Ahmed Bahgat

Cairo University, Electronics Research Institute

Papers

Total Citations

H-Index

About

Ahmed Bahgat is a robotics and computer vision researcher whose work spans two critical domains: autonomous robot navigation and pose estimation. His most influential contribution, "Uncalibrated Stereo Vision with Deep Learning for 6-DOF Pose Estimation for a Robot Arm System" (2021), has garnered 35 citations, demonstrating significant community interest in his approach to solving complex spatial awareness challenges without traditional camera calibration constraints — a meaningful advancement for practical robotic deployment. Bahgat has also made notable strides in mobile robot path planning, investigating both classical and bio-inspired algorithms. His comparative analysis of informed versus uninformed path planning algorithms and his work applying Modified Particle Swarm Optimization and Genetic Algorithms to navigation optimization reflect a sustained commitment to making robot movement more efficient, collision-free, and computationally effective across diverse environments. Collectively, his research addresses fundamental challenges in modern robotics: how machines perceive their spatial environment and how they navigate through it intelligently. His trajectory suggests a researcher bridging deep learning with classical optimization techniques, contributing tools that are increasingly relevant as mobile robots become embedded in industrial, medical, and everyday applications. Students exploring autonomous systems and computer vision will find his work a valuable entry point into these intersecting fields.