A Sensing Strategy Combining Kinematic Model With FBG Sensors for Continuum Robots

Continuum manipulators (CMs) are commonly used in surgical robots due to their compact size and flexibility; however, these features also lead to low rigidity and limited accuracy. This article proposes a particle filter-based localization algorithm that combines kinematic and fiber Bragg grating (FBG) sensor data to enhance localization accuracy. First, a tiny ( $\Phi ~4$ mm) CM embedded with FBG sensors was designed, its kinematic model was derived based on the constant curvature model, and its measurement results were obtained by calculating curvature, interpolation, and integration according to the FBG sensor data. Subsequently, an adaptive weighted particle filter algorithm is proposed, which can prevent erroneous outputs when the kinematic model fails A series of experiments were conducted to demonstrate the proposed method. Bending experiments showed that, when the model is valid, the proposed method reduces error by 50% compared to measurement alone, with an error consistently under 0.8 mm across experimental conditions regardless of model validity. In trajectory ablation experiments on phantoms, the proposed method aligned more accurately with the ablation path.