IEEE CSS Houston Webinar: Motion Dynamics Modeling and Fault Detection of a Soft Trunk Robot

#Robotics #control #Machine #Learning #engineering
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Abstract:

The field of soft robotics has been experiencing rapid growth, with researchers and engineers showing increasing interest due to the unique capabilities of these robots. Soft robots, characterized by their soft bodies and flexible structures, have demonstrated great potential in addressing real-world challenges across various domains, including medical applications. Effective modeling and control are vital for fully harnessing the potential of soft robots, particularly in applications involving human interaction. However, creating models for soft robots made of soft materials, diverse shapes, and actuators 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 research explores the fault detection process by simulating four distinct faults that could impair system control performance, such as diminishing tracking accuracy or inducing instability. Furthermore, the paper examines the identification of fault occurrences during the operation of soft robots.


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  • Date: 26 Oct 2023
  • Time: 07:30 PM to 08:15 PM
  • All times are (UTC-05:00) Central Time (US & Canada)
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  • Starts 18 October 2023 12:58 PM
  • Ends 26 October 2023 07:30 PM
  • All times are (UTC-05:00) Central Time (US & Canada)
  • No Admission Charge

  Speakers

Emadodin Jandaghi

Biography:

Emadodin Jandaghi holds both a B.S. and M.S. in Civil Engineering from Iran in 2018
and started his PhD in Robotics at the University of Rhode Island in 2022. His early
research focused on Soft Robotics, fabrication, modeling, and control. Using Radial
Basis Function NN, he developed a fault detection framework, which not only yielded
three published articles but also earned one of them a coveted finalist spot for the ”Best
Student Paper” at AIM2023. Currently, he is working on developing a framework for
controlling and identifying fully uncertain nonlinear dynamics in multi-agent robotic
manipulators using AI. He is looking to extend this framework into various robotic
domains like UGVs. Beyond academia, Emad enjoys strumming his guitar and dreams
of sharing his music with the world in the future.

Address:United States




Agenda

7:30pm | Begin event, introductions
7:35am | Presentation:  Motion Dynamics Modeling and Fault Detection of a Soft Trunk Robot
8:00pm | Q&A