Langwen Zhang
Papers
4
Total Citations
19
H-Index
2
About
Langwen Zhang is an emerging researcher whose work spans intelligent robotics, industrial automation, and real-time perception systems. Their primary research areas include multi-AGV path planning, warehouse robotics scheduling, and LiDAR-inertial odometry for autonomous navigation. Zhang’s most impactful contribution is an improved heuristic path planning algorithm that minimizes energy consumption in distributed multi-AGV systems—a critical advance for automated warehouses, earning 11 citations. They also proposed a novel hierarchical scheduling strategy for multi-warehouse mobile robots using Industrial Cyber-Physical Systems (ICPS), which optimizes computing resource allocation and work efficiency (5 citations). More recently, Zhang has pushed into real-time stereo matching with multi-volume attention mechanisms and developed a fast, accurate tightly coupled LiDAR-inertial odometry method based on sparse voxel maps and Gauss-Newton optimization. This latter work, published in 2025, addresses key bottlenecks in LiDAR data processing for navigation in complex environments. With a growing citation footprint and contributions that bridge theoretical modeling and practical deployment, Zhang is establishing a reputation for advancing both the efficiency and robustness of autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4