A maintenance training simulator system by the method of hardware-in-loop simulation is developed to train the maintenance personal of a certain command and control equipment. This paper introduces the main functions, structure and design of the system's console and core hardware component. A case of fault point setting shows that the system can realize successfully the maintenance training functions of this equipment.

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The surgical robot, a crucial instrument in the emerging field of minimally invasive surgery (MIS), has clear advantages over traditional MIS in terms of flexibility, accuracy, and 3D vision. A crucial computer vision task that helps surgeons is the real-time and precise segmentation of the region of interest during robotic surgery. In this paper, a brand-new and reliable learning framework for image segmentation is proposed, and it is based on a single RGB camera. The major study goal is presented after a brief introduction to the current state of affairs and the visual capabilities of surgical robots at first. Then, a brief review and discussion of the existing research on deep learning-based computer vision in robotic surgery is given, followed by the presentation of our suggested deep neural network structure. Additionally, the comprehensive extraction of the multi-scale feature information makes use of the atrous convolutional procedure. Finally, a variety of image segmentation evaluation metrics are introduced, and the performance is promising for the clinical domain.

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