Evaluation of a vision-based tactile sensor

Kamiyama, Kazuto; Kajimoto, Hiroyuki; Kawakami, Naoki; Tachi, Susumu

doi:10.1109/robot.2004.1308043

Cited by 91 publications

(45 citation statements)

References 9 publications

(8 reference statements)

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“…We evaluate the resolution of the force magnitude along the three directions: x, y, and z. Each directional force, fx, fy, and fz, is represented by a one-dimensional force vector and the summations of the force denoted by Fx, Fy, and Fz, respectively, are calculated as shown in equation (6). x…”

Section: B Resolution Of Magnitudementioning

confidence: 99%

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Measurement of force vector field of robotic finger using vision-based haptic sensor

Sato

Kamiyama

Nii

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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It is expected that the use of haptic sensors to measure the magnitude, direction, and distribution of a force will enable a robotic hand to perform dexterous manipulations. Therefore, we have developed a new type of finger-shaped haptic sensor that can measure a three-dimensional force vector field over a contact surface. The sensor consists of a transparent elastic body, two layers of internal blue and red markers, and a CCD camera to capture the movements of the markers. Using the elastic theory, we can calculate the force vector field from the captured movements of the markers. However, the elastic theory cannot be applied to the finger-shaped sensor because of its complicated shape. Therefore, we use actual measurements for the calibration in order to develop a prototype of the sensor. Then, we evaluate its basic performance. The result shows that the sensor performance can be improved further, and the sensor can be successfully used in a robotic hand.

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Section: B Resolution Of Magnitudementioning

confidence: 99%

“…The technology used by this sensor is the same as that used in the vision-based haptic sensor developed by us [6]. This sensor has a simple structure; hence, it satisfies the second and third requirements, i.e., having small size and high resolution.…”

Section: Introductionmentioning

confidence: 99%

Measurement of force vector field of robotic finger using vision-based haptic sensor

Sato

Kamiyama

Nii

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

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“…Combining the vision with force/torque or tactile data has been tried by many researchers in recent years [15][16][17][18][19], mainly for task-orientated (i.e. high-level) operations of robotic manipulators such as opening doors [18] or grasping a stationary object [15,19].…”

Section: Introductionmentioning

confidence: 99%

Vision and force/torque integration for realtime estimation of fast-moving object under intermittent contacts

2016

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This paper considers the fast and accurate estimation of motion variables of a rigid body object whose movement occurs from intermittent contacts with coordinating manipulators in nonprehensile manipulation tasks. The estimator operates under multiple sensory data including visual, joint torque, joint position and/or tactile measurements which are combined at the lower level to compensate for the latency and the slow sampling of the visual data. The estimator is real-time in the sense that it provides the motion data of the target object at the same fast sample rate of the servo controller without delay. The basic formulation is the multi-rate Kalman filter with the contact force vector as its process input, and the visual observation as its measurement output signal which is the down-sampled and delayed version of the configuration of the target object. Experimental tests are conducted for the case of planar object manipulation as well as the non-centroidal rotation under gravity using a robotic hand, and their results are presented to demonstrate the validity of the proposed estimation scheme.

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“…Humans can grasp and manipulate objects mostly without looking, guided only by haptics. But to date, there is no technical equivalent to human skin, in spite of research on different sensor designs [1], [2], [3], [4], [5], [6], [7]. So we still lack the "CCDChip" for tactile sensing.…”

Section: Introductionmentioning

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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Evaluation of a vision-based tactile sensor

Cited by 91 publications

References 9 publications

Measurement of force vector field of robotic finger using vision-based haptic sensor

Measurement of force vector field of robotic finger using vision-based haptic sensor

Vision and force/torque integration for realtime estimation of fast-moving object under intermittent contacts

Acquisition and Application of a Tactile Database

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