Intelligent Shape Decoding of a Soft Optical Waveguide Sensor

Abstract

Optical waveguides create interesting opportunities in the area of soft sensing and electronic skins due to their potential for high flexibility, quick response time, and compactness. The loss or change of light intensities inside a waveguide can be measured and converted into useful sensing feedback such as strain or shape sensing. Compared to other approaches such as those based on microelectromechanical system modules or flexible conductors, the entire sensor state can be characterized by fewer sensing nodes and less encumbering wiring, allowing greater scalability. Herein, simple light‐emitting diodes (LEDs) and photodetectors (PDs) combined with an intelligent shape decoding framework are utilized to enable 3D shape sensing of a self‐contained flexible substrate. Multiphysics finite element analysis is leveraged to optimize the PDs/LEDs layout and enrich ground‐truth data from sparse to dense points for model training. The mapping from light intensities to overall sensor shape is achieved with an autoregression‐based model that considers temporal continuity and spatial locality. The sensing framework is evaluated on an A5‐sized flexible sensor prototype and a fish‐shaped prototype, where sensing accuracy (RMSE = 0.27 mm) and repeatability (Δ light intensity <0.31% over 1000 cycles) are tested underwater.

Keywords

• autoregressive modeling
• multiphysics finite element analysis (FEA)
• optical waveguide
• soft sensing