Soft Robot Shape Estimation With IMUs Leveraging PCC Kinematics for Drift Filtering

Francesco Stella, Cosimo Della Santina, Josie Hughes

Research output: Contribution to journalArticlepeer-review


The control possibilities for soft robots have long been hindered by the need for reliable methods to estimate their configuration. Inertial measurement units (IMUs) can solve this challenge, but they are affected by well-known drift issues. This letter proposes a method to eliminate this limitation by leveraging the Piecewise Constant Curvature model assumption. We validate the reconstruction capabilities of the algorithm in simulation and experimentally. To this end, we also present a novel large-scale, foam-based manipulator with embedded IMU sensors. Using the filter, we bring the accuracy in IMU-based reconstruction algorithms to 93% of the soft robot's length and enable substantially longer measurements than the baseline. We also show that the proposed technique generates reliable estimations for closed-loop control of the robot's shape.

Original languageEnglish
Pages (from-to)1945-1952
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - 1 Feb 2024
Externally publishedYes


  • Calibration and identification
  • and learning for soft robots
  • control
  • modeling
  • soft sensors and actuators


Dive into the research topics of 'Soft Robot Shape Estimation With IMUs Leveraging PCC Kinematics for Drift Filtering'. Together they form a unique fingerprint.

Cite this