Soft robotics最新文献

Tendon-driven continuum robots suffer from crosstalk of driving forces between sections, typically resulting in motion coupling between sections, which affects their motion accuracy and complicates the control strategies. To address these issues, this article proposes a mechanically designed variable-pitch flexible-screw-driven continuum robot (FSDCR) that enables motion decoupling between sections. The continuum section of the FSDCR comprises a series of orthogonally arranged vertebrae and is driven by customized variable-pitch flexible screws. The variable-pitch flexible screws apply driving forces and constraints to several threaded vertebrae in the continuum section, improving positioning accuracy and loading capacity. The flexible screws effectively balance the driving force and torque within one section through antagonistic torsional actuation, thereby achieving motion decoupling between sections. Characterization experiments have been conducted to compare the motion accuracy and load capacity of the variable-pitch FSDCR with those of the constant-pitch FSDCR. The results demonstrate that the variable-pitch FSDCR exhibits improved positioning accuracy, minimizing an average error of 0.79 mm (0.60% relative to its total length), which is 82.09% lower than that of the constant-pitch FSDCR. The load capacity of the variable-pitch FSDCR is enhanced by up to 129.09% compared with the constant-pitch FSDCR. Experiments on the motion decoupling performance of the FSDCR show that the maximum motion coupling error is 0.32 mm (0.24% relative to the section length). Additionally, the motion coupling error is minimally influenced by the rotational speed of the screw. Finally, a three-section FSDCR is constructed, and its load capacity and motion flexibility are demonstrated.

This paper explores online stiffness modulation within a single tail stroke for swimming soft robots. Despite advances in stiffening mechanisms, little attention has been given to dynamically adjusting stiffness in real-time, presenting a challenge in developing mechanisms with the requisite bandwidth to match tail actuation. Achieving an optimal balance between thrust and efficiency in swimming soft robots remains elusive, and the paper addresses this challenge by proposing a novel mechanism for independent stiffness control, leveraging fluid-driven stiffening within a patterned pouch. Inspired by fluidic-driven actuation, this approach exhibits high bandwidth and facilitates significant stiffness changes. We perform experiments to demonstrate how this mechanism enhances both thrust and swimming efficiency. The tail actuation and fluid-driven stiffening can be optimized for a specific combination of thrust and efficiency, tailored to the desired maneuver type. The paper further explores the complex interaction between the soft body and surrounding fluid and provides fluid dynamics insights gained from the vortices created during actuation. Through frequency modulation and online stiffening, the study extends the Pareto front of achievable thrust generation and swimming efficiency.

Currently, pneumatic soft actuators are widely used due to their impressive adaptability, but they still face challenges for more extensive practical applications. One of the primary issues is the bulky and noisy air compressors required to generate air pressure. To circumvent this critical problem, this work proposes a new type of air pressure source, based on the vapor pressure at the gas-liquid equilibrium to replace conventional air pumps. Compared with the previous phase transition method, this approach gains advantages such as generating gas even at low temperatures (instead of boiling point), more controllable gas output, and higher force density (since both ammonia and water contribute to the gas pressure). This work built mathematical models to explain the mechanism of converting energy to output action force from electrical energy and found the aqua ammonia system is one of the optimal choices. Multiple prototypes were created to demonstrate the capability of this method, including a pouch actuator that pushed a load 20,555 times heavier than its dead weight. Finally, based on the soft actuator, an untethered crawling robot was implemented with onboard batteries, showing the potentially extensive applications of this methodology.

The unique rigid-flex connection between the fin-rays and fin-surface in a bionic undulatory fin robot endows the fin-surface with both active flexibility and load-bearing capacity, enabling this robot to perform amphibious motions in underwater, terrestrial, and even marshy environments. However, investigations into dynamic modeling problems for the undulatory fin robot, considering the impact of nonlinear deformation and frictional contact on ground locomotion performance, are scarce. Given this, based on the absolute nodal coordinate formulation (ANCF), this paper presents an efficient and accurate nonlinear dynamic model for this robot to elucidate the fin's flexible deformation and motion law. This model considers material, geometric, and boundary nonlinearities, utilizing ANCF thin plate elements and reference nodes to individually describe the fin-surface and fin-rays of the undulatory fin. Then, by using the master-slave technique, a frictional contact formulation for the fin and the ground is proposed. Furthermore, we conduct in-depth research and analysis on the formation and undulatory motion of the undulatory fin, encompassing its static deformation, static contact deformation, and frictional contact motion, and successfully obtain its responses under various conditions. Research indicates that during fin-surface motion, longitudinal sliding or a tendency for sliding at the contact points results in the undulatory fin moving in a crawling gait. The proposed theoretical model correctly captures the fin's complex nonlinear deformations and frictional characteristics and reveals its ground locomotion mechanism, whose effectiveness and superiority are validated through numerical examples and experiments.

Robotic grasping plays a pivotal role in real-world interactions for robots. Existing grippers often limit functionality to a single grasping mode-picking or suction. While picking handles smaller objects and suction adapts to larger ones, integrating these modes breaks scale boundaries, expanding the robot's potential in real applications. This article introduces grasping modes transformable soft gripper capable of achieving amphibious cross-scale objects grasping. Despite its compact and fully scalable design (20 mm in diameter prototype), it morphs into two configurations, gripping objects from 10% (2 mm) to over 1000% (200 mm) of its size, spanning a vast 100-fold range. To enhance its grasping efficacy, we derived theoretical analytical models for the two distinct grasping modes. Subsequently, we present a detailed illustration of the gripper's fabrication process. Experimental validation demonstrates the gripper's success in attaching or detaching everyday items and industrial products, achieving high success rates in both air and underwater scenarios. Amphibious grasping and card manipulation demonstrations underscore the practicality of this transformative soft robotics approach.

