Soft Robotics最新文献

Soft actuators provide an attractive means for locomotion, gripping, and deployment of those machines and robots used in biomedicine, wearable electronics, automated manufacturing, etc. In this study, we focus on the shape-morphing ability of soft actuators made of pneumatic networks (pneu-nets), which are easy to fabricate with inexpensive elastomers and to drive with air pressure. As a conventional pneumatic network system morphs into a single designated state, achieving multimodal morphing has required multiple air inputs, channels, and chambers, making the system highly complex and hard to control. In this study, we develop a pneu-net system that can change its shape into multiple forms as a single input pressure increases. We achieve this single-input and multimorphing by combining pneu-net modules of different materials and geometry, while harnessing the strain-hardening characteristics of elastomers to prevent overinflation. Using theoretical models, we not only predict the shape evolution of pneu-nets with pressure change but also design pneu-nets to sequentially bend, stretch, and twist at distinct pressure points. We show that our design strategy enables a single device to carry out multiple functions, such as grabbing-turning a light bulb and holding-lifting a jar.

Inspired by human hands and wrists, an anthropomorphic soft manipulator (ASM) driven by water hydraulics is proposed for underwater operations and exploration. Compared with traditional rigid manipulator, ASM has highly evolved grasping ability with better flexibility and adaptability, while it has better load capacity, grasping ability, and flexibility in comparison with the pneumatic gripper. ASM wrist is composed of rigid-flexible coupling structure with three bellows and a spindle, which generates continuous wrist pitching. The linear elongate characteristics of bellows and pitching performance of ASM wrist are simulated by finite element modeling (FEM) method and tested experimentally. The mathematical model of bending deformation for the water hydraulic soft gripper (WHSG) is established. The bending deformation and contact force of WHSG are simulated by FEM and measured experimentally. The ASM prototype is fabricated, and the grasping experiments in the air and underwater are conducted. It is confirmed that the developed ASM can switch between standard and expanded grasping position to adopt and grasp objects of different shapes and dimensions. And living animals with rough or smooth surfaces such as turtle and carp can also be caught harmlessly. ASM also exhibits preferable adaptability when the objects are out of grasping range or deviating from the grasping center. This study confirms that the developed ASM has enormous application potentials and broader prospects in the field of underwater operation, underwater fishing, underwater sampling, etc.

To be fully integrated into the activities of our daily lives, robots need to be capable of traversing unstructured environments and interacting safely with their surroundings. Soft robots are perfect candidates since they can adapt to their surroundings through passive material compliance, rather than relying on complex control. However, the same compliance hinders the generation of propelling forces, and current approaches face a trade-off between traveling speed, action range, and control complexity. We overcome this trade-off by developing a locomotion mechanism based on the synergistic interaction between symmetric vibrations, elasticity, and asymmetric morphology. We then realize a rapid soft locomotor using inexpensive off-the-shelf components and requiring only elementary actuation and control. A single robotic unit can travel at speeds up to 100 mm/s when tethered and 35 mm/s when untethered. We derive a model that predicts the speed of the robot as a function of several design parameters and physical properties, highlighting the role of geometric asymmetries in the resulting anisotropic motion. Moreover, these elementary units can be added together to create more complex behaviors. By adding 2 units in parallel, the assembly can locomote and be steered following nonholonomic constraints. Our approach opens the door to a new class of low-cost soft robots that can travel fast and far with elementary fabrication and control, and which can be combined to achieve complex functions without compromising their essential simplicity.

Magnetic continuum robots (MCRs), which are free of complicated structural designs for transmission, can be miniaturized and are therefore widely used in the medical field. However, the deformation shapes of different segments, including deflection directions and curvatures, are difficult to control simultaneously under an external programmable magnetic field. This is because the latest MCRs have designs with an invariable magnetic moment combination or profile of one or more actuating units. Therefore, the limited dexterity of the deformation shape causes the existing MCRs to collide readily with their surroundings or makes them unable to approach difficult-to-reach regions. These prolonged collisions are unnecessary or even hazardous, especially for catheters or similar medical devices. In this study, a novel magnetic moment intraoperatively programmable continuum robot (MMPCR) is introduced. By applying the proposed magnetic moment programming method, the MMPCR can deform under three modalities, that is, J, C, and S shapes. Additionally, the deflection directions and curvatures of different segments in the MMPCR can be modulated as desired. Furthermore, the magnetic moment programming and MMPCR kinematics are modeled, numerically simulated, and experimentally validated. The experimental results exhibit a mean deflection angle error of 3.3° and correspond well with simulation results. Comparisons between navigation capacities of the MMPCR and MCR demonstrate that the MMPCR has a higher capacity for dexterous deformation.

Fluidic soft robots have the advantages of inherent compliance and adaptability, but they are significantly restricted by complex control systems and bulky power devices, including fluidic valves, fluidic pumps, electrical motors, as well as batteries, which make it challenging to operate in narrow space, energy shortage, or electromagnetic sensitive situations. To overcome the shortcomings, we develop portable human-powered master controllers to provide an alternative solution for the master-slave control of the fluidic soft robots. Each controller can supply multiple fluidic pressures to the multiple chambers of the soft robots simultaneously. We use modular fluidic soft actuators to reconfigure soft robots with various functions as control objects. Experimental results show that flexible manipulation and bionic locomotion can be simply realized using the human-powered master controllers. The developed controllers which eliminate energy storage and electronic components can provide a promising candidate of soft robot control in surgical, industrial, and entertainment applications.

