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Research on small‐scale soft robots has emerged in recent years, and various soft actuators have been developed to implement various actions. Nevertheless, realizing self‐sensing alongside environmental sensing capabilities remains a challenge, largely due to the constraints imposed by compact dimensions and the limited load‐bearing capacity intrinsic to these robots. In this study, an innovative approach is introduced through the development of a visualized sensing integrated soft actuator, which harnesses the actuator's color as a straightforward and real‐time sensing medium. Additionally, a multi‐responsive actuation mechanism is adopted in which the actuator responds concurrently to both electric and magnetic fields. To exemplify the efficacy of this concept, the actuators are engineered as flexible soft grippers, thereby accommodating functions encompassing grasping and transporting. Relying on the color distribution manifested by the actuator, the actuator's self‐sensing ability alongside its capacity to discern object temperatures is demonstrated. Such a soft actuator provides new perspectives in the realm of soft robotics, showing great potential in diverse robotic applications. [ABSTRACT FROM AUTHOR]

In article number 1806698, Guoying Gu, Qi Ge, and co‐workers develop a paradigm to design and manufacture fast‐response, stiffness‐tunable (FRST) soft actuators via hybrid multimaterial 3D printing. The robotic gripper with three FRST actuators demonstrates a high load capacity and shape adaptivity by lifting objects with arbitrary shapes and various weights spanning from less than 10 g to 1.5 kg. [ABSTRACT FROM AUTHOR]

Soft pneumatic robotics have attracted considerable attention in recent years due to their deformation capabilities, which far exceed those of conventional robotics. However, precise control of soft pneumatic actuators remains a challenge due to the lack of model-based control techniques. This work aims to employ a high-precision and low-cost backpropagation (BP) neural network-based model method to control a 3D soft pneumatic actuator. Experiments show that this BP neural network-based model control method performs well in terms of precision, in which the errors of bending angle and deflection angle are within 0.8° and 1.2°, respectively, and the end point position error of the soft actuator is less than 2.5 mm, which is significantly better than traditional modeling methods, demonstrating the application potential of soft robots for high-precision operations. [ABSTRACT FROM AUTHOR]

Successful soft robot modeling approaches appearing in the recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machine learning. Though specific modeling techniques have been developed for and validated against already realized systems, their strengths and weaknesses have not been explicitly compared against each other. In this article, we show how three distinct model structures, a lumped-parameter model, a continuum mechanical model, and a neural network, compare in capturing the gross trends and specific features of the force generation of soft robotic actuators. In particular, we study models for fiber-reinforced elastomeric enclosures (FREEs), which are a popular choice of soft actuator and that are used in several soft articulated systems, including soft manipulators, exoskeletons, grippers, and locomoting soft robots. We generated benchmark data by testing eight FREE samples that spanned broad design and kinematic spaces and compared the models on their ability to predict the loading–deformation relationships of these samples. This comparison shows the predictive capabilities of each model on individual actuators and each model's generalizability across the design space. While the neural net achieved the highest peak performance, the first principles-based models generalized best across all actuator design parameters tested. The results highlight the essential roles of mathematical structure and experimental parameter determination in building high-performing, generalizable soft actuator models with varying effort invested in system identification. [ABSTRACT FROM AUTHOR]

Soft robotics is projected to have a significant impact on healthcare, industry, and the military to deliver assistance in rehabilitation, daily living activities, repetitive motion tasks, and human performance augmentation. Many attempts have been made for application-specific robotic joints, robots, and exoskeletons using various actuator types, materials, and designs. The progress of creating soft robotic systems can be accelerated if a set of actuators with defined characteristics were developed, similar to conventional robotic actuators, which can be assembled to create desired systems including exoskeletons and end effectors. This work presents the design methodology of such a modular actuator, created with a novel corrugated diaphragm that can apply linear displacement, angular displacement, and force. This modular actuator approach allows for creating various robotic joints by arranging them into different configurations. The modular corrugated diaphragm actuator concept was validated through numerical simulation, fabrication, and testing. Linear displacement, angular displacement, and force characteristics were shown for a single module and in multi-module assemblies. Actuator assemblies that are configured in a serial and parallel manner were investigated to demonstrate the applicability and versatility of the concept of the modular corrugated diaphragm actuator for creating single and multi-degree-of-freedom joints. [ABSTRACT FROM AUTHOR]

Soft actuators with embedded fluidic channels are promising for soft robotics, but their continuous nature presents challenges in accurate and efficient modeling. Existing studies on dynamic behavior have shown that both approximate and exact methods have limitations in accurately predicting the deflection of soft actuators. Additionally, conventional methods require experimental parameter extraction for each individual actuator. To address these challenges, we propose a modified analytical method and use finite element analysis as the defined correction functions to improve accuracy and computational efficiency, providing a promising tool for the design and control of soft robotic systems. In this work, we propose a solution methodology for simulating the dynamic behavior of fluidic soft actuators, which are widely used in soft robotics applications. Our approach takes into account the cross-sectional geometry and the number of channels in the actuator's configuration. By scrutinizing various fluidic actuation designs, we can deduce the most suitable cross-sectional configuration for optimal performance. Compared to existing analytical methods, our approach is more accurate and does not require additional calculation time. As the method is adaptable with experimental models, it can be used to design and optimize soft actuators while can be refined and improved through experimental model updating. [ABSTRACT FROM AUTHOR]

