Papers
1
Total Citations
35
H-Index
1
About
Yi Quan is a prominent researcher in robotics and optimization, whose work centers on advancing autonomous navigation and path planning. His most cited paper, "Bidirectional artificial potential field-based ant colony optimization for robot path planning" (2024, 35 citations), introduces a novel hybrid algorithm that integrates bidirectional search strategies with ant colony optimization, significantly improving efficiency and obstacle avoidance in complex environments. This contribution addresses critical challenges in real-time robot motion planning, offering a more robust solution than traditional methods. Quan’s research bridges theoretical optimization and practical robotics, with impacts spanning industrial automation, autonomous vehicles, and swarm robotics. His work is widely recognized for its innovative fusion of bio-inspired algorithms and artificial potential fields, earning citations from peers developing next-generation autonomous systems. Beyond this flagship paper, Quan continues to explore adaptive and multi-objective optimization techniques, solidifying his reputation as a rising leader in computational intelligence and robotics. His achievements underscore a commitment to solving real-world navigation problems, making his research essential reading for students and engineers in intelligent systems.
Research Focus
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Top Papers
- 1