Soft Robotics最新文献

The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.

Meso- or micro-scale(or insect-scale) robots that are capable of realizing flexible locomotion and/or carrying on complex tasks in a remotely controllable manner hold great promise in diverse fields, such as biomedical applications, unknown environment exploration, in situ operation in confined spaces, and so on. However, the existing design and implementation approaches for such multifunctional, on-demand configurable insect-scale robots are often focusing on their actuation or locomotion, while matched design and implementation with synergistic actuation and function modules under large deformation targeting varying task/target demands are rarely investigated. In this study, through systematical investigations on synergistical mechanical design and function integration, we developed a matched design and implementation method for constructing multifunctional, on-demand configurable insect-scale soft magnetic robots. Based on such a method, we report a simple approach to construct soft magnetic robots by assembling various modules from the standard part library together. Moreover, diverse soft magnetic robots with desirable motion and function can be (re)configured. Finally, we demonstrated (re)configurable soft magnetic robots shifting into different modes to adapt and respond to varying scenarios. The customizable physical realization of complex soft robots with desirable actuation and diverse functions can pave a new way for constructing more sophisticated insect-scale soft machines that can be applied to practical applications soon.

Soft robots are predicted to operate well in unstructured environments due to their resilience to impacts, embodied intelligence, and potential ability to adapt to uncertain circumstances. Soft robots are of further interest for space and extraterrestrial missions, owing to their lightweight and compressible construction. Most soft robots in the literature to-date are made of elastomer bodies. However, limited data are available on the material characteristics of commonly used elastomers in extreme environments. In this study, we characterize four commonly used elastomers in the soft robotics literature-EcoFlex 00-30, Dragon Skin 10, Smooth-Sil 950, and Sylgard 184-in a temperature range of -40°C to 80°C and humidity range of 5-95% RH. We perform pull-to-failure, stiffness, and stress-relaxation tests. Furthermore, we perform a case study on soft elastomers used in stretchable capacitive sensors to evaluate the implications of the constituent material behavior on component performance. We find that all elastomers show temperature-dependent behavior, with typical stiffening of the material and a lower strain at failure with increasing temperature. The stress-relaxation response to temperature depends on the type of elastomer. Limited material effects are observed in response to different humidity conditions. The mechanical properties of the capacitive sensors are only dependent on temperature, but the measured capacitance shows changes related to both humidity and temperature changes, indicating that component-specific properties need to be considered in tandem with the mechanical design. This study provides essential insights into elastomer behavior for the design and successful operation of soft robots in varied environmental conditions.

The art of origami has gained traction in various fields such as architecture, the aerospace industry, and soft robotics, owing to the exceptional versatility of flat sheets to exhibit complex shape transformations. Despite the promise that origami robots hold, their use in high-capacity environments has been limited due to the lack of rigidity. This article introduces novel, origami-inspired, self-locking pneumatic modular actuators (SPMAs), enabling them to operate in such environments. Our innovative approach is based on origami patterns that allow for various types of shape morphing, including linear and rotational motion. We have significantly enhanced the stiffness of the actuators by embedding magnets in composite sheets, thus facilitating their application in real-world scenarios. In addition, the embedded self-adjustable valves facilitate the control of sequential origami actuations, making it possible to simplify the pneumatic system for actuating multimodules. With just one actuation source and one solenoid valve, the valves enable efficient control of our SPMAs. The SPMAs can control robotic arms operating in confined spaces, and the entire system can be modularized to accomplish various tasks. Our results demonstrate the potential of origami-inspired designs to achieve more efficient and reliable robotic systems, thus opening up new avenues for the development of robotic systems for various applications.

