Fabric Dynamic Motion Modeling and Collision Avoidance With Oriented Bounding Box

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.

Authors

Institutions

Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-08-04
DOI
https://doi.org/10.1109/lra.2025.3595026
Citations
2
Primary Topic
3D Shape Modeling and Analysis
Type
article
Field-Weighted Citation Impact
2.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fabric Dynamic Motion Modeling and Collision Avoidance With Oriented Bounding Box

Fuyuki Tokuda, Akira Seino, Akinari Kobayashi, Letian Li et al.
2 citations
IEEE Robotics and Automation Letters
3D Shape Modeling and Analysis
2.67
article

Fabric Dynamic Motion Modeling and Collision Avoidance With Oriented Bounding Box

Fuyuki Tokuda, Akira Seino, Akinari Kobayashi, Letian Li, Kazuhiro Kosuge, Norman C. Tien
article en
2 citations

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.

IEEE Robotics and Automation LettersVol. 10(9)
University of Hong Kong (HK)
Openalex Percentile: Top 11%
3D Shape Modeling and Analysis
2.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Fabric Dynamic Motion Modeling and Collision Avoidance With Oriented Bounding Box — Fuyuki Tokuda, Akira Seino, et al. · IEEE Robotics and Automation Letters (2025) | TGRS Research Map | TGRS