Zekun Bai
Papers
2
Total Citations
105
H-Index
2
About
Zekun Bai is a leading researcher in autonomous robotics, specializing in path planning and deep reinforcement learning for mobile robots operating in unknown environments. His work addresses critical challenges in real-world autonomous navigation, including environmental dependency, slow inference times, and limited disturbance resistance. Bai’s most influential paper, “Path Planning of Autonomous Mobile Robot in Comprehensive Unknown Environment Using Deep Reinforcement Learning” (2024), has garnered 94 citations, reflecting its significant impact on the field. In this work, he proposes a novel framework that enhances robot adaptability and decision-making in complex, unstructured settings. Earlier, Bai introduced an improved Q-Learning algorithm integrated with a flower pollination approach for obstacle avoidance and optimized path planning for unmanned ground robots (2022, 11 citations), demonstrating his sustained focus on overcoming convergence and collision issues. His contributions are pivotal for advancing autonomous systems in logistics, exploration, and defense. Bai’s research not only pushes the boundaries of reinforcement learning in robotics but also offers practical solutions for real-time, safe navigation, making him a key figure in the next generation of intelligent autonomous vehicles.
Research Focus
Key Achievements
Top Papers
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