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About

Mingzhang Pan is a leading researcher in medical robotics, specializing in robot-assisted surgery, autonomous path planning, and human-robot interaction. His work focuses on enhancing the safety and precision of surgical procedures through intelligent collision avoidance and skill assessment systems. Pan’s most cited paper, "Collision Risk Assessment and Automatic Obstacle Avoidance Strategy for Teleoperation Robots" (2022, 26 citations), introduces a novel framework for real-time collision detection and avoidance in teleoperated surgical environments, significantly improving patient safety. He further advanced this field with "Autonomous Path Planning for Robot-Assisted Pelvic Fracture Closed Reduction with Collision Avoidance" (2022, 16 citations), which optimizes surgical pathways to minimize risk and procedural time. Pan also developed "An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery" (2023, 12 citations), a groundbreaking method that quantifies surgical skill using motion data from instrument tips, offering objective feedback for training. Additionally, his work on "A Robot Learning from Demonstration Method Based on Neural Network and Teleoperation" (2023, 7 citations) demonstrates how robots can learn complex surgical tasks from expert demonstrations, paving the way for more autonomous systems. With over 60 total citations, Pan’s contributions are shaping the future of intelligent, safe, and efficient robotic surgery.