It has been a great challenge to develop robots that are able to perform complex movement patterns with high speed and, simultaneously, high accuracy. Copepods are animals found in freshwater and saltwater habitats that can have extremely fast escape responses when a predator is sensed by performing explosive curved jumps. In this study, we present a design and build prototypes of a combustion-driven underwater soft robot, the “copebot,” which, similar to copepods, is able to accurately reach nearby predefined locations in space within a single curved jump. Because of an improved thrust force transmission unit, causing a large initial acceleration peak (850 body length·s−2), the copebot is eight times faster than previous combustion-driven underwater soft robots, while able to perform a complete 360° rotation during the jump. Thrusts generated by the copebot are tested to quantitatively determine the actuation performance, and parametric studies are conducted to investigate the sensitivity of the kinematic performance of the copebot to the input parameters. We demonstrate the utility of our design by building a prototype that rapidly jumps out of the water, accurately lands on its feet on a small platform, wirelessly transmits data, and jumps back into the water. Our copebot design opens the way toward high-performance biomimetic robots for multifunctional applications.

Thermal actuation is a common actuation method for soft robots. However, a major limitation is the relatively slow actuation speed. Here we report significant increase in the actuation speed of a bimorph thermal actuator by harnessing the snap-through instability. The actuator is made of silver nanowire/polydimethylsiloxane composite. The snap-through instability is enabled by simply applying an offset displacement to part of the actuator structure. The effects of thermal conductivity of the composite, offset displacement, and actuation frequency on the actuator speed are investigated using both experiments and finite element analysis. The actuator yields a bending speed as high as 28.7 cm−1/s, 10 times that without the snap-through instability. A fast crawling robot with locomotion speed of 1.04 body length per second and a biomimetic Venus flytrap were demonstrated to illustrate the promising potential of the fast bimorph thermal actuators for soft robotic applications.

The stiffness of a soft robot with structural cavities can be regulated by controlling the pressure of a fluid to render predictable changes in mechanical properties. When the soft robot interacts with the environment, the mediating fluid can also be considered an inherent information pathway for sensing. This approach to using structural tuning to improve the efficacy of a sensing task with specific states has not yet been well studied. A tunable stiffness soft sensor also renders task-relevant contact dynamics in soft robotic manipulation tasks. This article proposes a type of adaptive soft sensor that can be directly three-dimensional printed and controlled using pneumatic pressure. The tunability of such a sensor helps to adjust the sensing characteristics to better capturing specific tactile features, demonstrated by detecting texture with different frequencies. We present the design, modeling, Finite Element Simulation, and experimental characterization of a single unit of such a tunable stiffness sensor. How the sensing characteristics are affected by adjusting its stiffness is studied in depth. In addition to the tunability, the results show that such types of adaptive sensors exhibit good sensitivity (up to 2.6 KPa/N), high sensor repeatability (average std <0.008 KPa/N), low hysteresis (<6%), and good manufacturing repeatability (average std = 0.0662 KPa/N).

A reconfigurable dexterous gripper is designed which can switch states, including rigidity and flexibility, for different application scenarios. Moreover, the stiffness of the fingers in the flexible state can also be tuned for different objects. Three fingers are connected to the revolute joints of the palm, and each finger has a reshape mechanism with a slider moving up and down to lock or release the fingertip joint. When the slider moves upward, the gripper works in the rigid state and the fingers are actuated by the servos. When the slider moves downward, the gripper works in the flexible state that the fingertip is supported by a spring, and the fingertip joint is rotated by an embedded motor with two group cables for tuning stiffness. This novel design provides the gripper with the advantages of high precision and strong load capacity of rigid grippers and shape adaptability and safety of soft grippers. The reconfigurable mechanism allows the gripper great versatility for grasping and manipulation, which facilitates the planning and execution of the motion of objects with different shapes and stiffness. We discuss the stiffness-tunable mechanism with different states, analyze the kinematic characteristics, and test the manipulator performance to investigate the application in rigid-flexible collaborative works. Experimental results show the practicability of this gripper under different requirements and the rationality of this proposed concept.

