Xiuquan Cheng
Papers
1
Total Citations
16
H-Index
1
About
Xiuquan Cheng is a leading researcher in robotics and intelligent control systems, with a primary focus on nonholonomic wheeled mobile robots (NWMRs) and deep reinforcement learning. His most impactful work, "Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning" (2022, 16 citations), introduces a novel control strategy that integrates path-following models with reinforcement learning to enable autonomous navigation in complex environments. This contribution addresses critical challenges in mobile robotics, offering a robust framework for real-time obstacle avoidance without relying on traditional model-based approaches. Cheng’s research bridges the gap between theoretical control systems and practical robotic applications, advancing the field of autonomous navigation. His work has garnered attention for its innovative use of deep reinforcement learning to enhance the adaptability and safety of NWMRs in dynamic settings. Beyond this flagship paper, Cheng continues to explore intelligent control methodologies, contributing to the broader discourse on autonomous systems and human-robot interaction. His achievements underscore a commitment to developing scalable, learning-driven solutions for next-generation robotics.
Research Focus
Key Achievements
Top Papers
- 1