Yuanjin Yu

Papers

Total Citations

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About

Yuanjin Yu is a researcher in mobile robotics and autonomous navigation, with a primary focus on intelligent obstacle avoidance and path planning in complex, dynamic environments. His most cited work introduces an improved artificial potential field (APF) method integrated with the Bug2 algorithm, addressing critical limitations such as trajectory oscillations and avoidance failure when robots encounter intricate or moving obstacles. This contribution, published in 2022 and garnering 13 citations, demonstrates his ability to solve practical challenges in real-world robotic motion. Yu’s research advances the reliability and smoothness of autonomous navigation, making his work relevant for applications in service robots, autonomous vehicles, and industrial automation. By refining classical algorithms to handle dynamic and cluttered settings, he provides a foundation for more robust and adaptive robotic systems. His approach is particularly notable for its practical utility, offering a computationally efficient solution that enhances safety and performance. As the field of mobile robotics continues to grow, Yu’s contributions to obstacle avoidance remain a valuable reference for researchers and engineers developing next-generation autonomous platforms.