Rasmus Eckholdt Andersen
Papers
8
Total Citations
104
H-Index
6
About
Rasmus Eckholdt Andersen is a robotics researcher whose work bridges industrial automation, autonomous inspection, and intelligent perception. His research focuses on developing collaborative robotic systems for smart manufacturing environments, autonomous visual inspection for marine vessels, and deep learning-based navigation under uncertainty. Andersen’s major contributions include the development of Little Helper 6 (LH6), an integrated collaborative mobile manipulator for flexible part feeding in Industry 4.0 facilities (45 citations), and pioneering approaches to uncertainty-aware visually-attentive navigation using deep neural networks. His work on autonomous inspection systems for water ballast tanks of marine vessels has been particularly impactful, addressing the critical challenge of defect classification during robotic exploration of confined spaces. Andersen has also contributed to fine-grained plant analysis for agricultural robotics and inverse reinforcement learning for human-to-robot skill transfer. His research has accumulated over 100 citations, with notable publications in IEEE and Springer venues. Through the Inspectrone project, he is advancing autonomous corrosion detection in marine vessels, demonstrating his commitment to solving real-world industrial challenges with cutting-edge robotic perception and control systems.
Research Focus
Key Achievements
Top Papers
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- 3Uncertainty-aware visually-attentive navigation using deep neural networks11 citations · 2023
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- 8Deep stochastic image segmentation for autonomous robotic inspection2 citations · 2023