Papers
6
Total Citations
43
H-Index
5
About
Bao Song is a robotics and automation researcher whose work spans industrial robot control, motion planning, kinematic calibration, and dynamic modeling. His research addresses fundamental challenges in making robotic systems more precise, adaptive, and practically deployable in manufacturing environments. Song's most notable contributions include pioneering adaptive impedance control methods for robot force-position tracking on arbitrarily inclined surfaces, tackling the real-world complexity of unknown surface orientations and varying environmental conditions. His work on permanent magnet synchronous motor cascade control directly improves the precision of PMSM-actuated industrial robots, bridging the gap between motor-level performance and system-level manufacturing efficiency. In dynamic modeling, he developed an improved particle swarm optimization algorithm incorporating cross-mutation functions to enhance robotic parameter identification accuracy — a critical foundation for high-performance multi-joint control. Song has also advanced trajectory planning for parallel kinematic manipulators using novel centre sphere methods, collision detection for palletizing robots using oriented bounding box algorithms, and hybrid kinematic calibration models that minimize linearization errors in six-DoF serial robots. His cumulative citation record reflects steady influence across the robotics community. His body of work is particularly valuable for researchers and engineers seeking reliable control and planning solutions for precision industrial robotics applications.
Research Focus
Key Achievements
Top Papers
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