The computational design of a fractal-inspired soft robotic
Soft robotics, with their application in biomedical fields like flexible surgical tools and wearable exo-suits, have revolutionized biomedical practices. To adapt to different scales of application scenarios, our research emphasizes fractal-inspired soft robotics, leveraging the unique scalable and...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823009675 |
| _version_ | 1797401788235644928 |
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| author | Xuanang Chen |
| author_facet | Xuanang Chen |
| author_sort | Xuanang Chen |
| collection | DOAJ |
| description | Soft robotics, with their application in biomedical fields like flexible surgical tools and wearable exo-suits, have revolutionized biomedical practices. To adapt to different scales of application scenarios, our research emphasizes fractal-inspired soft robotics, leveraging the unique scalable and reconfigurable properties of fractal structures. This study focuses on the computational design and simulation of these robots. Traditional modeling methods, apt for rigid robotic arms, falter for soft materials due to their extensive flexibility. We introduced a deep learning strategy with physics-informed neural network for accurate soft robot dynamics modeling. Unlike existing networks optimized for rigid systems, our approach integrates elastic beam theory and deformation laws into Lagrangian Dynamics, making it ideal for simulating fractal soft robotic arms. Compared to conventional finite element simulations and learning approaches, our method shows superior effectiveness. We also conducted simple simulation experiments to show that fractal soft arm is potentially suitable for medical procedures (such as dilating passages) and proved that this fractal-designed soft robotic arm exhibits good portability and adaptability to various scenarios, making it a promising candidate for guiding the design of future surgical robots. |
| first_indexed | 2024-03-09T02:16:20Z |
| format | Article |
| id | doaj.art-e9e2e8aac2344c0185a24162275dc446 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-09T02:16:20Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-e9e2e8aac2344c0185a24162275dc4462023-12-07T05:27:55ZengElsevierAlexandria Engineering Journal1110-01682023-12-01843746The computational design of a fractal-inspired soft roboticXuanang Chen0University of Pennsylvania, 3131 Walnut St, Philadelphia, 19104, PA, USASoft robotics, with their application in biomedical fields like flexible surgical tools and wearable exo-suits, have revolutionized biomedical practices. To adapt to different scales of application scenarios, our research emphasizes fractal-inspired soft robotics, leveraging the unique scalable and reconfigurable properties of fractal structures. This study focuses on the computational design and simulation of these robots. Traditional modeling methods, apt for rigid robotic arms, falter for soft materials due to their extensive flexibility. We introduced a deep learning strategy with physics-informed neural network for accurate soft robot dynamics modeling. Unlike existing networks optimized for rigid systems, our approach integrates elastic beam theory and deformation laws into Lagrangian Dynamics, making it ideal for simulating fractal soft robotic arms. Compared to conventional finite element simulations and learning approaches, our method shows superior effectiveness. We also conducted simple simulation experiments to show that fractal soft arm is potentially suitable for medical procedures (such as dilating passages) and proved that this fractal-designed soft robotic arm exhibits good portability and adaptability to various scenarios, making it a promising candidate for guiding the design of future surgical robots.http://www.sciencedirect.com/science/article/pii/S1110016823009675Soft roboticsBiomedical applicationFractalDeep learningLagrangian dynamicsPhysics-informed neural networks |
| spellingShingle | Xuanang Chen The computational design of a fractal-inspired soft robotic Alexandria Engineering Journal Soft robotics Biomedical application Fractal Deep learning Lagrangian dynamics Physics-informed neural networks |
| title | The computational design of a fractal-inspired soft robotic |
| title_full | The computational design of a fractal-inspired soft robotic |
| title_fullStr | The computational design of a fractal-inspired soft robotic |
| title_full_unstemmed | The computational design of a fractal-inspired soft robotic |
| title_short | The computational design of a fractal-inspired soft robotic |
| title_sort | computational design of a fractal inspired soft robotic |
| topic | Soft robotics Biomedical application Fractal Deep learning Lagrangian dynamics Physics-informed neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823009675 |
| work_keys_str_mv | AT xuanangchen thecomputationaldesignofafractalinspiredsoftrobotic AT xuanangchen computationaldesignofafractalinspiredsoftrobotic |