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

Francesco Stella; Cosimo Della Santina; Josie Hughes

doi:10.1109/LRA.2023.3339063

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

Francesco Stella, Cosimo Della Santina, Josie Hughes

Learning & Autonomous Control

Research output: Contribution to journal › Article › Scientific › peer-review

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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.

Original language	English
Pages (from-to)	1945-1952
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	9
Issue number	2
DOIs	https://doi.org/10.1109/LRA.2023.3339063
Publication status	Published - 2024

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Calibration and Identification;
Kinematics
Magnetic separation
Modeling, Control, and Learning for Soft Robots
Robot sensing systems
Robots
Sensors
Shape
Soft robotics
Soft sensors and actuators

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Soft_Robot_Shape_Estimation_With_IMUs_Leveraging_PCC_Kinematics_for_Drift_FilteringFinal published version, 1.8 MB

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title = "Soft robot shape estimation with IMUs leveraging PCC kinematics for drift filtering",

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.",

keywords = "Calibration and Identification;, Kinematics, Magnetic separation, Modeling, Control, and Learning for Soft Robots, Robot sensing systems, Robots, Sensors, Shape, Soft robotics, Soft sensors and actuators",

author = "Francesco Stella and Santina, {Cosimo Della} and Josie Hughes",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.",

year = "2024",

doi = "10.1109/LRA.2023.3339063",

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AU - Stella, Francesco

AU - Santina, Cosimo Della

AU - Hughes, Josie

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Calibration and Identification;

KW - Kinematics

KW - Magnetic separation

KW - Modeling, Control, and Learning for Soft Robots

KW - Robot sensing systems

KW - Robots

KW - Sensors

KW - Shape

KW - Soft robotics

KW - Soft sensors and actuators

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SP - 1945

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JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

IS - 2

ER -

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

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Keywords

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