Chenzhi Tan

Papers

Total Citations

H-Index

About

Chenzhi Tan is a researcher focused on advancing autonomous navigation and path planning for mobile robots, with a particular emphasis on optimizing classical algorithms for real-world efficiency. Their most-cited work, “Research on path planning of three-neighbor search A* algorithm combined with artificial potential field” (2021, 26 citations), addresses critical limitations of the traditional A* algorithm—specifically, excessive search nodes and prolonged computation times. By integrating a three-neighbor search strategy with artificial potential fields, Tan’s approach significantly streamlines path planning, reducing computational overhead while maintaining robust obstacle avoidance. This hybrid methodology offers a practical solution for dynamic environments, bridging the gap between heuristic search and reactive control. Tan’s contributions are particularly valuable for applications in robotics, autonomous vehicles, and logistics, where real-time decision-making is essential. With 26 citations, this work has already influenced subsequent studies in intelligent navigation, demonstrating its relevance to both academic research and industrial deployment. Tan’s innovative synthesis of established techniques showcases a talent for refining foundational algorithms to meet modern performance demands, marking them as a promising voice in the field of mobile robotics and artificial intelligence.