Soft robotics最新文献

Traditional robots derive their capabilities of movement through rigid structural "links" and discrete actuated "joints." Alternatively, soft robots are composed of flexible materials that permit movement across a continuous range of their body and appendages and thus are not restricted in where they can bend. While trade-offs between material choices may restrain robot functionalities within a narrow spectrum, we argue that bridging the functional gaps between soft and hard robots can be achieved from a hybrid design approach that utilizes both the reconfigurability and the controllability of traditional soft and hard robot paradigms. In this study, we present a hybrid robot with soft inflated "linkages," and rigid internal joints that can be spatially reconfigured. Our method is based on the geometric pinching of an inflatable beam to form mechanical pinch-joints connecting the inflated robot linkages. Such joints are activated and controlled via internal motorized modules that can be relocated for on-demand joint-linkage configurations. We demonstrate two applications that utilize joint reconfigurations: a deployable robot manipulator and a terrestrial crawling robot with tunable gaits.

The somatosensory system is crucial for living beings to survive and thrive in complex environments and to interact with their surroundings. Similarly, rapidly developed soft robots need to be aware of their own posture and detect external stimuli. Bending and force sensing are key for soft machines to achieve embodied intelligence. Here, we present a soft inductive bimodal sensor (SIBS) that uses the strain modulation of magnetic permeability and the eddy-current effect for simultaneous bidirectional bending and force sensing with only two wires. The SIBS is made of a flexible planar coil, a porous ferrite film, and a soft conductive film. By measuring the inductance at two different frequencies, the bending angle and force can be obtained and decoupled. Rigorous experiments revealed that the SIBS can achieve high resolution (0.44° bending and 1.09 mN force), rapid response, excellent repeatability, and high durability. A soft crawling robot embedded with one SIBS can sense its own shape and interact with and respond to external stimuli. Moreover, the SIBS is demonstrated as a wearable human-machine interaction to control a crawling robot via wrist bending and touching. This highlights that the SIBS can be readily implemented in diverse applications for reliable bimodal sensing.

In this study, a vacuum-based modular actuator system named reconfigurable origami-based vacuum pneumatic artificial muscles (ROV-PAMs) is presented. The system consists of six types of actuating modules and three types of fluidic supporting modules each embedded with magnet-based connectors so that the modules can be assembled to modify the system behavior. The module can be used in a myriad of ways, including extending their working range, creating complex geometries upon deformation, and cooperating to improve overall performance. A simple analytical model for the actuating modules is derived based on the law of conservation of energy, and the model is verified experimentally which shows that this intuitive model can provide a reasonable prediction of performance. A block sorting robot is built using three different types of actuating modules with multiple fluidic supporting modules, and the robot shows that it is possible to flexibly and easily assemble modules to build a robot capable of completing diverse tasks. The ROV-PAM module and its concept can be applied to realize robotic designs, which can be altered on-the-fly to adjust its functionality to meet the evolving requirements required for truly flexible automation.

This study develops a biomimetic soft octopus suction device with integrated self-sensing capabilities designed to enhance the precision and safety of cardiac surgeries. The device draws inspiration from the octopus's exceptional ability to adhere to various surfaces and its sophisticated proprioceptive system, allowing for real-time adjustment of adhesive force. The research integrates thin-film pressure sensors into the soft suction cup design, emulating the tactile capabilities of an octopus's sucker to convey information about the contact environment in real time. Signals from sensors within soft materials exhibiting complex strain characteristics are processed and interpreted using the grey wolf optimizer-back propagation (GWO-BP) algorithm. The tissue stabilizer is endowed with the self-sensing capabilities of biomimetic octopus suckers, and real-time feedback on the adhesion state is provided. The embedding location of the thin-film pressure sensors is determined through foundational experiments with flexible substrates, standard spherical tests, and biological tissue trials. The newly fabricated suction cups undergo compression pull-off tests to collect data. The GWO-BP algorithm model accurately identifies and predicts the suction cup's adhesion force in real time, with an error rate below 0.97% and a mean prediction time of 0.0027 s. Integrating this technology offers a novel approach to intelligent monitoring and attachment assurance during cardiac surgeries. Hence, the probability of potential cardiac tissue damage is reduced, with future applications for integrating intelligent biomimetic adhesive soft robotics.

Suction grippers offer a distinct advantage in their ability to handle a wide range of items. However, attaching these grippers to irregular and rough surfaces presents an ongoing challenge. To address this obstacle, this study explores the integration of magnetic intelligence into a soft suction gripper design, enabling fast external magnetic actuation of the attachment process. Additionally, miniaturization options are enhanced by implementing a compliant deploying mechanism. The resulting design is the first-of-its-kind magnetically-actuated deployable suction gripper featuring a thin magnetic membrane (Ø 50 mm) composed of carbonyl iron particles embedded in a silicone matrix. This membrane is supported by a frame made of superelastic nitinol wires that facilitate deployment. During experiments, the proof-of-principle prototype demonstrates successful attachment on a diverse range of curved surfaces in both dry and wet environments. The gripper achieves attachment on curved surfaces with radii of 50-75 mm, exerting a maximum attachment force of 2.89 ± 0.54 N. The current gripper design achieves a folding percentage of 75%, enabling it to fit into a Ø 12.5 mm tube and access hard-to-reach areas while maintaining sufficient surface area for attachment forces. The proposed prototype serves as a foundational steppingstone for further research in the development of reliable and effective magnetically-actuated suction grippers across various configurations. By addressing the limitations of attachment to irregular surfaces and exploring possibilities for miniaturization and precise control, this study opens new avenues for the practical application of suction grippers in diverse industries and scenarios.

