See, Plan, Cut: MPC-Based Autonomous Volumetric Robotic Laser Surgery with OCT Guidance

Abstract

Robotic laser systems enable sub-millimeter, non- contact tissue resection, yet existing platforms lack volumetric planning and intraoperative feedback. We present RATS (Robot-Assisted Tissue Surgery), an intelligent optical coherence tomography (OCT)-guided robotic platform for autonomous volumetric soft tissue resection. RATS integrates macro-scale RGB-D imaging, micro-scale OCT, and a fiber- coupled surgical laser, calibrated through a novel multistage alignment pipeline that achieves OCT-to-laser calibration accuracy of 0.161 ± 0.031 mm. A super-Gaussian laser–tissue interaction (LTI) model characterizes ablation morphology with an average RMSE of 0.231 ± 0.121 mm, outperforming Gaussian baselines. A sampling-based model predictive control (MPC) framework operates directly on OCT voxel data to generate closed-loop, constraint-aware resection trajectories, achieving 0.842 mm RMSE (root-mean-square error) and improving intersection-over-union agreement by 64.8% compared to feedforward execution. RATS also detects and preserves subsurface structures, demonstrating the first closed-loop autonomous volumetric robotic laser resection with OCT guidance. To our knowledge, this is the first demonstration of closed-loop autonomous volumetric robotic laser resection with OCT guidance, enabling precise, obstacle-aware tissue removal with potential in neurosurgery.