Soft Robotics最新文献

Soft robots aim to revolutionize how robotic systems interact with the environment thanks to their inherent compliance. Some of these systems are even able to modulate their physical softness. However, simply equipping a robot with softness will not generate intelligent behaviors. Indeed, most interaction tasks require careful specification of the compliance at the interaction point; some directions must be soft and others firm (e.g., while drawing, entering a hole, tracing a surface, assembling components). On the contrary, without careful planning, the preferential directions of deformation of a soft robot are not aligned with the task. With this work, we propose a strategy to prescribe variations of the physical stiffness and the robot's posture so to implement a desired Cartesian stiffness and location of the contact point. We validate the algorithm in simulation and with experiments. To perform the latter, we also present a new tendon-driven soft manipulator, equipped with variable-stiffness segments and proprioceptive sensing and capable to move in three dimensional. We show that, combining the intelligent hardware with the proposed algorithm, we can obtain the desired stiffness at the end-effector over the workspace.

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.

Hand gesture recognition, one of the most popular research topics in human-machine interaction, is extensively used in visual and augmented reality, sign language translation, prosthesis control, and so on. To improve the flexibility and interactivity of wearable gesture sensing interfaces, flexible electronic systems for gesture recognition have been widely studied. However, these systems are limited in terms of wearability, stability, scalability, and robustness. Herein, we report a flexible wearable hand gesture recognition system that is based on an iontronic capacitive pressure sensing array and deep convolutional neural networks. The entire capacitive array is integrated into a flexible silicone wristband and can be comfortably and conveniently wrapped around the wrist. The pressure sensing array, which is composed of an iontronic film sandwiched between two flexible screen-printed electrode arrays, exhibits a high sensitivity (775.8 kPa^-1), fast response time (65 ms), and high durability (over 6000 cycles). Image processing techniques and deep convolutional neural networks are applied for sensor signal feature extraction and hand gesture recognition. Several contexts such as intertrial test (average accuracy of 99.9%), intersession rewearing (average accuracy of 93.2%), electrode shift (average accuracy of 83.2%), and different arm positions during measurement (average accuracy of 93.1%) are evaluated.

Crawling animals with bendable soft bodies use the friction anisotropy of their asymmetric body structures to traverse various substrates efficiently. Although the effect of friction anisotropy has been investigated and applied to robot locomotion, the dynamic interactions between soft body bending at different frequencies (low and high), soft asymmetric surface structures at various aspect ratios (low, medium, and high), and different substrates (rough and smooth) have not been studied comprehensively. To address this lack, we developed a simple soft robot model with a bioinspired asymmetric structure (sawtooth) facing the ground. The robot uses only a single source of pressure for its pneumatic actuation. The frequency, teeth aspect ratio, and substrate parameters and the corresponding dynamic interactions were systematically investigated and analyzed. The study findings indicate that the anterior and posterior parts of the structure deform differently during the interaction, generating different frictional forces. In addition, these parts switched their roles dynamically from push to pull and vice versa in various states, resulting in the robot's emergent locomotion. Finally, autonomous adaptive crawling behavior of the robot was demonstrated using sensor-driven neural control with a miniature laser sensor installed in the anterior part of the robot. The robot successfully adapted its actuation frequency to reduce body bending and crawl through a narrow space, such as a tunnel. The study serves as a stepping stone for developing simple soft crawling robots capable of navigating cluttered and confined spaces autonomously.

Electrostatic adhesion (EA) clutches are widely applied in robots, wearable devices, and virtual reality, due to their compliance, lightweight, ultrathin profile, and low power consumption. Higher force density has been constantly perpetuated in the past decades since EA was initially proposed. In this study, by composing terpolymer poly(vinylidene fluoride-trifluoroethylene-chlorotrifluoroethylene) [P(VDF-TrFE-CTFE)] and two-dimensional Ti₃C₂T_x nanosheets (MXene), nanocomposite films with high dielectric constant (${\delta }_r^{\text{'}}$ > 2300) and low loss tangent are achieved. The force representative index ${\delta }_r^{\text{'}}{E}_{bd}^2$ (the relative dielectric constant times the square of breakdown electric field) is enhanced by 5.91 times due to the charge accumulation at matrix-filler interfaces. Superhigh shear stress (85.61 N cm^-2) is generated, 408% higher than the previous maximum value. One of the EA clutches fabricated in this study is only 160 μm thin and 0.4 g heavy. Owing to the low current (<1 μA), the power consumption is <60 mW/cm². It can hold a 2.5 kg weight by only 0.32 cm² area and support an adult (45 kg) (Clinical Trial Registration number: 20210090). With this technology, a dexterous robotic hand is displayed to grasp and release a ball, showing extensive applications of this technique.

