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

• Published in: IEEE sensors journal Vol. 23; no. 3; pp. 1962 - 1971
• Main Authors: Al, Gorkem Anil, Martinez-Hernandez, Uriel
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Artificial neural networks; Circuit boards; Contact pressure; Convolutional neural network (CNN)-based contact recognition; Force; Gyroscopes; Impulse response; Inertial platforms; Inertial sensing devices; multimodal tactile sensor; Position (location); Printed circuits; Robot arms; Robot control; Robot sensing systems; Robotics; Robots; Sandwich structures; Sensors; Tactile discrimination; Tactile sensors; Tactile sensors (robotics)
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3229227

In this article, we present a low-cost multimodal tactile sensor capable of providing accelerometer, gyroscope, and pressure data using a seven-axis chip as a sensing element. This approach reduces the complexity of the tactile sensor design and collection of multimodal data. The tactile device is composed of a top layer (a printed circuit board (PCB) and a sensing element), a middle layer (soft rubber material), and a bottom layer (plastic base) forming a sandwich structure. This approach allows the measurement of multimodal data when force is applied to 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 analyzed with accelerometer, gyroscope, and pressure data systematically collected from the sensor. Second, the 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.