Soft Robotics最新文献

The design of soft actuators is often focused on achieving target trajectories or delivering specific forces and torques, rather than controlling the impedance of the actuator. This article outlines a new soft, tunable pneumatic impedance module based on an antagonistic actuator setup of textile-based pneumatic actuators intended to deliver bidirectional torques about a joint. Through mechanical programming of the actuators (select tuning of geometric parameters), the baseline torque to angle relationship of the module can be tuned. A high bandwidth fluidic controller that can rapidly modulate the pressure at up to 8 Hz in each antagonistic actuator was also developed to enable tunable impedance modulation. This high bandwidth was achieved through the characterization and modeling of the proportional valves used, derivation of a fluidic model, and derivation of control equations. The resulting impedance module was capable of modulating its stiffness from 0 to 100 Nm/rad, at velocities up to 120°/s and emulating asymmetric and nonlinear stiffness profiles, typical in wearable robotic applications.

This work experimentally investigates a model-predictive motion planning strategy to impose oscillatory and undulation movements in a macro fiber composite (MFC)-actuated robotic fish. Most of the results in this field exploit sinusoidal input signals at the resonance frequency, which reduces the device's maneuverability. Differently, this work uses body/caudal fin locomotion patterns as references in a motion planning strategy formulated as a model-based predictive control (MPC) scheme. This open-loop scheme requires the modeling of the device, which is accomplished by deriving a gray box state-space model using experimental modal data. This state-space model considers the electromechanical coupling of the actuators. Based on the references and the model, the MPC scheme derives the input signals for the MFC actuators. An experimental campaign is carried out to verify two references for mimicking the locomotion patterns of a fish under limited actuation. The experimental results confirm the motion planning scheme's capability to impose oscillatory and undulation movements.

The development of the field of soft robotics has led to the exploration of novel techniques to manufacture soft actuators, which provide distinct advantages for wearable assistive robotics. One subset of these soft pneumatic actuators is conventionally developed from silicone, fabrics, and thermoplastic polyurethane (TPU). Each of these materials in isolation possesses limitations of low-stress capacity, low-design complexity, and high-input pressure requirements, respectively. Combining these materials can overcome some limitations and maintain their desirable properties. In this article, we explore one such composite design scheme using a combination of silicone polymer-based bladder and reconfigurable fabric skin made from an anisotropic extensible fabric. The silicone polymer bladder acts as the hermetic seal, while this skin acts as the constraint. Bending and torsional actuators were designed utilizing the anisotropy of these fabrics. The torsional actuator designs can achieve over 540° of twist, significantly larger than previously reported in the literature, owing to the lower mechanical impedance of the extensible fabrics. Actuators with 360° of bending were also fabricated using this method. In addition, the lack of TPU-backed or inextensible fabrics reduces the actuator's stiffness, leading to lower actuation pressures. Skin-based designs also confer the advantage of modularity, reconfigurability, and the ability to achieve complex motions by tuning the properties of the bladder and the skin. For applications with high-force requirements, such as wearable exoskeletons, we demonstrate the utility of multilayer design schemes. A multilayer bending actuator generated 190 N of force at 100 kPa and was shown to be a candidate for wearable assistive devices. In addition, torsional designs were shown to have utility in practical scenarios such as screwing on a bottle cap and turning knobs. Thus, we present a novel fabric-skin-based design concept that is highly versatile and customizable for various application requirements.

Aquatic swimmers, whether natural or artificial, leverage their maneuverability and morphological adaptability to operate successfully in diverse, complex underwater environments. Maneuverability allows swimmers the agility to change speed and direction within a constrained operating space, while morphological adaptability allows their bodies to deform as they avoid obstacles and pass through narrow gaps. In this work, we design a soft, modular, nonbiomorphic swimming robot that emulates the maneuverability and adaptability of biological swimmers. This tethered swimming robot is actuated by a two degree-of-freedom (2-DOF) cable-driven mechanism that enables not only common maneuvers, such as undulatory surging and pitch/yaw rotations, but also a roll rotation maneuver that is steady and controllable. This simple 2-DOF system demonstrates full 3D swimming abilities in a space-constrained underwater test bed. The soft compliant body and passive foldable fins of the swimming robot lend to its morphological adaptability, allowing it to move through narrow gaps, channels, and tunnels and to avoid obstacles without the need for a low-level feedback control strategy. The passive adaptability and maneuvering capabilities of our swimming robot offer a new approach to achieving underwater navigation in complex real-world settings.

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.

