Shi-Lin Ho
Papers
3
Total Citations
15
H-Index
2
About
Shi-Lin Ho is a robotics researcher whose work focuses on the intersection of artificial intelligence and autonomous navigation, particularly for mobile robots and vehicles operating in complex, dynamic environments. Ho’s major contributions center on developing intelligent path planning and control strategies that enable robots to navigate safely and efficiently without human intervention. Notably, Ho has advanced Q-learning-based approaches for collision-free path planning and optimal trajectory generation, as demonstrated in their most-cited work (10 citations), which addresses the critical challenge of real-time obstacle avoidance in dynamic settings. Additionally, Ho has integrated Voronoi diagrams with A* algorithms to enhance path efficiency in cluttered environments, and designed novel tracking control and slope-climbing strategies for autonomous mobile robots and flatbed vehicles, contributing to Industry 4.0 automation. With a growing citation record, Ho’s research is gaining recognition for its practical applications in rescue, service, and industrial logistics. Their work bridges reinforcement learning and classical robotics, offering scalable solutions for autonomous systems operating under uncertainty.
Research Focus
Key Achievements
Top Papers
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