Papers
15
Total Citations
88
H-Index
5
About
Guodong Lu is a robotics researcher whose work spans mobile robotics, aerial-ground hybrid systems, human-robot interaction, and intelligent control. His research bridges theoretical modeling with practical mechanical design, addressing real-world challenges in autonomous systems and wearable technology. Lu's most cited contribution (18 citations) introduced a novel semantic segmentation approach for 2D indoor lidar maps, combining distance transform watershed pre-segmentation with neural network classification — a meaningful advancement for mobile robot task execution. His broader robotics portfolio is notably diverse: CapsuleBot, a hybrid aerial-ground bi-copter robot, has attracted 11 citations for its innovative long-endurance design, while his tailless flapping-wing robot with bio-inspired passive legs demonstrates a flair for nature-inspired engineering solutions. His 2017 work on model-weighted adaptive neural backstepping sliding-mode control for cooperative manipulators addressed a frequently overlooked gap between simulation accuracy and real-world performance. Beyond locomotion, Lu has made contributions to garment fit prediction, humanoid robot teleoperation with posture constraints, and human skeleton action recognition using masked autoencoders. His robotic mannequin design (15 citations) also reflects an unusual but impactful application of robotics to the fashion industry. Collectively, Lu's work demonstrates a productive interdisciplinary perspective that pushes autonomous systems toward greater adaptability and real-world utility.
Research Focus
Key Achievements
Top Papers
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- 2Design of robotic mannequin formed by flexible belt net15 citations · 2019
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- 10Aggressive Collision-Inclusive Motion Planning3 citations · 2024