Department of Electrical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan (R.O.C.).
Division of Gastroenterology, Department of Internal Medicine, Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan (R.O.C.).
Sci Rep. 2021 Aug 13;11(1):16491. doi: 10.1038/s41598-021-95760-7.
This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional-integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA*) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA* path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.
本文提出了一种基于力的传感器、执行器、比例积分控制器和实时启发式搜索方法的经济型磁辅助结肠镜检查自主导航系统。力感测系统使用安装在机械臂和外部永磁体之间的称重传感器来获取吸引力数据，作为实时手术安全监测和跟踪信息的基础，以导航一次性磁性结肠镜。在磁场导航器 (MFN) 平台上，x 轴和 y 轴的平均跟踪精度分别为 1.14 ± 0.59 毫米和 1.61 ± 0.45 毫米，均以平均值 ± 标准差表示。跟踪系统的平均可检测半径为 15 厘米。本文提出了三种路径规划算法的仿真，具有我们提出的方向启发式评估设计的学习实时 A* (LRTA*) 算法具有最佳性能。在未知的合成结肠图谱中完成旅行需要 75 步。通过集成基于力的传感技术和 LRTA*路径规划算法，在 MFN 平台上完成高度逼真的结肠镜检查训练模型的自主导航所需的平均时间为 15 分 38 秒，插管率为 83.33%。所有自主导航实验均无需操作人员干预完成。
