Biomimetic Design

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Biomimetic Design James Tacey, Aditi Shinde

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Biomimetic Design. James Tacey , Aditi Shinde. Biomimetic Systems. Neural Networks Biological Redundancy Self-Repair Artificial Intelligence. Neural Networks. Groups of connected nodes or neurons Capable of finding non-linear patterns Highly adaptable to new inputs stimuli - PowerPoint PPT Presentation

Transcript of Biomimetic Design

Page 1: Biomimetic  Design

Biomimetic Design

James Tacey, Aditi Shinde

Page 2: Biomimetic  Design

Biomimetic Systems Neural Networks Biological Redundancy Self-Repair Artificial Intelligence

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Neural Networks Groups of connected nodes or neurons Capable of finding non-linear patterns Highly adaptable to new inputs stimuli Excellent choice for control systems of

biomimetic locomotion

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Biological Redundancy Many animals have redundant systems Systems allow continued functionality after a

limb or other system is damaged Redundancy prevents single point-of-failure in

the system Adaptation to a robotics allows for more

longer and more reliable functionality

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Self-Repair Animals are capable of healing and repairing

damage done Repair algorithms and self-repairing materials Allows robot to become more durable and

capable of lasting in more harsh environments

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Artificial Intelligence Still in early stages of research Robots can be ‘taught’ very simple tasks Would allow robots to adapt Wide variety of application in robotics, ability

to ‘learn’ variety of tasks

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Artificial Muscles Shape Memory Effects Electroactive Polymers Chemical Muscles

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Shape Memory Effects Use of materials that ‘remember’ shapes Material is ‘taught’ a shape Temperature controlled ‘muscle’ Slow but more durable ‘muscle’ for robotic

applications

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Electroactive Polymers Polymers that change shape Electrically stimulated Highly pliable materials with a wide-variety of

applications

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Chemical Muscles Similar to biological muscles Non-combustible chemical reaction drives

cylinder Higher energy density than electricity

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Evolutionary Robotics Hardware and software co-evolution Use of genetic algorithms and neural networks Allows more adaptive design Requires less trial and error Effective for developing robotic gaits

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Robotic lobster

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Lamprey-based undulatory vehicle

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CWRU’s Robot V

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CWRU’s Cricket microrobot

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Standford’s iSprawl

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Fraunhofer Institute’s Scorpion

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Mesoscale robot quadruped

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Snake robot S7

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RoboTuna

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VCUUV

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Georgia Tech’s Entomopter

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Neural oscillator control network for Rodney

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Oct-1b

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Sony quadruped robot

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Koharo

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Random morphology robot

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Self-modeling robot

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Golem

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Genobots

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CCSLs Nonaped