Mohamed Wageeh
Papers
1
Total Citations
3
H-Index
1
About
Mohamed Wageeh is an emerging researcher whose work sits at the intersection of artificial intelligence and mobile robotics, with a particular focus on autonomous navigation in dynamic environments. His most recognized contribution explores deep learning-based approaches to robot navigation, a field that has undergone remarkable transformation over recent decades. In this work, Wageeh systematically examines multiple deep learning methodologies, investigating how intelligent algorithms can enable mobile robots to perceive, interpret, and respond to complex, ever-changing surroundings — a fundamental challenge in modern robotics research. His research addresses one of the most critical tasks in mobile robotics: enabling robots to navigate reliably without human intervention. By leveraging deep learning architectures, Wageeh contributes to bridging the gap between theoretical AI advancements and practical robotic deployment, a challenge that remains central to the field. While his citation record is still in its early stages — reflecting a researcher at the beginning of their academic journey — the relevance of his chosen domain positions him well within a rapidly expanding research community. Students and practitioners working on autonomous systems, reinforcement learning for robotics, or intelligent navigation algorithms will find his foundational investigations a worthwhile entry point into the literature.
Research Focus
Key Achievements
Top Papers
- 1
Key Collaborators
Related papers
- Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach
- Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach
- The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO)
- Robot Navigation with Map-Based Deep Reinforcement Learning
- Robot Navigation with Map-Based Deep Reinforcement Learning
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