Soft Robotics最新文献

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 $L∕D$ 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.

We present a compression sensor based on a strain-sensitive carbon black-silicone composite cast on top of a printed circuit board with interdigitated electrodes. This results in a very sensitive and soft capacitive compression sensor not requiring a structured dielectric or compliant electrodes. We show how the optimal loading of carbon black to maximize the sensitivity depends on the type of carbon black and the stiffness of the silicone matrix. The optimal quantity of carbon black leads to a high sensitivity of 252% for an input force of 10 N (this corresponds to an input pressure of 17 kPa), without stiffening the silicone matrix or increasing the viscoelastic losses noticeably. The fabrication process of the sensors is much simpler than that of other soft capacitive sensors, and unlike carbon black-silicone resistive sensors, these capacitive sensors do not exhibit time-dependent impedance creep. They can be made thick without affecting their base capacitance or sensitivity, leading to compliant and conformable sensing interfaces suitable for a variety of applications, such as robotic tactile sensors.

Twisted string actuators (TSAs) have exhibited great promise in robotic applications by generating high translational force with low input torque. To further facilitate their robotic applications, it is strongly desirable but challenging to enhance their consistent strain generation while maintaining compliance. Existing studies predominantly considered overtwisting and coiling after the regular twisting stage to be undesirable-nonuniform and unpredictable knots, entanglements, and coils formed to create an unstable and failure-prone structure. Overtwisting would work well for TSAs when uniform coils can be consistently formed. In this study, we realize uniform and consistent coil formation in overtwisted TSAs, which greatly increases their strain. Furthermore, we investigate methods for enabling uniform coil formation upon overtwisting the strings in a TSA and present a procedure to systematically "train" the strings. To the authors' best knowledge, this is the first study to experimentally investigate overtwisting for TSAs with different stiffnesses and realize consistent uniform coil formation. Ultrahigh molecular-weight polyethylene strings form the stiff TSAs, whereas compliant TSAs are realized with stretchable and conductive supercoiled polymer strings. The strain, force, velocity, and torque of each overtwisted TSA were studied. Overtwisting and coiling resulted in ∼70% strain in stiff TSAs and ∼60% strain in compliant TSAs. This is more than twice the strain achieved through regular twisting. Finally, the overtwisted TSA was successfully demonstrated in a robotic bicep.

Current aerial robots demonstrate limited interaction capabilities in unstructured environments when compared with their biological counterparts. Some examples include their inability to tolerate collisions and to successfully land or perch on objects of unknown shapes, sizes, and texture. Efforts to include compliance have introduced designs that incorporate external mechanical impact protection at the cost of reduced agility and flight time due to the added weight. In this work, we propose and develop a lightweight, inflatable, soft-bodied aerial robot (SoBAR) that can pneumatically vary its body stiffness to achieve intrinsic collision resilience. Unlike the conventional rigid aerial robots, SoBAR successfully demonstrates its ability to repeatedly endure and recover from collisions in various directions, not only limited to in-plane ones. Furthermore, we exploit its capabilities to demonstrate perching where the three-dimensional collision resilience helps in improving the perching success rates. We also augment SoBAR with a novel hybrid fabric-based bistable (HFB) grasper that can utilize impact energies to perform contact-reactive grasping through rapid shape conforming abilities. We exhaustively study and offer insights into the collision resilience, impact absorption, and manipulation capabilities of SoBAR with the HFB grasper. Finally, we compare the performance of conventional aerial robots with the SoBAR through collision characterizations, grasping identifications, and experimental validations of collision resilience and perching in various scenarios and on differently shaped objects.

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.

Smart materials such as magnetoactive elastomers (MAEs) combine elastic and magnetic properties that can be significantly changed in response to a magnetic field and therefore offer enormous potential for applications in both scientific research and engineering. When such an elastomer contains microsized hard magnetic particles, it can become an elastic magnet once magnetized in a strong magnetic field. This article studies a multipole MAE with the aim of utilizing it as an actuation element of vibration-driven locomotion robots. The elastomer beam has three magnetic poles overall with the same poles at the ends and possesses silicone bristles protruding from its underside. The quasi-static bending of the multipole elastomer in a uniform magnetic field is investigated experimentally. The theoretical model exploits the magnetic torque to describe the field-induced bending shapes. The unidirectional locomotion of the elastomeric bristle-bot is realized in two prototype designs using magnetic actuation of either an external or an integrated source of an alternating magnetic field. The motion principle is based on cyclic interplay of asymmetric friction and inertia forces caused by field-induced bending vibrations of the elastomer. The locomotion behavior of both prototypes shows a strong resonant dependency of the advancing speed on the frequency of applied magnetic actuation.

Advances in science and engineering often reveal the limitations of classical approaches initially used to understand, predict, and control phenomena. With progress, conceptual categories must often be re-evaluated to better track recently discovered invariants across disciplines. It is essential to refine frameworks and resolve conflicting boundaries between disciplines such that they better facilitate, not restrict, experimental approaches and capabilities. In this essay, we address specific questions and critiques which have arisen in response to our research program, which lies at the intersection of developmental biology, computer science, and robotics. In the context of biological machines and robots, we explore changes across concepts and previously distinct fields that are driven by recent advances in materials, information, and life sciences. Herein, each author provides their own perspective on the subject, framed by their own disciplinary training. We argue that as with computation, certain aspects of developmental biology and robotics are not tied to specific materials; rather, the consilience of these fields can help to shed light on issues of multiscale control, self-assembly, and relationships between form and function. We hope new fields can emerge as boundaries arising from technological limitations are overcome, furthering practical applications from regenerative medicine to useful synthetic living machines.

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.