This paper presents a tunable stiffness actuator with a soft-rigid combined layer jamming mechanism. The tunable stiffness actuator is aimed to be integrated into an exosuit to prevent ankle sprain and avoid or mitiga...
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This paper presents a tunable stiffness actuator with a soft-rigid combined layer jamming mechanism. The tunable stiffness actuator is aimed to be integrated into an exosuit to prevent ankle sprain and avoid or mitigate the development of chronic ankle instability. The main purpose of the soft-rigid layer jamming mechanism is to produce large stiffness with a small volume and achieve a linear stiffness characteristic. To this end, the actuator is designed to include rigid retainer pieces within the soft silicone layers, and each soft-rigid layer is jammed to induce stiffness changes. To validate the stiffness characteristics of the proposed soft-rigid actuator, a series of experiments were performed and stiffness changes were investigated for varying jamming states from unjammed to fully jammed states. Increasing the number of jamming layer effectively increased the actuator stiffness, which was consistent with expected results from the analytical model. Soft-rigid actuator's stiffness at the fully jammed state was 212.1% and 123.1% higher than the unjammed state for one-side and both sides anchored conditions, respectively. Compared to the soft actuator without the rigid retainer, the soft-rigid actuator exhibited a more linear characteristic (Pearson correlation coefficient = 0.990 and 0.997 for one-side and both sides anchored conditions, respectively). Moreover, the soft-rigid actuator achieved significantly higher stiffness than the soft actuator in all jamming states (at least 41.3% increase in each jamming state). The results suggest a potential use of the tunable stiffness actuator to develop a soft ankle exosuit with highly variable but linear stiffness characteristics.
This paper presents a novel TSA mechanism with an adjustable offset between strings, which enables a variable transmission system. TSA is designed to be used in a variety of applications, including exoskeletons, and r...
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To control the lower-limb exoskeleton robot effectively, it is essential to accurately recognize user status and environmental conditions. Previous studies have typically addressed these recognition challenges through...
To control the lower-limb exoskeleton robot effectively, it is essential to accurately recognize user status and environmental conditions. Previous studies have typically addressed these recognition challenges through independent models for each task, resulting in an inefficient model development process. In this study, we propose a Multitask learning approach that can address multiple recognition challenges simultaneously. This approach can enhance data efficiency by enabling knowledge sharing between each recognition model. We demonstrate the effectiveness of this approach using Gait phase recognition (GPR) and Terrain classification (TC) as examples, the most conventional recognition tasks in lower-limb exoskeleton robots. We first created a high-performing GPR model that achieved a Root mean square error (RMSE) value of 2.345 ± 0.08 and then utilized its knowledge-sharing backbone feature network to learn a TC model with an extremely limited dataset. Using a limited dataset for the TC model allows us to validate the data efficiency of our proposed Multitask learning approach. We compared the accuracy of the proposed TC model against other TC baseline models. The proposed model achieved 99.5 ± 0.044% accuracy with a limited dataset, outperforming other baseline models, demonstrating its effectiveness in terms of data efficiency. Future research will focus on extending the Multitask learning framework to encompass additional recognition tasks.
To control the lower-limb exoskeleton robot effectively, it is essential to accurately recognize user status and environmental conditions. Previous studies have typically addressed these recognition challenges through...
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This paper proposes a method of constructing weak control-Lyapunov functions for nonlinear systems by introducing a topological geometric assumption called a Morse-Smale system. A Lyapunov function is one of the most ...
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This paper proposes a method of constructing weak control-Lyapunov functions for nonlinear systems by introducing a topological geometric assumption called a Morse-Smale system. A Lyapunov function is one of the most important tools to study stability and stabilization of nonlinear systems. However, a general way of finding Lyapunov functions has not been found yet. First, we confirm there is a weak Lyapunov function for Morse-Smale systems. Next, we define the escapability for singular structures of the weak Lyapunov function. If all singular structures are escapable, then the Morse-Smale system is a globally asymptotically stabilizable one. Finally, we present the method of constructing a set of weak control-Lyapunov functions to achieve global stabilization. The method is described in terms of a recursive sequence of singular structures. We call the sequence a weak Lyapunov filtration.
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