A-SEE2.0: Active-Sensing End-Effector for Robotic Ultrasound Systems with Dense Contact Surface Perception Enabled Probe Orientation Adjustment
Yernar Zhetpissov, Xihan Ma, Kehan Yang, Haichong Zhang
AI summary
Problem
Conventional freehand ultrasound relies heavily on operator skill, leading to inconsistent results and physical strain, while existing robotic systems struggle to maintain consistent probe orientation on complex, deformable body surfaces without preoperative data.
Approach
The authors developed A-SEE2.0, a compact end-effector integrating dual side-mounted RGB-D cameras with a robotic ultrasound probe to continuously perceive surface geometry and automatically adjust probe alignment in real time.
Key results
- Achieved 2.47 ± 1.25° normal positioning error on flat surfaces and 12.19 ± 5.81° on curved mannequin surfaces
- Enabled real-time full-field surface perception and automatic orthogonal probe alignment using fused dual RGB-D point clouds
- Demonstrated in-vivo forearm ultrasound image quality comparable to manual scanning by a human sonographer
- Implemented a shared autonomy workflow integrating self-normal positioning, contact force control, and teleoperation
Why it matters
Advances autonomous medical robotics by providing a reliable, real-time solution for probe alignment on complex anatomy, enabling standardized and remote diagnostic ultrasound imaging.
Abstract
Conventional freehand ultrasound (US) imaging is highly dependent on the skill of the operator, leading to inconsistent results and increased physical burden on sonographers. Robotic Ultrasound Systems (RUSS) aim to address these limitations by providing standardized and automated imaging solutions, especially in environments with limited access to skilled operators. This paper presents the development of a RUSS system that employs a novel end-effector, A-SEE2.0, which uses dual RGB-D depth cameras to maintain the US probe normal to the skin surface, a default starting configuration for anatomical landmarks identification. Our RUSS integrates RGB-D camera data with robotic control algorithms to maintain orthogonal probe alignment on uneven surfaces without preoperative data. Validation tests using a phantom model show that the system achieves robust normal positioning accuracy. A-SEE2.0 demonstrates 2.47 ± 1.25 degrees normal positioning error on a flat surface and 12.19 ± 5.81 degrees error on a mannequin surface. This work highlights the clinical potential of A-SEE2.0 by demonstrating that, during in-vivo forearm ultrasound examinations, it achieves image quality comparable to manual scanning by a human sonographer.