Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery
This work addresses the challenge of efficient and accurate tumor localization in robotic surgery, which is incremental as it integrates multiple existing conditions into a novel framework.
The paper tackles the problem of automatically localizing and finding the shapes of tumors and stiff inclusions in robotic surgery by developing an approach that uses Gaussian processes and active learning to optimize palpation paths, resulting in accurate estimation of locations and boundaries while reducing exploration time.
In this work, we develop an approach for guiding robots to automatically localize and find the shapes of tumors and other stiff inclusions present in the anatomy. Our approach uses Gaussian processes to model the stiffness distribution and active learning to direct the palpation path of the robot. The palpation paths are chosen such that they maximize an acquisition function provided by an active learning algorithm. Our approach provides the flexibility to avoid obstacles in the robot's path, incorporate uncertainties in robot position and sensor measurements, include prior information about location of stiff inclusions while respecting the robot-kinematics. To the best of our knowledge this is the first work in literature that considers all the above conditions while localizing tumors. The proposed framework is evaluated via simulation and experimentation on three different robot platforms: 6-DoF industrial arm, da Vinci Research Kit (dVRK), and the Insertable Robotic Effector Platform (IREP). Results show that our approach can accurately estimate the locations and boundaries of the stiff inclusions while reducing exploration time.