Papers
5
Total Citations
33
H-Index
3
About
Qingchun Zheng is a robotics and intelligent systems researcher whose work spans mobile robot navigation, robotic manipulation, surgical robotics, and visual simultaneous localization and mapping (SLAM). With a focus on applying deep reinforcement learning to real-world robotic challenges, Zheng has made notable contributions to autonomous path planning and obstacle avoidance, developing novel approaches that address critical limitations such as sparse rewards and slow early-stage learning efficiency. His highly cited 2024 study on mobile robot path planning in dynamic environments and his 2023 work integrating Proximal Policy Optimization with YOLOv5 for object recognition and grasping — each garnering 11 citations — demonstrate his ability to combine cutting-edge machine learning techniques with practical robotic applications. Beyond autonomous navigation, Zheng has extended his expertise into medical robotics, authoring a temperature prediction model for bone drilling in surgical rehabilitation that has attracted 7 citations. His more recent work on DE-RGBD SLAM highlights his growing interest in robust perception systems for dynamic environments. Collectively, Zheng's research reflects a broad yet coherent vision of making robotic systems smarter, safer, and more adaptable across industrial and clinical settings.
Research Focus
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Top Papers
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