A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior
Hao Jiang, Zhanchi Wang, Xinghua Liu, Xiaotong Chen, Yusong Jin, Xuanke You, Xiaoping Chen
Abstract
Soft compliant materials and novel actuation mechanisms ensure flexible motions and high adaptability for soft robots, but also increase the difficulty and complexity of constructing control systems. In this work, we provide an efficient control algorithm for a multi-segment extensible soft arm in 2D plane. The algorithm separate the inverse kinematics into two levels. The first level employs gradient descent to select optimized arm's pose (from task space to configuration space) according to designed cost functions. With consideration of viscoelasticity, the second level utilizes neural networks to figure out the pressures from each segment's pose (from configuration space to actuation space). In experiments with a physical prototype, the control accuracy and effectiveness are validated, where the control algorithm is further improved by an optional feedback strategy.
BibTeX
@inproceedings{icra2017_atwolevelapproac,
title = {A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior},
author = {Hao Jiang and Zhanchi Wang and Xinghua Liu and Xiaotong Chen and Yusong Jin and Xuanke You and Xiaoping Chen},
booktitle = {ICRA 2017},
year = {2017}
}