Manipulation of Unknown Objects to Improve the Grasp Quality Using Tactile Information

Montano, Andres; Suárez, Raúl

doi:10.3390/s18051412

Cited by 21 publications

(17 citation statements)

References 43 publications

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“…Aplicación de la estrategia deseada. Conocido el vector Q k , se calculan unos nuevos puntos de contacto, C ik+1 * , de forma que al mover los dedos hacia estas posiciones se produzca el efecto deseado (Montaño y Suárez, 2018).…”

Section: Capacidad De Manipulación Diestraunclassified

Aspectos técnicos de los robots co-trabajadores con operadores humanos

Suárez

Rosell

2019

DyO

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La robótica colaborativa, en la que los robots comparten el espacio de trabajo con los humanos e interactúan con ellos, toma un papel cada vez más relevante. En este contexto, el artículo presenta desarrollos realizados en diferentes aspectos técnicos necesarios para que estos robots funcionen adecuadamente y cumplan las expectativas en cuanto a su integración. En particular, se enfatizan las capacidades de planificación autónoma de tareas y movimientos, de manipulación diestra, de manipulación bimanual y de interacción con los humanos. Todos los desarrollos presentados han sido validados experimentalmente, y en el artículo se incluyen ejemplos ilustrativos de los experimentos realizados.Palabras clave: Robótica, manipuladores móviles, robots co-trabajadores, cooperación humano-robot.

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Section: Capacidad De Manipulación Diestraunclassified

Aspectos técnicos de los robots co-trabajadores con operadores humanos

Suárez

Rosell

2019

DyO

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“…Tactile sensors have many benefits in various robotics applications; in particular, they have proved useful for challenging manipulation tasks. (1) They provide information about the external environment, such as the contact area, temperature, vibration, and pressure, as well as providing sliding detection. (2) Typically, tactile sensors are more sensitive than other sensors, such as visual or acoustic sensors.…”

Section: Introductionmentioning

confidence: 99%

Biomimetic Hybrid Tactile Sensor with Ridged Structure That Mimics Human Fingerprints to Acquire Surface Texture Information

Kim¹,

Choi²,

Moon³

et al. 2020

Sensors and Materials

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For robots manipulating objects, more sophisticated manipulations would be possible if surface information about the object were available. This information becomes even more critical when the manipulation is to be carried out in an unstructured environment. Tactile sensing has been the norm for collecting such surface information. In this paper, a sensor for recognizing surface texture was developed, in which a ridged structure on its surface, which generates vibrations when moved along the surface of the object of interest, is used. The pattern used for the ridged structure combines two distinct patterns that are found in different parts of a human fingerprint. In this work, sensors with two distinct fingerprint-like structures were first developed and tested to acquire surface texture information. Texture features were also extracted and identified with these sensors. Then a hybrid pattern was created by combining the two previously tested distinct structures. Finally, a series of experiments was conducted to compare the performance of sensors with a ridged structure having a single fingerprint pattern and a hybrid sensor whose ridged structure consists of two fingerprint patterns. In the experiments, the sensors collected surface information from nine different textured samples. Through these experiments, it was confirmed that the sensor using the hybrid pattern has better surface identification capabilities than the sensors using a single pattern.

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“…The interest in the application of tactile sensors is consequently very desirable for humanoids [1] and its use is growing in the field of robotic manipulation [2]. These sensors can provide a wide range of information like surface features, material properties and acting forces during the contact experienced by the sensor, which are potentially useful for dealing with manipulation tasks [3].…”

Section: Introductionmentioning

confidence: 99%

Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection

Zapata-Impata

Gil

Torres

2019

Sensors

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Robotic manipulators have to constantly deal with the complex task of detecting whether a grasp is stable or, in contrast, whether the grasped object is slipping. Recognising the type of slippage—translational, rotational—and its direction is more challenging than detecting only stability, but is simultaneously of greater use as regards correcting the aforementioned grasping issues. In this work, we propose a learning methodology for detecting the direction of a slip (seven categories) using spatio-temporal tactile features learnt from one tactile sensor. Tactile readings are, therefore, pre-processed and fed to a ConvLSTM that learns to detect these directions with just 50 ms of data. We have extensively evaluated the performance of the system and have achieved relatively high results at the detection of the direction of slip on unseen objects with familiar properties (82.56% accuracy).

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Manipulation of Unknown Objects to Improve the Grasp Quality Using Tactile Information

Cited by 21 publications

References 43 publications

Aspectos técnicos de los robots co-trabajadores con operadores humanos

Aspectos técnicos de los robots co-trabajadores con operadores humanos

Biomimetic Hybrid Tactile Sensor with Ridged Structure That Mimics Human Fingerprints to Acquire Surface Texture Information

Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection

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