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DTSTAMP:20231027T022957Z
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DTSTART;TZID=America/Chicago:20231026T193000
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DESCRIPTION:Abstract:\n\nThe field of soft robotics has been experiencing r
 apid growth\, with researchers and engineers showing increasing interest d
 ue to the unique capabilities of these robots. Soft robots\, characterized
  by their soft bodies and flexible structures\, have demonstrated great po
 tential in addressing real-world challenges across various domains\, inclu
 ding medical applications. Effective modeling and control are vital for fu
 lly harnessing the potential of soft robots\, particularly in applications
  involving human interaction. However\, creating models for soft robots ma
 de of soft materials\, diverse shapes\, and actuators poses significant ch
 allenges. Moreover\, accurate fault detection in soft robots necessitates 
 precise modeling. This paper introduces a novel machine learning approach\
 , termed deterministic learning\, for training a soft robot model using a 
 radial basis function neural network. The research explores the fault dete
 ction process by simulating four distinct faults that could impair system 
 control performance\, such as diminishing tracking accuracy or inducing in
 stability. Furthermore\, the paper examines the identification of fault oc
 currences during the operation of soft robots.\n\nSpeaker(s): Emadodin Jan
 daghi\, \n\nAgenda: \n7:30pm | Begin event\, introductions\n7:35am | Prese
 ntation: Motion Dynamics Modeling and Fault Detection of a Soft Trunk Robo
 t\n8:00pm | Q&A\n\nVirtual: https://events.vtools.ieee.org/m/379670
LOCATION:Virtual: https://events.vtools.ieee.org/m/379670
ORGANIZER:chang.duan@gmail.com
SEQUENCE:11
SUMMARY:IEEE CSS Houston Webinar: Motion Dynamics Modeling and Fault Detect
 ion of a Soft Trunk Robot
URL;VALUE=URI:https://events.vtools.ieee.org/m/379670
X-ALT-DESC:Description: <br /><p>Abstract:</p>\n<p>The field of soft roboti
 cs has been experiencing rapid growth\, with researchers and engineers sho
 wing increasing interest due to the unique capabilities of these robots. S
 oft robots\, characterized by their soft bodies and flexible structures\, 
 have demonstrated great potential in addressing real-world challenges acro
 ss various domains\, including medical applications. Effective modeling an
 d control are vital for fully harnessing the potential of soft robots\, pa
 rticularly in applications involving human interaction. However\, creating
  models for soft robots made of soft materials\, diverse shapes\, and actu
 ators poses significant challenges. Moreover\, accurate fault detection in
  soft robots necessitates precise modeling. This paper introduces a novel 
 machine learning approach\, termed deterministic learning\, for training a
  soft robot model using a radial basis function neural network. The resear
 ch explores the fault detection process by simulating four distinct faults
  that could impair system control performance\, such as diminishing tracki
 ng accuracy or inducing instability. Furthermore\, the paper examines the 
 identification of fault occurrences during the operation of soft robots.<b
 r /><br /></p><br /><br />Agenda: <br /><p><strong>7:30pm | </strong>Begin
  event\, introductions<br /><strong>7:35am | </strong>Presentation: &nbsp\
 ;Motion Dynamics Modeling and Fault Detection of a Soft Trunk Robot<br /><
 strong>8:00pm | </strong>Q&amp\;A<br /><br /></p>
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