Quanjun Yin
Papers
9
Total Citations
172
H-Index
5
About
Quanjun Yin is a researcher whose work spans autonomous robot navigation, deep reinforcement learning, and spatial representation — fields that sit at the intersection of artificial intelligence and robotics. His most influential contribution, the MK-A3C algorithm (2019, 70 citations), introduced a memory and knowledge-enhanced deep reinforcement learning approach enabling non-holonomic robots to navigate complex, dynamic environments with continuous control — a significant advance over earlier methods that struggled with moving obstacles. Complementing this, his hierarchical JPS-IA3C framework elegantly combined classical path planning with adaptive motion control, demonstrating that hybrid architectures can outperform standalone approaches in real-world robotic scenarios. Yin's earlier work laid important groundwork: his modified potential field method for dynamic obstacle avoidance (2013) and his swarm formation control algorithm both addressed fundamental multi-robot coordination challenges. His research on Generalized Voronoi Diagrams and grid-based distance maps reflects a sustained commitment to efficient spatial representation, critical for real-time robotic applications. More recently, Yin has expanded into vision-language navigation, publishing a widely-cited survey (2023, 38 citations) that has quickly become a key reference in this rapidly growing field. Across more than a decade of research, Yin has consistently worked to make autonomous robots more capable, efficient, and adaptable in realistic environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Vision-language navigation: a survey and taxonomy38 citations · 2023
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- 5Formation Control of Robotic Swarm Using Bounded Artificial Forces9 citations · 2013
- 6Speed Optimization for Incremental Updating of Grid-Based Distance Maps5 citations · 2019
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