Beyond the Manual Touch: Situational-aware Force Control for Increased Safety in Robot-assisted Skullbase Surgery

TL;DR

This paper tackles safety and precision in robot-assisted skullbase drilling by introducing a situational-aware, adaptive force control framework that leverages a Digital Twin to incorporate real-time tool-tissue interaction forces and surgical context. The approach combines a cooperative robot (REMS) with adaptive admittance control and context-driven gain tuning to keep contact forces within safe limits while preserving sensorimotor transparency. Key contributions include extending the Digital Twin to model contact dynamics, real-time operating-structure estimation, and a dynamic force-control scheme that adjusts to anatomy-specific safety thresholds. Feasibility experiments with a cadaveric temporal bone and mixed-level users suggest meaningful reductions in undesired interactions, paving the way for safer, more precise skullbase procedures.

Abstract

Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods - We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results - To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions - The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base.

Figures (5)

• Figure 1: System overview showing force sensing drill, surgical environment, and digital twin. $S$ denotes the operating anatomical structure. $x_T$ is the drill tip pose and $\Dot{q}$ is a joint velocity. $F_T$, $F_W$, $F_D$ denote tool-tissue interaction force at the drill tip, and forces measured from the wrist force sensor and the drill sensor, respectively.
• Figure 2: The closest distances between the drill tip and the anatomical structures, $d_n$, are calculated in real-time. ($d_1$: Facial Nerve, $d_2$: Tegmen, $d_3$: Sigmoid, $d_4$: Cortical bone, $d_5$: Trabecular bone).
• Figure 3: Experimental setup. Medical student uses the surgical drill attached to the cooperative robot under microscopic view, while an adjacent optical tracker monitors both the drill and the anatomy.
• Figure 4: Positional data (x, y, z) of the drill from a participant's procedure, with a green line indicating drill contact with anatomy (High signifies contact) and labeled operating structures.
• Figure 5: Comparison of the interaction force with and without proposed method. Contact, High and Undesired denote when the tissue interaction force being ($F_T > \textcolor{black}{C} = 0.3$[N]), ($F_T > \lambda_n$[N]) and ($F_T > \lambda_n +0.2$[N]) respectively. Lower value indicates better result.