Many marine creatures, gastropods, and earthworms generate continuous traveling waves in their bodies for locomotion within marine environments, complex surfaces, and inside narrow gaps. In this work, we study theoretically and experimentally the use of embedded pneumatic networks as a mechanism to mimic nature and generate bidirectional traveling waves in soft robots. We apply long-wave approximation to theoretically calculate the required distribution of pneumatic network and inlet pressure oscillations needed to create desired moving wave patterns. We then fabricate soft robots with internal pneumatic network geometry based on these analytical results. The experimental results agree well with our model and demonstrate the propagation of moving waves in soft robots, along with locomotion capabilities. The presented results allow fabricating soft robots capable of continuous moving waves using the common approach of embedded pneumatic networks and requiring only two input controls.

The growing need for soft robots with secure, adaptive, and autonomous functioning in unforeseen environments favors designs with multiple functionalities. This has driven soft robotic grippers to be explored to integrate perceptual capability for augmented multifunctionality. In nature, sea anemones can detect and catch preys of various shapes and sizes effectively with extremely simple bodies because of the efficient coupling of sensing and actuation capability. Inspired by their body structures, we present a bistable gripper with multifunctionality that includes sensing (proprioceptive and exteroceptive) and multimodal gripping (grasping and pinching). The gripper exploits an array of tapered pins on the external surface of a dome membrane for gripping and a set of cylindrical markers on the internal surface of the membrane for optical sensing. The membrane is bistable and can settle in either of two equilibrium states “natural” and “retracted.” Gripping functionality is achieved by the centripetal enveloping movement of the pins, along with the passive snap-through process of the membrane. By analyzing the distribution of markers within the view of an embedded camera, sophisticated sensing functionality can be achieved. We first characterized each function separately and then implemented an object handling system, combining the sensing and gripping functionality, to demonstrate the potential for more advanced robotic applications. This work delivers a compact universal gripper design with an efficient and elegant integration of multifunctionality.

Soft actuators and their sensors have always been separate entities with two distinct roles. The omnidirectional compliance of soft robots thus means that multiple sensors have to be used to sense different modalities in the respective planes of motion. With the recent emergence of self-sensing actuators, the two roles have gradually converged to simplify sensing requirements. Self-sensing typically involves embedding a conductive sensing element into the soft actuator and provides multiple state information along the continuum. However, most of these self-sensing actuators are fabricated through manual methods, which results in inconsistent sensing performance. Soft material compliance also imply that both actuator and sensor exhibit nonlinear behaviors during actuation, making sensing more complex. In this regard, machine learning has shown promise in characterizing the nonlinear behavior of soft sensors. Beyond characterization, we show that applying machine learning to soft actuators eliminates the need to implant a sensing element to achieve self-sensing. Fabrication is done using 3D printing, thus ensuring that sensing performance is consistent across the actuators. In addition, our proposed technique is able to estimate the bending curvature of a soft continuum actuator and the external forces applied to the tip of the actuator in real time. Our methodology is generalizable and aims to provide a novel way of multimodal sensing for soft robots across a variety of applications.

Recent advances in soft robotics demonstrate the requirement of modular actuation to enable the rapid replacement of actuators for maintenance and functionality extension. There remain challenges to designing soft actuators capable of different motions with a consistent appearance for simplifying fabrication and modular connection. Origami structures reshaping along with their unique creases became a powerful tool to provide compact constraint layers for soft pneumatic actuators. Inspired by Waterbomb and Kresling origami, this article presents three types of vacuum-driven soft actuators with a cubic shape and different origami skins, featuring contraction, bending, and twisting-contraction combined motions, respectively. In addition, these modular actuators with diversified motion patterns can be directly fabricated by molding silicone shell and constraint layers together. Actuators with different geometrical parameters are characterized to optimize the structure and maximize output properties after establishing a theoretical model to predict the deformation. Owing to the shape consistency, our actuators can be further modularized to achieve modular actuation via mortise and tenon-based structures, promoting the possibility and efficiency of module connection for versatile tasks. Eventually, several types of modular soft robots are created to achieve fragile object manipulation and locomotion in various environments to show their potential applications.

