Xing Lin
Papers
1
Total Citations
16
H-Index
1
About
Xing Lin is a pioneering researcher in mobile robotics and intelligent path planning, whose work bridges computational optimization and real-world autonomous navigation. His most-cited paper, "Designing the optimal path curve based on spline functions for mobile robot using the combination of bee colony algorithm and genetic algorithm" (2024, 16 citations), introduces a novel hybrid meta-heuristic approach that fuses bee colony optimization with genetic algorithms to generate smooth, energy-efficient trajectories. This research addresses critical trade-offs in robotic movement—balancing time efficiency, path length, and energy consumption—by leveraging spline functions for continuous curvature paths. Lin’s contributions extend beyond classical and probabilistic methods, offering a robust framework that outperforms traditional techniques in complex environments. His work has significant implications for autonomous systems, from warehouse logistics to search-and-rescue operations, where optimal routing directly impacts performance and battery life. By integrating bio-inspired algorithms with mathematical curve design, Lin has advanced the state of the art in mobile robot navigation, providing a scalable solution for real-time path optimization. His research continues to inspire new directions in intelligent robotics and computational intelligence.
Research Focus
Key Achievements
Top Papers
- 1