A-SEE2.0: Active-Sensing End-Effector for Robotic Ultrasound Systems with Dense Contact Surface Perception Enabled Probe Orientation Adjustment

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,...

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Vydáno v:IEEE robotics and automation letters Ročník 10; číslo 9; s. 9557 - 9564
Hlavní autoři: Zhetpissov, Yernar, Ma, Xihan, Yang, Kehan, Zhang, Haichong K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Cameras
Computer vision for medical robotics
Control algorithms
End effectors
Flat surfaces
Image quality
Mannequins
medical robots and systems
Point cloud compression
Probes
Real-time systems
Robot control
Robot kinematics
Robot vision systems
Robotics
robotics and automation in life sciences
Robots
sensor-based control
Sensors
Ultrasonic imaging
Vectors
United States > US
medical robots and systems
robotics and automation in life sciences
sensor-based control
Computer vision for medical robotics
ISSN:2377-3766, 2377-3766
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Shrnutí: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 letter 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.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2025.3595036