Yuankai Qiao

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About

Yuankai Qiao is a researcher advancing the frontiers of intelligent automation and precision robotics. His work centers on two critical, interconnected challenges: enabling high-accuracy perception for automated maintenance and enhancing the absolute positioning accuracy of industrial robots. In his foundational paper, "Tiny Screw and Screw Hole Detection for Automated Maintenance Processes" (2022, 2 citations), Qiao tackles a fundamental bottleneck in automated disassembly by integrating deep learning with machine vision to reliably detect minuscule fasteners. This work is essential for developing fully autonomous maintenance systems. Building on this, his 2023 study, "A Kinematic Calibration Method Based on Residual Network Combining Joint Angles and Robot Pose" (2 citations), addresses the industry-wide need for improved robot precision. By introducing a novel residual network architecture that fuses joint angle data with end-effector pose, Qiao provides a sophisticated compensation method that significantly boosts absolute positioning accuracy—a key performance metric for advanced manufacturing. Through these contributions, Qiao is bridging the gap between perception and precise action, laying the groundwork for more capable and reliable robotic systems in complex industrial environments.