57 research outputs found
Integrated force and displacement sensing in an untethered dielectric elastomer actuator with a piezoresistive element
No full textDielectric elastomer actuators, owing to their fully electrical control and silent operation, are becoming increasingly popular for the development of terrestrial and underwater mobile robots with versatile locomotion capabilities. It is essential to embed the ability to sense their state and external interactions in these robots to facilitate the development of future autonomous capabilities. However, sensorizing dielectric elastomer actuators for untethered robotic applications is challenging due to their use of high voltage and the nonlinear mechanics of the elastomers utilized in them. To address this challenge, we developed a novel technique based on embedded piezoresistive sensing and high voltage feedback to simultaneously estimate the actuator displacement and external force in a fully untethered actuator driven by a miniature low-cost voltage amplifier. A data-driven regression model has been developed to accurately estimate force and displacement from the measured data. Validation tests conducted on three actuators demonstrate promising results. We achieve RMSE values as low as 29.736 mN for force estimation and 0.023 mm for displacement estimation in the zero-voltage condition, where the actuator is subjected to a triangular wave with a mechanical frequency of 0.1 Hz and an amplitude of 3 mm. Additionally, we have realized fully untethered operation by employing a power source, small-size voltage amplifier, microcontroller, and wireless connectivity module embedded in a compact form-factor. This work presents a significant advancement in soft robotics, offering a reliable and cost-effective solution for future autonomous robotic systems based on high-voltage dielectric elastomer actuators
Stimuli-responsive electrofluidic nervous system for autonomous soft robots
No full textMany organisms like earthworms with soft bodies and simple nervous systems can sense and respond to stimuli, conducting complex tasks such as navigation, foraging and transporting objects. However, most soft robots currently require rigid semiconductor-based electronics for sensing and control, limiting the benefits of their soft bodies and posing challenges for integration. To address these limitations, we propose a stimuli-responsive electrofluidic nervous system (SENS) composed of soft materials to realize signal generation, multimodal stimuli-sensing and decision making for multi-actuator soft electroactive robots.SENS is composed of multiple fluidic switches, which are driven by electroactive actuators and by external stimuli such as force and heat transduced into fluidic movement by sensing receptors. Electrofluidic circuits are created using these switches to achieve self-starting oscillating circuits that control input voltages to actuators and mode-selection units that activate specific oscillating circuits based on applied external stimuli to achieve stimuli-responsive behaviors. Utilizing SENS, we realized a soft crawling robot that can change its direction of motion in response to tactile and heat stimuli. The robot is made of a dielectric elastomer actuator and two electroadhesion actuators. Furthermore, an untethered soft robot has been developed with a miniaturized SENS and anonboard constant voltage power source, which can exhibit unidirectional motion. This work constitutes a step towards developing electronics-free, entirely soft autonomous robots capable of versatile and adaptive tasks.<br/
Compact planar low-voltage electroadhesion pads for reversible tissue and hydrogel adhesion
No full textRecent breakthroughs in low-voltage electroadhesion (EA) have demonstrated adhesion of hydrogels and biological tissues to metals at less than 10 V, offering significant promise for biomedical and soft robotic applications. However, the current arrangements rely on a parallel electrode configuration that sandwiches the adhesion target (e.g., tissue or hydrogel) between two electrodes, introducing two main limitations. Reversing voltage polarity causes re-adhesion to the opposite electrode, and bilateral electrode access is often impractical in confined settings such as robotic surgery or internal device anchoring. Addressing these challenges, this work presents a novel, compact, planar EA pad that achieves reversible adhesion with access to just a single surface. The effect of interfacial length, inter-electrode gap, and electrode width ratio on EA forces is investigated experimentally, and finite element electrostatic simulations are used to investigate the effect of these parameters on electric field strength and distribution. The optimized design achieves a 279% difference in adhesion force between forward and reverse polarity. Single-contact lifting and release of kidney tissue is demonstrated using the normal EA forces and a proof-of-concept EA tissue grasper that minimizes the required pinch force for grasping is realized
A surface diffusion model for Dip Pen Nanolithography line writing
No full textDip Pen Nanolithography is a direct write process that creates nanoscale dots and lines. Models typically predict dot and line size via assumption of constant ink flow rate from tip to substrate. This is appropriate for dot writing. It is however well-known, though models rarely reflect, that the ink flow rate depends upon writing speed during line writing. Herein, we explain the physical phenomenon that governs line writing and use this to model tip-substrate diffusion in line writing. We accurately predict (i) the increase in flow rate with writing speed and (ii) line width within 12.5%
