Papers
3
Total Citations
11
H-Index
2
About
Quan Liang’s research focuses on intelligent robotics, autonomous navigation, and acoustic sensing, with a particular emphasis on reinforcement learning for mobile robot path planning in unstructured environments. In his most cited work, “Vision navigation of agricultural mobile robot based on reinforcement learning” (2014, 8 citations), Liang pioneered the integration of visual perception with Q-learning algorithms to enable adaptive, sensor-driven navigation for agricultural robots—addressing critical challenges in field autonomy. His earlier foundational study, “Reinforcement learning based mobile robot path planning in unknown environment” (2012, 2 citations), proposed a self-learning framework that allowed robots to navigate without prior environmental maps, advancing real-time decision-making in unknown terrains. More recently, Liang has explored acoustic scattering effects on close-packed array elements (2025, 1 citation), contributing to the miniaturization and multifunctional integration of underwater acoustic payloads for robotic systems. Though his citation counts are modest, Liang’s work represents a meaningful bridge between reinforcement learning theory and practical robotic applications in agriculture and underwater domains. His research continues to influence adaptive navigation strategies, particularly for resource-constrained autonomous systems operating in complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3