Control of contact via tactile sensing

Zhang, H.; Chen, N.N.

doi:10.1109/70.880799

Cited by 69 publications

(54 citation statements)

References 22 publications

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“…al [24] extended the work of Chen [4,3] and proposed a first tactile servoing framework. They draw on elasticity theory to formulate a tactile sensor model and the inverse model.…”

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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“…al [24] extended the work of Chen [4,3] and proposed a first tactile servoing framework. They draw on elasticity theory to formulate a tactile sensor model and the inverse model.…”

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

“…The interaction matrix selects how a robot's pose is adjusted based on the feature error. Since the equations 4, 3 relate the error in feature space with an increment of an end-effector pose in the Cartesian space, a resulting mapping can be refereed as an inverse tactile Jacobian matrix J −1 [6]. Therefore, the tactile feature transformation matrix is referred as J −1 .…”

Section: Contact Frame Controllermentioning

confidence: 99%

ZMP Features for Touch Driven Robot Control via Tactile Servo

Kappassov

Ramón

Perdereau

2017

Springer Proceedings in Advanced Robotics

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Abstract. In most robotic applications, tactile sensors are modeled as rigid matrices of adjacent pressure sensing elements so that the geometric deformations of their surfaces are neglected. This paper proposes to define these deformations as rotational and translational compliances that are later used as new tactile features for tactile servoing tasks. In fact, a novel two-layered inverse tactile Jacobian matrix is developed in order to map errors of these features into cartesian errors required for touch-driven exploration and manipulation robotic tasks. The performance of this new tactile servoing approach is demonstrated in several real experiments with a 6x14 tactile array mounted on a 7-dof robotic manipulator.

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“…Several papers deal with the shape reconstruction from tactile images [17], [18], [19]. Zhang and Chen use tactile sensing to control a robot trajectory [20], while Melchiori detects the slippage of objects via tactile sensing [21]. Once again, every new algorithm working on tactile data has to prove its performance in an experiment.…”

Section: A Related Workmentioning

confidence: 99%

Acquisition and Application of a Tactile Database

Schöpfer

Ritter

Heidemann

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-We present a database of 2D pressure profile timeseries as a testbed for tactile object and surface recognition. The tactile database captures the surfaces of household and toy objects by moving a 2D pressure sensor mounted to an industrial robot arm around the objects using real-time trajectory calculation. Thus, it represents different "views" of the objects in a similar way as the well known Columbia Object Image Library (COIL) captures different views of an object by a camera. As a first application, objects in the database are classified using a neural network architecture.

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Control of contact via tactile sensing

Cited by 69 publications

References 22 publications

A Control Framework for Tactile Servoing

A Control Framework for Tactile Servoing

ZMP Features for Touch Driven Robot Control via Tactile Servo

Acquisition and Application of a Tactile Database

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