Theoretical Research of Tactile Shape Sensor for Complex Surfaces Based on Fiber-Optic Distributed Sensors

Tactile shape sensor is a crucial research focus in the field of soft robotics, and many researchers have developed intelligent tactile shape sensors. However, current research on tactile shape sensors predominantly focuses on simple shapes such as bending and twisting, neglecting a common but significant shapes – concave-convex shapes. This study introduces a tactile shape sensor based on Fiber-Optic distributed sensors, and it utilizes traditional optimization algorithms, avoiding the influence of training data associated with machine learning. In this research, we propose a method to predict shapes using strain data from fibers. The validation of this prediction method is primarily theoretical, conducted through simulations. This research conducts beyond validating basic shapes like bending, twisting, and stretching, and also includes various complex concave-convex shapes, single-point pressing, multi-point pressing (3 points), and reverse pressing (2 points) in case studies. In ideal conditions, the predicted shapes match the set sample shapes perfectly.