State Estimation and Control with a Robust Extended Kalman Filter for a Fabric Soft Robot

Abstract

Soft robots have shown great potential in safe human-robot interaction. However, their compliant nature makes posture measurement and trajectory tracking a challenging task. Accurate sensing systems such as motion capture devices aren't portable enough to use in any environment, while body-integrated soft sensors create problems like hysteresis. In this work, a robust extended Kalman filter (REKF) based sensor fusion method with an accelerometer, gyroscope, and draw wire sensor is introduced to estimate the bending angle of a fabric-based inflatable bending actuator. The REKF improves upon similar estimation techniques by ensuring a robust estimate regardless of non-linearity or uncertainty in the model. Linear Quadratic Gaussian (LQG) control is integrated with the proposed REKF to demonstrate the REKF based sensor fusion method is useful in control. The results show that the REKF based sensor fusion system presents a portable, robust, accurate estimation of the actuator's bending angle.