Shengjie Li
Papers
4
Total Citations
37
H-Index
4
About
Shengjie Li is a robotics researcher whose work centers on semantic mapping, motion planning, and teleoperation for autonomous and industrial robotic systems. His key contributions include a novel approach to 2D indoor room map semantic segmentation, which combines distance transform watershed pre-segmentation with a neural network for lidar information sampling classification—a method that directly supports mobile robots in understanding and navigating complex environments. Li has also advanced robotic motion planning through incremental accelerated gradient descent and adaptive fine-tuning heuristics, improving performance in real-time scenarios. His research addresses practical constraints in industrial manipulation, proposing distributed variable density path search and simplification methods that respect end-effector attitude constraints essential for tasks like welding and spraying. Additionally, he has developed real-time explicit mapping and teleoperation control methods for humanoid robots under posture constraints. With his most cited work accumulating 18 citations and several recent papers from 2022–2023 gaining traction, Li is establishing a reputation for bridging theoretical optimization with deployable robotic solutions.
Research Focus
Key Achievements
Top Papers
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