Papers
13
Total Citations
625
H-Index
9
About
Dejun Guo is a robotics researcher whose work spans mobile robot control, aerial robotics, multi-robot systems, and robotic applications in challenging environments. He is particularly distinguished for his pioneering contributions to vision-based control, where his adaptive image-based frameworks allow wheeled mobile robots and aerial vehicles to navigate and track trajectories using uncalibrated cameras — eliminating the need for precise sensor calibration that traditionally limits real-world deployment. His 2015 and 2016 papers on uncalibrated image-based trajectory tracking and leader-follower formation control have together garnered over 240 citations, establishing him as a key voice in autonomous robot perception and coordination. Guo's highly cited 2020 survey on robots under the COVID-19 pandemic (262 citations) demonstrates his broader impact beyond technical contributions, synthesizing how robotic and autonomous systems can address critical societal challenges during global crises. His research on aerial robots is equally notable, encompassing GPS-denied navigation, package delivery with cable-suspended payloads, and high-speed flight through multiple openings. His work on multi-robot formation control with performance and feasibility constraints further reflects his ambition to tackle complex, real-world deployment scenarios. Across his body of work, Guo has consistently advanced the intersection of computer vision, adaptive control, and autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Robots Under COVID-19 Pandemic: A Comprehensive Survey262 citations · 2020
- 2Adaptive Vision-Based Leader–Follower Formation Control of Mobile Robots143 citations · 2016
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