An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs

TL;DR

This paper introduces a cost-effective, sensor-fusion method combining IMUs and bend sensors with a Kalman filter for accurate, long-term shape estimation of soft robots, outperforming IMU-only approaches.

Contribution

The study develops an integrated sensor fusion approach using off-the-shelf sensors and a Kalman filter to enhance soft robot proprioception and accuracy over extended periods.

Findings

Root mean square error of 16.96 mm achieved

56% reduction in error compared to IMU-only methods

Maintains high accuracy during diverse interactions

Abstract

This study presents an enhanced proprioceptive method for accurate shape estimation of soft robots using only off-the-shelf sensors, ensuring cost-effectiveness and easy applicability. By integrating inertial measurement units (IMUs) with complementary bend sensors, IMU drift is mitigated, enabling reliable long-term proprioception. A Kalman filter fuses segment tip orientations from both sensors in a mutually compensatory manner, improving shape estimation over single-sensor methods. A piecewise constant curvature model estimates the tip location from the fused orientation data and reconstructs the robot's deformation. Experiments under no loading, external forces, and passive obstacle interactions during 45 minutes of continuous operation showed a root mean square error of 16.96 mm (2.91% of total length), a 56% reduction compared to IMU-only benchmarks. These results demonstrate that our approach not only enables long-duration proprioception in soft robots but also maintains high accuracy and robustness across these diverse conditions.