The compliance of soft grippers contributes to their great superiority over rigid grippers in grasping irregularly shaped objects and forming soft contact with environments. Due to a relatively small pressure, soft grippers lack the stiffness required for wider applications. Particle jamming has been frequently reported as a means of stiffness control. Unlike previous research using vacuum for particle jamming, this paper proposes a novel passive particle jamming principle that does not need any vacuum power or other control means. The proposed method is by simply patching a silicone rubber soft actuator and a pack (made of strain-limiting membrane) of particles to form an integral gripping finger. The inflation of the soft actuator applies a pressure to the particle pack causing particles inside it to jam. A larger squeezing pressure will result in tighter particle jamming, thus increasing the stiffness of the finger. The stiffness of the finger is controllable as it is proportional to the actuator's air pressure, which has been verified by experiments in this research. The stiffness can increase more than six fold when air pressure changes from 20 to 80 kPa in the experimental studies. The reported discovery may enhance the capabilities of soft robotic grippers so that more robotic picking operations could be performed by soft grippers. [ABSTRACT FROM PUBLISHER]

Rehabilitation robots have become one of the main technical instruments that Treat disorder patients in the biomedical engineering field. The robotic glove for the rehabilitation is basically made of specialized materials which can be designed to help the post-stroke patients. In this paper, a review of the different types of robotic glove for Rehabilitation have been discussed and summarized. This study reviews a different mechanical system of robotic gloves in previous years. The selected studies have been classified into four types according to the Mechanical Design: The first type is a tendon-driven robotic glove. The second type of robotic glove works with a soft actuator as a pneumatic which is operated by air pressure that passes through a plastic pipe, pressure valves, and air compressor. The third type is the exoskeleton robotic gloves this type consists of a wearable mechanical design that can used a finger-based sensor to measure grip strength or is used in interactive video applications. And the fourth type is the robotic glove with a liner actuator this type consists of a tape placed on the fingers and connected to linear actuators to open and close the fingers during the rehabilitation process. [ABSTRACT FROM AUTHOR]

Autonomous robots that work in the same environment as humans are preferred to ensure mechanical safety with respect to soft contact with their surroundings and adaptivity to handle various tools and to manage partial malfunctions. To ensure that these requirements for robots are satisfied, this study proposes an approach for obtaining a robot structure and its application to building controller for dynamic motion of a robot. It is assumed that the physical relations between the sensor variables are unknown. On the basis of dependency network construction using mutual information, controllers are generated and tested by finding appropriate causal chains of the sensor variables. The proposed controller generation methods were tested using the control tasks of a musculoskeletal robotic arm. Thus, the proposed controller generation algorithm finds appropriate controllers, and the framework of this generation is robust to the changes in the body of the body. • Automatic controller generation based on sensor variables' dependency detection. • Multi-modal sensor relations found automatically using mutual information. • Applicable to soft-actuator robot system with unknown physical properties. • Analyzable partial relations identified in the controller generator. • Robust and quick adaptation of controller to partial change of the robot system. [ABSTRACT FROM AUTHOR]

Soft actuator technology and its role in robotic manipulation have been rapidly gaining ground. However, less attention has been given to the potential advantages of its application to the agricultural sector, where soft robotics may be a game changer due to its greater adaptability, lower cost and simplicity of manufacture. This article presents a new design approach for soft grippers based on modules that incorporate the concept of bellows and combine it with the versatility and replicability of a 3D printed structure. In this way, the modules can be freely configured to obtain grippers adaptable to crops of different diameters. Furthermore, the definition of a method to determine the soft grippers features is also presented, with the aim of serving as the basis for a future benchmarking study on soft actuators. The experimental tests carried out demonstrated the feasibility and capability of the end-effectors to manipulate various fruits, ensuring a sufficient contact area for the safe handling of the targets and avoiding damaging the products. [ABSTRACT FROM AUTHOR]

Recently, soft materials such as silicone elastomers are widely used in soft robotics due to their high flexibility and safe physical interaction with humans. Focusing on three aspects— highly elastic materials, sacrificial materials, and actuation units , we aim to develop an additive manufacturing strategy that allows the fabrication of highly elastic soft structures more efficiently. First, silicone thinner is used to tailor the mechanical properties of the soft silicone elastomer. We found out that adding 20% volume thinner to the silicone improved the ultimate tensile elongation of 3D printed silicone samples upto 1260%, which is the highest among all the 3D printed soft materials previously reported. However, in a cyclic tensile test, this strain is not achieved; instead, the maximum strain was 600%. Second, carbohydrate glasses are introduced as sacrificial materials for 3D printing silicone with hollow channels. Few configurations of pneumatic and hydraulic actuators are developed by forming channels in the silicone elastomer via 3D printed sacrificial carbohydrate structures and subsequently dissolving. The 3D printed structures actuated successfully to get morphed shapes, which showed that our method is effective for manufacturing of functional soft robotic structures. Unlabelled Image • Material processing parameters for additive manufacturing process of very soft structures are presented. • 3D printing silicone samples with 20% thinner resulted in an ultimate tensile elongation of 1260% • Extensive material characterization results for the 3D printed soft materials are presented. • Soft actuators are fabricated via 3D printing carbohydrate glass and soft silicone for application in soft robots. [ABSTRACT FROM AUTHOR]