Guangjie Lin
Papers
2
Total Citations
20
H-Index
2
About
Guangjie Lin is a robotics researcher specializing in bio-inspired locomotion, particularly the motion planning and control of snake robots. His work addresses critical challenges in gait transition and autonomous navigation for serpentine robotic systems. Lin’s most cited paper, “Adaptive Transition Gait Planning of Snake Robot Based on Polynomial Interpolation Method” (2022, 14 citations), introduces a novel approach to smoothly shifting between straight and turning gaits by updating control parameters via ROS nodes, significantly enhancing maneuverability in constrained environments. His follow-up study, “Trajectory prediction and visual localization of snake robot based on BiLSTM neural network” (2023, 6 citations), integrates deep learning to improve spatial awareness and path forecasting, enabling more robust autonomous operation. These contributions have practical implications for search-and-rescue missions and industrial inspection in confined spaces. Lin’s research bridges classical kinematics with modern machine learning, offering efficient, adaptive solutions for complex robotic locomotion. His work continues to influence the development of flexible, terrain-adaptive robots, making him a rising figure in the field of bio-inspired robotics.
Research Focus
Key Achievements
Top Papers
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- 2