Agile maneuvers are essential for robot-enabled complex tasks such as surgical procedures. Prior explorations on surgery autonomy are limited to feasibility study of completing a single task without systematically addressing generic manipulation safety across different tasks. We present an integrated planning and control framework for 6-DoF robotic instruments for pipeline automation of surgical tasks.We leverage the geometry of a robotic instrument and propose the nodal state space (NSS) to represent the robot state in SE(3) space. Each elementary robot motion could be encoded by regulation of the state parameters via a dynamical system. This theoretically ensures that every in-process trajectory is globally feasible and stably reached to an admissible target, and the controller is of closed-form without computing 6-DoF inverse kinematics. Then, to plan the motion steps reliably, we propose an interactive (instant) goal state of the robot that transforms manipulation planning through desired path constraints into a goal-varying manipulation (GVM) problem. We detail how GVM could adaptively and smoothly plan the procedure (could proceed or rewind the process as needed) based on on-the-fly situations under dynamic or disturbed environment. Finally, we extend the above policy to characterize complete pipelines of various surgical tasks. Simulations show that our framework could smoothly solve twisted maneuvers while avoiding collisions. Physical experiments using the da Vinci Research Kit (dVRK) validates the capability of automating individual tasks including tissue debridement, dissection, and wound suturing. The results confirm good task-level consistency and reliability compared to state-of-the-art automation algorithms.

翻译：敏捷操作对于机器人执行的复杂任务（如外科手术）至关重要。先前关于手术自主性的探索仅限于完成单一任务的可行性研究，未能系统性地解决跨不同任务的通用操作安全性问题。我们提出了一种针对六自由度机器人器械的集成规划与控制框架，用于手术任务的流水线自动化。我们利用机器人器械的几何特性，提出了节点状态空间（NSS）来在SE(3)空间中表示机器人状态。每个基本机器人运动可通过动态系统对状态参数的调节进行编码。这在理论上保证了每个过程轨迹全局可行且能稳定到达可接受目标，同时控制器具有闭式解形式，无需计算六自由度逆运动学。然后，为可靠规划运动步骤，我们提出了机器人交互式（即时）目标状态，将经过期望路径约束的操作规划转化为目标变化操作（GVM）问题。我们详细阐述了在动态或干扰环境下，GVM如何根据实时情况自适应且平滑地规划流程（可根据需要前进或回退过程）。最后，我们将上述策略扩展到描述各类手术任务的完整流水线。仿真结果表明，我们的框架能平滑解决扭曲操作并避免碰撞。使用达芬奇研究套件（dVRK）的物理实验验证了自动化单个任务（包括组织清创、解剖和伤口缝合）的能力。与最先进的自动化算法相比，结果确认了良好的任务级一致性和可靠性。