Soft robot shape estimation with IMUs leveraging PCC kinematics for drift filtering

Journal Article (2024)
Author(s)

Francesco Stella (TU Delft - Learning & Autonomous Control, École Polytechnique Fédérale de Lausanne)

C. Della Santina (TU Delft - Learning & Autonomous Control)

Josie Hughes (École Polytechnique Fédérale de Lausanne)

Research Group
Learning & Autonomous Control
Copyright
© 2024 F. Stella, C. Della Santina, Josie Hughes
DOI related publication
https://doi.org/10.1109/LRA.2023.3339063
More Info
expand_more
Publication Year
2024
Language
English
Copyright
© 2024 F. Stella, C. Della Santina, Josie Hughes
Research Group
Learning & Autonomous Control
Issue number
2
Volume number
9
Pages (from-to)
1945-1952
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

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.

Files

Soft_Robot_Shape_Estimation_Wi... (pdf)
(pdf | 1.91 Mb)
- Embargo expired in 04-06-2024
License info not available