Abstract
Manual endoscopic submucosal dissection (ESD) is technically demanding, and existing single-segment robotic tools offer limited dexterity. These limitations motivate the development of more advanced solutions. To address this, DESectBot, a novel dual segment continuum robot with a decoupled structure and integrated surgical forceps, enabling 6 degrees of freedom (DoFs) tip dexterity for improved lesion targeting in ESD, was developed in this work. Deep learning controllers based on gated recurrent units (GRUs) for simultaneous tip position and orientation control, effectively handling the nonlinear coupling between continuum segments, were proposed. The GRU controller was benchmarked against Jacobian based inverse kinematics, model predictive control (MPC), a feedforward neural network (FNN), and a long short-term memory (LSTM) network. In nested-rectangle and Lissajous trajectory tracking tasks, the GRU achieved the lowest position/orientation RMSEs: 1.11 mm/ 4.62° and 0.81 mm/ 2.59°, respectively. For orientation control at a fixed position (four target poses), the GRU attained a mean RMSE of 0.14 mm and 0.72°, outperforming all alternatives. In a peg transfer task, the GRU achieved a 100% success rate (120 success/120 attempts) with an average transfer time of 11.8s, the STD significantly outperforms novice-controlled systems. Additionally, an ex vivo ESD demonstration grasping, elevating, and resecting tissue as the scalpel completed the cut confirmed that DESectBot provides sufficient stiffness to divide thick gastric mucosa and an operative workspace adequate for large this http URL results confirm that GRU-based control significantly enhances precision, reliability, and usability in ESD surgical training scenarios.
Abstract (translated)
手动内镜下粘膜剥离术（ESD）在技术上具有挑战性，现有的单节段机器人工具灵活性有限。这些限制促使开发更先进的解决方案。为此，在这项工作中开发了DESectBot——一种新型双节段连续体机器人，它配备了解耦结构和集成手术夹钳，实现了6个自由度（DoFs）的末端灵巧性，从而提高了ESD中病灶定位的能力。提出了一种基于门控循环单元（GRU）的深度学习控制器来同时控制末端的位置和方向，并有效地处理连续体节段之间的非线性耦合问题。在嵌套矩形轨迹跟踪任务和李萨茹轨迹跟踪任务中，GRU分别实现了最低的位置/方向均方根误差：1.11毫米/4.62度和0.81毫米/2.59度。 对于固定位置的定向控制（四个目标姿态），GRU达到了平均RMSE 0.14毫米和0.72度，优于所有其他方法。在钉子传输任务中，GRU实现了100%的成功率（120次成功尝试/120次总尝试）以及11.8秒的平均传输时间，标准差显著优于新手操控系统。 此外，在离体ESD演示中，DESectBot展示了夹取、提升和切除组织的能力，当手术刀完成切割时，这证实了DESectBot提供了足够的刚度来分割厚胃黏膜，并且操作空间足够大以适应此任务。这些结果确认基于GRU的控制在ESD手术培训场景中显著提高了精度、可靠性和易用性。 总的来说，这项工作展示了使用深度学习技术提高机器人辅助内镜下粘膜剥离术（ESD）效率和精确性的潜力，为未来的临床应用提供了一种有前景的方法。
URL
https://arxiv.org/abs/2602.03406
