Data‐Driven Kinematic Modeling of Physical Origami Robots
Origami‐inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate control of all degree of freedom (DoF). Herein...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400217 |
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| _version_ | 1832592552703819776 |
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| author | Mustafa Mete Alexander Schüßler Yves Martin des Taillades Bruno Trivelli Jamie Paik |
| author_facet | Mustafa Mete Alexander Schüßler Yves Martin des Taillades Bruno Trivelli Jamie Paik |
| author_sort | Mustafa Mete |
| collection | DOAJ |
| description | Origami‐inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate control of all degree of freedom (DoF). Herein, a data‐driven kinematic modeling approach tailored for physical origami robots to effectively address the inherent compliance is introduced. This approach is applied to a multiloop origami spherical joint, which features a minimalistic design comprising two parallel waterbomb structures. This design allows for the integration of four actuators, thereby enabling full control over the structure's three DoF and its inherent compliance. It is demonstrated that a small dataset is adequate for accurately learning the forward kinematics, which then informs an optimization‐based inverse kinematics. Additionally, through trajectory tracking experiments, it is verified that the modeling method is both rapid and accurate, making it suitable for real‐time applications. To showcase a practical application, the joint and its models are utilized as a force feedback origami joystick, designed for intuitive drone control. This joystick offers an enhanced control experience and conveys crucial information about collisions and external forces to drone operators. Overall, the data‐driven modeling approach introduces a new possibility of designing controllable compliant interfaces. |
| format | Article |
| id | doaj-art-1fa524849e234448b7ee45454c73662f |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-1fa524849e234448b7ee45454c73662f2025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400217Data‐Driven Kinematic Modeling of Physical Origami RobotsMustafa Mete0Alexander Schüßler1Yves Martin des Taillades2Bruno Trivelli3Jamie Paik4Reconfigurable Robotics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne SwitzerlandReconfigurable Robotics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne SwitzerlandReconfigurable Robotics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne SwitzerlandReconfigurable Robotics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne SwitzerlandReconfigurable Robotics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne SwitzerlandOrigami‐inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate control of all degree of freedom (DoF). Herein, a data‐driven kinematic modeling approach tailored for physical origami robots to effectively address the inherent compliance is introduced. This approach is applied to a multiloop origami spherical joint, which features a minimalistic design comprising two parallel waterbomb structures. This design allows for the integration of four actuators, thereby enabling full control over the structure's three DoF and its inherent compliance. It is demonstrated that a small dataset is adequate for accurately learning the forward kinematics, which then informs an optimization‐based inverse kinematics. Additionally, through trajectory tracking experiments, it is verified that the modeling method is both rapid and accurate, making it suitable for real‐time applications. To showcase a practical application, the joint and its models are utilized as a force feedback origami joystick, designed for intuitive drone control. This joystick offers an enhanced control experience and conveys crucial information about collisions and external forces to drone operators. Overall, the data‐driven modeling approach introduces a new possibility of designing controllable compliant interfaces.https://doi.org/10.1002/aisy.202400217force feedback joysticksmachine learningmodelingorigami spherical jointsphysical origami robots |
| spellingShingle | Mustafa Mete Alexander Schüßler Yves Martin des Taillades Bruno Trivelli Jamie Paik Data‐Driven Kinematic Modeling of Physical Origami Robots Advanced Intelligent Systems force feedback joysticks machine learning modeling origami spherical joints physical origami robots |
| title | Data‐Driven Kinematic Modeling of Physical Origami Robots |
| title_full | Data‐Driven Kinematic Modeling of Physical Origami Robots |
| title_fullStr | Data‐Driven Kinematic Modeling of Physical Origami Robots |
| title_full_unstemmed | Data‐Driven Kinematic Modeling of Physical Origami Robots |
| title_short | Data‐Driven Kinematic Modeling of Physical Origami Robots |
| title_sort | data driven kinematic modeling of physical origami robots |
| topic | force feedback joysticks machine learning modeling origami spherical joints physical origami robots |
| url | https://doi.org/10.1002/aisy.202400217 |
| work_keys_str_mv | AT mustafamete datadrivenkinematicmodelingofphysicalorigamirobots AT alexanderschußler datadrivenkinematicmodelingofphysicalorigamirobots AT yvesmartindestaillades datadrivenkinematicmodelingofphysicalorigamirobots AT brunotrivelli datadrivenkinematicmodelingofphysicalorigamirobots AT jamiepaik datadrivenkinematicmodelingofphysicalorigamirobots |