Papers
85
Total Citations
1,738
H-Index
21
About
Dezhen Song is a prominent robotics researcher whose work spans mobile robot navigation, localization, autonomous sensing, and infrastructure inspection. His most influential contributions center on skid-steered mobile robots, where his kinematic modeling and IMU-based localization frameworks — garnering nearly 200 and 98 citations respectively — have become foundational references for researchers tackling the notoriously complex wheel-ground interactions inherent to these platforms. His adaptive trajectory tracking control work further cemented his reputation as a leading voice in mobile robot dynamics. Beyond ground vehicle kinematics, Song has made significant strides in visual navigation, developing heterogeneous landmark-based approaches that intelligently exploit geometric constraints from diverse visual features, earning over 100 citations. His research portfolio also extends to radio source localization, where he pioneered cooperative multi-robot strategies for tracking transient, anonymous transmitters — a genuinely challenging signal-processing problem. Perhaps most practically impactful is his recent work fusing ground-penetrating radar with camera systems for subsurface pipeline mapping and airport runway defect detection, combining deep learning (GPR-RCNN) with sensor fusion to automate critical infrastructure inspection. With contributions spanning algorithmic theory, hardware integration, and real-world deployment, Song's research reflects a rare breadth that consistently bridges fundamental robotics science with pressing engineering challenges.
Research Focus
Key Achievements
Top Papers
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- 3IMU-based localization and slip estimation for skid-steered mobile robots98 citations · 2007
- 4Adaptive Trajectory Tracking Control of Skid-Steered Mobile Robots96 citations · 2007
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