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 lesions.These results confirm that GRU-based control significantly enhances precision, reliability, and usability in ESD surgical training scenarios.

翻译：手动内镜黏膜下剥离术（ESD）技术要求高，现有单段机器人器械灵活性有限。这些局限性推动了对更先进解决方案的开发。为此，本研究开发了DESectBot——一种具有解耦结构和集成手术钳的新型双段连续体机器人，能够实现6自由度末端灵活性，以改善ESD中的病灶定位。本文提出了基于门控循环单元（GRU）的深度学习控制器，用于同时控制末端位置和姿态，有效处理连续体段间的非线性耦合。将GRU控制器与基于雅可比矩阵的逆运动学、模型预测控制（MPC）、前馈神经网络（FNN）以及长短期记忆（LSTM）网络进行了基准比较。在嵌套矩形和利萨如轨迹跟踪任务中，GRU分别取得了最低的位置/姿态均方根误差：1.11毫米/4.62°和0.81毫米/2.59°。在固定位置（四个目标姿态）的姿态控制中，GRU实现了0.14毫米和0.72°的平均均方根误差，优于所有对比方法。在穿环转移任务中，GRU实现了100%的成功率（120次尝试/120次成功），平均转移时间为11.8秒，其性能标准差显著优于新手操作系统。此外，离体ESD演示中，器械在抓持、提拉组织的同时由手术刀完成切割，证实了DESectBot具备分割厚胃黏膜所需的足够刚度，以及处理大面积病灶的充足操作空间。这些结果证明，基于GRU的控制显著提升了ESD手术训练场景中的精度、可靠性和可用性。