Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new potential for autonomous resection. We present a vision-guided, autonomous approach for palliative resection of tracheal tumors. This system models the tracheal surface with a fifth-degree polynomial to plan tool trajectories, while a custom Faster R-CNN segmentation pipeline identifies the trachea and tumor boundaries. The electrocautery tool angle is optimized using handheld surgical demonstrations, and trajectories are planned to maintain a 1 mm safety clearance from the tracheal surface. We validated the workflow successfully in five consecutive experiments on ex-vivo animal tissue models, successfully clearing the airway obstruction without trachea perforation in all cases (with more than 90% volumetric tumor removal). These results support the feasibility of an autonomous resection platform, paving the way for future developments in minimally-invasive autonomous resection.

翻译：现有气管肿瘤切除方法常缺乏有效气道清理所需的精度，而机器人技术的进步为自主切除提供了新的潜力。本文提出一种用于气管肿瘤姑息性切除的视觉引导自主方法。该系统采用五次多项式对气管表面进行建模以规划器械轨迹，同时通过定制化的Faster R-CNN分割流程识别气管与肿瘤边界。电灼器械角度通过手持手术演示进行优化，轨迹规划保持距气管表面1毫米的安全边界。我们在离体动物组织模型上连续进行五次实验验证该工作流程，所有案例均成功清除气道梗阻且未发生气管穿孔（肿瘤体积切除率超过90%）。这些结果证实了自主切除平台的可行性，为未来微创自主切除技术的发展奠定了基础。