Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Closed Loop Control of an MR-Conditional Robot with Wireless Tracking Coil Feedback

This paper presents a hardware and software system to implement the task space control of an MR-conditional robot by integrating inductively coupled wireless coil based tracking feedback into the control loop. The main motivation of this work is to increase the accuracy performance and address the system uncertainties in the practical scenarios. We present the MR-conditional robot hardware design, wireless tracking method, and custom-designed communication software for real-time tracking data transfer. Based on these working principles, we fabricate the robot platform and evaluate the complete system with respect to various performance indices, i.e. data communication speed, targeting accuracy, tracking coil resolution, image quality, temperature variation, and task space control accuracy for static and dynamic targeting inside MRI scanner. The in-scanner targeting results show that the MR-conditional robot with wireless tracking coil feedback achieves the targeting error of 0.17 ± 0.08 mm, while the error calculated from the joint space optical encoder feedback is 0.68 ± 0.19 mm.

Principles of focused ultrasound

Focused ultrasound (FUS/HIFU) relies on ablation of pathological tissues by delivering a sufficiently high level of acoustic energy in situ of the human body. Magnetic Resonance guided FUS (MRgFUS/HIFU) and Ultrasound guided (USgFUS/HIFU) are image guided techniques combined with therapeutic FUS for monitoring purposes. The principles and technologies of FUS/HiFU are described in this paper including the basics of MR guidance techniques and MR temperature mapping. Clinical applications of FUS/HIFU gained CE and FDA approvals for the treatment of various benign and few malignant lesions in the last two decades. Current technical limitations of ultrasound guided and MRI guided Focused Ultrasound, as well as adverse effects for the application of this technique are outlined including challenges of ablating moving organs (liver and kidney). An outlook to possible applications is provided; exampling clinical trials discussing future options.