Yunlong Huang
Papers
2
Total Citations
17
H-Index
2
About
Yunlong Huang is a researcher at the forefront of intelligent robotics, specializing in path planning for high-stakes autonomous systems. His work addresses the fundamental challenge of navigating complex, hazardous environments—a nondeterministic polynomial time (NP) problem where traditional optimization methods often fail by becoming trapped in local minima. Huang’s major contribution lies in developing bio-inspired algorithms that fuse artificial intelligence with adaptive optimization. His most cited work, "Intelligent Technique for Robot Path Planning Using Artificial Neural Network and Adaptive Ant Colony Optimization" (2012, 10 citations), pioneered a hybrid approach that combines neural networks with swarm intelligence for more efficient route discovery. Building on this, his 2011 paper introduced the Danger Model Immune Wavelet Neural Network (DIWNN), a novel algorithm specifically designed for explosive ordnance disposal robots. By mimicking biological immune systems, DIWNN enables robots to dynamically avoid threats while optimizing paths in real time. Though early in his career, Huang’s focused contributions to adaptive, nature-inspired navigation systems are laying critical groundwork for safer autonomous operations in military and disaster-response applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2