Jianjun Yu

Papers

Total Citations

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About

Jianjun Yu is a robotics and autonomous systems researcher whose work centers on intelligent navigation, motion planning, and obstacle avoidance for mobile robots. His most recognized contribution lies in advancing artificial potential field (APF) methodologies for local path planning, addressing fundamental algorithmic limitations that have long challenged the robotics community. In his 2013 paper, which has garnered 21 citations, Yu tackled the notorious local minima problem inherent in traditional APF approaches — a critical shortcoming that causes robots to become trapped during obstacle avoidance and fail to reach intended destinations. By introducing virtual force constructs and refined algorithmic strategies, his work significantly improved the reliability and robustness of real-time path planning systems. This contribution has proven valuable to researchers and engineers developing autonomous mobile platforms across industrial, service, and research applications. Yu's research reflects a practical, problem-driven approach to robotics engineering, bridging theoretical algorithmic design with real-world navigation challenges. His work continues to serve as a reference point for scholars exploring enhanced potential field methods and intelligent robot motion control in dynamic environments.