Accelerating Inverse Kinematic Solutions for a Cable-Driven Soft Robotic Manipulator via Physics-Informed Neural Network
Rui Lin, Shuyou He, Ming Xu, Kangjia Fu, Xuesong Wu, Xiucong Sun, Qi Zhang, Sunquan Yu
Abstract
Cable-driven soft manipulators, with inherent compliance and hyper-redundancy, offer significant advantages in unstructured environments but present formidable challenges in modeling of inverse kinematics due to nonlinear deformations and underactuation. In this paper, building on a modified forward kinematic model, a physics-informed neural networks (PINN) framework based on spatiotemporal data is proposed for efficient inverse kinematics computation of cable-driven soft robotic manipulators. A geometrically exact forward kinematic model is constructed under the Piecewise Constant Curvature (PCC) assumption, extended to multi-section configurations, and enhanced by cable deflection compensation to account for practical routing constraints. Experimental validation shows a 40.11% reduction in end-effector positioning error (average 15.98 mm) when deflection effects are included. The proposed PINN architecture takes time and section count as inputs and outputs the corresponding manipulator configuration, enabling unified spatiotemporal trajectory tracking by minimizing elastic energy while satisfying kinematic constraints. Compared to particle swarm optimization (PSO), which requires iterative computation for each trajectory sample, the proposed method reduces computational time by over 71.9%, demonstrating superior efficiency in solving redundant inverse kinematics problems. This work bridges data-driven and mechanics-based approaches, offering a scalable solution for real-time control of soft manipulators.
BibTeX
@inproceedings{iros2025_acceleratinginve,
title = {Accelerating Inverse Kinematic Solutions for a Cable-Driven Soft Robotic Manipulator via Physics-Informed Neural Network},
author = {Rui Lin and Shuyou He and Ming Xu and Kangjia Fu and Xuesong Wu and Xiucong Sun and Qi Zhang and Sunquan Yu and Xiang Zhang},
booktitle = {IROS 2025},
year = {2025}
}