ROS2-based Cobot motion control for automated 3D muscle ultrasound scanning
Nizer, Abdul (2025)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025120934083
https://urn.fi/URN:NBN:fi:amk-2025120934083
Tiivistelmä
This thesis is on the design and implementation of a collaborative robotic system that contains motion control along with optical motion capture technologies to be used during ultrasound imaging. The set-up integrates Cobot with OptiTrack markers, which secure the utmost detective approach through precise tracking of motion, thus bringing improved quality for imaging. The whole system comes equipped with advanced motion planning tools that include ROS2 Humble, MoveIt2, RViz, and Gazebo to provide an opportunity for simulation, visualization, and excellent control. This is to ensure continuous reliability and reproducible ultrasound imaging during medical diagnosis.
The research aims for automation of ultrasound imaging while exploiting robot capability for pre-defined trajectory adherence and real-time adaptation to dynamic human anatomy by real-time motion capture. Besides, the whole system is configured for real-time processing and visualization of ultrasound data within the complete precision diagnostic framework. This project closes in on operator fatigue, variability in imaging results, and efficiency by incorporating robotics and motion capture in scanning procedures.
The implementation mainly consists of software setup, motion programming, and validation phases, with methods concentrated on linear and marker-guided probe sweeps as well as force threshold monitoring for consistent skin contact. Results indicate the diverse possibilities that ROS2-based collaborative robotics provide for further implementing healthcare automation and scalable yet efficient solutions for medical imaging in future development of diagnostic technologies and robot-assisted procedures.
The research aims for automation of ultrasound imaging while exploiting robot capability for pre-defined trajectory adherence and real-time adaptation to dynamic human anatomy by real-time motion capture. Besides, the whole system is configured for real-time processing and visualization of ultrasound data within the complete precision diagnostic framework. This project closes in on operator fatigue, variability in imaging results, and efficiency by incorporating robotics and motion capture in scanning procedures.
The implementation mainly consists of software setup, motion programming, and validation phases, with methods concentrated on linear and marker-guided probe sweeps as well as force threshold monitoring for consistent skin contact. Results indicate the diverse possibilities that ROS2-based collaborative robotics provide for further implementing healthcare automation and scalable yet efficient solutions for medical imaging in future development of diagnostic technologies and robot-assisted procedures.