In this paper, we propose an approach to detect incipient slip, i.e. predict slip, by using a high-resolution vision-based tactile sensor, GelSlim. The sensor dynamically captures the tactile imprints of the contact object and their changes with a soft gel pad. The method assumes the object is mostly rigid and treats the motion of object's imprint on sensor surface as a 2D rigid-body motion. We use the deviation of the true motion field from that of a 2D planar rigid transformation as a measure of slip. The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip). The method can detect both translational and rotational incipient slip without any prior knowledge of the object at 24 Hz. We test the method on 10 objects 240 times and achieve 86.25 detection accuracy. We further show how the slip feedback can be used to monitor the gripping force to avoid slip with a closed-loop bottle-cap screwing and unscrewing experiment with incipient slip detection feedback. The method was demonstrated to be useful for the robot to apply proper gripping force and stop screwing at the right point before breaking objects. The method can be applied to many manipulation tasks in both structured and unstructured environments.
Since relative motion between two rough surfaces results in high-frequency signals, many tactile sensors have been built to detect slip by sensing vibration @cite_4 @cite_9 . Sch "o pfer . @cite_7 proposed a incipient slip detection method by using piezo-resistive tactile sensor, which could measure the normal force at 83 Hz within 80 mm @math 80 mm sensing area. The time-sequence force data was reprocessed by using a Fourier transform first and then fed into a neural network to detect slip. Sch "u rmann . @cite_15 further improved the frame rate up to 1.9k Hz and successfully implemented grasp force control with slip detection feedback. The method is able to detect slip with unknown objects, however, the configuration of the tactile sensor is hard to directly to grasp objects with. Romano . @cite_12 used the vibration signals from an accelerometer (running at 3 kHz) and pressure cells (running at 24.4 Hz) embedded in the palm of the gripper to detect slip. They also performed a similar grasp force control experiment with slip feedback control at around 1K Hz with a PR2 robot.
@cite_4 @cite_7 @cite_9 @cite_15 @cite_12
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The detection of incipient slip is an important cornerstone in tactile based grasping. In this paper, we present an approach to detect incipient slip using a fast piezo-resistive, yet static tactile sensor pad. Our approach renders special slip sensors obsolete and therefore enables static and dynamic sensing with one sensing mechanism. For the detection of the slip, a fast fourier transform is used to pre-process the data. In a subsequent step, a standard artificial neural net is trained on the data from the frequency domain to detect slippage, as well as to discriminate different surface textures. Slip detection helps to prevent robotic hands from dropping grasped objects and would thus enable complex object manipulation. Here we present a method of detecting slip with a biomimetic optical tactile sensor-the TacTip-that operates by measuring the positions of internal pins embedded in its compliant skin. We investigate whether local pin movement is a strong signal of slip. Accurate and robust discrimination between static and slipping objects is obtained with a support vector machine (accuracy 99.88 ). We then demonstrate performance on a task in which a slipping object must be caught. For fast reaction times, a modified TacTip is made for high-speed data collection. Performance of the slip detection method is then validated under several test conditions, including varying the speed at which slip onset occurs and using novel shaped objects. The proposed methods should apply to tactile sensors that can detect the local velocities of surface movement. The sensor and slip detection methods are also well-suited for integration onto robotic hands for deploying slip control under manipulation. In this paper, we report a new flexible capacitive tactile sensor array with the capability of measuring both normal and shear force distribution using polydimethylsiloxane (PDMS) as a base material. A tactile cell consists of two thick PDMS layers with embedded electrodes, an air gap, and a pillar structure. The pillar structure is formed at the center of each tactile cell between the air gap under a large bump. There are four capacitors in a cell to decompose the contact force into normal and shear components. Four capacitors are arranged in a square form. If a normal force is applied on the bump, the PDMS layer on the pillar structure is compressed, and the air gap between the top and bottom electrodes decreases, resulting in the increase in all four capacitances. If a shear force is applied, a torque is induced around the pillar. Therefore, the capacitance of the two capacitors increases, whereas that of the other two decreases. The bump and the pillar structure play a critical role to generate a torque for shear force measurement. The sensor has been realized in an 8 x 8 array of unit sensors, and each unit sensor responds to normal and shear stresses in all three axes, respectively. Measurement of a single sensor shows that the full-scale range of detectable force is about 10 mN, which corresponds to 131 kPa in three directions. The sensitivities of a cell measured with a current setup are 2.5 mN, 3.0 mN, and 2.9 mN for the x-, y-, and z-directions, respectively. Normal and shear force images are also captured from a 4 x 4 array of the fabricated sensor. Distinctive characteristic patterns appear when a shear force is applied to the sensor. 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.