Papers
10
Total Citations
163
H-Index
6
About
Meng Wang is a versatile robotics and computer vision researcher whose work spans autonomous navigation, 3D perception, and affective computing. His most influential contributions focus on enhancing the intelligence and reliability of robotic systems in complex real-world environments. In mobile robotics, Wang developed an improved ant colony algorithm combined with Markov Decision Processes to generate smoother, safer robot trajectories in grid-based environments, garnering 62 citations and establishing him as a notable voice in path planning research. His work on RGB-D video segmentation, particularly object detection and tracking under occlusion, has attracted 40 citations and advanced scene understanding for robotic grasping applications. Wang has also made meaningful strides in 3D pose estimation, proposing reinforcement learning-based approaches for articulated object pose estimation and developing KPA-Tracker for real-time 6D pose tracking. His research extends into multimodal affective computing, point cloud analysis with transformer architectures, and pipeline inspection navigation systems, demonstrating remarkable breadth. With contributions touching educational robotics through the child-friendly LinkBricks construction kit, Wang exemplifies a researcher committed to bridging foundational computer vision with practical, human-centered applications across an impressive range of domains.
Research Focus
Key Achievements
Top Papers
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- 6Geometrically aware transformer for point cloud analysis9 citations · 2025
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- 10Research on mobile robot mapping based on improved Gmapping algorithm1 citations · 2025