  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.
