Yuhong Zhang
Papers
1
Total Citations
11
H-Index
1
About
Yuhong Zhang is a leading researcher in robotics and autonomous systems, specializing in secure motion planning and resilient control under cyber-physical threats. Their most influential work introduces a groundbreaking Generative Adversarial Network (GAN)-based robust motion planning framework for mobile robots, directly addressing the critical vulnerability of localization attacks—a problem often overlooked in conventional methods that assume attack-free state estimation. This 2023 paper has already garnered 11 citations, reflecting its timely impact on the field. Zhang’s contributions bridge the gap between machine learning and robotic safety, demonstrating how adversarial training can enable robots to navigate effectively even when sensor data is compromised. By integrating GANs into motion planning, they have provided a novel defense mechanism against malicious perturbations, advancing the reliability of autonomous navigation in security-sensitive environments. Their work is essential reading for researchers tackling the intersection of robotics, cybersecurity, and AI, offering both theoretical insights and practical algorithms for next-generation resilient mobile robots.
Research Focus
Key Achievements
Top Papers
- 1