Papers
2
Total Citations
52
H-Index
2
About
Linhui Han is a leading researcher in mobile robotics, specializing in biologically inspired algorithms for complete coverage path planning (CCPP). Their work addresses a fundamental challenge in autonomous systems—ensuring that robots, such as cleaning or inspection machines, efficiently traverse every reachable point in an environment without redundancy. Han’s major contributions include pioneering the integration of Q-learning with biologically inspired neural networks (BINN), overcoming traditional limitations like local optima and low coverage ratios. Their 2023 paper on a "Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning" has garnered 30 citations, while an improved BINN-based algorithm, published the same year, has earned 22 citations. These works demonstrate Han’s ability to merge reinforcement learning with neural dynamics, producing robust, adaptive solutions for real-world robotic navigation. By enhancing path efficiency and coverage, Han’s research directly impacts the design of smarter cleaning robots, agricultural drones, and inspection systems. Their achievements highlight a commitment to advancing autonomous navigation, making them a key figure in the field of intelligent robotics.
Research Focus
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Top Papers
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