Papers
2
Total Citations
61
H-Index
2
About
Dr. Ye Jin is a leading researcher in mobile robotics, specializing in intelligent navigation and path planning. Her work centers on integrating deep reinforcement learning with traditional algorithms to enhance autonomous decision-making in wheeled mobile robots (WMRs). Her most impactful contribution, "Navigation of Mobile Robots Based on Deep Reinforcement Learning: Reward Function Optimization and Knowledge Transfer" (2023, 47 citations), pioneers methods to optimize reward functions and enable knowledge transfer across tasks, significantly improving robot learning efficiency and adaptability. In her earlier work, "An improved target-oriented path planning algorithm for wheeled mobile robots" (2022, 14 citations), she developed a novel hybrid approach combining an improved A* algorithm with an optimized dynamic windows approach (DWA), enabling WMRs to plan optimal, target-oriented paths in complex environments. This work bridges global and local planning, addressing critical challenges in real-time navigation. Dr. Jin’s research has direct implications for autonomous logistics, service robotics, and industrial automation. Her innovative fusion of learning-based and classical methods marks her as a rising figure in robotics, with her citation record reflecting growing influence in the field.
Research Focus
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Top Papers
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