Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916240349462528 |
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| author | Tian, Huanyu Huber, Martin Mower, Christopher E. Han, Zhe Li, Changsheng Duan, Xingguang Bergeles, Christos |
| author_facet | Tian, Huanyu Huber, Martin Mower, Christopher E. Han, Zhe Li, Changsheng Duan, Xingguang Bergeles, Christos |
| contents | In this study, we introduce a novel shared-control system for key-hole docking operations, combining a commercial camera with occlusion-robust pose estimation and a hand-eye information fusion technique. This system is used to enhance docking precision and force-compliance safety. To train a hand-eye information fusion network model, we generated a self-supervised dataset using this docking system. After training, our pose estimation method showed improved accuracy compared to traditional methods, including observation-only approaches, hand-eye calibration, and conventional state estimation filters. In real-world phantom experiments, our approach demonstrated its effectiveness with reduced position dispersion (1.23\pm 0.81 mm vs. 2.47 \pm 1.22 mm) and force dispersion (0.78\pm 0.57 N vs. 1.15 \pm 0.97 N) compared to the control group. These advancements in semi-autonomy co-manipulation scenarios enhance interaction and stability. The study presents an anti-interference, steady, and precision solution with potential applications extending beyond laparoscopic surgery to other minimally invasive procedures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion Tian, Huanyu Huber, Martin Mower, Christopher E. Han, Zhe Li, Changsheng Duan, Xingguang Bergeles, Christos Robotics In this study, we introduce a novel shared-control system for key-hole docking operations, combining a commercial camera with occlusion-robust pose estimation and a hand-eye information fusion technique. This system is used to enhance docking precision and force-compliance safety. To train a hand-eye information fusion network model, we generated a self-supervised dataset using this docking system. After training, our pose estimation method showed improved accuracy compared to traditional methods, including observation-only approaches, hand-eye calibration, and conventional state estimation filters. In real-world phantom experiments, our approach demonstrated its effectiveness with reduced position dispersion (1.23\pm 0.81 mm vs. 2.47 \pm 1.22 mm) and force dispersion (0.78\pm 0.57 N vs. 1.15 \pm 0.97 N) compared to the control group. These advancements in semi-autonomy co-manipulation scenarios enhance interaction and stability. The study presents an anti-interference, steady, and precision solution with potential applications extending beyond laparoscopic surgery to other minimally invasive procedures. |
| title | Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.05817 |