Papers
1
Total Citations
2
H-Index
1
About
Yi Quan is a researcher specializing in robotics and computational intelligence, with a particular focus on path planning and optimization algorithms. Their most notable contribution is the development of a bidirectional artificial potential field-based ant colony optimization method for robot path planning, published in 2024. This work integrates two powerful bio-inspired techniques—artificial potential fields and ant colony optimization—to enhance the efficiency and safety of autonomous navigation in complex environments. By addressing key challenges such as local minima and convergence speed, Quan’s approach offers a robust solution for real-time robotic applications. Although their work is early in its impact, with 2 citations to date, it represents a promising step forward in the field. Quan’s research bridges theoretical algorithm design and practical deployment, making it valuable for students and researchers interested in intelligent robotics, swarm intelligence, and autonomous systems. Their innovative synthesis of methods highlights a commitment to advancing adaptive, nature-inspired solutions for real-world engineering problems.
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Top Papers
- 1