Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric information of subsurface anatomy, such as the location of tumors, which necessitates accurate and efficient surgical sensing. In this work, we propose an automated sensing method that maps 3D subsurface anatomy to provide such geometric knowledge. We model the anatomy via a Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D occupancy map, we plan sensing paths on the surface of the anatomy via a graph search algorithm, $A^*$ search, with a cost function that enables the trajectories generated to balance between exploration of unsensed regions and refining the existing probabilistic understanding. We demonstrate the performance of our proposed method by comparing it against 3 different methods in several anatomical environments including a real-life CT scan dataset. The experimental results show that our method efficiently detects relevant subsurface anatomy with shorter trajectories than the comparison methods, and the resulting occupancy map achieves high accuracy.

翻译：机器人手术子任务自动化有潜力减少每位患者所需的人类外科医生工作量。许多手术子任务需要皮下解剖的几何信息（如肿瘤位置），这要求精确且高效的手术感知。本文提出了一种自动化感知方法，通过映射三维皮下解剖结构来提供此类几何知识。我们基于贝叶斯希尔伯特图构建概率三维占据图以建模解剖结构。利用该三维占据图，我们通过图搜索算法$A^*$规划解剖表面上的感知路径，其代价函数使生成的轨迹能够平衡未感知区域的探索与现有概率理解的优化。我们通过将所提方法与三种不同方法在多个解剖环境（包括真实CT扫描数据集）中进行对比，验证了其性能。实验结果表明，我们的方法能够以比对比方法更短的轨迹高效检测相关皮下解剖结构，且生成的占据图达到了高精度。