Data-driven methods with deep neural networks demonstrate promising results for accurate modeling in soft robots. However, deep neural network models rely on voluminous data in discovering the complex and nonlinear representations inherent in soft robots. Consequently, while it is not always possible, a substantial amount of effort is required for data acquisition, labeling, and annotation. This article introduces a data-driven learning framework based on synthetic data to circumvent the exhaustive data collection process. More specifically, we propose a novel time series generative adversarial network with a self-attention mechanism, Transformer TimeGAN (TTGAN) to precisely learn the complex dynamics of a soft robot. On top of that, the TTGAN is incorporated with a conditioning network that enables it to produce synthetic data for specific soft robot behaviors. The proposed framework is verified on a widely used pneumatic-based soft gripper as an exemplary experimental setup. Experimental results demonstrate that the TTGAN generates synthetic time series data with realistic soft robot dynamics. Critically, a combination of the synthetic and only partially available original data produces a data-driven model with estimation accuracy comparable to models obtained from using complete original data.

Pneumatic actuators are widely studied in soft robotics as they are facile, low cost, scalable, and robust and exhibit compliance similar to many systems found in nature. The challenge is to harness high energy density chemical and biochemical reactions that can generate sufficient pneumatic pressure to actuate soft systems in a controlled and ecologically compatible manner. This investigation evaluates the potential of chemical reactions as both positive and negative pressure sources for use in soft robotic pneumatic actuators. Considering the pneumatic actuation demands, the chemical mechanisms of the pressure sources, and the safety of the system, several gas evolution/consumption reactions are evaluated and compared. Furthermore, the novel coupling of both gas evolution and gas consumption reactions is discussed and evaluated for the design of oscillating systems, driven by the complementary evolution and consumption of carbon dioxide. Control over the speed of gas generation and consumption is achieved by adjusting the initial ratios of feed materials. Coupling the appropriate reactions with pneumatic soft-matter actuators has delivered autonomous cyclic actuation. The reversibility of these systems is demonstrated in a range of displacement experiments, and practical application is shown through a soft gripper that can move, pick up, and let go of objects. Our approach presents a significant step toward more autonomous, versatile soft robots driven by chemo-pneumatic actuators.

With intrinsic compliance, soft pneumatic actuators are widely utilized in delicate tasks. However, complex fabrication approaches and limited tunability are still problems. Here, we propose a tunable folding assembly strategy to design and fabricate soft pneumatic actuators called FASPAs (folding assembly soft pneumatic actuators). A FASPA consists only of a folded silicone tube constrained by rubber bands. By designing local stiffness and folding manner, the FASPA can be designed to achieve four configurations, pure bending, discontinuous-curvature bending, helix, and discontinuous-curvature helix. Analytical models are developed to predict the deformation and the tip trajectory of different configurations. Meanwhile, experiments are performed to verify the models. The stiffness, load capacity, output force, and step response are measured, and fatigue tests are performed. Further, grippers with single, double, and triple fingers are assembled by utilizing different types of FASPAs. As such, objects with different shapes, sizes, and weights can be easily grasped. The folding assembly strategy is a promising method to design and fabricate soft robots with complex configurations to complete tough tasks in harsh environments.

The growing interest in soft materials to develop flexible devices involves the need to create accurate methodologies to determine parameter values of constitutive models to improve their modeling. In this work, a novel approach for the optimization of constitutive model parameters is presented, which consists of using a genetic algorithm (GA) to obtain a set of solutions from data of uniaxial tensile tests, which are later used to simulate the mechanical test using finite element analysis (FEA) software to find an optimal solution considering Drucker's stability criterion. This approach was applied to the elastomer Ecoflex 00-30 considering the Warner and Yeoh models and Rivlin's phenomenological theory. The correlation between the experimental and the predicted data by the models was determined using the root mean squared error (RMSE), where the found parameter sets provided a close fit to the experimental data with RMSE values of 0.022 (ANSYS) and 0.024 (ABAQUS) for Warner's model, while for Yeoh's model were 0.014 (ANSYS) and 0.012 (ABAQUS). It was found that the best parameter values accurately follow the experimental material behavior using FEA. The proposed GA not only optimizes the material parameters but also has a high reproducibility level with average RMSE values of 0.024 for Warner's model and 0.009 for Yeoh's model, fulfilling Drucker's stability criterion.

Although various soft pneumatic actuators have been studied, their performance, including load capacity, has not been satisfied yet. Enhancing their actuation capability and using them to develop soft robots with high performance is still an open and challenging issue. In this study, we developed novel pneumatic actuators based on fiber-reinforced airbags as a solution to this problem, of which the maximum pressure reaches more than 100 kPa. Through cellular rearrangement, the developed actuators could bend uni- or bidirectionally, achieving large driving force, large deformation, and high conformability. Hence, they could be used to develop soft manipulators with relatively large payload (up to 10 kg, about 50 times the body self-weight) and soft climbing robots with high mobility. In this article, we first present the design of the airbag-based actuators and then model the airbag to obtain the relationship between the pneumatic pressure, external force, and deformation. Subsequently, we validate the models by comparing the simulated and measured results and test the load capacity of the bending actuators. Afterward, we present the development of a soft pneumatic robot that can rapidly climb horizontal, inclined, and vertical poles with different cross-sectional shapes and even outdoor natural objects, like bamboos, at a speed of 12.6 mm/s generally. In particular, it can dexterously transition between poles at any angle, which, to the best of our knowledge, has not been achieved before.