Zuqiong Zhang
Papers
1
Total Citations
21
H-Index
1
About
Dr. Zuqiong Zhang is a leading researcher in mobile robotics and intelligent path planning, with a focus on advancing reinforcement learning algorithms for autonomous navigation. Her most cited work, "A DDQN Path Planning Algorithm Based on Experience Classification and Multi Steps for Mobile Robots" (2022, 21 citations), addresses a critical limitation of traditional Q-learning—its inability to handle continuous state and action spaces. Dr. Zhang innovatively enhanced the Double Deep Q-Network (DDQN) algorithm by introducing experience classification and multi-step learning mechanisms, significantly improving both the accuracy and efficiency of path planning in complex, dynamic environments. This contribution has practical implications for real-world robotic systems, enabling more adaptive and reliable navigation. Her research bridges the gap between theoretical deep reinforcement learning and applied robotics, offering scalable solutions for autonomous agents. With a growing citation impact, Dr. Zhang’s work is increasingly recognized for its technical rigor and practical relevance, positioning her as a rising voice in the intersection of AI and robotics. Her achievements underscore a commitment to solving real-world constraints in autonomous systems, making her research essential reading for students and engineers in the field.
Research Focus
Key Achievements
Top Papers
- 1