A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots

• URL: http://arxiv.org/abs/2404.12627v1
• Date: Fri, 19 Apr 2024 05:00:25 GMT
• Title: A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots
• Authors: Eric Vincent Galeta, Ayman A. Nada, Sabah M. Ahmed, Victor Parque, Haitham El-Hussieny,
• Abstract summary: The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics. Traditional rigid sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. This study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors.
• Score: 0.3495246564946556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics, where the limitations of traditional rigid sensors become evident. Encoders, piezoresistive, and potentiometer sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. To overcome these hurdles, our study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors. This sensor, designed to flawlessly integrate with the robot's structure, utilizes a resistive material that adjusts its resistance in response to the robot's movements and deformations. This adjustment facilitates the capture of multidimensional force measurements across the soft sensor layers. A deep Convolutional Neural Network (CNN) is employed to decode the sensor signals, enabling precise estimation of the robot's shape configuration based on the detailed data from the e-textile sensor. Our research investigates the efficacy of this e-textile sensor in determining the curvature parameters of soft continuum robots. The findings are encouraging, showing that the soft e-textile sensor not only matches but potentially exceeds the capabilities of traditional rigid sensors in terms of shape sensing and estimation. This advancement significantly boosts the safety and efficiency of robotic navigation systems.

Related papers

• Digitizing Touch with an Artificial Multimodal Fingertip [51.7029315337739]
  Humans and robots both benefit from using touch to perceive and interact with the surrounding environment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities.
  arXiv  Detail & Related papers  (2024-11-04T18:38:50Z)
• Recognizing Complex Gestures on Minimalistic Knitted Sensors: Toward Real-World Interactive Systems [0.13048920509133805]
  Our digitally-knitted capacitive active sensors can be manufactured at scale with little human intervention. This work advances the capabilities of such sensors by creating the foundation for an interactive gesture recognition system.
  arXiv  Detail & Related papers  (2023-03-18T04:57:46Z)
• Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
  Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. vision-based tactile sensors are being widely used for various robotic perception and control tasks. We present a method for interactive perception using vision-based tactile sensors for a part mating task.
  arXiv  Detail & Related papers  (2023-03-10T16:27:37Z)
• Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network [7.346580429118843]
  We present a learning-based method to detect slip using barometric tactile sensors. We train a temporal convolution neural network to detect slip, achieving high detection accuracies. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.
  arXiv  Detail & Related papers  (2022-02-19T08:21:56Z)
• Bayesian Imitation Learning for End-to-End Mobile Manipulation [80.47771322489422]
  Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities. We show that using the Variational Information Bottleneck to regularize convolutional neural networks improves generalization to held-out domains. We demonstrate that our method is able to help close the sim-to-real gap and successfully fuse RGB and depth modalities.
  arXiv  Detail & Related papers  (2022-02-15T17:38:30Z)
• ReSkin: versatile, replaceable, lasting tactile skins [28.348982687106883]
  ReSkin is a tactile soft sensor that leverages machine learning and magnetic sensing to offer a low-cost, diverse and compact solution for long-term use. Our self-supervised learning algorithm enables finer performance enhancement with small, inexpensive data collection procedures.
  arXiv  Detail & Related papers  (2021-10-29T20:21:37Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors [7.35805050004643]
  We present a learning-based method to detect slip using barometric tactile sensors. We are able to achieve slip detection accuracies of greater than 91%. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for many complex manipulation tasks.
  arXiv  Detail & Related papers  (2021-03-24T19:29:03Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)
• DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation [16.54834671357377]
  General purpose in-hand manipulation remains one of the unsolved challenges of robotics. We introduce DIGIT, an inexpensive, compact, and high-resolution tactile sensor geared towards in-hand manipulation.
  arXiv  Detail & Related papers  (2020-05-29T17:07:54Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.