A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing

Cirillo, Andrea; Cirillo, Pasquale; Maria, G. De; Natale, Ciro; Pirozzi, Salvatore

doi:10.1155/2017/1357061

Cited by 23 publications

(12 citation statements)

References 30 publications

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“…Human-robot collaboration has recently gained a lot of interest and received many contributions on both theoretical and practical aspects, including sensor development [82], design of robust and adaptive controllers [83,84], learning robots force-sensitive manipulation skills [85], human interfaces [86,87], and similar. Besides, some companies attempt to introduce collaborative robots in order for HRC to become more suited to enter manufacturing applications and production lines.…”

Section: Discussionmentioning

confidence: 99%

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Amin

Rezayati

Venn

et al. 2020

Sensors

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Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

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Section: Discussionmentioning

confidence: 99%

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Amin

Rezayati

Venn

et al. 2020

Sensors

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show abstract

“…In addition to interacting with inanimate objects and terrain, robots also need to physically interact with humans. Human robot interaction (HRI) applications span a wide range of different tasks including intuitive programming [192], putting on clothing [196], handing over objects [216], safe interaction with collaborative robots [217], [218] and shaving [193]. During these tasks, the robot need to ensure that the interaction forces are safe and adapt to the humans body.…”

Section: F Human Robot Interactionmentioning

confidence: 99%

A Review of Tactile Information: Perception and Action Through Touch

Kroemer

et al. 2020

IEEE Trans. Robot.

188

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Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data is however not limited to individual contacts and can be used to extract a wide range of information about the objects in the robots environment as well as the robots own actions during the interactions.In this paper, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The paper also includes an overview of different types of robot applications and the types of tactile information that they employ.The paper concludes with a discussion of tactile-based computational framework and future tactile applications which are still beyond current robot's capabilities.

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“…In addition, the simplified readout electronics has been investigated to some extent, leading to the extensive use of piezoresistive transduction mechanism in most of the commercially available tactile sensors [ 25 ]. However, hysteresis phenomena and crosstalk among the sensor elements result in insufficient sensitivity and repeatability, which limit their implementation in advanced human–robot interaction [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Flexible Robot Skin for Safe and Natural Human–Robot Collaboration

et al. 2018

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For industrial manufacturing, industrial robots are required to work together with human counterparts on certain special occasions, where human workers share their skills with robots. Intuitive human–robot interaction brings increasing safety challenges, which can be properly addressed by using sensor-based active control technology. In this article, we designed and fabricated a three-dimensional flexible robot skin made by the piezoresistive nanocomposite based on the need for enhancement of the security performance of the collaborative robot. The robot skin endowed the YuMi robot with a tactile perception like human skin. The developed sensing unit in the robot skin showed the one-to-one correspondence between force input and resistance output (percentage change in impedance) in the range of 0–6.5 N. Furthermore, the calibration result indicated that the developed sensing unit is capable of offering a maximum force sensitivity (percentage change in impedance per Newton force) of 18.83% N−1 when loaded with an external force of 6.5 N. The fabricated sensing unit showed good reproducibility after loading with cyclic force (0–5.5 N) under a frequency of 0.65 Hz for 3500 cycles. In addition, to suppress the bypass crosstalk in robot skin, we designed a readout circuit for sampling tactile data. Moreover, experiments were conducted to estimate the contact/collision force between the object and the robot in a real-time manner. The experiment results showed that the implemented robot skin can provide an efficient approach for natural and secure human–robot interaction.

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A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing

Cited by 23 publications

References 30 publications

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

A Review of Tactile Information: Perception and Action Through Touch

Development of Flexible Robot Skin for Safe and Natural Human–Robot Collaboration

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