Robotic laser systems offer the potential for sub-millimeter, non-contact, high-precision tissue resection, yet existing platforms lack volumetric planning and intraoperative feedback. We present RATS (Robot-Assisted Tissue Surgery), an intelligent opto-mechanical, optical coherence tomography (OCT)-guided robotic platform designed for autonomous volumetric soft tissue resection in surgical applications. 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+-0.031mm on tissue phantoms and ex vivo porcine tissue. A super-Gaussian laser-tissue interaction (LTI) model characterizes ablation crater morphology with an average RMSE of 0.231+-0.121mm, outperforming Gaussian baselines. A sampling-based model predictive control (MPC) framework operates directly on OCT voxel data to generate constraint-aware resection trajectories with closed-loop feedback, achieving 0.842mm RMSE and improving intersection-over-union agreement by 64.8% compared to feedforward execution. With OCT, RATS detects subsurface structures and modifies the planner's objective to preserve them, demonstrating clinical feasibility.

翻译：机器人激光系统具备亚毫米级、非接触式、高精度组织切除的潜力，但现有平台缺乏体积规划与术中反馈能力。本文提出RATS（机器人辅助组织手术系统），一种智能光机电一体化、由光学相干断层扫描（OCT）引导的机器人平台，专为外科手术中自主体积软组织切除而设计。RATS整合了宏观RGB-D成像、微观OCT及光纤耦合手术激光器，通过新型多级标定流程实现系统校准，在组织仿体与离体猪组织上达到0.161±0.031mm的OCT-激光标定精度。超高斯激光-组织相互作用模型以0.231±0.121mm的平均均方根误差表征消融坑形态，性能优于高斯基线模型。基于采样的模型预测控制框架直接在OCT体素数据上运行，生成具有闭环反馈的约束感知切除轨迹，实现0.842mm均方根误差，较前馈执行方式将交并比一致性提升64.8%。借助OCT，RATS能探测亚表层结构并调整规划目标以保护关键组织，展现了临床可行性。