Yuming Chen
Papers
1
Total Citations
2
H-Index
1
About
Yuming Chen is a robotics researcher whose work focuses on advancing autonomous navigation through bio-inspired and optimization-based approaches. Chen’s 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 innovative algorithm synergizes the global search efficiency of ant colony optimization with the local obstacle-avoidance capabilities of artificial potential fields, offering a robust solution for complex, dynamic environments. While still early in its citation impact, with 2 citations to date, this work represents a significant step toward more adaptive and collision-free navigation in robotics. Chen’s research sits at the intersection of swarm intelligence, motion planning, and autonomous systems, addressing critical challenges in real-time path optimization. By integrating bidirectional search strategies, the method reduces computational overhead and improves convergence speed, making it particularly relevant for applications in autonomous vehicles, drones, and mobile robots. As a rising voice in the field, Chen’s work promises to influence future developments in intelligent robotics and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1