Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery

Hadi Salman, Elif Ayvali, Arun Srivatsan Rangaprasad, Yifei Ma, Nicolas Zevallos, Rashid Yasin, Long Wang, Nabil Simaan and Howie Choset
Conference Paper, Proceedings of the international conference on robotics and automation (ICRA), May, 2018

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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.

author = {Hadi Salman and Elif Ayvali and Arun Srivatsan Rangaprasad and Yifei Ma and Nicolas Zevallos and Rashid Yasin and Long Wang and Nabil Simaan and Howie Choset},
title = {Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery},
booktitle = {Proceedings of the international conference on robotics and automation (ICRA)},
year = {2018},
month = {May},
publisher = {IEEE},
} 2018-02-21T11:01:01-04:00