Ahmed M. Walied
Papers
1
Total Citations
2
H-Index
1
About
Ahmed M. Walied is a researcher focused on advancing autonomous navigation for mobile robots, a field critical to applications ranging from warehouse logistics to assistive robotics. His work addresses the persistent challenge of error-free path planning in dynamic environments, particularly indoor settings where obstacles and conditions change unpredictably. Walied’s most notable contribution is his 2021 study on path planning using Q-learning, a reinforcement learning technique that enables robots to learn optimal routes through trial and error without requiring pre-programmed maps. This approach, cited twice to date, demonstrates a practical step toward more adaptive and intelligent robotic systems. By integrating machine learning with traditional navigation algorithms, Walied’s research offers a scalable solution for real-time decision-making in cluttered spaces. His work is especially relevant for students and engineers exploring the intersection of robotics and artificial intelligence, providing a clear example of how reinforcement learning can overcome the limitations of static planning methods. Walied’s contributions highlight the growing importance of data-driven approaches in robotics, paving the way for more autonomous and responsive mobile platforms.
Research Focus
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Top Papers
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