Previous research on wearable robotics focused on developing actuation mechanisms while overlooking influences of skin movement. During finger flexion, skins on the opisthenar and finger back are stretched. Impeding such skin movement will obstruct normal finger motions. In this research, a statistical study on skin movement is proposed and conducted to quantify skin movement on human hands. Results of 30 subjects (15 men and 15 women) reveal that skin at the finger back extends by an average of 29.3 ± 7.2% in fist clenching. Based on this study, design guidelines for robotic gloves are proposed, and nominal strain values at different hand regions are tabulated for references in robotic glove design. To explore the influence of skin movement on wearable robotics, an elastomer-constrained flat tube actuator is proposed based on which two prototype robotic gloves are developed: one with an ergonomic strap interface that has small constraint to skin motion, and the other based on the commonly used fabric glove that is supposed to have large constraint to skin motion. With the same power input to the robotic gloves, the strap-based design achieves a finger motion range of 2.5 times and a gripping force of 4.3 times that of the conventional fabric glove.

Computational design is a critical tool to realize the full potential of Soft Robotics, maximizing their inherent benefits of high performance, flexibility, robustness, and safe interaction. Practically, computational design entails a rapid iterative search process over a parameterized design space, with assessment using (frequently) computational modeling and (more rarely) physical experimentation. Bayesian approaches work well for these expensive-to-analyze systems and can lead to efficient exploration of design space than comparative algorithms. However, such computational design typically entails weaknesses related to a lack of fidelity in assessment, a lack of sufficient iterations, and/or optimizing to a singular objective function. Our work directly addresses these shortcomings. First, we harness a sophisticated nonlinear Finite Element Modeling suite that explicitly considers geometry, material, and contact nonlinearity to perform rapid accurate characterization. We validate this through extensive physical testing using an automated test rig and printed robotic fingers, providing far more experimental data than that reported in the literature. Second, we explore a significantly larger design space than comparative approaches, with more free variables and more opportunity to discover novel, high performance designs. Finally, we use a multiobjective Bayesian optimizer that allows for the identification of promising trade-offs between two critical objectives, compliance and contact force. We test our framework on optimizing Fin Ray grippers, which are ubiquitous throughout research and industry due to their passive compliance and durability. Results demonstrate the benefits of our approach, allowing for the optimization and identification of promising gripper designs within an extensive design space, which are then 3D printed and usable in reality.

For designing the assistive wearable rehabilitation robots, it is challenging to design the robot as energy efficient because the actuators have to be capable of overcoming human loads such as gravity of the body and spastic torque continuously during the assistance. To address these challenges, we propose a novel design of soft artificial muscle that utilizes shape memory alloy (SMA) spring actuators with pre-detwinning process. The SMA spring was fabricated through a process called pre-detwinning, which enhances the linearity of the SMA spring in martensite phase and unpowered restoring force, which is called passive force. The fabricated SMA spring can contract >60%. Finally, the soft wearable robot that can assist not only the gravitational torque exerted on the elbow by passive force, but also the elbow movements with active force was designed with a soft artificial muscle. A soft artificial muscle consists of the bundles of pre-detwinned SMA springs integrated with the stretchable coolant vessel. The stiffness of the muscle was measured as 1125 N/m in martensite phase and 1732 N/m in austenite phase. In addition, the muscle showed great actuation frequency performances, the bandwidth of which was measured as 0.5 Hz. The proposed wearable mechanism can fully compensate the gravitational torque for all the angles in passive mode. In addition, the proposed mechanism can produce high torque up to 3.5 Nm and movements in active mode.

Soft robotics promises to achieve safe and efficient interactions with the environment by exploiting its inherent compliance and designing control strategies. However, effective control for the soft robot-environment interaction has been a challenging task. The challenges arise from the nonlinearity and complexity of soft robot dynamics, especially in situations where the environment is unknown and uncertainties exist, making it difficult to establish analytical models. In this study, we propose a learning-based optimal control approach as an attempt to address these challenges, which is an optimized combination of a feedforward controller based on probabilistic model predictive control and a feedback controller based on nonparametric learning methods. The approach is purely data-driven, without prior knowledge of soft robot dynamics and environment structures, and can be easily updated online to adapt to unknown environments. A theoretical analysis of the approach is provided to ensure its stability and convergence. The proposed approach enabled a soft robotic manipulator to track target positions and forces when interacting with a manikin in different cases. Moreover, comparisons with other data-driven control methods show a better performance of our approach. Overall, this work provides a viable learning-based control approach for soft robot-environment interactions with force/position tracking capability.

Abstract Active control of the shape of soft robots is challenging. Despite having an infinite number of passive degrees of freedom (DOFs), soft robots typically only have a few actively controllable DOFs, limited by the number of degrees of actuation (DOAs). The complexity of actuators restricts the number of DOAs that can be incorporated into soft robots. Active shape control is further complicated by the buckling of soft robots under compressive forces; this is particularly challenging for compliant continuum robots due to their long aspect ratios. In this study, we show how variable stiffness enables shape control of soft robots by addressing these challenges. Dynamically changing the stiffness of sections along a compliant continuum robot selectively "activates" discrete joints. By changing which joints are activated, the output of a single actuator can be reconfigured to actively control many different joints, thus decoupling the number of controllable DOFs from the number of DOAs. We demonstrate embedded positive pressure layer jamming as a simple method for stiffness change in inflated beam robots, its compatibility with growing robots, and its use as an "activating" technology. We experimentally characterize the stiffness change in a growing inflated beam robot and present finite element models that serve as guides for robot design and fabrication. We fabricate a multisegment everting inflated beam robot and demonstrate how stiffness change is compatible with growth through tip eversion, enables an increase in workspace, and achieves new actuation patterns not possible without stiffening.