AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics

2025-05-15

AI Summary

In robot-assisted minimally invasive surgery, existing auxiliary camera path planning methods are limited to 2D feature tracking and neglect critical constraints—including camera pose, workspace boundaries, and robotic arm joint limits. To address this, we propose a fully automated 3D path planning and real-time tracking framework. Our approach introduces, for the first time on the da Vinci platform, a priority-based hierarchical control architecture that integrates heuristic geometric modeling with nonlinear optimization to achieve robust, autonomous viewpoint regulation under multiple constraints. Experimental evaluation demonstrates a target feature visibility rate of 99.84%, average pose estimation error of 4.36±2.11° (orientation) and 1.95±5.66 mm (position), and a mean computation time of 6.8±12.8 ms per planning cycle. Furthermore, novice users achieved significantly broader visual coverage compared to conventional methods—matching the performance of expert endoscopists.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Computer Vision: Motion & Tracking
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

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.

Problem

• Autonomous camera path planning for RAMIS visualization enhancement
• Addressing workspace constraints and joint limits in camera tracking
• Improving surgical scene coverage without manual viewpoint control

Innovation

• Priority-based workspace-constrained control algorithm
• Heuristic geometric placement with nonlinear optimization
• Autonomous tracking using da Vinci manipulators