Junlin Ou
Papers
5
Total Citations
33
H-Index
3
About
Junlin Ou is a researcher specializing in mobile robotics, path planning, and edge computing, with a focus on developing efficient, real-time navigation systems. Their major contributions include pioneering hybrid path planning methods that integrate adaptive visibility graphs (AVG) with Dijkstra’s algorithm and genetic algorithms (GA) on edge computing platforms, significantly improving both accuracy and computational efficiency for mobile robots. Ou also advanced indoor positioning systems by combining low-cost overhead cameras, ArUco markers, and data-driven modeling, making precise localization accessible for robotics research and education. Their work has garnered attention, with their 2023 paper on hybrid path planning accumulating 20 citations, reflecting its impact on the field. Notably, Ou’s 2025 study introduced a GPU-enabled approach that merges global evolutionary dynamic programming with local GA optimization, enabling real-time path planning in dynamic environments with moving obstacles. This innovative fusion of global and local strategies underscores Ou’s commitment to solving complex, real-world robotic challenges. Through these contributions, Junlin Ou is shaping the future of autonomous navigation, offering scalable, cost-effective solutions that bridge theory and practical application.
Research Focus
Key Achievements
Top Papers
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