A Rigid-Flexible Coupled Six-Dimensional Force Sensor and Its PINN-Based Decoupling Algorithm
Yinlong Zhu1, Zhengyu Xie1, Chuanwei Lu2
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|April 14, 2026
Summary
This study introduces a low-cost, compact six-axis force sensor using capacitive sensing and a neural network. The novel design significantly improves force and torque decoupling accuracy for robotic applications.
Area of Science:
- Robotics
- Sensor Technology
- Artificial Intelligence
Background:
- Six-dimensional force sensors are crucial for compliant robotic control and human-machine interaction.
- Conventional sensors face challenges with complex structures, large size, and high costs.
Purpose of the Study:
- To develop a compact, low-cost six-axis force sensor.
- To improve the decoupling accuracy of force and torque measurements.
- To enable high-precision sensing in lightweight robotic systems.
Main Methods:
- Utilized capacitive sensing with a novel arrangement of flexible sensing units.
- Implemented a Physically Informed Neural Network (PINN) for residual signal decoupling.
- Conducted experiments to validate decoupling accuracy.
Main Results:
- Achieved low force decoupling errors: 1.75% (Fx), 1.20% (Fy), 1.31% (Fz).
- Achieved low torque decoupling errors: 0.95% (Mx), 0.93% (My), 0.97% (Mz).
- Demonstrated significant improvements in decoupling accuracy.
Conclusions:
- The proposed capacitive sensor offers a compact and cost-effective solution.
- PINN effectively decouples coupled signals, enhancing sensor performance.
- The sensor is suitable for lightweight, high-precision robotic applications.
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