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

Han, Dong Heon, Mehta, Mayank, Zuo, Runze, Wanger, Zachary, Bruder, Daniel

arXiv.org Artificial Intelligence 

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 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 IMUonly 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. Soft robots possess intrinsic compliance and virtually infinite degrees of freedom, enabling continuous deformation [1].