Grip force codtrol for an elastic finger using vision-based incipient slip feedback

Ikeda, Atsutoshi; Kurita, Yuichi; Ueda, Jun; Matsumoto, Yoshio; Ogasawara, Tsukasa

doi:10.1109/iros.2004.1389452

Cited by 50 publications

(40 citation statements)

References 12 publications

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“…ARTC consists of a resonant cavity inside an elastic body and a receiver for ultrasound, and detects the direction of slip from the changes in the resonant frequency of ultrasonic waves. In addition, Ikeda et al [7] have observed the contact surface between an elastic body and a rigid board by using a camera, and have proposed a method for estimating initial slip. However, since these detection methods depend on processing such as the embedding of the detector element inside the finger, and due to their complex structure, the problems of miniaturization and reduction of weight emerge again.…”

Section: Introductionmentioning

confidence: 99%

Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand

Teshigawara

Tsutsumi

Shimizu

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Tactile sensors for slip detection are essential for implementing human-like gripping in a robot hand. In previous studies, we proposed flexible, thin and lightweight slip detection sensors utilizing the characteristics of pressuresensitive conductive rubber. This was achieved by using the high-frequency vibration component generated in the process of slipping of the gripped object in order to distinguish between slipping of the object and changes in the normal force. In this paper, we design a slip detection sensor for a multi-fingered robot hand and examine the influence of noise caused by the operation of such a hand. Finally, we describe an experiment focusing on the adjustment of the gripping force of a multifingered robot hand equipped with the developed sensors.

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Section: Introductionmentioning

confidence: 99%

Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand

Teshigawara

Tsutsumi

Shimizu

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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“…Ikeda et al [81] and Ueda et al [82] proposed a method of grip-force control based on feedback of a visual "slip margin", determined from the deformation of an elastic surface (the object) against a flat transparent sensor. The slip margin (a custom index: 1 when the contact area is completely stuck, and 0 when the contact interface completely slips) is estimated from the eccentricity of the perimeter of the contact area, which is determined by processing an image captured from behind the transparent sensor surface.…”

Section: Incipient Slip Detectionmentioning

confidence: 99%

Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

et al. 2018

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“…As an example of the first, Ikeda et al [7] use a camera to detect deformations of a fingertip. Problems due to occlusion were avoided by gripping a transparent object, limiting the possible applications.…”

Section: Related Workmentioning

confidence: 99%

Stabilizing novel objects by learning to predict tactile slip

Veiga

Hoof

Peters³

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-During grasping and other in-hand manipulation tasks maintaining a stable grip on the object is crucial for the task's outcome. Inherently connected to grip stability is the concept of slip. Slip occurs when the contact between the fingertip and the object is partially lost, resulting in sudden undesired changes to the objects state. While several approaches for slip detection have been proposed in the literature, they frequently rely on previous knowledge of the manipulated object. This previous knowledge may be unavailable, seeing that robots operating in real-world scenarios often must interact with previously unseen objects.In our work we explore the generalization capabilities of well known supervised learning methods, using random forest classifiers to create generalizable slip predictors. We utilize these classifiers in the feedback loop of an object stabilization controller. We show that the controller can successfully stabilize previously unknown objects by predicting and counteracting slip events.

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Grip force codtrol for an elastic finger using vision-based incipient slip feedback

Cited by 50 publications

References 12 publications

Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand

Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand

Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

Stabilizing novel objects by learning to predict tactile slip

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