Papers
21
Total Citations
452
H-Index
11
About
He Kong is a versatile robotics and control systems researcher whose work bridges precision agriculture, state estimation, and autonomous systems. Best known for his contributions to agricultural robotics, Kong has advanced the application of deep learning to crop-weed classification and semantic segmentation, with his 2021 data augmentation study accumulating 129 citations and establishing him as a leading voice in AI-driven farm automation. His real-time ryegrass detection work further demonstrates a commitment to practical, deployable solutions for precision farming. Beyond agriculture, Kong has made significant theoretical contributions to sensor fusion and localization. His research on TDOA-based sensor array calibration, SLAM-integrated microphone array systems, and Kalman filtering under unknown inputs reflects deep expertise in robust state estimation. His closed-form error propagation framework for invariant EKF on Lie groups addresses fundamental challenges in robot pose estimation with applications to visual-inertial navigation. Kong also tackles the operational realities of field robotics, developing energy-aware and resource-conscious path planning strategies that extend robot autonomy in unstructured outdoor environments. His backstepping control work for high-order uncertain systems further illustrates his breadth across theory and application. Collectively, his research has garnered over 390 citations, marking a compelling and growing influence across robotics, control theory, and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2Real time detection of inter-row ryegrass in wheat farms using deep learning58 citations · 2021
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- 4Kalman filtering under unknown inputs and norm constraints34 citations · 2021
- 5Energy Aware Mission Planning for WMRs on Uneven Terrains26 citations · 2019
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