1,721,066 research outputs found
Design and characterization of a 3D-printed pneumatically-driven bistable valve with tunable characteristics
Full text linkAlthough research studies in pneumatic soft robots develop rapidly, most pneumatic actuators are still controlled by rigid valves and conventional electronics. The existence of these rigid, electronic components sacrifices the compliance and adaptability of soft robots. Current electronics-free valve designs based on soft materials are facing challenges in behaviour consistency, design flexibility, and fabrication complexity. Taking advantages of soft material 3D printing, this letter presents a new design of a bi-stable pneumatic valve, which utilises two soft, pneumatically-driven, and symmetrically-oriented conical shells with structural bistability to stabilise and regulate the airflow. The critical pressure required to operate the valve can be adjusted by changing the design features of the soft bi-stable structure. Multi-material printing simplifies the valve fabrication, enhances the flexibility in design feature optimisations, and improves the system repeatability. In this work, both a theoretical model and physical experiments are introduced to examine the relationships between the critical operating pressure and the key design features. Results with valve characteristic tuning via material stiffness changing show better effectiveness compared to the change of geometry design features (demonstrated largest tunable critical pressure range from 15.3 to 65.2 kPa and fastest response time ≤1.8s )
On the development of a tactile sensor for fabric manipulation and classification for industrial applications
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Design of a 3D-printed soft robotic hand with integrated distributed tactile sensing
Full text linkHumans rely on distributed tactile sensing in their hands to achieve robust and dexterous manipulation of delicate objects. Soft robotic hands have received increased attention in recent years due to their adaptability to unknown objects and safe interactions with the environment. However, the integration of distributed sensing in soft robotic hands is lacking. This is largely due to the complexity in the integration of soft sensing solutions with the hands. This paper proposes a novel soft robotic hand that incorporates an active palm and distributed pneumatic tactile sensing in both the fingers and the palm. Multi-material 3D printing allows the tactile sensors to be directly printed on the hand, whereas conventional tactile approaches require the sensors to be attached as part of multiple fabrication procedures. Active degrees of freedom are introduced in the palm to achieve increased dexterity. The proposed hand successfully performed 32 of the 33 Feix taxonomy grasps and all 11 Kapandji thumb opposition poses
A Magnetorheological Elastomer‐Based Proportional Valve for Soft Pneumatic Actuators
Full text linkThe interest in soft pneumatic actuators has been growing rapidly in robotics, owing to the contact adaptability with the material softness. However, these actuators are mostly controlled by rigid electronic pneumatic valves, which can hardly be integrated into the robot itself, limiting its mobility and adaptability. Recent advances in soft or electronics‐free valve designs provide the potential to achieve an integrated soft robotic system with reduced weight and rigidity. Nevertheless, the challenge in valve response remains open. To enable dynamic control of a soft pneumatic actuator (SPA), a fast‐response proportional valve is needed. Herein, the potential of Ecoflex‐based magnetorheological elastomer (MRE) membrane to create a proportional valve that can be used in the control of a soft robot made from the same silicone material is explored. Experimental characterization shows that the proposed MRE valve (30 mm × 30 mm × 15 mm, 30 grams) can hold pressure up to 41.3 kPa and regulate the airflow in an analog manner. The valve is used to perform closed‐loop proportional–integral–differential (PID) control with 50 Hz on a SPA and is able to control the pressure within the actuator chamber with a root‐mean‐square error of 0.05 kPa
Magneto-Active Elastomer Filter for Tactile Sensing Augmentation Through Online Adaptive Stiffening
Full text linkThe mechanical properties of a sensor strongly affect its tactile sensing capabilities. The role of morphology and stiffness on the quality of the tactile data has already been the subject of several studies, which focus mainly on static sensor designs and design methodologies. However, static designs always come with trade-offs: considering stiffness, soft compliant sensors ensure a better contact, but at the price of mechanically filtering and altering the detected signal. Conversely, online adaptable filters can tune their characteristics, becoming softer or stiffer when needed. We propose a magneto-active elastomer filter which, when placed on top of the tactile unit, allows the sensor to change its stiffness on demand. We showcase the advantages provided by online stiffening adaptation in terms of information gained and data structure. Moreover, we illustrate how adaptive stiffening influences classification, using 9 standard machine learning algorithms, and how adaptive stiffening can increase the classification accuracy up to 34 with respect to static stiffness control
Tactile resilience of sensory whisker by adaptive morphology
Full text linkNature is featured by the resiliency, which enables adaptivity to sudden change under many circumstances. Meanwhile, the resiliency in robotic systems is far from comparable to that of the nature. If a robot is partially damaged, often the whole system fails to operate properly. While some approaches have been proposed, the majority of them are focusing on updating the control policy. Such approach, while rather complex, is not always applicable to mechanical damage of the robot body, especially parts that continuously interact with the surrounding environment. In the previous works Nguyen and Ho, (2022) and Nguyen and Ho, (2021) we introduced an artificial whiskered sensor that exhibited resilience against physical damage by active change of its morphology around the placement of sensory elements (strain gauges), which allowed compensation of location sensing when the whisker was trimmed. In this paper, we extend the approach by using the whisker sensor for texture discrimination tasks. We demonstrate that changing the morphology of the whisker again helps to reduce mismatching between prior knowledge in the frequency/time domain of the sensory signal. This allows the sensory whisker to recover the tactile perception on texture discrimination after the whisker is partially damaged. Furthermore, we also observe that using adaptive sensor morphology would augment tactile perception without the need of computationally expensive recognition and re-classification. This work is expected to shed a light on a new generation of robots that automatically work in the open world where self-maintenance against uncertainties is needed
A tactile-based fabric learning and classification architecture
No full textThis paper proposes an architecture for tactile-based fabric learning and classification. The architecture is based on a number of SVM-based learning units, which we call fabric classification cores, specifically trained to discriminate between two fabrics. Each core is based on a specific subset of the fully available set of features, on the basis of their discriminative value, determined using the p-value. During fabric recognition, each core casts a vote. The architecture collects votes and provides an overall classification result. We tested seventeen different fabrics, and the result showed that classification errors are negligible
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