Point cloud-guided ultrasound robotic scanning path planning for the kidney based on anatomical positioning

In order to tackle the path planning difficulties faced by ultrasound robots during renal ultrasonography procedures, this research integrates anatomical positioning with point cloud processing technology, proposing a specialized path planning algorithm tailored for renal ultrasonography. The study employs a depth camera to capture and preprocess three-dimensional point cloud data from the surface of the body. The orientation of the human model is enhanced through the automated identification of the vertebral line and the narrowest section of the waist, while the costovertebral angle is utilized to formulate an accurate scanning trajectory aimed at imaging the kidneys. To evaluate the efficacy of the algorithm, this study validates its path planning capabilities through simulation experiments and enables the robot to perform automatic scans on real human subjects. The experimental results indicate that the algorithm can effectively plan the desired scanning path across different positions and poses on standard human models, allowing the ultrasound robot to successfully acquire ultrasound images of the kidneys from volunteers under real physiological conditions. The success rate is 93.33 % and the average scanning time is 4.28 s. Experimental results demonstrate that the proposed path planning method, by incorporating anatomical features, accelerates and simplifies kidney localization in 2D ultrasound imaging. Utilizing point cloud technology, it achieves low-cost, fully automated scanning, rapidly and accurately detecting human feature lines, reducing dependence on external markers, and effectively addressing the challenges posed by different poses through pose correction. These advancements provide a strong foundation for the autonomous operation of ultrasound robots.