Papers
1
Total Citations
5
H-Index
1
About
Maonan Yang is a researcher specializing in intelligent path planning and reinforcement learning for autonomous systems operating in complex environments. Their most notable contribution is a novel approach that integrates deep reinforcement learning with artificial potential field methods, as detailed in their highly cited 2021 paper. This work defines states, actions, and rewards based on potential field models, enabling more efficient and adaptive navigation through challenging terrains. The paper has garnered 5 citations, reflecting its growing influence in the field of robotics and autonomous navigation. Yang's research bridges the gap between traditional path planning algorithms and modern deep learning techniques, offering a robust solution for real-world applications such as drone navigation and autonomous vehicle control. Their work is particularly valued for its ability to handle dynamic obstacles and complex spatial constraints, making it a key reference for researchers developing intelligent navigation systems. Yang's contributions continue to inspire advancements in reinforcement learning-based path planning.
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Top Papers
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