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Herein, the design, modeling, and validation of high‐flow, fluid‐driven, membrane valves tailored specifically for applications in soft robotic systems are described. Targeting the piping problem in hyper‐actuated soft robots, two fluid‐driven membrane valve designs that can admit flows of up to 871 mg s−1$871 \textrm{ } \textrm{ } \text{mg} \textrm{ } \left(\text{s}\right)^{- 1}$ while weighing less than 20 g$false$ are introduced. A mathematical model to predict fluid flow by representing the displacement of the membrane as a scalar quantity influenced by the balance of pressures applied across the valve's ports is established. The model incorporates six parameters with direct physical relevance, enhancing its usefulness in valve design and system integration. In an experimental validation, flow rates with deviations within 4% are predicted and the onset of flow is correctly identified with an error rate of less than 1%. In addition, applications of these valves for flow amplification and for the creation of a fluid‐driven oscillator are experimentally demonstrated. This research contributes to the advancement of soft robotics by providing a tool for designing, optimizing, and controlling fluid‐driven systems and it lays the groundwork for the future development of embedded, fluid‐controlled valve networks that can be used to realize hyper‐actuated soft robotic systems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of the Fluid Jacobian, which provides a description of the power transmission that operates between the fluid and mechanical domains in soft robotic systems. It can be understood as a generalization of the traditional kinematic Jacobian that relates the joint space torques and velocities to the task space forces and velocities of a robot. In a similar way, the Fluid Jacobian relates fluid pressure to task space forces and fluid flow to task space velocities. In addition, the Fluid Jacobian can also be regarded as a generalization of the piston cross-sectional area in a fluid-driven cylinder that extends to complex geometries and multiple dimensions. In the following, we present a theoretical derivation of this framework, focus on important special cases, and illustrate the meaning and practical applicability of the Fluid Jacobian in four brief examples. [ABSTRACT FROM AUTHOR]

Forward dynamic simulation provides a powerful framework for characterizing internal loads and for predicting changes in movement due to injury, impairment or surgical intervention. However, the computational challenge of generating simulations has greatly limited the use and application of forward dynamic models for simulating human gait. In this study, we introduce an optimal estimation approach to efficiently solve for generalized accelerations that satisfy the overall equations of motion and best agree with measured kinematics and ground reaction forces. The estimated accelerations are numerically integrated to enforce dynamic consistency over time, resulting in a forward dynamic simulation. Numerical optimization is then used to determine a set of initial generalized coordinates and speeds that produce a simulation that is most consistent with the measured motion over a full cycle of gait. The proposed method was evaluated with synthetically created kinematics and force plate data in which both random noise and bias errors were introduced. We also applied the method to experimental gait data collected from five young healthy adults walking at a preferred speed. We show that the proposed residual elimination algorithm (REA) converges to an accurate solution, reduces the detrimental effects of kinematic measurement errors on joint moments, and eliminates the need for residual forces that arise in standard inverse dynamics. The greatest improvements in joint kinetics were observed proximally, with the algorithm reducing joint moment errors due to marker noise by over 20% at the hip and over 50% at the low back. Simulated joint angles were generally within 1 deg of recorded values when REA was used to generate a simulation from experimental gait data. REA can thus be used as a basis for generating accurate simulations of subject-specific gait dynamics. [DOI: 10.1115/1.3005148] Keywords: gait, joint moments, residual forces, noise rejection, numerical integration, initial conditions, geometric constraints

Many concrete bridges around the world have passed their service life and are at high risk of collapse, especially in active seismic regions. This is particularly true in many developing and underdeveloped countries, where deficient construction and underpreparedness would result in a major catastrophe should a major seismic event occur. Thus, there lies a need to introduce an efficient, cost-effective, and readily available method of bridge rehabilitation. In this study, steel-reinforced grout (SRG) jacketing is used as a seismic strengthening technique for reinforced concrete bridge piers. Two bridge piers, one seismically damaged and one seismically deficient, were jacketed with SRG and then tested to observe their seismic response. The findings from the experimental program revealed that SRG jacketing can rehabilitate/repair a damaged bridge pier, and restore its flexural capacity, energy dissipation capacity, and ductility. While strengthening a seismically deficient pier, results showed that the flexural capacity and energy dissipation capacity could be improved, and ductility could be enhanced. [ABSTRACT FROM AUTHOR]

It has been possible to identify the stereocentres of the pacidamycins. All proteinogenic amino acids are of the natural, (S) configuration, with the DABA residue being of the (2S,3S) configuration.

User preference is a promising objective for the control of robotic exoskeletons because it may capture the multifactorial nature of exoskeleton use. However, to use it, we must first understand its characteristics in the context of exoskeleton control. Here, we systematically measured the control preferences of individuals wearing bilateral ankle exoskeletons during walking. We investigated users' repeatability identifying their preferences and how preference changes with walking speed, device exposure, and between individuals with different technical backgrounds. Twelve naive and 12 knowledgeable nondisabled participants identified their preferred assistance in repeated trials by simultaneously self-tuning the magnitude and timing of peak torque. They were blinded to the control parameters and relied solely on their perception of the assistance to guide their tuning. We found that participants' preferences ranged from 7.9 to 19.4 newton-meters and 54.1 to 59.2 percent of the gait cycle. Across trials, participants repeatably identified their preferences with a mean standard deviation of 1.7 newton-meters and 1.5 percent of the gait cycle. Within a trial, participants converged on their preference in 105 seconds. As the experiment progressed, naive users preferred higher torque magnitude. At faster walking speeds, these individuals were more precise at identifying the magnitude of their preferred assistance. Knowledgeable users preferred higher torque than naive users. These results highlight that although preference is a dynamic quantity, individuals can reliably identify their preferences. This work motivates strategies for the control of lower limb exoskeletons in which individuals customize assistance according to their unique preferences and provides meaningful insight into how users interact with exoskeletons. [ABSTRACT FROM AUTHOR]