Shape display devices composed of actuation pixels enable dynamic rendering of surface morphological features, which have important roles in virtual reality and metaverse applications. The traditional pin-array solution produces sidestep-like structures between neighboring pins and normally relies on high-density pins to obtain curved surfaces. It remains a challenge to achieve continuous curved surfaces using a small number of actuated units. To address the challenge, we resort to the concept of surface continuity in computational geometry and develop a C⁰-continuity shape display device with trichamber fiber-reinforced soft actuators. Each trichamber unit produces three-dimensional (3D) deformation consisting of elongation, pitch, and yaw rotation, thus ensuring rendered surface continuity using low-resolution actuation units. Inspired by human tactile discrimination threshold on height and angle gradients between adjacent units, we proposed the mathematical criteria of C⁰-continuity shape display and compared the maximal number of distinguishable shapes using the proposed device in comparison with typical pin-array. We then established a shape control model considering the nonlinearity of soft materials to characterize and control the soft device to display C⁰-continuity shapes. Experimental results showed that the proposed device with nine trichamber units could render typical sets of distinguishable C⁰-continuity shape sequence changes. We envision that the concept of C⁰-continuity shape display with 3D deformation capability could improve the fidelity of the rendered shapes in many metaverse scenarios such as touching human organs in medical palpation simulations.

Articular cartilage defects are among the most common orthopedic diseases, which seriously affect patients' health and daily activities, without prompt treatment. The repair biocarrier-based treatment has shown great promise. Total joint injection and open surgery are two main methods to deliver functional repair biocarriers into the knee joint. However, the exhibited drawbacks of these methods hinder their utility. The repair effect of total joint injection is unstable due to the low targeting rate of the repair biocarriers, whereas open surgery causes serious trauma to patients, thereby prolonging the postoperative healing time. In this study, we develop a magnetic soft catheter robot (MSCR) system to perform precise in situ repair of articular cartilage defects with minimal incision. The MSCR processes a size of millimeters, allowing it to enter the joint cavity through a tiny skin incision to reduce postoperative trauma. Meanwhile, a hybrid control strategy combining neural network and visual servo is applied to sequentially complete the coarse and fine positioning of the MSCR on the cartilage defect sites. After reaching the target, the photosensitive hydrogel is injected and anchored into the defect sites through the MSCR, ultimately completing the in situ cartilage repair. The in vitro and ex vivo experiments were conducted on a 3D printed human femur model and an isolated porcine femur, respectively, to demonstrate the potential of our system for the articular cartilage repair.

The combustion actuation method opens a unique pathway for high-performance soft robots, allowing for high accelerations in multifunctional applications. Along with multifunctionality come great challenges in effective robot structure design, accurate control and prediction of combustion-actuated motions, and practical implementation of various applications. However, research in this nascent field remains fragmented, lacking central guiding principles. To systematize these works, this review article summarizes state-of-the-art technologies in combustion-actuated soft robots, addressing three key questions: How to design a combustion-enabled soft robot? How to predict its movements and control it? and How to practically apply it.

High maneuverability and energy efficiency are crucial for underwater robots to perform tasks in engineering practice. Natural evolution empowers aquatic species with skills of agile and efficient swimming, which can be deliberately employed for better robotic swimmers. A critical issue for efficient robotic swimmers is the design and control of an appropriate propulsion system. This study, therefore, presents a completely different realization of a highly flexible and controllable bistable nonlinear mechanism as a "fishtail." The mechanism combines an elastic spine and a lightweight parallel linkage mechanism. Through active control of the endpoint of the elastic spine, the compliant tail can be empowered with exceptional controllability and tunable bistability for a much more efficient and also the first-ever accurately controlled bistable elastic propulsion system. Experimental results demonstrate that the new bistable fishtail can achieve a faster speed of its size (up to an average speed of 0.8 m·s^-1) with an associated higher energy efficiency (corresponding cost of transport as low as 9 J·m^-1·kg^-1), and greater maneuverability (with an average turning speed of up to 107°/s at a much smaller turning radius of 0.31 body length). This study will definitely provide an efficient controllable and feasible approach to the design of nonlinear compliant propulsion systems for underwater vehicles by exploring nonlinear dynamics.

Dielectric elastomer actuators (DEAs) enable to create soft robots with fast response speed and high-energy density, but the fast optimization design of DEAs still remains elusive because of their continuous electromechanical deformation and high-dimensional design space. Existing approaches usually involve repeating and vast finite element calculation during the optimization process, leading to low efficiency and time consuming. The advance of deep learning has shown the potential to accelerate the optimization process, but the high-dimensional design space leads to challenge on the accuracy and generality of the deep learning model. In this work, we propose a deep learning-based automatic design framework for DEAs, capable of rapidly generating high-dimensional distributed electrode patterns based on different design objects. This framework is developed as follows: (1) a dataset construction strategy combining with a finite element model is developed to optimize the data distribution within the high-dimensional design space; (2) a neural network-embedded physical information is designed and trained to achieve accurate prediction of the continuous deformation within $0.011s$; and (3) a genetic algorithm with the neural network is proposed to automatically and rapidly optimize the electrode pattern of DEAs based on various design objects. To verify the effectiveness, a series of case studies (including maximum displacement, specific displacement, multiplicity of solutions, multiple degree-of-freedom actuations, and complex actuations) has been conducted. Both simulation results and experimental data demonstrate that our design framework can automatically design the electrode pattern within 2 min and obviously improve the performance of DEAs. This work proposes a deep learning-based design approach with automatic and rapid property, thereby paving the way for broader applications of DEAs.