Zhengru Fang
Papers
1
Total Citations
17
H-Index
1
About
Zhengru Fang is a leading researcher in collaborative perception and task-oriented communications for multi-robot and vehicular networks. His work addresses critical challenges in real-time adaptive systems, particularly the fusion of sensory data from multiple agents to enhance perception accuracy and sensing range. Fang's most cited paper, "R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications" (2025, 17 citations), introduces a novel framework that tackles the persistent issue of extrinsic calibration errors caused by mobility and non-rigid sensor mounts. By enabling online calibration and robust communication, this work significantly improves the reliability of collaborative sensing in dynamic environments. Fang's contributions are foundational to advancing autonomous systems, where precise, real-time perception is essential for safety and efficiency. His research bridges the gap between theoretical communication models and practical deployment, offering scalable solutions for intelligent transportation and multi-agent coordination. With a growing citation impact, Fang is recognized for pushing the boundaries of task-oriented communications, making him a key figure in the next generation of collaborative autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1