Papers
2
Total Citations
11
H-Index
2
About
Rongke Gao is a researcher advancing the frontier of intelligent industrial robotics and precision measurement. His work focuses on two critical, interconnected areas: automated path planning for 3D scanning and high-accuracy robot calibration. Gao’s major contribution lies in replacing inefficient, manual "teach-in" methods with automated, model-based approaches. His 2024 paper on path planning for line scanning measurement robots, which has garnered 8 citations, proposes a method that uses a part’s CAD model to autonomously generate optimal scanning trajectories, significantly boosting efficiency in industrial inspection. Complementing this, his work on robot calibration introduces a novel kinematic parameter calibration method. By integrating the R-optimal criterion with an improved IOOPS algorithm, Gao directly addresses the challenge of end-effector positioning accuracy, a fundamental bottleneck for precision manufacturing. This dual focus—on both how a robot moves and how accurately it knows its position—demonstrates a comprehensive approach to industrial automation. With his recent publications already attracting early citations, Gao is establishing himself as a key voice in making robotic measurement systems more autonomous, accurate, and practical for real-world factory floors.
Research Focus
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Top Papers
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