A Reactive Autonomous Camera System for the RAVEN II Surgical Robot

TL;DR

This paper introduces a proof-of-concept autonomous camera system for the RAVEN II surgical robot that uses transfer learning and control rules to improve camera positioning, aiming to reduce surgeon workload and enhance safety during procedures.

Contribution

It presents a novel autonomous camera control system utilizing transfer learning for object localization and classification in surgical robotics.

Findings

Perception module achieved 61.21% accuracy in object identification.

System effectively localized graspers and multiple environment objects.

Proposed camera movements aligned well with surgeon preferences.

Abstract

The endoscopic camera of a surgical robot provides surgeons with a magnified 3D view of the surgical field, but repositioning it increases mental workload and operation time. Poor camera placement contributes to safety-critical events when surgical tools move out of the view of the camera. This paper presents a proof of concept of an autonomous camera system for the Raven II surgical robot that aims to reduce surgeon workload and improve safety by providing an optimal view of the workspace showing all objects of interest. This system uses transfer learning to localize and classify objects of interest within the view of a stereoscopic camera. The positions and centroid of the objects are estimated and a set of control rules determines the movement of the camera towards a more desired view. Our perception module had an accuracy of 61.21% overall for identifying objects of interest and was able to localize both graspers and multiple blocks in the environment. Comparison of the commands proposed by our system with the desired commands from a survey of 13 participants indicates that the autonomous camera system proposes appropriate movements for the tilt and pan of the camera.