Ibrahim Hroob
Papers
7
Total Citations
50
H-Index
4
About
Ibrahim Hroob is an emerging robotics researcher whose work sits at the intersection of autonomous mobile systems, agricultural robotics, and long-term robot deployment in dynamic environments. His research addresses some of the most pressing challenges in field robotics: enabling robots to localize accurately, navigate safely, and operate reliably over extended periods in ever-changing outdoor settings such as vineyards and crop fields. Among his most recognized contributions is the Bacchus Long-Term (BLT) dataset (2023, 24 citations), a landmark multimodal agricultural dataset that has become a valuable benchmark for the field robotics community. His work on adaptive localization and stable-point segmentation — spanning both 2D scan filtering and 3D LiDAR-based approaches — demonstrates a sustained effort to make robot perception robust under continuous environmental change, collectively attracting over a dozen citations across multiple publications. His resilient trajectory replanning framework and narrow-space navigation system further reflect his drive to close the gap between laboratory robotics and real-world agricultural deployment. With contributions spanning perception, localization, motion planning, and multisensory harvesting systems, Hroob is establishing himself as a versatile and impactful voice in precision agriculture robotics, with work that directly supports the automation demands of modern food production.
Research Focus
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Top Papers
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