Qinzhao Wang
Papers
2
Total Citations
14
H-Index
2
About
Qinzhao Wang is a researcher specializing in intelligent robotics and autonomous navigation, with a focus on path planning and reinforcement learning-based control systems. Their work addresses fundamental challenges in robot mobility, particularly in environments where traditional methods fall short. Wang’s most cited paper, "A Deep Reinforcement Learning Based Mapless Navigation Algorithm Using Continuous Actions" (2019, 8 citations), introduces a novel approach that leverages deep reinforcement learning to enable robots to navigate without pre-existing maps, using continuous action spaces to improve maneuverability and adaptability. This work is complemented by their earlier study, "Path Planning of Robot Based on Improved Artificial Potential Field Method" (2017, 6 citations), which resolves common pitfalls in classical potential field algorithms—such as unreachable targets near obstacles and local minima—by incorporating kinematic constraints. Together, these contributions demonstrate Wang’s impact in advancing both learning-based and classical navigation techniques, with cumulative citations reflecting growing recognition in the robotics community. Their research is particularly valuable for students and engineers developing autonomous systems for dynamic, unstructured environments, offering practical solutions that bridge theoretical algorithms and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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- 2