Jiangming Kan
Papers
15
Total Citations
181
H-Index
6
About
Jiangming Kan is a robotics and intelligent systems researcher whose work spans autonomous navigation, path planning, and computer vision, with a growing focus on intelligent forestry applications. He is perhaps best known for his contributions to robot path planning algorithms, particularly his development of improved ant colony optimization techniques for 3D environments, which has earned over 50 citations and addressed longstanding challenges such as local optima entrapment and excessive computation time. His earlier work on fuzzy PID-based trajectory tracking control for mobile robots (44 citations) demonstrated a strong foundation in control systems and simulation. Kan has made significant contributions to Visual SLAM, introducing novel loop closure detection methods combining points, lines, and information entropy, as well as a deep-learning-enhanced VSLAM system with adaptive motion modeling suited for autonomous driving contexts. A distinctive thread in his research is the application of robotics and computer vision to forestry — from automated tree diameter measurement to forest scene 3D reconstruction using conditional GANs. With a cumulative citation profile reflecting both breadth and depth, Kan represents a researcher bridging classical robotics methodology with modern machine learning to solve real-world environmental and autonomous systems challenges.
Research Focus
Key Achievements
Top Papers
- 13D Path Planning for the Ground Robot with Improved Ant Colony Optimization50 citations · 2019
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- 6Ground Robot Path Planning Based on Simulated Annealing Genetic Algorithm10 citations · 2018
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- 8Improved Ant Colony Optimization for Ground Robot 3D Path Planning6 citations · 2018
- 9A novel forestry information-collecting mobile system5 citations · 2024
- 10CGAN-Based Forest Scene 3D Reconstruction from a Single Image5 citations · 2024