Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controls
| Autor: | Qi, Jiaming, Ran, Guangtao, Wang, Bohui, Liu, Jian, Ma, Wanyu, Zhou, Peng, Navarro-Alarcon, David |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | The robotic manipulation of deformable linear objects has shown great potential in a wide range of real-world applications. However, it presents many challenges due to the objects' complex nonlinearity and high-dimensional configuration. In this paper, we propose a new shape servoing framework to automatically manipulate elastic rods through visual feedback. Our new method uses parameterized regression features to compute a compact (low-dimensional) feature vector that quantifies the object's shape, thus, enabling to establish an explicit shape servo-loop. To automatically deform the rod into a desired shape, the proposed adaptive controller iteratively estimates the differential transformation between the robot's motion and the relative shape changes; This valuable capability allows to effectively manipulate objects with unknown mechanical models. An auto-tuning algorithm is introduced to adjust the robot's shaping motions in real-time based on optimal performance criteria. To validate the proposed framework, a detailed experimental study with vision-guided robotic manipulators is presented. Comment: 8 pages, 12 figures |
| Databáze: | arXiv |
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