Design of a flexible tactile sensor for classification of rigid and deformable objects

Drimus, Alin; Kootstra, Gert; Bilberg, Arne; Kragić, Danica

doi:10.1016/j.robot.2012.07.021

Cited by 203 publications

(149 citation statements)

References 24 publications

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“…It was used to identify soft toys, hard books, and bottles and cans containing different amounts of liquid. Drimus et al 16 described a self-designed tactile sensor with 64 tactile sensing modules arranged in an 8 Â 8 grid. This sensor was attached to the Schunk2 and Schunk3 fingers, which were used to grasp various objects to build a tactile data set.…”

Section: Tactile Perception For Deformable Objectsmentioning

confidence: 99%

“…Three kinds of representative objects which are usually used for tactile recognition research. Left: rigid objects used in the study by Luo et al 14 ; middle: textured material used in the study by Sinapov et al 15 ; right: deformable objects used in the study by Drimus et al 16 The figures in left and middle panels have been reproduced with permission from IEEE. The figure in right panel has been reproduced with permission from Elsevier.…”

Section: Introductionmentioning

confidence: 99%

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Recent progress on tactile object recognition

Liu

Sun

et al. 2017

International Journal of Advanced Robotic Systems

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Conventional visual perception technology is subject to many restrictions, such as illumination, background clutter, and occlusion. Many intrinsic properties of objects, like stiffness, hardness, and internal state, cannot be effectively perceived by visual sensors. For robots, tactile perception is a key approach to obtain environmental and object information. Different from vision sensors, tactile sensors can directly measure some physical properties of objects and environment. At the same time, humans also utilize touch sensory receptors as an important means to perceive and interact with the environment. In this article, we present a detailed discussion on tactile object recognition problem. We divide the current studies on the tactile object recognition into three subcategories and detailed analysis has been put forward on them. In addition, we also discuss some advanced topics such as visual-tactile fusion, exploratory procedure, and data sets.

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Section: Tactile Perception For Deformable Objectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recent progress on tactile object recognition

Liu

Sun

et al. 2017

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…It uses a biomimetic tactile sensor, which is capable of triaxial force sensing. In [32], a novel tactile sensor is presented using flexible piezoresistive rubber.…”

Section: Grippers For Known Environmentsmentioning

confidence: 99%

State of the Art Robotic Grippers and Applications

et al. 2016

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Abstract:In this paper, we present a recent survey on robotic grippers. In many cases, modern grippers outperform their older counterparts which are now stronger, more repeatable, and faster. Technological advancements have also attributed to the development of gripping various objects. This includes soft fabrics, microelectromechanical systems, and synthetic sheets. In addition, newer materials are being used to improve functionality of grippers, which include piezoelectric, shape memory alloys, smart fluids, carbon fiber, and many more. This paper covers the very first robotic gripper to the newest developments in grasping methods. Unlike other survey papers, we focus on the applications of robotic grippers in industrial, medical, for fragile objects and soft fabrics grippers. We report on new advancements on grasping mechanisms and discuss their behavior for different purposes. Finally, we present the future trends of grippers in terms of flexibility and performance and their vital applications in emerging areas of robotic surgery, industrial assembly, space exploration, and micromanipulation. These advancements will provide a future outlook on the new trends in robotic grippers.

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“…Considerable research has been conducted to infer tactile object properties using robots as well. Two recent examples are Drimus et al [2] and Chu et al [3], who introduced methods to infer multiple object properties by analyzing touch sensor input during several controlled exploration procedures.…”

Section: Inroductionmentioning

confidence: 99%

Estimating object hardness with a GelSight touch sensor

Yuan

Srinivasan

Adelson³

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Hardness sensing is a valuable capability for a robot touch sensor. We describe a novel method of hardness sensing that does not require accurate control of contact conditions. A GelSight sensor is a tactile sensor that provides high resolution tactile images, which enables a robot to infer object properties such as geometry and fine texture, as well as contact force and slip conditions. The sensor is pressed on silicone samples by a human or a robot and we measure the sample hardness only with data from the sensor, without a separate force sensor and without precise knowledge of the contact trajectory. We describe the features that show object hardness. For hemispherical objects, we develop a model to measure the sample hardness, and the estimation error is about 4% in the range of 8 Shore 00 to 45 Shore A. With this technology, a robot is able to more easily infer the hardness of the touched objects, thereby improving its object recognition as well as manipulation strategy.

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Design of a flexible tactile sensor for classification of rigid and deformable objects

Cited by 203 publications

References 24 publications

Recent progress on tactile object recognition

Recent progress on tactile object recognition

State of the Art Robotic Grippers and Applications

Estimating object hardness with a GelSight touch sensor

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