted significant research attention in recent years. They have found interesting applications in industrial automation where soft robotic hands are fitted as end-effector on traditional rigid robotic arms to handle delicate objects. Their inherent compliance with the shape of the object reduces the complexity of sensing and actuation mechanisms required for the safe operation of traditional robotic hands. They also have the potential application in the home automation, since the operation of robots in indoor environment impose a stringent requirement on safety and compliant design. Despite this, the dynamic manipulation of soft robots remains challenging because their inherent flexibility makes their mathematical model highly nonlinear. Existing works either use model-free control, e.g., PID, which owing to its general formulation, does not account for the peculiarity of soft robots, or they use the Finite-Element-Method based approach, which, apart from being computationally expensive, requires an exact model of the soft robots. In this paper, we take a holistic approach by first developing a low-order approximate mathematical model for computational efficiency and then adding a feedback loop using an inverse dynamics controller to compensate for modeling errors. Theoretical analysis is presented to prove the convergence and stability of the proposed controller. Extensive experimental and comparison results also prove the superiority of the proposed controller over other algorithms.

Microporous Induced Fully Printed Pressure Sensor for Wearable Soft Robotics Machine Interfaces

Soft robotics machine interfaces are desirable for facilitating handling of objects in soft robotics applications. High‐speed responses of the interfaces are crucial for achieving statement conversions in novel robotics systems. Herein, a novel scheme for synthesizing a functional ink for producing fully printed soft pressure sensors that are highly responsive for detection of an applied vertical force is presented. The sensor consists of carbon nanotubes and polymeric soft materials, and achieves good response characteristics because of the microporous sensing layer. The fabricated sensor shows high performance for detecting forces with a high‐speed response. Novel wearable robotics machine interfaces for the printed soft sensor and a soft robotic hand are fabricated to facilitate object manipulation. The artificial sensor for a switching system demonstrates successful gripping and release of an object when controlled by a switch. Further, these findings show that the switching performance of the sensor is suitable for the machine interface for switching applications. This implies that the fabrication of a sensing system for remotely controlled soft robots is possible. Thus, high‐sensitivity printed devices for tactile machine interfaces in the form of a wearable e‐skin are experimentally demonstrated.

A Dexterous Soft Robotic Hand for Delicate In-Hand Manipulation

In this letter, we show that soft robotic hands provide a robust means of performing basic primitives of in-hand manipulation in the presence of uncertainty. We first discuss the design of a prototype hand with dexterous soft fingers capable of moving objects within the hand using several basic motion primitives. We then empirically validate the ability of the hand to perform the desired object motion primitives while still maintaining strong grasping capabilities. Based on these primitives, we examine a simple, heuristic finger gait which enables continuous object rotation for a wide variety of object shapes and sizes. Finally, we demonstrate the utility of our dexterous soft robotic hand in three real-world cases: unscrewing the cap of a jar, orienting food items for packaging, and gravity compensation during grasping. Overall, we show that even for complex tasks such as in-hand manipulation, soft robots can perform robustly without the need for local sensing or complex control.