Ibrahim Sayed Ahmad

Papers

Total Citations

H-Index

About

Ibrahim Sayed Ahmad is a researcher whose work sits at the intersection of robotics, artificial intelligence, and autonomous systems. His primary research areas include intelligent control systems, neural network-based navigation, and multi-agent coordination between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). His most cited paper, “Proposed system of artificial Neural Network for positioning and navigation of UAV-UGV” (2018, 20 citations), introduces a novel neural network framework for path planning and control of mobile robots in unstructured environments. The system fuses data from GPS, a Robot Vision System (RVS), and a Quad-Vision System (QVS) to enable safe, adaptive navigation. This work is notable for its integration of multiple sensor modalities into a single intelligent control architecture, addressing a key challenge in autonomous robotics: reliable operation in unpredictable settings. Ahmad’s contributions have practical implications for search-and-rescue, surveillance, and industrial automation, where robust positioning and navigation are critical. His research continues to influence the development of smarter, more resilient autonomous systems, bridging the gap between theoretical neural network models and real-world robotic applications.