A single-chip multimodal tactile sensor for a robotic gripper

Al, Gorkem Anil; Oussallam, Bilal El Achab; Martinez-Hernandez, Uriel

doi:10.1109/icar53236.2021.9659408

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Our multimodal sensor is capable of providing tactile data from a large surface using one chip or sensing element. We have shown in [40] that the tactile sensor can be used to cover larger areas with a robotic finger forming a multimodal sensing area of 65 mm x 34.5 mm. The multiple data available in these tactile sensors placed in the robotic finger have been used for grasping objects and handover applications in [41].…”

Section: Discussionmentioning

confidence: 99%

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

Martinez-Hernandez

2023

IEEE Sensors J.

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In this paper, we present a low-cost multimodal tactile sensor capable of providing accelerometer, gyroscope and pressure data using a 7-axis chip as sensing element. This approach reduces the complexity of tactile sensor design and collection of multimodal data. The tactile device is composed of a top layer (a printed circuit board and sensing element), a middle layer (soft rubber material), and bottom layer (plastic base) forming a sandwich structure. This approach allows the measurement of multimodal data when force is applied on different parts of the top layer of the sensor. The multimodal tactile sensor is validated with analyses and experiments in both offline and real-time. First, the spatial impulse response and sensitivity of the sensor are analysed with accelerometer, gyroscope and pressure data systematically collected from the sensor. Second, estimation of contact location from a range of sensor positions and force values is evaluated using accelerometer and gyroscope data together with a Convolutional Neural Network (CNN) method. Third, the estimation of contact location is used to control the position of a robot arm. The results show that the proposed multimodal tactile sensor has the potential for robotic applications such as tactile perception for robot control, human-robot interaction and object exploration.

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Section: Discussionmentioning

confidence: 99%

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

Martinez-Hernandez

2023

IEEE Sensors J.

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“…The chip ICM-20789 has been used by previous works for tactile sensor design [14]- [16], and the tactile sensor presented in [15] has been used for the design of a robotic finger and employed in different applications [17], [18]. The tactile sensor in the DiGeTac unit is designed following the fabrication steps described in [15].…”

Section: A Sensing Module Designmentioning

confidence: 99%

DiGeTac Unit for Multimodal Communication in Human–Robot Interaction

Al,

Martinez-Hernandez

2024

IEEE Sens. Lett.

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Sensing technologies are required to enhance the safety and interaction between humans and robots in the concept of Human-Robot Collaboration (HRC). In this work, a multimodal sensing unit, DiGeTac, composed of two layers with distance, gesture and tactile elements is presented. Distance and gesture elements are placed on the top layer of the DiGeTac, while the tactile element is placed on the bottom layer. The sensing unit DiGeTac and its data acquisition board together are designed as a sensing module. The sensor performance for detection of distance, hand gestures and touch perception are analysed with offline and real-time experiments. The sensing module recognises hand gestures with 96% accuracy and contact location with 88% accuracy using Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), respectively. The sensing capabilities of the proposed module are validated in real-time with a collaborative task using the Universal Robot arm. The sensing capabilities of the proposed DiGeTac, as demonstrated in real-time collaborative tasks with the robot arm, highlight its potential to enhance interaction in HRC settings by effectively recognizing and responding to human commands.

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“…3C. When pressure output from any of three sensors in the tactile finger exceeds the predefined threshold value of 500, the gripper is controlled to perform a grasping motion for posterior handover actions in collaborative tasks [16]. A linear filter is also used on the gyroscope and pressure signals of the tactile sensor to ensure robust grasping and handover actions.…”

Section: Tactile Finger For Grasping and Handovermentioning

confidence: 99%

“…This sensing modality allows robots to interact with objects and humans safely. Handover actions have been studied with a variety of tactile technologies and data processing methods in assembly tasks and human-robot interaction scenarios [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Multimodal sensor-based human-robot collaboration in assembly tasks

Male

Shabani

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This work presents a framework for Human-Robot Collaboration (HRC) in assembly tasks that uses multimodal sensors, perception and control methods. First, vision sensing is employed for user identification to determine the collaborative task to be performed. Second, assembly actions and hand gestures are recognised using wearable inertial measurement units (IMUs) and convolutional neural networks (CNN) to identify when robot collaboration is needed and bring the next object to the user for assembly. If collaboration is not required, then the robot performs a solo task. Third, the robot arm uses time domain features from tactile sensors to detect when an object has been touched and grasped for handover actions in the assembly process. These multimodal sensors and computational modules are integrated in a layered control architecture for HRC collaborative assembly tasks. The proposed framework is validated in real-time using a Universal Robot arm (UR3) to collaborate with humans for assembling two types of objects 1) a box and 2) a small chair, and to work on a solo task of moving a stack of Lego blocks when collaboration with the user is not needed. The experiments show that the robot is capable of sensing and perceiving the state of the surrounding environment using multimodal sensors and computational methods to act and collaborate with humans to complete assembly tasks successfully.

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A single-chip multimodal tactile sensor for a robotic gripper

Cited by 4 publications

References 16 publications

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

DiGeTac Unit for Multimodal Communication in Human–Robot Interaction

Multimodal sensor-based human-robot collaboration in assembly tasks

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