Albion Paliqi

Papers

Total Citations

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About

Albion Paliqi is a robotics researcher whose work centers on the intersection of computer vision and autonomous locomotion. His primary research focus involves applying deep learning techniques—specifically Convolutional Neural Networks (CNNs)—to enhance the navigational capabilities of legged robots. Paliqi’s most cited work, "Path Control of Quadruped Robot through Convolutional Neural Networks" (2018), demonstrates a practical integration of visual data processing with robotic control. In this project, he developed a system where a quadruped robot uses a camera and OpenCV to detect objects, with Python-based CNNs processing the visual input to guide the robot’s path. This contribution is notable for bridging the gap between neural network-based perception and real-time robotic movement, offering a scalable approach to autonomous navigation in unstructured environments. While his citation count (9 for this paper) reflects a focused, early-stage impact, his work has been recognized for its hands-on engineering approach, combining theoretical deep learning with tangible robotic applications. Paliqi’s research is particularly relevant for students and engineers interested in embedded AI, robotic control systems, and the practical deployment of neural networks in hardware-constrained platforms.