Geometric-Aware Attention Networks with Adaptive Constraint Adjustment for Static Modeling of the Tendon-driven Continuum Robot
Xu, Xin Song, Yang Su, Bowen Wu, Ke Wu, Di Liang, Wendi Zheng, Gang Chu, Henry K.
Xu, Xin
Song, Yang
Su, Bowen
Wu, Ke
Wu, Di
Liang, Wendi
Zheng, Gang
Chu, Henry K.
Supervisor
Department
Robotics
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Tendon-driven continuum robots (TDCRs) with hyper-redundant DOFs are widely developed for sophisticated tasks such as minimally invasive surgery. However, the static modeling of the TDCRs is challenging due to the nonlinear characteristics of the TDCRs' structures and material properties. To tackle the above-mentioned challenges, we develop a novel end-to-end deep learning approach called geometric-aware attention network (GAANet) by combining neural network architecture and domain-specific geometric constraints. To acquire the datasets and evaluate the applicability of our developed network, we built up a two-segment TDCR with variable cross-sections, which is equipped with load cells and a motion capture system to measure the tension of tendons and the tip position of each segment simultaneously. To prove the efficacy of the developed GAANet, a multilayer perceptron (MLP) trained with the same dataset and model-based approach called pseudo rigid body model (PRBM) are both implemented for comparison. The results show that the developed GAANet outperforms the MLP and PRBM by 70.33% and 73.36% respectively in terms of the tip position prediction accuracy of each segment, demonstrating its potential for precise and real-time static modeling for our developed TDCRs with variable cross-sections. © 2025 IEEE.
Citation
X. Xu et al., "Geometric-Aware Attention Networks with Adaptive Constraint Adjustment for Static Modeling of the Tendon-driven Continuum Robot," 2025 IEEE 8th International Conference on Soft Robotics (RoboSoft), Lausanne, Switzerland, 2025, pp. 1-7, doi: 10.1109/RoboSoft63089.2025.11020942
Source
2025 IEEE 8th International Conference on Soft Robotics, RoboSoft 2025
Conference
8th IEEE International Conference on Soft Robotics, RoboSoft 2025
Keywords
deep learning, static mod-eling, Tendon-driven continuum robots
Subjects
Source
8th IEEE International Conference on Soft Robotics, RoboSoft 2025
Publisher
IEEE