A Soft Robotic Gripper with Anti-Freezing Ionic Hydrogel-Based Sensors for Learning-Based Object Recognition

Zuo, Runze; Zhou, Zhanfeng; Ying, Binbin; Liu, Xinyu

doi:10.1109/icra48506.2021.9561287

Cited by 16 publications

(15 citation statements)

References 26 publications

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“…This fracture can also be addressed by directly bonding the hydrogel layer with two elastomers. 58 Electrical Properties of the iTENG. Considering its application for wearable and stretchable electronics, we characterized the iTENG in the single-electrode mode by simply connecting the antifreezing ionic hydrogel to the ground by a metal wire through an external load (Figure 2A).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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An Ionic Hydrogel-Based Antifreezing Triboelectric Nanogenerator

Ying

Zuo

Wan

et al. 2022

ACS Appl. Electron. Mater.

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The rapid development of stretchable electronics and soft robotics requires a sustainable power source that can match their mechanical stretchability in various working environments. Ionic hydrogel-based soft triboelectric nanogenerators (TENGs) show great promise for those application scenarios. However, ionic hydrogel-based TENGs suffer from the freezing issue under subzero temperatures. In this study, a low-cost, highly stretchable, and antifreezing ionic triboelectric nanogenerator (iTENG) is designed to involve a dielectric elastomer and a freeze-tolerant ionic hydrogel as the electrification layer and the electrode, respectively. The iTENG design achieves a unique combination of merits such as robust hydrogel–elastomer bonding, high stretchability (300%), and excellent tolerance of extremely low temperature (down to −53 °C). Because of the reliable interfacial bonding, the iTENG shows a good mechanical durability under different stretching conditions. The iTENG can harvest mechanical energies from various human motions and can also serve as a self-powered wearable sensor in both regular and extremely cold environments. The stretchable iTENG overcomes the strain-induced performance degradation of existing stretchable materials with percolated conductive fillers and the water freezing-induced degradation of conductive ionic hydrogels, providing a feasible design of stretchable and sustainable power sources for stretchable electronics and soft robotics operating in harsh environments.

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Section: ■ Results and Discussionmentioning

confidence: 99%

“…Our experiments show that it has little impact on the integration of iTENG and its performance. This fracture can also be addressed by directly bonding the hydrogel layer with two elastomers …”

Section: Resultsmentioning

confidence: 99%

An Ionic Hydrogel-Based Antifreezing Triboelectric Nanogenerator

Ying

Zuo

Wan

et al. 2022

ACS Appl. Electron. Mater.

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“…These sensors can detect the deformation of bending actuators so that the grasping pose of different fingers can be estimated. Long short-term memory (LSTM) ( Xie and Zhong, 2016b ) is typically utilized to process the data and classify the objects from the SoftMax function ( Zuo et al, 2021 ). However, since the strain sensors are normally made for one axis detection, they are usually insufficient for detection and need to be used together with other sensors such as tactile ( Zuo et al, 2021 ) and vision ( Jiao et al, 2020 ) sensors.…”

Section: Methods and Recent Developmentsmentioning

confidence: 99%

“…To enable object detection/classification when grasping using soft grippers, the sensors are usually integrated on the soft fingers to perceive the grasping mode. To detect the deformation of each finger, strain sensors are implemented into the soft grippers (Elgeneidy et al, 2018;Jiao et al, 2020;Souri et al, 2020;Zuo et al, 2021). These sensors can detect the deformation of bending Frontiers in Robotics and AI frontiersin.org actuators so that the grasping pose of different fingers can be estimated.…”

Section: F Soft Gripping Technologymentioning

confidence: 99%

Learning-based robotic grasping: A review

2023

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As personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as logistics, fast-moving consumer goods, and food delivery, these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without much modification or downtime. Most solutions in the market are based on traditional object recognition and are, therefore, not suitable for grasping unknown objects with varying shapes and textures. Adequate learning policies enable robotic grasping to accommodate high-mix and low-volume manufacturing scenarios. In this paper, we review the recent development of learning-based robotic grasping techniques from a corpus of over 150 papers. In addition to addressing the current achievements from researchers all over the world, we also point out the gaps and challenges faced in AI-enabled grasping, which hinder robotization in the aforementioned industries. In addition to 3D object segmentation and learning-based grasping benchmarks, we have also performed a comprehensive market survey regarding tactile sensors and robot skin. Furthermore, we reviewed the latest literature on how sensor feedback can be trained by a learning model to provide valid inputs for grasping stability. Finally, learning-based soft gripping is evaluated as soft grippers can accommodate objects of various sizes and shapes and can even handle fragile objects. In general, robotic grasping can achieve higher flexibility and adaptability, when equipped with learning algorithms.

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“…This has been used in previous work to construct elastic strain sensors [25]. Conductive hydrogels can also be used, but degrade over time [26], [27]. Other researchers experiment with conductive liquids in microfluidic channels or dispersing conductive liquids in elastomers [28].…”

Section: E Stretchable Electronicsmentioning

confidence: 99%

Elastic Tactile Sensor Skin on Double-Curved Surfaces for Robots and Wearables

Ruppel¹,

Hendrich²,

Zhang³

2022

IEEE Access

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Tactile perception is an important modality for dexterous manipulation, but faces unique challenges in that the sensor has to wrap around the system. Biological skin solves these problems extremely well. Not only can it detect contacts, but it also grows on complex three-dimensional shapes, stretches with underlying soft tissue and around joints, and leaves enough space inside for other organs. Previous artificial sensors could only achieve different subsets of these features. We present a novel tactile sensor skin which is completely made from elastic silicone rubber and can stretch to multiple times its original length without damage. It can be produced in three-dimensional double-curved shapes, is thin enough to accommodate other robot components or human body parts, and it can measure forces and locations of multiple simultaneous contacts. We develop production methods and material formulations, algorithms for computer-aided design, compact readout electronics using small, low-power FPGAs, firmware, and software.INDEX TERMS Tactile sensors, robot sensing systems, robot learning, soft robotics.

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A Soft Robotic Gripper with Anti-Freezing Ionic Hydrogel-Based Sensors for Learning-Based Object Recognition

Cited by 16 publications

References 26 publications

An Ionic Hydrogel-Based Antifreezing Triboelectric Nanogenerator

An Ionic Hydrogel-Based Antifreezing Triboelectric Nanogenerator

Learning-based robotic grasping: A review

Elastic Tactile Sensor Skin on Double-Curved Surfaces for Robots and Wearables

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