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

arXiv: https://arxiv.org/abs/2505.10398 · HTML: https://mlsanity.com/paper/2505.10398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 85 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 6 of 6)

- [SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking](https://mlsanity.com/paper/2502.11534/) (2025)
- [Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping](https://mlsanity.com/paper/2309.16782/) (2023)
- [Automatic Calibration of a Multi-Camera System with Limited Overlapping Fields of View for 3D Surgical Scene Reconstruction](https://mlsanity.com/paper/2501.16221/) (2025)
- [Improving Surgical Situational Awareness with Signed Distance Field: A Pilot Study in Virtual Reality](https://mlsanity.com/paper/2303.01733/) (2023)
- [Autonomous Field-of-View Adjustment Using Adaptive Kinematic Constrained Control with Robot-Held Microscopic Camera Feedback](https://mlsanity.com/paper/2309.10287/) (2023)
- [Setup-Invariant Augmented Reality for Teaching by Demonstration with Surgical Robots](https://mlsanity.com/paper/2504.06677/) (2025)

## Built on by (most cited first, 1 of 1)

- [Strategy-Supervised Autonomous Laparoscopic Camera Control via Event-Driven Graph Mining](https://mlsanity.com/paper/2602.20500/) (2026)

## Abstract (arXiv)

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 ± 2.11 degrees and 1.95 ± 5.66 mm. The controller was computationally efficient, with a loop time of 6.8 ± 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.