Soft Robotics最新文献

To make robots more human-like and safer to use around humans, artificial muscles exhibiting compliance have gained significant attention from researchers. However, despite having excellent performance, pneumatic artificial muscles (PAMs) have failed to gain significant traction in commercial mobile applications due to their requirement to be tethered to a pneumatic source. This study presents a thermo-PAM called Thermo-PAM that relies on heating of a volume of air to produce a deformation. This allows for pneumatic actuation using only an electrical power source and thus enables pumpless pneumatic actuation. The actuator uses a high ratio between the heating volume and the deformable volume to produce a high actuation force throughout its entire motion and can produce either contractile or extension motions. The controllability of the actuator was demonstrated as well as its ability to handle heavy payloads. Moreover, it is possible to rely on either positive or negative pressure actuation modes where the positive pressure actuation mode actuates when heated and the negative pressure actuation mode relaxes when heated. The ability to use Thermo-PAMs for different modes of actuation with different operation methods makes the proposed actuator highly versatile and demonstrates its potential for advanced pumpless robotic applications.

This article presents the design and fabrication of a variable stiffness soft gripper based on layer jamming. Traditional layer jamming units have some limitations, such as complicated multistep fabrication, difficulties in system integration, and diminishing in stiffen effect. In this article, a variable stiffness soft gripper is proposed based on the rotational jamming layers to reduce the slippery phenomenon between layers. To fabricate the proposed complex design, a two-step fabrication method is presented. First, multimaterial 3D printing is applied to directly print out the soft finger body with jamming layers. Second, mold casting is used to fabricate the outer vacuum chamber. The proposed gripper contains a main framework and three identical variable stiffness soft fingers. To demonstrate the effectiveness of the design, the soft gripper is mounted on a robotic arm to test its ability of grasping heavy objects while following complex grasping trajectory. The gripper can successfully grasp an object up to 360 g. Grasping robustness of the proposed gripper can be guaranteed when the robotic arm is moving at acceleration up to 7 m/s². The results prove that the proposed design of the soft gripper can improve the grippers grasping robustness during high-speed movement.

Magnetic control has gained popularity recently due to its ability to enhance soft robots with reconfigurability and untethered maneuverability, among other capabilities. Several advancements in the fabrication and application of reconfigurable magnetic soft robots have been reported. This review summarizes novel fabrication techniques for designing magnetic soft robots, including chemical and physical methods. Mechanisms of reconfigurability and deformation properties are discussed in detail. The maneuverability of magnetic soft robots is then briefly discussed. Finally, the present challenges and possible future work in designing reconfigurable magnetic soft robots for biomedical applications are identified.

Origami provides an opportunity to construct a wide range of 3D functional structures by folding a flat sheet. It can be used to develop various soft functional robots by combining soft smart actuators. However, a simple and an effective model that can address the challenging problem of designing origami patterns to connect origami design with robotics is lacking, thereby greatly increasing the threshold of soft origami robots and hindering its development. This study proposes an easy-to-use inverse origami design model to generate the flat crease pattern from the desired folded shape automatically while simulating origami morphing by simply providing the shape parameters or 2D shape graphics. This method overcomes the difficulty of origami design and enables a close connection between origami and robotics. Through this method, various soft origami robots can be developed with low design complexity and time cost to achieve different functions, thereby promoting the development of soft origami robots.

For decades, it has been difficult for small-scale legged robots to conquer challenging environments. To solve this problem, we propose the introduction of a bioinspired soft spine into a small-scale legged robot. By capturing the motion mechanism of rat erector spinae muscles and vertebrae, we designed a cable-driven centrally symmetric soft spine under limited volume and integrated it into our previous robotic rat SQuRo. We called this newly updated robot SQuRo-S. Because of the coupling compliant spine bending and leg locomotion, the environmental adaptability of SQuRo-S significantly improved. We conducted a series of experiments on challenging environments to verify the performance of SQuRo-S. The results demonstrated that SQuRo-S crossed an obstacle of 1.07 body height, thereby outperforming most small-scale legged robots. Remarkably, SQuRo-S traversed a narrow space of 0.86 body width. To the best of our knowledge, SQuRo-S is the first quadruped robot of this scale that is capable of traversing a narrow space with a width smaller than its own width. Moreover, SQuRo-S demonstrated stable walking on mud-sand, pipes, and slopes (20°), and resisted strong external impact and repositioned itself in various body postures. This work provides a new paradigm for enhancing the flexibility and adaptability of small-scale legged robots with spine in challenging environments, and can be easily generalized to the design and development of legged robots with spine of different scales.

Soft and continuum robots present the opportunity for extremely large ranges of motion, which can enable dexterous, adaptive, and multimodal locomotion behaviors. However, as the number of degrees of freedom (DOF) of a robot increases, the number of actuators should also increase to achieve the full actuation potential. This presents a dilemma in mobile soft robot design: physical space and power requirements restrict the number and type of actuators available and may ultimately limit the movement capabilities of soft robots with high-DOF appendages. Restrictions on actuation of continuum appendages ultimately may limit the various movement capabilities of soft robots. In this work, we demonstrate multimodal behaviors in an underwater robot called "Hexapus." A hierarchical actuation design for multiappendage soft robots is presented in which a single high-power motor actuates all appendages for locomotion, while smaller low-power motors augment the shape of each appendage. The flexible appendages are designed to be capable of hyperextension for thrust, and flexion for grasping with a peak pullout force of 32 N. For propulsion, we incorporate an elastic membrane connected across the base of each tentacle, which is stretched slowly by the high-power motor and released rapidly through a slip-gear mechanism. Through this actuation arrangement, Hexapus is capable of underwater locomotion with low cost of transport (COT = 1.44 at 16.5 mm/s) while swimming and a variety of multimodal locomotion behaviors, including swimming, turning, grasping, and crawling, which we demonstrate in experiment.

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.