Papers
2
Total Citations
17
H-Index
2
About
Farhat Naseer is a rising researcher at the intersection of robotics, human-robot interaction, and artificial intelligence. Her work primarily focuses on developing intuitive control systems and advancing bio-inspired locomotion for robotic platforms. Naseer’s most cited paper, “Deep Learning-Based Unmanned Aerial Vehicle Control with Hand Gesture and Computer Vision” (2022, 13 citations), pioneers a novel approach to human-drone interaction, replacing conventional joystick and remote controls with natural hand gestures interpreted by deep learning models. This work directly addresses the electromagnetic interference and usability limitations of traditional drone controllers, offering a more seamless and accessible interface for operators. In her second notable study, “Study of Joint Symmetry in Gait Evolution for Quadrupedal Robots Using a Neural Network” (2022, 4 citations), Naseer explores how variations in joint symmetry influence gait effectiveness on uneven terrain. By employing neural networks to analyze and evolve gaits, she contributes foundational insights to bio-inspired robotics, enabling more adaptive and efficient locomotion. Though early in her career, Naseer’s work demonstrates a clear trajectory toward making robots—both aerial and legged—more responsive, intuitive, and capable in real-world environments. Her research holds promise for applications in search-and-rescue, surveillance, and autonomous exploration.
Research Focus
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Top Papers
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