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<p>Patients with infectious diseases often exhibit severe respiratory distress and breathing difficulties in isolation wards. Tracheal intubation is performed manually to secure the airway by health care providers wearing personal protective equipment to prevent infection spread and getting infected. This paper presents the navigation and preoperative positioning of an autonomous mobile intubation robot capable of independently moving to the patient’s bed, positioning the intubation device inside the mouth while the operator completes the intubation procedure remotely, and the robot exits the location independently. The mobile base navigation module includes mapping, localization and path planning using the R3 live multi-sensor fusion algorithm utilizing sensor imputs from lidar, inertia measurement unit (IMU) and RGB-D camera, YOLOv5s object detection and avoidance algorithm and improved A* global path planning algorithm together with time elastic band (TEB) local path planning algorithm respectively. The preoperative positioning module based on facial pose estimation using the retinaface algorithm combined with 6DRepNet to obtain facial pose. This is validated in simulation and physical experiments. The system’s navigation time and positioning accuracy compared with related indicators of endotracheal intubation surgery obtained significantly improved results. </p>

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Preoperative positioning of robot-assisted tracheal intubation system in intensive care unit

  • 期刊论文

  • 发表日期：

    2025年5月25日

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  • Randolph Osivue Odekhe,
  • Huiping Shi,
  • Hongji Xia,
  • Qixin Cao

摘要

Patients with infectious diseases often exhibit severe respiratory distress and breathing difficulties in isolation wards. Tracheal intubation is performed manually to secure the airway by health care providers wearing personal protective equipment to prevent infection spread and getting infected. This paper presents the navigation and preoperative positioning of an autonomous mobile intubation robot capable of independently moving to the patient’s bed, positioning the intubation device inside the mouth while the operator completes the intubation procedure remotely, and the robot exits the location independently. The mobile base navigation module includes mapping, localization and path planning using the R3 live multi-sensor fusion algorithm utilizing sensor imputs from lidar, inertia measurement unit (IMU) and RGB-D camera, YOLOv5s object detection and avoidance algorithm and improved A* global path planning algorithm together with time elastic band (TEB) local path planning algorithm respectively. The preoperative positioning module based on facial pose estimation using the retinaface algorithm combined with 6DRepNet to obtain facial pose. This is validated in simulation and physical experiments. The system’s navigation time and positioning accuracy compared with related indicators of endotracheal intubation surgery obtained significantly improved results.