Soft robots equipped with multifunctionalities have been increasingly needed for secure, adaptive, and autonomous functioning in unknown and unpredictable environments. Robotic stacking is a promising solution to increase the functional diversity of soft robots, which are required for safe human-machine interactions and adapting in unstructured environments. However, most existing multifunctional soft robots have a limited number of functions or have not fully shown the superiority of the robotic stacking method. In this study, we present a novel robotic stacking strategy, Netting-Rolling-Splicing (NRS) stacking, based on a dimensional raising method via 2D-to-3D rolling-and-splicing of netted stackable pneumatic artificial muscles to quickly and efficiently fabricate multifunctional soft robots based on the same, simple, and cost-effective elements. To demonstrate it, we developed a TriUnit robot that can crawl 0.46 ± 0.022 body length per second (BL/s) and climb 0.11 BL/s, and can carry a 3 kg payload while climbing. Also, the TriUnit can be used to achieve novel omnidirectional pipe climbing including rotating climbing, and conduct bionic swallowing-and-regurgitating, multi-degree-of-freedom manipulation based on their multimodal combinations. Apart from these, steady rolling, with a speed of 0.19 BL/s, can be achieved by using a pentagon unit. Furthermore, we applied the TriUnit pipe climbing robot in panoramic shooting and cargo transferring to demonstrate the robot's adaptability for different tasks. The NRS stacking-driven soft robot here has demonstrated the best overall performance among existing stackable soft robots, representing a new and effective way for building multifunctional and multimodal soft robots in a cost-effective and efficient way.

Inchworm-inspired bionic soft crawling robot (SCR) composed of soft materials possesses preeminent active compliant deformation ability and has obvious advantages over traditional hard robots when moving in a confined space, which is up-and-coming candidate in robotic community. Nevertheless, there are rare investigations on dynamic modeling problems of the SCR allowing for its nonlinear deformation properties and frictional contact that affects its crawling performance. In view of this, within the theoretical framework of absolute nodal coordinate formulation, in consideration of material, geometry, and boundary nonlinearities, combining a multiple-point contact model with the Coulomb friction model, an effective and accurate nonlinear dynamic model for a bioinspired SCR with one single limb is proposed to elucidate its motion law. We implement an in-depth dynamic research and analysis on the SCR in terms of average velocity, stick–slip characteristic, gaits and successfully simulate its successive forward crawling locomotion meanwhile gaining dynamic response. The proposed theoretical dynamic model correctly captures the SCR’ complex geometry configurations and nonlinear deformations, discloses its stick–slip dynamic behaviors and crawling locomotion mechanism, whose effectiveness and superiority are validated experimentally, which inspires a deep insight to motion analysis of other types of soft robots, and enlightens new ideas of their diversified architecture designs.

It is still challenging to achieve agility and trajectory control for untethered soft robots on an insect scale given their low mechanical impedance and compact structures. In this study, fast translational movements and swift turning motions are demonstrated on a power autonomous soft robot with a piezoelectric-thin-film-actuated body and electrostatic turning footpads. A high relative running speed of 2.5 body length per second compared with existing untethered robots is realized on a 24-mm-long untethered prototype integrated with power source, control, and wireless communication modules. An arc-shaped leg structure is adopted to self-regulate the frication forces on different footpads during turning by an inclination-induced redistribution of the payload gravity on legs and footpads. The trajectory maneuverability is demonstrated by navigating a 380 mg robot prototype with an 1810 mg payload to pass through a 58-cm-long S-shaped path with wireless control in 43.4 s. Due to the flexibility of the all-polymer body structure, the robustness of the untethered robot to large strain is demonstrated when compressed by 91 times the weight of the robot. A maximum travel distance of 58.6 m is achieved for the robot equipped with a 40 mA·h lithium battery, corresponding to the cost of transport of 261. This work provides a feasible solution to achieve high agility and advance the practicability of untethered soft robots on an insect scale.

Humans can feel and grasp efficiently in the dark through tactile feedback, whereas it is still a challenging task for robots. In this research, we create a novel soft gripper named JamTac, which has high-resolution tactile perception, a large detection surface, and integrated sensing-grasping capability that can search and grasp in low-visibility environments. The gripper combines granular jamming and visuotactile perception technologies. Using the principle of refractive index matching, a refraction-free liquid-particle rationing scheme is developed, which makes the gripper itself to be an excellent tactile sensor without breaking its original grasping capability. We simultaneously acquire color and depth information inside the gripper, making it possible to sense the shape, texture, hardness, and contact force with high resolution. Experimental results demonstrate that JamTac can be a promising tool to search and grasp in situations when vision is not available.

