Lijun Yu

Papers

Total Citations

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About

Lijun Yu is a researcher whose work lies at the intersection of autonomous robotics, path planning, and deep learning-based perception. His most impactful contribution, the "Path optimization of AUV based on smooth-RRT algorithm" (2017), has garnered 33 citations and addresses a critical challenge in autonomous underwater vehicle (AUV) navigation: generating smooth, feasible paths in complex, real-time environments. By introducing convergence and angle factors to the Rapidly-exploring Random Tree (RRT) algorithm, Yu significantly improved path quality and computational efficiency, a key advance for underwater missions. Building on this, his 2018 work on a hybrid algorithm for robot path planning combined gradient-based optimization with the A* algorithm to reduce path oscillation in cluttered spaces. Yu also ventured into deep learning with his 2019 study on target detection and recognition, which aimed to enhance both classification and localization—a step beyond traditional methods. Though his citation counts reflect a focused, early-career impact, his contributions to smooth, hybrid path planning are particularly notable for their practical relevance in autonomous navigation, offering clear improvements for real-world robotic systems operating in constrained environments.