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Autori principali: Banks, Alexandre, Moore, Randy, Zaman, Sayem Nazmuz, Abdelaal, Alaa Eldin, Salcudean, Septimiu E.
Natura: Preprint
Pubblicazione: 2025
Soggetti:
Accesso online:https://arxiv.org/abs/2505.10398
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author Banks, Alexandre
Moore, Randy
Zaman, Sayem Nazmuz
Abdelaal, Alaa Eldin
Salcudean, Septimiu E.
author_facet Banks, Alexandre
Moore, Randy
Zaman, Sayem Nazmuz
Abdelaal, Alaa Eldin
Salcudean, Septimiu E.
contents Incorporating an autonomous auxiliary camera into robot-assisted minimally invasive surgery (RAMIS) enhances spatial awareness and eliminates manual viewpoint control. Existing path planning methods for auxiliary cameras track two-dimensional surgical features but do not simultaneously account for camera orientation, workspace constraints, and robot joint limits. This study presents AutoCam: an automatic auxiliary camera placement method to improve visualization in RAMIS. Implemented on the da Vinci Research Kit, the system uses a priority-based, workspace-constrained control algorithm that combines heuristic geometric placement with nonlinear optimization to ensure robust camera tracking. A user study (N=6) demonstrated that the system maintained 99.84% visibility of a salient feature and achieved a pose error of 4.36 $\pm$ 2.11 degrees and 1.95 $\pm$ 5.66 mm. The controller was computationally efficient, with a loop time of 6.8 $\pm$ 12.8 ms. An additional pilot study (N=6), where novices completed a Fundamentals of Laparoscopic Surgery training task, suggests that users can teleoperate just as effectively from AutoCam's viewpoint as from the endoscope's while still benefiting from AutoCam's improved visual coverage of the scene. These results indicate that an auxiliary camera can be autonomously controlled using the da Vinci patient-side manipulators to track a salient feature, laying the groundwork for new multi-camera visualization methods in RAMIS.
format Preprint
id arxiv_https___arxiv_org_abs_2505_10398
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics
Banks, Alexandre
Moore, Randy
Zaman, Sayem Nazmuz
Abdelaal, Alaa Eldin
Salcudean, Septimiu E.
Robotics
Human-Computer Interaction
Machine Learning
Systems and Control
Signal Processing
J.3.2; J.2.7; I.2.9
Incorporating an autonomous auxiliary camera into robot-assisted minimally invasive surgery (RAMIS) enhances spatial awareness and eliminates manual viewpoint control. Existing path planning methods for auxiliary cameras track two-dimensional surgical features but do not simultaneously account for camera orientation, workspace constraints, and robot joint limits. This study presents AutoCam: an automatic auxiliary camera placement method to improve visualization in RAMIS. Implemented on the da Vinci Research Kit, the system uses a priority-based, workspace-constrained control algorithm that combines heuristic geometric placement with nonlinear optimization to ensure robust camera tracking. A user study (N=6) demonstrated that the system maintained 99.84% visibility of a salient feature and achieved a pose error of 4.36 $\pm$ 2.11 degrees and 1.95 $\pm$ 5.66 mm. The controller was computationally efficient, with a loop time of 6.8 $\pm$ 12.8 ms. An additional pilot study (N=6), where novices completed a Fundamentals of Laparoscopic Surgery training task, suggests that users can teleoperate just as effectively from AutoCam's viewpoint as from the endoscope's while still benefiting from AutoCam's improved visual coverage of the scene. These results indicate that an auxiliary camera can be autonomously controlled using the da Vinci patient-side manipulators to track a salient feature, laying the groundwork for new multi-camera visualization methods in RAMIS.
title AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics
topic Robotics
Human-Computer Interaction
Machine Learning
Systems and Control
Signal Processing
J.3.2; J.2.7; I.2.9
url https://arxiv.org/abs/2505.10398