Qingze Li
Papers
2
Total Citations
14
H-Index
2
About
Qingze Li is a researcher whose work bridges artificial intelligence and surgical robotics, with a focus on autonomous navigation and minimally invasive procedures. His key research areas include deep reinforcement learning for mobile robot path planning and robotic-assisted surgery. In his highly cited 2021 paper, "A Deep Reinforcement Learning Method for Mobile Robot Path Planning in Unknown Environments" (10 citations), Li proposed a novel approach that enables robots to navigate without relying on pre-existing global maps, overcoming limitations of traditional methods that require complex environmental models. This contribution has significant implications for autonomous systems operating in dynamic, unstructured settings. Li also made notable contributions to clinical robotics through his 2024 study, "Da Vinci robot-assisted retroperitoneal tumor resection in 105 patients: a single-center experience" (4 citations), which provides valuable insights into the safety and efficacy of the Da Vinci Surgical System for retroperitoneal tumor removal—a procedure with limited prior documentation. This work highlights Li’s ability to translate robotic technologies into practical medical applications, advancing both theoretical frameworks and real-world surgical outcomes.
Research Focus
Key Achievements
Top Papers
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