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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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GradTac: Spatio-Temporal Gradient Based Tactile Sensing.

Kanishka Ganguly1, Pavan Mantripragada1, Chethan M Parameshwara1

  • 1Perception and Robotics Group, University of Maryland, College Park, MD, United States.

Frontiers in Robotics and AI
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed GradTac, a novel algorithm for fluid-based tactile sensors in robotics. This method enhances signal-to-noise ratio (SNR) and improves tactile data processing for more accurate robotic interaction.

Keywords:
active-perceptionbio-inspiredevent-basedtactile-eventstactile-sensing

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Area of Science:

  • Robotics
  • Sensor Technology
  • Computational Neuroscience

Background:

  • Fluid-based tactile sensors offer a balance of anthropomorphic design and measurement accuracy for robotics.
  • Existing fluid-based sensors suffer from low signal-to-noise ratio (SNR) due to inherent data "damping" that is difficult to model.
  • Neuromorphic principles, particularly event-based sensing, offer inspiration for novel signal processing techniques.

Purpose of the Study:

  • To introduce a new algorithm, GradTac, for processing data from fluid-based tactile sensors.
  • To enhance the robustness and accuracy of tactile sensing in robotic applications by mitigating noise.
  • To enable more precise tracking of tactile information and object interactions.

Main Methods:

  • Developed GradTac, an algorithm that transforms discrete tactile sensor data into spatio-temporal surfaces.
  • Utilized spatio-temporal gradient representation inspired by neuromorphic event-based sensing.
  • Applied the algorithm to data from BioTac SP sensors on the Shadow Dexterous Hand.

Main Results:

  • GradTac processing demonstrates robustness against noise inherent in fluid-based sensors.
  • The algorithm enables accurate tracking of touch regions and tactile contours.
  • Successfully demonstrated efficacy in real-world experiments for force measurement, slip detection, and edge tracking.

Conclusions:

  • The spatio-temporal domain processing via GradTac significantly improves tactile data quality and reliability.
  • GradTac offers a robust solution for enhancing the performance of fluid-based tactile sensors in robotics.
  • An accompanying task-agnostic dataset for BioTac SP sensors is released to foster further research and benchmarking.