Edge detection in tactile images

Muthukrishnan, C.; Smith, D.N.; Myers, David Jaz; Rebman, Jack; Koivo, A.J.

doi:10.1109/robot.1987.1088067

Cited by 29 publications

(15 citation statements)

References 10 publications

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“…Another approach to extract the orientation of an object edge w.r.t. the sensor surface is to apply a Hough transform to fit a line to the sensor image [13,2] and subsequently compute the orientation of this line w.r.t. the sensor's coordinate frame.…”

Section: State Of the Artmentioning

confidence: 99%

A Control Framework for Tactile Servoing

Li¹,

Schürmann²,

Haschke³

et al. 2013

Robotics: Science and Systems IX

117

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Abstract-The advent of sensor arrays providing tactile feedback with high spatial and temporal resolution asks for new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks.In this paper, we introduce a control framework to realize a whole set of tactile servoing tasks, i.e. control tasks, which intend to realize a specific tactile interaction pattern. This includes such simple tasks like tracking a touched object, maintaining both contact location and contact force, as well as more elaborate tasks like tracking an object's pose or tactile object exploration.Exploiting methods known from image processing, we introduce robust feature extraction methods to estimate the 2D contact position, the contact force, and the orientation of an object edge being in contact to the sensor. The flexible control framework allows to adapt the PID-type controller to a large range of different tasks by specification of a projection matrix toggling certain control components on and off. We demonstrate and evaluate the capabilities of the proposed control framework in a series of experiments employing a 16×16 tactile sensor array attached to a Kuka LWR as a "large" fingertip.

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Section: State Of the Artmentioning

confidence: 99%

A Control Framework for Tactile Servoing

Li¹,

Schürmann²,

Haschke³

et al. 2013

Robotics: Science and Systems IX

117

View full text Add to dashboard Cite

show abstract

“…A second challenge is the subsequent computer processing of the transduced data to obtain useful information (tactile data processing). Approaches have included vision-based processing [6,26], neural networks [5], and solid mechanics modeling [9,10], but a rigorous comparison of methods is lacking. A final challenge is determining the most effective means for relaying the tactile information to a human user (tactile display).…”

mentioning

confidence: 99%

Human vs. robotic tactile sensing: Detecting lumps in soft tissue

Gwilliam

Pezzementi

Jantho

et al. 2010

2010 IEEE Haptics Symposium

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Humans can localize lumps in soft tissue using the distributed tactile feedback and processing afforded by the fingers and brain. This task becomes extremely difficult when the fingers are not in direct contact with the tissue, such as in laparoscopic or robot-assisted procedures. Tactile sensors have been proposed to characterize and detect lumps in robot-assisted palpation. In this work, we compare the performance of a capacitive tactile sensor with that of the human finger. We evaluate the response of the sensor as it pertains to robot-assisted palpation and compare the sensor performance to that of human subjects performing an equivalent task on the same set of artificial tissue models. Furthermore, we investigate the effects of various tissue parameters (lump size, lump depth, and surrounding tissue stiffness) on the performance of both the human finger and the tactile sensor. Using signal detection theory for determining tactile sensor lump detection thresholds, the tactile sensor outperforms the human finger in a palpation task.

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“…In the case of group 3 shown in Fig. 4, the sensor is moved to the upper direction when the signal unit is like that shown in (2), to the right direction when the signal unit is like that shown in (5), to the lower direction when the signal unit is like that shown in (4), and to the left direction when the signal unit is like (a) Considered case.…”

Section: Algorithmmentioning

confidence: 99%

“…A new Hough Transform algorithm is applied to the edge points to determine the line parameters of the detected edges. Muthukrishnan et al extended some of the existing edge detection methods in a vision system that can be used for tactile images with modifications if necessary [5]. For large objects, a straight line extraction method was employed to obtain the polygonal approximation of the shape of the object.…”

Section: Introductionmentioning

confidence: 99%

An enhanced edge tracking method using a low resolution tactile sensor

Phung

Ihn

Koo

et al. 2010

Int. J. Control Autom. Syst.

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Edge detection plays an important role in object recognition and exploration. In this paper, we propose an efficient tracking algorithm, which uses a coarse resolution tactile sensor set, for edge detection of a 2D shape object. Although many researchers have often used a tactile sensor with high resolution such as a 10x10 or higher for edge detection, in this research, we use a 2x2 tactile sensor to track the edges of an object. Using this type of low resolution sensor, we can reduce the manufacturing cost of the sensor and simplify the calculation and control process. In our algorithm, we only need to control the sensor to make it move along the vertical axis, Oy, or the horizontal axis, Ox. The sensor is moved along a suitable direction according to the sensing signals receiving from it. A set of simulations for both convex and concave shapes has been done to verify the algorithm. In addition, methods to increase the accuracy of the algorithm are also discussed.

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Edge detection in tactile images

Cited by 29 publications

References 10 publications

A Control Framework for Tactile Servoing

A Control Framework for Tactile Servoing

Human vs. robotic tactile sensing: Detecting lumps in soft tissue

An enhanced edge tracking method using a low resolution tactile sensor

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