Muscle driving is a critical actuation mode of soft or flexible robots and plays a key role in the motion of most animals. Although the system development of soft robots has been extensively investigated, the general kinematic modeling of soft bodies and the design methods used for muscle-driven soft robots (MDSRs) are inadequate. With a focus on homogeneous MDSRs, this article presents a framework for kinematic modeling and computational design. Based on continuum mechanics theory, the mechanical characteristics of soft bodies were first described using a deformation gradient tensor and energy density function. The discretized deformation was then depicted using a triangular meshing tool according to the piecewise linear hypothesis. Deformation models of MDSRs caused by external driving points or internal muscle units were established by the constitutive modeling of hyperelastic materials. The computational design of the MDSR was then addressed based on kinematic models and deformation analysis. Algorithms were proposed to infer the design parameters from the target deformation and to determine the optimal muscles. Several MDSRs were developed, and experiments were conducted to verify the effectiveness of the presented models and design algorithms. The computational and experimental results were compared and evaluated using a quantitative index. The presented framework of deformation modeling and computational design of MDSRs can facilitate the design of soft robots with complex deformations, such as humanoid faces.

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.

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.

Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment.

In this study, we present a method to construct meter-scale deformable structures for underwater robotic applications by discretely assembling mechanical metamaterials. We address the challenge of scaling up nature-like deformable structures while remaining structurally efficient by combining rigid and compliant facets to form custom unit cells that assemble into lattices. The unit cells generate controlled local anisotropies that architect the global deformation of the robotic structure. The resulting flexibility allows better unsteady flow control that enables highly efficient propulsion and optimized force profile manipulations. We demonstrate the utility of this approach in two models. The first is a morphing beam snake-like robot that can generate thrust at specific anguilliform swimming parameters. The second is a morphing surface hydrofoil that, when compared with a rigid wing at the same angles of attack (AoAs), can increase the lift coefficient up to 0.6. In addition, in lower AoAs, the ratio improves by 5 times, whereas in higher angles it improves by 1.25 times. The resulting hydrodynamic performance demonstrates the potential to achieve accessible, scalable, and simple to design and assemble morphing structures for more efficient and effective future ocean exploration and exploitation.

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.

Continuum robots have the advantages of agility and adaptability. However, existing continuum robots have limitations of low stiffness and complex motion modes, and the existing variable stiffness methods cannot achieve a wide range of stiffness changes and fast switching stiffness simultaneously. A continuum robot structure, switching stiffness method, and motion principle are proposed in this article. The continuum robot is made up of three segments connected in series. Each segment comprises multiple spherical joints connected in series, and the joints can be locked by their respective airbag. A valve controls each airbag, quickly switching the segment between rigidity and flexibility. The motion of the segments is driven by three cables that run through the robot. The segment steers only when it is unlocked. When a segment becomes locked, it acts as a rigid body. As a result, by locking and unlocking each segment in sequence, the cables can alternately drive all the segments. The stiffness variation and movement of the continuum robot were tested. The segment's stiffness varies from 36.89 to 1300.95 N/m and the stiffness switching time is 0.25-0.48 s. The time-sharing control mode of segment stiffness and motion is validated by establishing a specific test platform and a mathematical model. The continuum robot's flexibility is demonstrated by controlling the fast bending of different segments sequentially.

Jellyfish are among the widely distributed nature creatures that can effectively control the fluidic flow around their transparent soft body, thus achieving movements in the water and camouflage in the surrounding environments. Till now, it remains a challenge to replicate both transparent appearance and functionalities of nature jellyfish in synthetic systems due to the lack of transparent actuators. In this work, a fully transparent soft jellyfish robot is developed to possess both transparency and bio-inspired omni motions in water. This robot is driven by transparent dielectric elastomer actuators (DEAs) using hybrid silver nanowire networks and conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)/waterborne polyurethane as compliant electrodes. The electrode exhibits large stretchability, low stiffness, high transmittance, and excellent conductivity at large strains. Consequently, the highly transparent DEA based on this hybrid electrode, with Very-High-Bond membranes as dielectric layers and polydimethylsiloxane as top coating, can achieve a maximum area strain of 146% with only 3% hysteresis loss. Driven by this transparent DEA, the soft jellyfish robot can achieve vertical and horizontal movements in water, by mimicking the actual pulsating rhythm of an Aurelia aurita. The bio-inspired robot can serve multiple functions as an underwater soft robot. The hybrid electrodes and bio-inspired design approach are potentially useful in a variety of soft robots and flexible devices.