Design of a Soft Gripper With Improved Microfluidic Tactile Sensors for Classification of Deformable Objects

Deng, Linan; Shen, Yi; Fan, Genglin; He, Xin; Li, Zhi; Yuan, Ye

doi:10.1109/lra.2022.3158440

Cited by 16 publications

(11 citation statements)

References 40 publications

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Section: Tactile Sensing Modalities and Methodologiesmentioning

confidence: 99%

“…Object classification [164] Force sensitive resistor Tactile images CNN-Bayes Detect the size and shape of objects [165] Strain gauges Linear displacements Hidden Markov Model (HMM) Slip prediction [166] GelSight tactile sensor Tactile images Deep neural network (DNN) Slip detection [167] TacTip tactile sensor Tactile images SVM Slip detection for stable grasp [168] state of the soft gripper can be directly obtained through the perception of contact force including its magnitude, direction, and distribution. [163] The soft grippers can adjust the grasping force and posture adaptively when there exists a control strategy on the basis of force feedback.…”

Section: Force Sensingmentioning

confidence: 99%

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Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

Qu,

Mao,

et al. 2023

Adv Funct Materials

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Tactile sensing technology is crucial for soft grippers. Soft grippers equipped with intelligent tactile sensing systems based on various sensors can interact safely with the unstructured environments and obtain precise properties of objects (e.g., size and shape). It is essential to develop state‐of‐the‐art sensing technologies for soft grippers to handle different grasping tasks. In this review, the development of tactile sensing techniques for robotic hands is first introduced. Then, the principles and structures of different types of sensors normally adopted in soft grippers, including capacitive tactile sensors, piezoresistive tactile sensors, piezoelectric tactile sensors, fiber Bragg grating (FBG) sensors, vision‐based tactile sensors, triboelectric tactile sensors, and other advanced sensors developed recently are briefly presented. Furthermore, sensing modalities and methodologies for soft grippers are also described in aspects of force measurement, perception of object properties, slip detection, and fusion of perception. The application scenarios of soft grippers are also summarized based on these advanced sensing technologies. Finally, the challenges of tactile sensing technologies for soft grippers that need to be tackled are discussed and perspectives in addressing these challenges are pointed out.

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Section: Tactile Sensing Modalities and Methodologiesmentioning

confidence: 99%

Section: Force Sensingmentioning

confidence: 99%

Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

Qu,

Mao,

et al. 2023

Adv Funct Materials

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“…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. Tactile sensors are widely used for detecting objects ( Jiao et al, 2020 ; She et al, 2020 ; YangHan et al, 2020 ; Subad et al, 2021 ; Zuo et al, 2021 ; Deng et al, 2022 ). They can be built and fabricated on a small scale and embedded into soft grippers.…”

Section: Methods and Recent Developmentsmentioning

confidence: 99%

“…They can be built and fabricated on a small scale and embedded into soft grippers. Tactile sensors can be made from capacitance sensing ( Jiao et al, 2020 ; Zuo et al, 2021 ), optical fiber-based sensing ( YangHan et al, 2020 ), microfluidics, ( Deng et al, 2022 ), or even vision-based tactile sensors ( She et al, 2020 ). Since the grasping motion dynamically changes with time, LSTM is usually adopted to process the data and classify the objects.…”

Section: Methods and Recent Developmentsmentioning

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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“…They can detect the hardness of objects, whether the sensor is vision-based or electrical transductionbased [11]- [14]. Classification of objects can be accomplished using multiple tactile sensors in a single grasp [15] or with vision-based tactile sensors [16]. However, the majority of these sensors are designed for classifying larger or deformable objects and may not be appropriate for small object classification in cluttered environments due to issues such as sensing resolution and gripper size.…”

Section: Related Workmentioning

confidence: 99%

DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction

Do¹,

Kennedy²

2022

2022 International Conference on Robotics and Automation (ICRA)

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Collaborative robots stand to have an immense impact both on human welfare in domestic service applications and industrial superiority in advanced manufacturing requires dexterous assembly. The outstanding challenge is providing robotic fingertips with a physical design that makes them adept at performing dexterous tasks that require high-resolution, calibrated shape reconstruction and force sensing. In this work, we present DenseTact 2.0, an optical-tactile sensor capable of visualizing the deformed surface of a soft fingertip and using that image in a neural network to perform both calibrated shape reconstruction and 6-axis wrench estimation. We demonstrate the sensor accuracy of 0.3633mm per pixel for shape reconstruction, 0.410N for forces, 0.387mmN •m for torques, and the ability to calibrate new fingers through transfer learning, achieving comparable performance that trained more than four times faster with only 12% of the dataset size.

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Design of a Soft Gripper With Improved Microfluidic Tactile Sensors for Classification of Deformable Objects

Cited by 16 publications

References 40 publications

Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

Learning-based robotic grasping: A review

DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction

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