Tongpo Zhang
Papers
3
Total Citations
10
H-Index
2
About
Tongpo Zhang is a researcher focused on advancing autonomous navigation and multi-robot systems through vision-based tracking and intelligent path planning. His work centers on integrating edge computing and deep learning to solve critical challenges in dynamic environments, particularly addressing the common problem of target loss due to partial visual occlusion. Zhang’s most cited paper, "Improved Camshift Algorithm in AGV Vision-based Tracking with Edge Computing" (2021, 6 citations), enhances real-time tracking performance for automated guided vehicles. He further explores deep learning solutions for mobile robot tracking in specific environments (2022, 3 citations), demonstrating how neural networks can maintain robust visual tracking under challenging conditions. Additionally, his research on multi-robot planning introduces a novel quad-tree map division algorithm for handling irregular obstacles (2022, 1 citation), combining optimal shortest path computation with motion coordination and waiting strategies. While his citation counts are still building, Zhang’s contributions are particularly relevant to the growing field of industrial automation and collaborative robotics, where efficient, occlusion-robust tracking and scalable multi-agent coordination are essential for real-world deployment.
Research Focus
Key Achievements
Top Papers
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