Zonghan Cao

Shenyang Institute of Automation, State Key Laboratory of Robotics

Papers

Total Citations

302

H-Index

About

Zonghan Cao is a leading researcher in autonomous robotics, with a primary focus on path planning, obstacle avoidance, and environmental perception for mobile robots. His work spans ground robots, underwater vehicles, and unmanned aerial systems, addressing core challenges in autonomous navigation. Cao’s most impactful contribution is his comprehensive review of autonomous path planning algorithms for mobile robots (2023), which has garnered 286 citations—a testament to its value as a foundational resource for researchers and engineers. In this work, he systematically analyzes classical and modern approaches, highlighting trade-offs between efficiency, safety, and adaptability. He further advances real-time perception with his ScorePillar method (2024), which tackles the difficult problem of small object detection in LiDAR data, achieving accurate and efficient pedestrian avoidance—a critical capability for safe robot navigation. Additionally, his research on traversable area recognition using 3D CNNs and attention mechanisms (2023) enhances ground robots’ ability to navigate rough, unstructured terrains for search-and-rescue and bomb-disposal missions. Cao’s work directly enables more capable, safer autonomous systems, making him a key figure in the field of mobile robotics.