PhD Thesis Proposal: Kyle Motter

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  • Date/Time:
    • Thursday November 29, 2018
      10:00 am - 12:00 pm
  • Location: Love 210
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Summary Sentence: Fabric Control via Decentralized Multi-Agent Path Planning through a Distributed Active Environment

Full Summary: Fabric Control via Decentralized Multi-Agent Path Planning through a Distributed Active Environment


Dr. Wayne Book (advisor), School of Mechanical Engineering, Georgia Institute of Technology

Dr. Sundaresan Jayaraman, School of Materials Science and Engineering, Georgia Institute of Technology

Dr. Ling Liu, School of Computer Science, Georgia Institute of Technology

Dr. Anirban Mazumdar, School of Mechanical Engineering, Georgia Institute of Technology

Dr. Tucker Balch, School of Interactive Computing, Georgia Institute of Technology





The garment manufacturing industry has not benefited from the rapid advances in robotics and automation due to the inherent difficulty in handling flexible materials.  Currently, the vast majority of sewing operations and material handling is still performed by humans in low-wage conditions.  However, the industry is undergoing a paradigm shift toward custom and on-demand manufacturing, increasing the need for automated handling of single-ply cut and printed fabric.  For this purpose, we have developed a system of novel distributed actuators ("budgers") for fabric manipulation and control.
Using these distributed actuators as a foundation, this proposed thesis is broken into two sections - fabric control and large-scale routing, presented within the context of treating the fabric as an “unactuated robot” traversing through the “actuated environment” of a budger array.  At the local level, confined to a set of budgers on a single table, we propose a set of algorithms to provide feasible, realtime, closed-loop control and visual tracking of both fabric trajectory and wrinkle state.  As the system scales up to a large number of inter-connected budger tables, we propose using internet-protocol-like routing algorithms using the inherent intelligence local to the actuated environment to solve the large-scale multi-agent path planning problem.

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In Campus Calendar

Decision and Control Lab (DCL)

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Faculty/Staff, Public, Undergraduate students
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  • Created By: mamstutz3
  • Workflow Status: Draft
  • Created On: Nov 14, 2018 - 3:56pm
  • Last Updated: Nov 15, 2018 - 11:20am