Lanyong Zhang
Papers
8
Total Citations
63
H-Index
4
About
Lanyong Zhang is a leading researcher in intelligent robotics and autonomous navigation, with a focus on reinforcement learning (RL) for motion planning in complex, dynamic environments. His major contributions include pioneering improvements to deep RL algorithms—such as the enhanced TD3 and Deep Q-Learning approaches—that significantly boost the success rate, training speed, and adaptability of mobile robots, UAVs, and autonomous ground vehicles. Notably, his 2024 paper on path planning using an improved TD3 algorithm has garnered 28 citations, reflecting its impact on addressing critical flaws in existing methods. Zhang’s work also extends to practical applications, including the design of natural gas pipeline inspection robots and position correction methods for AUVs in deep-sea navigation. His recent multi-goal RL frameworks enable quadrotors and ground vehicles to navigate cluttered 3D environments with unseen random goals, pushing the boundaries of autonomous decision-making. With over 60 total citations across his most-cited works, Zhang’s research is essential reading for students and engineers seeking to advance real-world robotic systems that operate reliably in unpredictable settings.
Research Focus
Key Achievements
Top Papers
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- 4The design of natural gas pipeline inspection robot system7 citations · 2015
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- 7Geometric Path Planning and Synchronization for Multiple Vehicles1 citations · 2025
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