Soft Robotics最新文献

Soft and stretchable force sensors are widely used for health monitoring, robotics, prosthetics, and other applications. Soft force sensors with the capability of measuring both normal and shear force could offer even greater functionality and provide more information, particularly in the field of biomechanics. In this work, a new solid-state force sensor is proposed that can measure both normal and shear forces at the same time. The soft and stretchable sensor was fabricated using an ionic liquid (IL)/polymer network. Two separate IL-based polymer membranes were used to detect normal and shear forces. Sensor architecture and electrical wiring for normal, shear, and combined sensing were developed, and various material compositions for different sensor layers were investigated to find the combination that could achieve the optimum sensor performance. A basic material formulation for carbon nanotube-based electrodes, the IL/polymer network, and polymeric insulation layers was proposed. To configure a combined (normal and shear) sensor, separate sensors for normal and shear deformations were first designed and investigated. Later, a combined sensor was fabricated using a mold via screen printing, photocuring, and thermal curing. The combined sensor was evaluated under different force conditions. The results show that the sensor can reliably measure normal and shear forces. Moreover, the findings demonstrate a way to successfully modulate the sensitivity for normal and shear sensing by varying the material composition or geometric configuration, which provides flexibility for application-specific designs.

The advent of soft robots has solved many issues posed by their rigid counterparts, including safer interactions with humans and the capability to work in narrow and complex environments. While much work has been devoted to developing soft actuators and bioinspired mechatronic systems, comparatively little has been done to improve the methods of actuation. Hydraulically soft actuators (HSAs) are emerging candidates to control soft robots due to their fast responses, low noise, and low hysteresis compared to compressible pneumatic ones. Despite advances, current hydraulic sources for large HSAs are still bulky and require high power availability to drive the pumping plant. To overcome these challenges, this work presents a new bioinspired soft and high aspect ratio pumping element (Bio-SHARPE) for use in soft robotic and medical applications. This new soft pumping element can amplify its input volume to at least 8.6 times with a peak pressure of at least 40 kPa. The element can be integrated into existing hydraulic pumping systems like a hydraulic gearbox. Naturally, an amplification of fluid volume can only come at the sacrifice of pumping pressure, which was observed as a 19.1:1 reduction from input to output pressure. The new concept enables a large soft robotic body to be actuated by smaller fluid reservoirs and pumping plant, potentially reducing their power and weight, and thus facilitating drive source miniaturization. The high amplification ratio also makes soft robotic systems more applicable for human-centric applications such as rehabilitation aids, bioinspired untethered soft robots, medical devices, and soft artificial organs. Details of the fabrication and experimental characterization of the Bio-SHARPE and its associated components are given. A soft robotic squid and an artificial heart ventricle are introduced and experimentally validated.

Magnetically actuated miniature robots have immeasurable potential in lab-on-a-chip and biomedical due to their ability to navigate in constrained space. However, current soft robots made by elastomers have limited functionalities and are prevented from very narrow environments such as channel much smaller than their size because of their non- or limited deformability. In this study, a soft and multifunctional robot based on liquid metal (magnetic liquid-metal droplet robot [MLDR]) with high output force is reported. It is fabricated by engulfing iron particles into a Galinstan droplet. By changing the shape and motion of permanent magnets, the MLDR can be reshaped and moved. The MLDR can also be split in batches and merged efficiently. It shows good softness and flexibility when navigating freely in a narrow channel, and thus can pass through a confined space smaller than its size easily. Furthermore, the MLDR can also push and spread the accumulated liquid in a desired path, and manipulate the motions of small objects well. Benefiting from the solidification-like phenomenon, an MLDR can output milli-Newton-level force much higher than the output force of ferrofluid droplet robots in micro-Newton level. The demonstrated capabilities of the MLDR are promising for the applications in lab-on-a-chip or biomedical devices.

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.

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.

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.

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.