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About

Orhan Ozguner is a leading researcher in the field of medical robotics, with a primary focus on advancing autonomous capabilities for robotic surgery. His work centers on the da Vinci Surgical System, where he has made pivotal contributions to camera-robot calibration, surgical tool tracking, and autonomous suturing. His most cited paper, "Camera-Robot Calibration for the Da Vinci Robotic Surgery System" (2020, 58 citations), introduces a novel method for hand-eye calibration between the endoscopic camera and patient-side manipulators, a critical step for enabling semi-autonomous surgical robots. He further advanced surgical tool tracking in "Vision-Based Surgical Tool Pose Estimation for the da Vinci® Robotic Surgical System" (2018, 42 citations), combining robot kinematics with computer vision. Ozguner also pioneered needle localization and tracking using stereo endoscopic images (2018, 20 citations) and developed a visually-guided autonomous needle driving algorithm for suturing (2021). His work has accumulated over 127 citations, demonstrating significant impact in the surgical robotics community. Ozguner's research bridges the gap between teleoperated and autonomous surgery, with notable achievements in creating algorithms that enhance precision and reduce human error in minimally invasive procedures.