Menglong Li

North University of China, Shanxi Provincial Traffic Planning Survey and Design Institute

Papers

Total Citations

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About

Menglong Li is a researcher at the forefront of robotics and autonomous systems, with a primary focus on mobile robot navigation, deep learning for robotic perception, and industrial parallel robot positioning. His most impactful work introduces the IA-DWA algorithm, a novel fusion of the A* global path planner with the Dynamic Window Approach (DWA) for local obstacle avoidance. This hybrid method enables mobile robots to efficiently search for globally optimal, collision-free paths while dynamically reacting to unknown obstacles, addressing a critical challenge in autonomous navigation—his top-cited paper (15 citations) highlights its practical significance. Li has also advanced industrial automation through deep learning hybrid methods. His RP-YOLOX-DL approach combines YOLOX object detection with Deeplabv3+ segmentation to achieve precise target positioning for parallel robots, improving classification pickup efficiency and response time. Additionally, he developed a 3D pickup estimation method using point cloud simplification and registration, further enhancing robotic manipulation accuracy. With contributions spanning navigation, perception, and industrial robotics, Li’s work demonstrates a strong commitment to bridging algorithmic innovation with real-world robotic applications, making him a notable figure in the field.