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Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors

Baaij, Thomas and Holkenborg, Marn Klein and Stölzle, Maximilian and van der Tuin, Daan and Naaktgeboren, Jonatan and Babuška, Robert and Della Santina, Cosimo (2022) Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors. Soft Matter, 19 (1), pp. 44-56. Royal Society of Chemistry. doi: 10.1039/D2SM00914E. ISSN 1744-683X.

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Official URL: https://dx.doi.org/10.1039/D2SM00914E


Sensing the shape of continuum soft robots without obstructing their movements and modifying their natural softness requires innovative solutions. This letter proposes to use magnetic sensors fully integrated into the robot to achieve proprioception. Magnetic sensors are compact, sensitive, and easy to integrate into a soft robot. We also propose a neural architecture to make sense of the highly nonlinear relationship between the perceived intensity of the magnetic field and the shape of the robot. By injecting a priori knowledge from the kinematic model, we obtain an effective yet data-efficient learning strategy. We first demonstrate in simulation the value of this kinematic prior by investigating the proprioception behavior when varying the sensor configuration, which does not require us to re-train the neural network. We validate our approach in experiments involving one soft segment containing a cylindrical magnet and three magnetoresistive sensors. During the experiments, we achieve mean relative errors of 4.5%.

Item URL in elib:https://elib.dlr.de/193621/
Document Type:Article
Additional Information:https://www.github.com/tud-cor-sr/promasens
Title:Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Baaij, ThomasDelft University of TechnologyUNSPECIFIEDUNSPECIFIED
Holkenborg, Marn KleinDelft University of TechnologyUNSPECIFIEDUNSPECIFIED
Stölzle, MaximilianDelft University of TechnologyUNSPECIFIEDUNSPECIFIED
van der Tuin, DaanDelft University of TechnologyUNSPECIFIEDUNSPECIFIED
Naaktgeboren, JonatanDelft University of TechnologyUNSPECIFIEDUNSPECIFIED
Babuška, RobertCzech Technical UniversityUNSPECIFIEDUNSPECIFIED
Della Santina, CosimoUNSPECIFIEDhttps://orcid.org/0000-0003-1067-1134UNSPECIFIED
Date:30 November 2022
Journal or Publication Title:Soft Matter
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 44-56
Publisher:Royal Society of Chemistry
Keywords:soft robots
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Robot Dynamics & Simulation [RO]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Analysis and Control of Advanced Robotic Systems
Institute of Robotics and Mechatronics (since 2013)
Deposited By: Strobl, Dr. Klaus H.
Deposited On:27 Jan 2023 14:46
Last Modified:28 Jun 2023 13:36

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