Shape Estimation of a 3D Printed Soft Sensor Using Multi-Hypothesis Extended Kalman Filter

This study develops a multi-hypothesis extended Kalman filter (MH-EKF) for the online estimation of the bending angle of a 3D printed soft sensor attached to soft actuators. Despite the advantage of compliance and low interference, the 3D printed soft sensor is susceptible to the hysteresis property and nonlinear effects. Improving measurement accuracy for sensors with hysteresis is a common challenge. Current studies mainly apply complex models and highly nonlinear functions to characterize the hysteresis, requiring a complicated parameter identification process and challenging real-time applications. This study enhances the model simplicity and the real-time performance for the hysteresis characterization. We identify the hysteresis by combining multiple polynomial functions and improving the sensor estimation with the proposed MH-EKF. We examine the performance of the filter in the real-time closed-loop control system. Compared with the baseline methods, the proposed approach shows improvements in the estimation accuracy with low computational complexity.

Automated summary

This study presents a multi-hypothesis extended Kalman filter (MH-EKF) for online estimation of bending angle of 3D printed soft sensor attached to soft actuators, improving measurement accuracy for sensors with hysteresis. The method simplifies the model, enhances real-time performance, identifies hysteresis using multiple polynomial functions, and shows improvements in estimation accuracy compared to baseline methods.