Henrik Skov Midtiby
Papers
8
Total Citations
163
H-Index
6
About
Henrik Skov Midtiby is a leading researcher at the intersection of robotics, precision agriculture, and autonomous systems. His work primarily focuses on developing intelligent algorithms for robotic weeding, fault diagnosis, and energy-efficient autonomous aerial robots. Midtiby’s most impactful contributions include pioneering model-based fault diagnosis algorithms for robotic systems—such as the nonlinear adaptive observer approach—which have garnered 45 citations and set a benchmark for reliability in robotics. In precision agriculture, he has advanced selective herbicide application through dicotyledon weed quantification in maize crops (33 citations) and robust plant species classification by combining plant and leaf shapes (25 citations), directly addressing EU regulations on herbicide leaching. His research on context-based crop classification for robotic weeding (27 citations) further underscores his role in reducing chemical usage. Midtiby has also made notable strides in energy modeling for heterogeneous embedded systems and autonomous aerial robots, including a novel planning-scheduling approach for coverage paths. With over 160 total citations, his work bridges computational efficiency and practical deployment, making him a key figure in sustainable, autonomous agricultural technology.
Research Focus
Key Achievements
Top Papers
- 1Model-Based Fault Diagnosis Algorithms for Robotic Systems45 citations · 2023
- 2
- 3
- 4Estimation of plant species by classifying plants and leaves in combination25 citations · 2017
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- 6Energy-Aware Planning-Scheduling for Autonomous Aerial Robots10 citations · 2022
- 7
- 8Mechanical and Computational Energy Estimation of a Fixed-Wing Drone5 citations · 2020