2D Shape Estimation of a Pneumatic‐Driven Soft Finger with a Large Bending Angle Based on Learning from Two Sensing Modalities

Abstract

A shape estimation method that utilizes two sensing modalities of a customized fiber Bragg grating (FBG) sensor and a commercial air pressure sensor for a pneumatically driven soft finger with an extensive bending angle range based on an artificial neural network model is proposed. The proposed FBG sensor utilizes two tiny nitinol rods as a backbone to attach the long‐grating FBG element fiber, enabling high strain transfer, shape sensing for large bending deformation, and preventing chirping failure and fiber sliding when bending. Its distal end is set free to slide and synchronizes with the extended length and reflects shapes for large bending deformation (up to 320° with a linearity of 99.96%), while its proximal end is fixed. The small packaged sensor unit enables modular design, easy assembly, and high repeatability with negligible effects on the soft finger's bending performances. The artificial neural network model is utilized to process the input of two sensing modalities, reducing errors from material nonlinearity, fabrication, and assembly of soft fingers while improving shape estimation's accuracy and transferability with average errors of 0.90 mm (0.69%) and 1.55 mm (1.19%) for whole shape and distal end position, respectively. Preliminary experiments also verify the potential for pressing force prediction and hardness recognition.

Keywords

• artificial neural networks
• curvature sensors
• fiber Bragg grating
• shape estimation/sensing
• soft manipulators