Papers
4
Total Citations
64
H-Index
4
About
Lidong Gu is a rising figure in robotics and optimization, whose work bridges nature-inspired algorithms and real-world robotic control. His primary research areas include meta-heuristic optimization, inverse kinematics, and dynamics control for robotic manipulators and mobile robots. Gu’s most notable contribution is the **Wild Geese Migration Optimization (GMO) algorithm**, a novel meta-heuristic inspired by the social swarming behavior of wild geese, which he applied to solve inverse kinematics for robots. This paper has garnered **37 citations** since 2022, signaling strong interest from the optimization and robotics communities. He further advanced the field by hybridizing the Battle Royale Optimization (BRO) algorithm with chicken swarm mechanisms, achieving improved performance for 7R 6DOF robot kinematics. In control theory, Gu proposed a **parallel network-based sliding mode tracking controller** to handle uncertain robot dynamics and external disturbances, and developed a **digital twin-based parameter compensation method** for mobile robot dynamics, enhancing positioning accuracy in high-risk environments like power distribution and petrochemical plants. With a growing citation record and a focus on practical, industry-relevant challenges, Gu’s work is shaping the future of intelligent robotic systems.
Research Focus
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Top Papers
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