Imperial College London
Portrait Picture

Dr Dandan Zhang

Faculty of Engineering, Department of Bioengineering

Lecturer in Artificial Intelligence & Machine Learning
 
 
 
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Contact

 

d.zhang17 Website

 
 
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Location

 

402Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Fan:2023:10.1109/ICRA48891.2023.10160288,
author = {Fan, W and Yang, M and Xing, Y and Lepora, NF and Zhang, D},
doi = {10.1109/ICRA48891.2023.10160288},
pages = {10373--10379},
title = {Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing},
url = {http://dx.doi.org/10.1109/ICRA48891.2023.10160288},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to high-resolution optical tactile sensors. Given the recent successes of Graph Neural Network (GNN) and the effectiveness of Voronoi features, we developed a Tactile Voronoi Graph Neural Network (Tac-VGNN) to achieve reliable pose-based tactile servoing relying on a biomimetic optical tactile sensor (TacTip). The GNN is well suited to modeling the distribution relationship between shear motions of the tactile markers, while the Voronoi diagram supplements this with area-based tactile features related to contact depth. The experiment results showed that the Tac-VGNN model can help enhance data interpretability during graph generation and model training efficiency significantly than CNN-based methods. It also improved pose estimation accuracy along vertical depth by 28.57% over vanilla GNN without Voronoi features and achieved better performance on the real surface following tasks with smoother robot control trajectories. For more project details, please view our website: https://sites.google.com/view/tac-vgnn/home
AU  - Fan,W
AU  - Yang,M
AU  - Xing,Y
AU  - Lepora,NF
AU  - Zhang,D
DO  - 10.1109/ICRA48891.2023.10160288
EP  - 10379
PY  - 2023///
SN  - 1050-4729
SP  - 10373
TI  - Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing
UR  - http://dx.doi.org/10.1109/ICRA48891.2023.10160288
ER  -
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