Papers
2
Total Citations
12
H-Index
2
About
Bowen Niu is a researcher focused on advancing autonomous navigation through bio-inspired and reinforcement learning techniques. His primary research areas include mobile robot path planning, optimization algorithms, and intelligent control systems. Niu’s major contributions lie in enhancing the efficiency and adaptability of path planning for autonomous mobile robots. In his 2019 work, "Mobile Robot Path Planning Based on Improved Reinforcement Learning Optimization," he refined Q-learning by addressing the limitations of fixed parameters in adaptive functions, achieving more effective navigation solutions. Building on this, his 2020 study, "Mobile Robot Path Planning Based on Improved Ant Colony Optimization Algorithm," integrated ant colony optimization with Q-learning to tackle complex planning problems, demonstrating significant potential for real-world applications. Each of these papers has garnered 6 citations, reflecting their growing influence in the robotics and optimization communities. Niu’s work bridges classical bionic algorithms with modern reinforcement learning, offering practical improvements for autonomous systems. His research is particularly valuable for students and engineers seeking robust, scalable solutions in mobile robotics, showcasing how hybrid approaches can overcome traditional path planning challenges.
Research Focus
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