Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917636559863808 |
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| author | Wang, Yuan McCandless, Max Donder, Abdulhamit Pittiglio, Giovanni Moradkhani, Behnam Chitalia, Yash Dupont, Pierre E. |
| author_facet | Wang, Yuan McCandless, Max Donder, Abdulhamit Pittiglio, Giovanni Moradkhani, Behnam Chitalia, Yash Dupont, Pierre E. |
| contents | The ability to accurately model mechanical hysteretic behavior in tendon-actuated continuum robots using deep learning approaches is a growing area of interest. In this paper, we investigate the hysteretic response of two types of tendon-actuated continuum robots and, ultimately, compare three types of neural network modeling approaches with both forward and inverse kinematic mappings: feedforward neural network (FNN), FNN with a history input buffer, and long short-term memory (LSTM) network. We seek to determine which model best captures temporal dependent behavior. We find that, depending on the robot's design, choosing different kinematic inputs can alter whether hysteresis is exhibited by the system. Furthermore, we present the results of the model fittings, revealing that, in contrast to the standard FNN, both FNN with a history input buffer and the LSTM model exhibit the capacity to model historical dependence with comparable performance in capturing rate-dependent hysteresis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots Wang, Yuan McCandless, Max Donder, Abdulhamit Pittiglio, Giovanni Moradkhani, Behnam Chitalia, Yash Dupont, Pierre E. Robotics Artificial Intelligence The ability to accurately model mechanical hysteretic behavior in tendon-actuated continuum robots using deep learning approaches is a growing area of interest. In this paper, we investigate the hysteretic response of two types of tendon-actuated continuum robots and, ultimately, compare three types of neural network modeling approaches with both forward and inverse kinematic mappings: feedforward neural network (FNN), FNN with a history input buffer, and long short-term memory (LSTM) network. We seek to determine which model best captures temporal dependent behavior. We find that, depending on the robot's design, choosing different kinematic inputs can alter whether hysteresis is exhibited by the system. Furthermore, we present the results of the model fittings, revealing that, in contrast to the standard FNN, both FNN with a history input buffer and the LSTM model exhibit the capacity to model historical dependence with comparable performance in capturing rate-dependent hysteresis. |
| title | Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2404.07168 |