부산대학교 도서관

Radiography is one of the most widely used imaging techniques in the world. Since its inception, it has continued to evolve, leading to the development of intelligent and automated radiography systems that are able to perceive parts of their environment and respond accordingly. However, such systems do not provide a complete view of the examination space and are therefore unable to detect multiple objects and fully ensure the safety of patients, staff and equipment during the execution of the movement. In this paper, we present a system architecture based on ROS (Robot Operating System) to solve these challenges and integrate an autonomous X-ray device. The architecture retrieves point clouds from range sensors placed at specific locations in the examination room. By integrating different subsystems, the architecture merges the data from the different sensors to map the space. It also implements downsampling and clustering methods to identify objects and later distinguish obstacles. A subsystem generates bounding boxes based on the detected obstacles and feeds them to a motion planning framework (MoveIt!) to enable collision avoidance during motion execution. At the same time, a subsystem implements a deep neural network model (PointNet) to classify the detected obstacles. Finally, the developed system architecture provided promising results after being deployed in a Gazebo simulated examination space and on a use case test platform.