Cable-driven continuum robots with hyper-redundant deformable backbones show great promise in applications, such as inspection in unstructured environments, where traditional rigid robots with discrete links and joints fail to operate. However, the motion of existing continuum robots is still constrained by their homogeneous backbones, and limited to environments with modest geometrical complexity. In this study, inspired by highly deformable elephant trunks, we presented a modular tensegrity structure with preprogrammable stiffness for continuum robots. Then we derived a mechanical model based on a positional formulation finite element method for predicting the configuration of the structure in different deformation scenarios. Theoretical predictions revealed that the curvature of each segment could be regulated by preprogramming their spring stiffness. Hence, our customizable design could offer an effective route for efficient robotic interactions. We further fabricated a continuum robot consisting of 12 modules, and showcased its deformation patterns under multiple scenarios. By regulating the distribution of spring stiffness, our robot could move through channels with varying curvatures, exhibiting its potential for applications where varying curvature, and conformal and efficient interactions are needed. Leveraging the inherent intelligence, this robotic system could simplify the complexity of the required actuation and control systems.

Robot grippers that lack physical compliance have a difficult time dealing with uncertainty, such as fragile objects that may not have well-defined shapes. Existing soft robotic grippers require a large empty workspace for their actuated fingers to curl around the objects of interest, limiting their performance in clutter. This article presents a three-dimensional structure that exhibits negative stiffness in every bending direction used as fingers in a class of soft robotic grippers. Our approach exploits a compliant mechanism in a conical shape such that a transverse external contact force causes the fingers to bend toward the contact, enabling passive conformation for an adaptive grasp, even in clutter. We show analytically and experimentally that the proposed fingers have a negative bending response and that they conform to objects of various diameters. We demonstrate a soft robotic gripper with three self-conforming fingers performing the following: (1) fingertip grasping, (2) power grasping, and (3) semipassive grasping in clutter. Grasping experiments focus on picking fruits, which exemplify delicate objects with unmodeled shapes with significant variation. The experimental results reveal the ability of the self-conforming structure to smoothly envelope a broad range of objects and demonstrate a 100% grasp success rate in the experiments performed. The proposed passively conforming fingers enable picking of complex and unknown geometries without disturbing nearby objects in clutter and without the need for complex grasping algorithms. The proposed structures can be tailored to deform in desired ways, enabling a robust strategy for the engineering of physical compliance for adaptive soft structures.

Skeletal muscles are critical to human-limb motion dynamics and energetics, where their mechanical states are seldom explored in vitro due to practical limitations of sensing technologies. This article aims to capture mechanical deformations of muscle contraction using wearable flexible sensors, which is justified with model calibration and experimental validation. The capacitive sensor is designed with the composite of conductive fabric electrodes and the porous dielectric layer to increase the pressure sensitivity and prevent lateral expansions. In this way, the compressive displacement of muscle deformation is captured in the muscle-sensor coupling model in terms of sensor deformation and parameters of pretension, material, and shape properties. The sensing model is calibrated in a linear form using ultrasound medical imaging. The sensor is capable of measuring muscle strain of 70% with an error of <3.6% and temperature disturbance of <5.6%. After 10K cycles of compression, the drift is only 3.3%. Immediate application of the proposed method is illustrated by gait pattern identification, where the K-nearest neighbor prediction accuracy of squats, level walking, stair ascent/descent, and ramp ascent is over 97% with a standard deviation below 2.6% compared to that of 94.61 ± 4.24% for ramp descent, and the response time is 14.37 ± 0.52 ms. The wearable sensing method is valid for muscle deformation monitoring and gait pattern identification, and it provides an alternative approach to capture mechanical motions of muscles, which is anticipated to contribute to understand locomotion biomechanics in terms of muscle forces and metabolic landscapes.

Equipping soft robotic grippers with sensing and perception capabilities faces significant challenges due to their high compliance and flexibility, limiting their ability to successfully interact with the environment. In this work, we propose a sensorized soft robotic finger with embedded marker pattern that integrates a high-speed neuromorphic event-based camera to enable finger proprioception and exteroception. A learning-based approach involving a convolutional neural network is developed to process event-based heat maps and achieve specific sensing tasks. The feasibility of the sensing approach for proprioception is demonstrated by showing its ability to predict the two-dimensional deformation of three points located on the finger structure, whereas the exteroception capability is assessed in a slip detection task that can classify slip heat maps at a temporal resolution of 2 ms. Our results show that our proposed approach can enable complete sensorization of the finger for both proprioception and exteroception using a single camera without negatively affecting the finger compliance. Using such sensorized finger in robotic grippers may provide safe, adaptive, and precise grasping for handling a wide category of objects.