Papers
2
Total Citations
125
H-Index
2
About
Chen Tian is a leading researcher in autonomous robotics and intelligent control systems, with a primary focus on underwater and snake-like robotic platforms. His most impactful contribution is the development of a complete coverage path planning algorithm for Autonomous Underwater Vehicles (AUVs) using a Grid-Based Neural Network (GBNN), a work that has garnered 122 citations and is widely recognized for advancing autonomous navigation in complex underwater environments. In his more recent work, Tian tackles the challenge of high-voltage cable inspection by designing a snake robot capable of rapid, stable crawling along cables. By employing a Hopf oscillator for gait generation and the Simulated Annealing Algorithm to optimize step size for the spiral-winding gait, he has significantly improved the robot’s crawling speed and efficiency. This innovation holds practical promise for safer, more reliable infrastructure maintenance. Tian’s research bridges theoretical control engineering with real-world robotic applications, demonstrating a clear trajectory from foundational path planning to specialized, high-impact field robotics.
Research Focus
Key Achievements
Top Papers
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