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

Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery