Shape Detection and Reconstruction of Soft Robotic Arm Based on Fiber Bragg Grating Sensor Array

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2018-12-01 DOI:10.1109/ROBIO.2018.8665266

Zhiyuan Zhang, Xueqian Wang, S. Wang, Deshan Meng, Bin Liang

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引用次数: 9

Abstract

For the problem of shape detection and reconstruction of soft robotic arm with extensive degrees of freedom, in this paper, a distributed Fiber Bragg Grating (FBG) sensor array is designed and the shape reconstruction algorithm based on curvature information and the Frenet frame is simplified. The considered soft robotic arm consists of two extension pneumatic muscles (EPMs) in series. Firstly, the distributed FBG sensor array is built. Secondly, the curvature information of each grating point is collected and a continuous curvature curve is obtained by using the interpolation algorithm. Thirdly, the two-dimensional shape curves are reconstructed for the two EPMs according to the simplified shape reconstruction algorithm. Finally, the shape curves of the two EPMs are integrated into the three-dimensional shape curve of the soft robotic arm. Experimental results show that the maximum position error between the end of the reconstructed soft robotic arm and the end of the soft robotic arm is 0.008 $m$, and the percentage error relative to the overall length of the soft robotic arm is 2.58%.

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基于光纤布拉格光栅传感器阵列的软机械臂形状检测与重构

针对具有广泛自由度的软机械臂的形状检测和重建问题，本文设计了一种分布式光纤布拉格光栅（FBG）传感器阵列，并简化了基于曲率信息和 Frenet 框架的形状重建算法。所考虑的软机械臂由两个串联的伸展气动肌肉（EPM）组成。首先，建立分布式 FBG 传感器阵列。其次，收集每个光栅点的曲率信息，并通过插值算法获得连续的曲率曲线。第三，根据简化的形状重建算法重建两个 EPM 的二维形状曲线。最后，将两个 EPM 的形状曲线整合到软机械臂的三维形状曲线中。实验结果表明，重建后的软机械臂末端与软机械臂末端之间的最大位置误差为 0.008 $m$，相对于软机械臂总长度的百分比误差为 2.58%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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