Designing soft robots that have greater toughness and better resistance to damage propagation while at the same time retaining their properties of compliance is fundamentally important for soft robotics applications. This study's main contribution is proposing a framework for nonlinear multimaterial architectural design of soft structures to increase their toughness and delay damage propagation. What are the limits when combining significantly different materials in one structure that will delay crack propagation while significantly maintaining postdamage toughness? Through this study, we observed that there is a very dynamic interplay when combining significantly different materials in one structure; this interplay could weaken or strengthen the multimaterial structure's toughness. In biological evolutionary terms, the Pangolin, Seashell, and Arapaima have found their answer for deflecting the crack and maintaining strength in their bodies. How does nature put these multimaterial structures together? Our research led us to find that the multimaterial toughness limits depend largely on the following parameters: components' relative morphology, architecture, spatial distribution, surface areas, and Young's Modulus. We found that a linear geometry, when it comes to morphology and/or architecture relative to surface area in multimaterial design, significantly reduces total toughness and fails to delay crack propagation. In contrast, incorporating geometric nonlinearities in both morphology and architecture significantly maintains higher total toughness even after damage, and significantly delays crack propagation. We believe that this study can open the door to further research and ultimately to promising and wide applications in soft robotics.

In this work, a simulation model for the optimal control of dielectric elastomer actuated flexible multibody dynamics systems is presented. The dielectric elastomer actuator (DEA) behaves like a flexible artificial muscle in soft robotics. It is modeled as an electromechanically coupled geometrically exact beam, where the electric charges serve as control variables. The DEA-beam is integrated as an actuator into multibody systems consisting of rigid and flexible components. The model also represents contact interaction via unilateral constraints between the beam actuator and, for example, a rigid body during the grasping process of a soft robot. With a mathematically concise and physically representative formulation, a reduced free energy function is developed for the electromechanically coupled beam. In the optimal control problem, an objective function is minimized while the electromechanically coupled dynamic balance equations for the multibody system have to be fulfilled together with the complementarity conditions for the contact and boundary conditions. The optimal control problem is solved via a direct transcription method, transforming it into a constrained nonlinear optimization problem. The electromechanically coupled geometrically exact beam is firstly semidiscretized with one-dimensional finite elements and then the multibody dynamics is temporally discretized with a variational integrator leading to the discrete Euler-Lagrange equations, which are further reduced with the null space projection. The discrete Euler-Lagrange equations and the boundary conditions serve as equality constraints, whereas the contact constraints are treated as inequality constraints in the optimization of the discretized objective. The constrained optimization problem is solved using the Interior Point Optimizer solver. The effectiveness of the developed model is demonstrated by three numerical examples, including a cantilever beam, a soft robotic worm, and a soft robotic grasper.

Soft robots have received a great deal of attention from both academia and industry due to their unprecedented adaptability in unstructured environment and extreme dexterity for complicated operations. Due to the strong coupling between the material nonlinearity due to hyperelasticity and the geometric nonlinearity due to large deflections, modeling of soft robots is highly dependent on commercial finite element software packages. An approach that is accurate and fast, and whose implementation is open to designers, is in great need. Considering that the constitutive relation of the hyperelastic materials is commonly expressed by its energy density function, we present an energy-based kinetostatic modeling approach in which the deflection of a soft robot is formulated as a minimization problem of its total potential energy. A fixed Hessian matrix of strain energy is proposed and adopted in the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, which significantly improves its efficiency for solving the minimization problem of soft robots without sacrificing prediction accuracy. The simplicity of the approach leads to an implementation of MATLAB with only 99-line codes, which provides an easy-to-use tool for designers who are designing and optimizing the structures of soft robots. The efficiency of the proposed approach for predicting kinetostatic behaviors of soft robots is demonstrated by seven pneumatic-driven and cable-driven soft robots. The capability of the approach for capturing buckling behaviors in soft robots is also demonstrated. The energy-minimization approach, as well as the MATLAB implementation, could be easily tailored to fulfill various tasks, including design, optimization, and control of soft robots.

Legged robots have remarkable terrestrial mobility, but are susceptible to falling and leg malfunction during locomotion. The use of a large number of legs, as in centipedes, can overcome these problems, but it makes the body long and leads to many legs being constrained to contact with the ground to support the long body, which impedes maneuverability. A mechanism for maneuverable locomotion using a large number legs is thus desirable. However, controlling a long body with a large number of legs requires huge computational and energy costs. Inspired by agile locomotion in biological systems, this study proposes a control strategy for maneuverable and efficient locomotion of a myriapod robot based on dynamic instability. Specifically, our previous study made the body axis of a 12-legged robot flexible and showed that changing the body-axis flexibility produces pitchfork bifurcation. The bifurcation not only induces the dynamic instability of a straight walk but also a transition to a curved walk, whose curvature is controllable by the body-axis flexibility. This study incorporated a variable stiffness mechanism into the body axis and developed a simple control strategy based on the bifurcation characteristics. With this strategy, maneuverable and autonomous locomotion was achieved, as demonstrated by multiple robot experiments. Our approach does not directly control the movement of the body axis; instead, it controls body-axis flexibility, which significantly reduces computational and energy costs. This study provides a new design principle for maneuverable and efficient locomotion of myriapod robots.