Xiangcheng Ding
Papers
2
Total Citations
31
H-Index
2
About
Xiangcheng Ding is a researcher focused on advancing autonomous navigation and path planning for mobile robots, particularly in dynamic and unpredictable environments. His primary contributions lie in enhancing deep reinforcement learning algorithms, with a specific emphasis on improving the Deep Deterministic Policy Gradient (DDPG) framework. Ding’s most cited work, “Research on Dynamic Path Planning of Mobile Robot Based on Improved DDPG Algorithm” (2021, 24 citations), addresses critical limitations of standard DDPG—namely low success rates and slow learning speeds—by integrating the RAdam optimizer to stabilize training and accelerate convergence. He further extended this approach in “Research on Path Planning of Cloud Robot in Dynamic Environment Based on Improved DDPG Algorithm” (2021, 7 citations), incorporating prioritized experience replay to boost sample efficiency and learning robustness. Collectively, his research offers practical solutions for real-time robot navigation in complex settings, bridging the gap between algorithmic theory and real-world deployment. Ding’s work is particularly valuable for students and engineers seeking to understand how adaptive optimization and memory-based learning can make autonomous systems more reliable and efficient in cluttered or changing environments.
Research Focus
Key Achievements
Top Papers
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