Space-Time Continuum: Continuous Shape and Time State Estimation for Flexible Robots
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916400155590656 |
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| author | Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. |
| author_facet | Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. |
| contents | This extended abstract introduces a novel method for continuous state estimation of continuum robots. We formulate the estimation problem as a factor-graph optimization problem using a novel Gaussian-process prior that is parameterized over both arclength and time. We use this to introduce the first continuous-time-and-space state estimation method for continuum robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12302 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Space-Time Continuum: Continuous Shape and Time State Estimation for Flexible Robots Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. Robotics This extended abstract introduces a novel method for continuous state estimation of continuum robots. We formulate the estimation problem as a factor-graph optimization problem using a novel Gaussian-process prior that is parameterized over both arclength and time. We use this to introduce the first continuous-time-and-space state estimation method for continuum robots. |
| title | Space-Time Continuum: Continuous Shape and Time State Estimation for Flexible Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.12302 |