Embedded Force Sensor for Soft Robots With Deep Transformation Calibration

Abstract

A novel soft sensor calibration method is proposed for minimally invasive surgery, based on our developed gelatin-graphite sensor with high compliance and adaptability. This approach uses convolutional deep learning that accounts for a sensor’s non-linear behavior and reduces noise amplification. This technique offers a smaller minimum detectable force than other approaches and is particularly useful in sensitive surgical scenarios. The sensor’s performance is characterized by its fine resolution (<inline-formula> <tex-math notation="LaTeX">$\leq 1$ </tex-math></inline-formula>mN) and accurate force estimation, especially for forces below 400 mN of amplitude. The best calibration (Morse) scheme provides high performance, with a Mean Absolute Error of <inline-formula> <tex-math notation="LaTeX">$\leq 7.9$ </tex-math></inline-formula> mN. This work was validated through comparison among other representative studies and offered a path toward future directions for optimizing and implementing soft robotic sensors in minimally invasive surgeries. The application of this sensor can revolutionize surgical procedures and capitalize on the benefits of soft robotics, potentially enhancing precision and reducing trauma in surgeries.