A hFSM based cognitive control architecture for assistive task in R-MIS

Nowadays, one of the most appealing and debated challenge in robotic surgery is the introduction of certain levels of autonomy in robot behaviour [1] implying technical advances in scene understanding and situation awareness, decision making, collision-free motion planning and environment interaction. The growth of R&D projects for autonomous surgical robotics (e.g. EU funded I-SUR, MURAB and SARAS) demonstrates the confidence and the expectations of the medical community on the benefits of such technologies. SARAS aims to develop assistive surgical robots for laparoscopic MIS, autonomously operating in the same workspace of either a teleoperated surgical robot or a manually driven surgical tool. The auxiliary robots autonomously decide which task perform to assist the main surgeon, planning motions for executing the task considering the dynamics of human driven tools and patient's organs (predictable only within a short time horizon). This paper proposes a control architecture for surgical robotic assistive tasks in MIS using a hierarchical multi-level Finite State Machine (hFSM) as the cognitive control and a two-layered motion planner for the execution of the task. The hFSM models the operation starting from atomic actions to progressively build up more complex levels. The twolayer architecture of the motion planner merges the benefits of an offline geometric path construction method with those of online trajectory reconfiguration and reactive adaptation. At a global level, the path is built according to the initial knowledge of the operating scene and the requirements of the surgical tasks. Then, the path is reconfigured with respect to the dynamic environment using artificial potential fields [2]. Finally, a local level computes the robot trajectory, preserving collision-free property even in presence of obstacles with small diameter (i.e. the manually driver surgical instruments), by enforcing a velocity modulation technique derived from the Dynamical Systems (DS) based approach of [3].