Industrial robots are widely deployed to perform pick-and-place tasks at high speeds to minimize manufacturing time and boost productivity. When dealing with delicate or fragile goods, soft robotic grippers are better end effectors than rigid grippers due to their softness and safe interaction. However, high-speed motion causes the soft robotic gripper to vibrate, leading to damage of the objects or failed grasping. Soft grippers with variable stiffness are considered to be effective in suppressing vibrations by adding damping devices, but it is quite challenging to compromise between stiffness and compliance. In this article, a controller based on deep reinforcement learning is proposed to control the stiffness of the soft robotic gripper, which can accurately suppress the vibration with only a minor influence on its compliance and softness. The proposed controller is a real-time vibration control strategy, which estimates the output of the controller based on the current operating environment. To demonstrate the effectiveness of the proposed controller, experiments were done with a UR5 robotic arm. For different situations, experimental results show that the proposed controller responds quickly and reduces the amplitude of the oscillation substantially.

There has been a growing need for soft robots operating various force-sensitive tasks due to their environmental adaptability, satisfactory controllability, and nonlinear mobility unique from rigid robots. It is of desire to further study the system instability and strongly nonlinear interaction phenomenon that are the main influence factors to the actuations of lightweight soft actuators. In this study, we present a design principle on lightweight pneumatically elastic backbone structure (PEBS) with the modular construction for soft actuators, which contains a backbone printed as one piece and a common strip balloon. We build a prototype of a lightweight (<80 g) soft actuator, which can perform bending motions with satisfactory output forces (∼20 times self-weight). Experiments are conducted on the bending effects generated by interactions between the hyperelastic inner balloon and the elastic backbone. We investigated the nonlinear interaction and system instability experimentally, numerically, and parametrically. To overcome them, we further derived a theoretical nonlinear model and a numerical model. Satisfactory agreements are obtained between the numerical, theoretical, and experimental results. The accuracy of the numerical model is fully validated. Parametric studies are conducted on the backbone geometry and stiffness, balloon stiffness, thickness, and diameter. The accurate controllability, operation safety, modularization ability, and collaborative ability of the PEBS are validated by designing PEBS into a soft laryngoscope, a modularized PEBS library for a robotic arm, and a PEBS system that can operate remote surgery. The reported work provides a further applicability potential of soft robotics studies.

The performance of the human finger is a significant inspiration for designing soft robotic fingers that can achieve high speed and high force or perform delicate and complex tasks. Existing soft grippers and actuators can be excellent in specific capabilities. However, it is still challenging for them to meet an all-around performance as the human finger, characterized by high actuation speed, wide grasping range, sensing ability, and gentle and high-load grasping capability. The proposed tendon pulley quadrastable (TPQ) finger has combined these qualities in the conducted gripping tasks. A pair of elastic tendons is utilized as the sole energy reservoir to create a novel energy distribution pattern: energy-coupled quadrastability. An energy model is built to analyze and predict the behaviors of the TPQ finger. Mechanical instability is utilized to enhance the actuation speed. The proposed soft lever mechanism endows the TPQ finger with sensing ability. The energy barrier adjusting plates control the energy barrier, adjusting the sensitivity of both active and passive actuation mechanisms. The transition of four stable states forms preplanned trajectories that are applied to create multiple grasping manners. Experiments show that it can respond to stimuli and finish a grasping task in merely 31 ms, and its payload can reach 33.25 kg. At the same time, it can also handle fragile objects such as a piece of rose and grasp a wide range of objects ranging from a thin nut (3.3 mm in height) or a thin card (0.76 mm thick) to a football (220 mm).

Hygroscopic soft actuators offer an attractive means to convert environmental energy to mechanical motions as they use water vapor, a ubiquitous substance in the atmosphere. To overcome the limits of existing hygroactuators, such as simplistic actuation mode, slow response, and low efficiency, here we present three kinds of humidity-powered soft machines adopting directionally electrospun hygroresponsive nanofibrous sheets. The wheels, seesaws, and vehicles developed in this work utilize spatial humidity gradient naturally established near moist surfaces such as human skin, so that they operate spontaneously, realizing energy scavenging or harvesting. We also constructed a theoretical framework to mechanically analyze their dynamics, which allowed us to optimize their design to obtain the highest motion speed physically possible.

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.