ELTac: a vision-based electroluminescent tactile sensing skin for force localization and magnitude estimation
No full textLarge-area tactile sensing for robotic manipulators is an important capability to enable robots to perceive interactions with environment around them and for intuitive human–robot collaboration. In this article, we introduce a novel vision-based tactile sensing methodology that employs electroluminescent (EL) panels with a deformable soft skin that modulates light intensity based on applied forces to realize force localization and magnitude estimation for multipoint contact scenarios. The tactile sensing module is composed of a transparent rigid skeleton, a sensing skin composed of a thin and flexible EL panel, a deformable translucent elastomer layer with a pyramid pattern and an opaque outer layer. When a force is applied onto the skin, the deformable layer deforms modulating the intensity of light passing through the transparent layer which is detected by a camera embedded inside the module. We utilize image processing, camera models, and statistical fitting to localize single and multiple touch points as well as estimate the magnitude of the forces applied. Finally, the proposed algorithm is tested with five different indenters, and the localization error and the intensity-force mapping are analyzed. A localization accuracy of 6.63 mm has been achieved and normal forces from 3.1 to 9.4 N can be detected with an accuracy of 9.3%–11.7% error range. This work provides a simple and effective solution for the acquisition of position and force magnitude information in human–robot interaction tasks such as guidance and demonstration
Characterization of the Dip Pen Nanolithography Process for Nanomanufacturing
No full textDip pen nanolithography (DPN) is a flexible nanofabrication process for creating 2-D nanoscale features on a surface using an “inked” tip. Although a variety of ink-surface combinations can be used for creating 2-D nanofeatures using DPN, the process has not yet been characterized for high throughput and high quality manufacturing. Therefore, at present it is not possible to (i) predict whether fabricating a part is feasible within the constraints of the desired rate and quality and (ii) select/design equipment appropriate for the desired manufacturing goals. Herein, we have quantified the processing rate, tool life, and feature quality for DPN line writing by linking these manufacturing metrics to the process/system parameters. Based on this characterization, we found that (i) due to theoretical and practical constraints of current technology, the processing rate cannot be increased beyond about 20 times the typical rate of ∼1 μm2 /min, (ii) tool life for accurate line writing is limited to 1–5 min, and (iii) sensitivity of line width to process parameters decreases with an increase in the writing speed. Thus, we conclude that for a high throughput and high quality system, we need (i) parallelization or process modification to improve throughput and (ii) accurate fixtures for rapid tool change. We also conclude that process control at high speed writing is less stringent than at low speed writing, thereby suggesting that DPN has a niche in high speed writing of narrow lines.National Science Foundation (U.S.) (Grant No. 0914790
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Embodied sensing and stimuli-responsive behaviour for electroactive soft robots
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An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows
No full textCow diseases are a major source of concern for people. Some diseases in animals that are discovered in their early stages can be treated while they are still treatable. If lumpy skin disease (LSD) is not properly treated, it can result in significant financial losses for the farm animal industry. Animals like cows that sign this disease have their skin seriously affected. A reduction in milk production, reduced fertility, growth retardation, miscarriage, and occasionally death are all detrimental effects of this disease in cows. Over the past three months, LSD has affected thousands of cattle in nearly fifty districts across Bangladesh, causing cattle farmers to worry about their livelihood. Although the virus is very contagious, after receiving the right care for a few months, the affected cattle can be cured. The goal of this study was to use various deep learning and machine learning models to determine whether or not cows had lumpy disease. To accomplish this work, a Convolution neural network (CNN) based novel architecture is proposed for detecting the illness. The lumpy disease-affected area has been identified using image preprocessing and segmentation techniques. After the extraction of numerous features, our proposed model has been evaluated to classify LSD. Four CNN models, DenseNet, MobileNetV2, Xception, and InceptionResNetV2 were used to classify the framework, and evaluation metrics were computed to determine how well the classifiers worked. MobileNetV2 has been able to achieve 96% classification accuracy and an AUC score of 98% by comparing results with recently published relevant works, which seems both good and promising
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