About

Hongtai Cheng is a robotics and automation researcher whose work spans intelligent perception, autonomous navigation, robotic manipulation, and human-robot interaction. With a career rooted in both foundational algorithms and applied systems, Cheng has made significant contributions to mobile robotics, including a widely recognized topological indoor localization and navigation framework for autonomous mobile robots (2014, 63 citations) that addressed real-world constraints of limited onboard resources. His work in semiconductor manufacturing examined the accuracy of dynamic wafer-handling robotic systems, reflecting an early commitment to precision automation (2013, 36 citations). Cheng's research has increasingly embraced computer vision and deep learning, most notably through a YOLO-DeepSORT-based network for tomato tracking and yield prediction in agricultural robotics (2022, 76 citations), his most cited contribution to date. He has also advanced compliant robotic systems through gesture-based teleoperation (2019, 33 citations), variable stiffness soft arm design (2018, 36 citations), and obstacle avoidance algorithms for manipulators (2021, 58 citations). His work on 3D point cloud descriptors further demonstrates a commitment to robust robotic grasping. Collectively, Cheng's research reflects a productive integration of perception, control, and intelligent decision-making across diverse robotic platforms.

Research Focus

Key Achievements

13
H-Index
48
Papers
604
Total Citations
13
Avg Citations/Paper
🏆 Most Cited Paper
Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot
76 citations · 2022
📈 Most Prolific Year: 2013 (7 Papers)
🤝 Key Collaborators: 83
🏛 Institutions: Beijing Academy of Agricultural and Forestry Sciences, Northeastern University, Texas State University, Harbin Institute of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago