TY - GEN
T1 - Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery
AU - Salman, Hadi
AU - Ayvali, Elif
AU - Srivatsan, Rangaprasad Arun
AU - Ma, Yifei
AU - Zevallos, Nicolas
AU - Yasin, Rashid
AU - Wang, Long
AU - Simaan, Nabil
AU - Choset, Howie
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85063137812&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063137812&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2018.8460936
DO - 10.1109/ICRA.2018.8460936
M3 - Conference contribution
AN - SCOPUS:85063137812
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5356
EP - 5363
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Y2 - 21 May 2018 through 25 May 2018
ER -