Remote Robotic Palpation With Depth-Vision-Driven Autonomous-Dimensionality-Reduction Shared Control

Abstract

Teleoperated medical robots have the potential to revolutionize healthcare. However, when developing systems for tasks like remote palpation, state-of-the-art literature still uses test phantoms of oversimplified geometries, due to the complexity of the required mechanical robot–patient interaction. In reality, human bodies have complex 3-D shapes and require fine-tuning of all six manipulator's degrees of freedom, controlled by the user. In this article, we argue that the implementation of depth-vision-driven autonomous dimensionality-reduction (DVD ADR) shared control can greatly improve the users' performance. The proposed control method keeps the user in control of the end-effector’s position, while automatically adjusting its orientation in order to maintain the tactile sensor normal to the phantom's surface. A depth camera and a computer vision algorithm are used to infer the phantom's shape and achieve DVD ADR shared control. Experimental results showcase how this leads to statistically significant performance improvement. Not only were the participants able to achieve more precise palpations, with up to 29.5% and 22.4% more accuracy in position and orientation, respectively, but the DVD ADR shared control allowed them to achieve a 8.8% better detection accuracy while needing 13.8% less time. The abovementioned results are all tested for statistical significance and achieved a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i>-value lower than 0.05.