A Simple Electric Soft Robotic Gripper with High-Deformation Haptic Feedback

Chin, Lillian; Yuen, Michelle C.; Lipton, Jeffrey; Trueba, Luis H.; Kramer‐Bottiglio, Rebecca; Rus, Daniela

doi:10.1109/icra.2019.8794098

Cited by 38 publications

(28 citation statements)

References 30 publications

(41 reference statements)

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“…Finally, research by Chin et al has proposed compliant electric actuators based on Handed Shearing Auxetics for a two-fingered gripper, and shows the gripping of several house-hold objects [18]. Continuation of the work included high-deformation haptic feedback to enable object classification [19].…”

Section: A Related Workmentioning

confidence: 99%

Soft Robotic Gripper With Compliant Cell Stacks for Industrial Part Handling

Netzev

Angleraud

Pieters

2020

IEEE Robot. Autom. Lett.

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Robot object grasping and handling requires accurate grasp pose estimation and gripper/end-effector design, tailored to individual objects. When object shape is unknown, cannot be estimated, or is highly complex, parallel grippers can provide insufficient grip. Compliant grippers can circumvent these issues through the use of soft or flexible materials that adapt to the shape of the object. This paper proposes a 3D printable soft gripper design for handling complex shapes. The compliant properties of the gripper enable contour conformation, yet offer tunable mechanical properties (i.e., directional stiffness). Objects that have complex shape, such as non-constant curvature, convex and/or concave shape can be grasped blind (i.e., without grasp pose estimation). The motivation behind the gripper design is handling of industrial parts, such as jet and Diesel engine components. (Dis)assembly, cleaning and inspection of such engines is a complex, manual task that can benefit from (semi-)automated robotic handling. The complex shape of each component, however, limits where and how it can be grasped. The proposed soft gripper design is tunable by compliant cell stacks that deform to the shape of the handled object. Individual compliant cells and cell stacks are characterized and a detailed experimental analysis of more than 600 grasps with seven different industrial parts evaluates the approach.

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Section: A Related Workmentioning

confidence: 99%

Soft Robotic Gripper With Compliant Cell Stacks for Industrial Part Handling

Netzev

Angleraud

Pieters

2020

IEEE Robot. Autom. Lett.

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“…Although a similar soft gripper design was used in [11,12], significant improvements were made in order to improve the sensitivity and resolution of readings for material classification, primarily for the pressure sensor. Previous designs relied on simply letting a neoprene foam liner or soft pressure sensor rest directly on the HSA actuators within the glove.…”

Section: B Sensorized Gripper Integrationmentioning

confidence: 99%

“…In order to extend our object size and stiffness measurements from [12] to apply to material properties, we would have to perform a more rigorous classification of compliance. Rather than simply perform a simple regression to map measured sensor values to compliance ratings, we use sensors in both fingers to create a more complex feature set that is more robust to variations between objects.…”

Section: Materials Classifiermentioning

confidence: 99%

“…Then, we estimate the size of the object. This size estimation is a simple linear regression determined by a set of training objects from [12]. This gives us a mapping between the fingers' strain sensor readings to the size of the object.…”

Section: Materials Classifiermentioning

confidence: 99%

“…This gripper is built with handed shearing auextic (HSA) actuators and soft capacitive silicone pressure and strain sensors [10,11]. This gripper has been demonstrated to accurately sense object size and stiffness [12], which we then extend to create a material-based classifier to sort objects as paper, plastic or metal. We characterize this classification algorithm and demonstrate its viability on a mock recycling setup, correctly classifying 27 objects with 85% accuracy with a stationary gripper and 63% accuracy on the recycling setup.…”

Section: Introductionmentioning

confidence: 99%

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Automated Recycling Separation Enabled by Soft Robotic Material Classification

Chin

Lipton

Yuen

et al. 2019

2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)

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Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting. Soft robotics offers a natural solution as compliant robots require less computation to plan paths and grasp objects in a cluttered environment. However, most soft robots are not robust enough to handle the many sharp objects present in a recycling facility. In this work, we present a soft sensorized robotic gripper which is fully electrically driven and can detect the difference between paper, metal and plastic. By combining handed shearing auxetics with high deformation capacitive pressure and strain sensors, we present a new puncture resistant soft robotic gripper. Our materials classifier has 85% accuracy with a stationary gripper and 63% accuracy in a simulated recycling pipeline. This classifier works over a variety of objects, including those that would fool a purely vision-based system.

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