Xuzhuo Zhang
Papers
1
Total Citations
32
H-Index
1
About
Xuzhuo Zhang is a researcher advancing the frontier of autonomous robotics through deep reinforcement learning. His most-cited work, "Robot Search Path Planning Method Based on Prioritized Deep Reinforcement Learning" (2022), has garnered 32 citations, establishing a novel framework that integrates prioritized experience replay with deep Q-networks to optimize robot navigation in complex, dynamic environments. This contribution addresses critical challenges in real-time path planning, enabling robots to efficiently balance exploration and exploitation during search tasks. Zhang’s approach improves convergence speed and decision-making quality, offering practical solutions for applications in disaster response, warehouse automation, and autonomous exploration. By bridging reinforcement learning theory with robotic systems, his research demonstrates how prioritized sampling can enhance learning efficiency in spatial reasoning problems. The paper’s citation impact reflects its influence on subsequent studies in intelligent path planning and adaptive control. Zhang’s work stands as a key reference for researchers developing next-generation autonomous systems, highlighting the potential of deep RL to transform robotic search and rescue operations.
Research Focus
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Top Papers
- 1