A Rigid-Flexible Coupled Six-Dimensional Force Sensor and Its PINN-Based Decoupling Algorithm

Six-dimensional force sensors are widely used in compliant robotic control and safe human-machine interactions due to their mature sensing mechanisms and high accuracy. However, conventional six-dimensional force sensors often suffer from complex structures, bulky size, and high manufacturing costs. To address these limitations, this paper proposes a compact and low-cost six-axis force sensor based on capacitive sensing. By employing a tailored arrangement of flexible sensing units, partial structural decoupling of force and torque in specific directions is achieved. A Physically Informed Neural Network (PINN) is further introduced to decouple the residual coupled signals. Experimental results demonstrate that the proposed method significantly improves decoupling accuracy, achieving force decoupling errors of 1.75%, 1.20%, and 1.31% for F_x, F_y, and F_z, respectively, and torque decoupling errors of 0.95%, 0.93%, and 0.97% for M_x, M_y, and M_z. The proposed sensor offers low-cost fabrication, compact integration, and high sensitivity, making it well suited for lightweight and high-precision sensing applications.