Research on the pose reconstruction method of soft mechanical arm based on fiber optic sensing

Soft robotic arms, due to their high flexibility and environmental adaptability, have shown broad application prospects in minimally invasive surgeries, precise operations, and special tasks. However, their continuous deformation characteristics pose challenges to pose perception and precise modeling. This paper proposes a pose reconstruction method for soft robotic arms based on fiber Bragg grating sensing and variable curvature modeling. A fiber sensing network capable of sensing combined stretching and bending deformations was constructed, and the distribution positions of the sensing network were optimized using the particle swarm optimization algorithm. Through numerical simulations and experiments, the results show that the proposed method has high reconstruction accuracy and significantly reduces the end position error. The research results provide effective technical support for the high-precision perception and closed-loop control of soft robotic arms.