Waleed Al Maawali

Papers

Total Citations

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About

Waleed Al Maawali is a researcher advancing the field of autonomous robotics through the integration of deep learning and computer vision. His primary research areas include mobile robot navigation, obstacle avoidance, and the application of convolutional neural networks (CNNs) to real-world robotic systems. Al Maawali’s most notable contribution is his 2020 work, "Obstacle-Avoidance Algorithm Using Deep Learning Based on RGBD Images and Robot Orientation," which pioneered a CNN-based approach that processes raw RGBD images alongside robot orientation data to enable intelligent, real-time obstacle avoidance in indoor environments. By leveraging deep learning’s hierarchical feature extraction, his algorithm allows mobile robots to navigate complex spaces without relying on pre-mapped environments, marking a significant step toward fully autonomous navigation. While his citation count is still growing, this work has been recognized for its practical application of AI to robotics, offering a scalable solution for autonomous systems. Al Maawali’s research sits at the intersection of robotics and artificial intelligence, providing a foundation for future innovations in safe, adaptive robot movement—a critical area for applications ranging from service robots to autonomous vehicles.