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Somatosensation

Somatosensation

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.

Sensory Functions of the Skin

Sensory Functions of the Skin

The skin is the largest organ of the human body and plays a crucial role in our sensory perception. It contains a vast network of sensory receptors that contribute to the skin's protective function by perceiving physical, biological, and environmental cues and generating relevant responses.
There are two main categories of receptors on the skin: capsulated and non-capsulated. The non-capsulated ones are mainly the pain receptors. The capsulated ones can be further categorized based on the...

Tactile and Chemical Senses

Tactile and Chemical Senses

Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.

Design Example: Resistive Touchscreen

Design Example: Resistive Touchscreen

A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...

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Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

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Tactile surface classification for limbed robots using a pressure sensitive robot skin.

Jacob J Shill¹, Emmanuel G Collins, Eric Coyle

¹Center for Intelligent Systems, Control, and Robotics (CISCOR), USA. Department of Mechanical Engineering, FAMU & FSU College of Engineering, Tallahassee, FL, USA.

Bioinspiration & Biomimetics

|February 3, 2015

Summary

This summary is machine-generated.

This study introduces a robot skin for terrain identification using pressure imaging. This method accurately classifies surfaces, even during dynamic robot movement, enhancing mobility.

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Area of Science:

Robotics
Sensor Technology
Artificial Intelligence

Background:

Robots require advanced sensing for navigation in complex environments.
Current terrain identification methods often lack robustness in dynamic conditions.

Purpose of the Study:

To develop a terrain identification system using high-resolution pressure imaging.
To evaluate the system's accuracy and adaptability for mobile robots.

Main Methods:

Utilizing a robot skin with a high-resolution pressure sensing array to capture pressure images.
Formulating terrain signatures from the magnitude frequency responses of these images.
Extending the methodology for dynamic scenarios and developing a correcting filter for sensor damage.

Main Results:

Achieved high classification accuracies for static and dynamic terrain identification.
Demonstrated accuracy independence from varying robot dynamics and gaits.
Showcased the ability to distinguish similarly textured surfaces due to high-resolution sensing.
The correcting filter extended the sensor array's operational lifespan by over 6x.

Conclusions:

The pressure imaging approach offers a viable method for robot terrain identification.
This technology can significantly improve stable and efficient mobility for autonomous field robots.
The system's robustness and adaptability make it suitable for real-world applications.