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Related Concept Videos

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Sensory impulses related to touch, pressure, vibration, and proprioception from various body parts, such as the limbs, trunk, neck, and posterior head, travel to the cerebral cortex through the posterior column-medial lemniscus pathway. The pathway’s name derives from the two white-matter tracts that convey the impulses: the spinal cord's posterior column and the brainstem's medial lemniscus. First-order sensory neurons extend their axons into the spinal cord, forming the...
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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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Proprioceptive wake classification by a body with a passive tail.

Colin Rodwell1, Beau Pollard2, Phanindra Tallapragada1

  • 1Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, United States of America.

Bioinspiration & Biomimetics
|April 14, 2023
PubMed
Summary

Marine animals use passive tails to sense their environment. Machine learning deciphers this flow information, improving robotic swimmer navigation and efficiency.

Keywords:
machine learningpassive tailproprioceptivewake classification

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Area of Science:

  • Fluid dynamics
  • Robotics
  • Bio-inspired engineering

Background:

  • Marine animals utilize non-visual sensors like lateral lines and whiskers for flow detection.
  • Proprioceptive sensing through body kinematics, specifically fin or tail movements, is also employed by some fish.
  • Understanding these biological mechanisms can enhance artificial robotic swimmers.

Purpose of the Study:

  • To investigate if the kinematics of a passive tail encode ambient flow information.
  • To determine if machine learning can decipher this encoded flow data.
  • To assess the impact of passive tails on the sensing capabilities of robotic swimmers.

Main Methods:

  • Experimental data collection of a hydrofoil with a passive tail in an upstream oscillating body's wake.
  • Analysis of angular velocity data from the downstream body.
  • Application of convolutional neural networks (CNNs) for wake classification.

Main Results:

  • Kinematic data from a body with a passive tail effectively encodes ambient flow information.
  • CNNs successfully classified wakes using kinematic data from the body with a tail.
  • The presence of a tail improved wake classification accuracy compared to a body without a tail, even using only main body kinematics.

Conclusions:

  • Passive tails provide valuable hydrodynamic sensing capabilities by modulating body response.
  • This modulation is decipherable through machine learning, enhancing flow detection.
  • Findings support the development of bio-inspired robotic swimmers with improved autonomous navigation and sensing.