Zeyad Mohsen
Papers
1
Total Citations
3
H-Index
1
About
Zeyad Mohsen is an emerging researcher specializing in mobile robotics and autonomous systems, with a particular focus on deep learning applications for robot navigation. His work addresses one of the most challenging problems in modern robotics: enabling robots to navigate intelligently and safely within dynamic, real-world environments. His most notable contribution, the 2021 thesis "Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach," systematically investigates and evaluates various deep learning methodologies applied to mobile robot navigation — a field that has undergone remarkable transformation over recent decades. By synthesizing advances in neural network architectures with practical robotics challenges, Mohsen's research contributes to bridging the gap between theoretical machine learning techniques and deployable autonomous systems. Though early in his research career with 3 citations to date, his work targets a high-impact domain at the intersection of artificial intelligence and robotics, areas experiencing exponential growth in both academic interest and industrial application. His research lays groundwork relevant to autonomous vehicles, warehouse automation, and assistive robotics — making his contributions increasingly relevant as demand for intelligent autonomous systems continues to accelerate globally.
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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