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Fabric Dynamic Motion Modeling and Collision Avoidance With Oriented Bounding Box

  • Letian Li*
  • , Fuyuki Tokuda
  • , Akira Seino
  • , Akinari Kobayashi
  • , Norman C. Tien
  • , Kazuhiro Kosuge
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Avoiding collision between the fabric and the obstacle is critical to transport fabric piece in the garment factory. If the fabric collides with the sharp-edged obstacle, it can be scratched or contaminated, resulting in poor product quality and increased waste. However, when we consider the fabric model, we find that current fabric models are not accurate enough for this real-world application. It is almost impossible to model the dynamic motion of the fabric with high accuracy, because its motion and deformation are affected by many hard-to-estimate factors. In this paper, instead of using an accurate fabric model, we propose a new fabric motion modeling method using the proposed OBB-Transformer, which models the fabric motion as a time series of oriented bounding boxes (OBBs). Using OBB-Transformer, a dynamic collision avoidance method is designed to plan a robot trajectory connecting the start point and the goal point without collision between the fabric and the obstacle. The performance of the fabric dynamic motion modeling is compared between the proposed and conventional methods. Then, the collision avoidance of a piece of fabric using the proposed method is demonstrated on a real robot system in both 2D and 3D scenarios. © 2025 The Authors.
Original languageEnglish
Pages (from-to)9542-9549
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume10
Issue number9
Online published4 Aug 2025
DOIs
Publication statusPublished - Sept 2025
Externally publishedYes

Funding

This work was supported in part by the JC STEM Lab of Robotics for Soft Materials funded by The Hong Kong Jockey Club Charities Trust and in part by the Innovation and Technology Commission of the HKSAR Government through the InnoHK initiative.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Keywords

  • Collision avoidance
  • deep learning methods
  • deformable object modeling
  • motion and path planning

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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