Basit Muhammad Imran
Papers
2
Total Citations
22
H-Index
2
About
Basit Muhammad Imran is a robotics and autonomous systems researcher whose work spans bio-inspired sensing, aerial robotics, and multi-agent control. His research uniquely bridges the gap between biological sensory mechanisms and practical engineering applications, most notably demonstrated in his highly cited 2021 work on Mel-spectrogram and deep convolutional neural network-based representation learning, which garnered 19 citations. In that study, Imran pioneered the use of biosonar sensors — inspired by echolocating bats — mounted on Unmanned Aerial Vehicles (UAVs) to estimate tree leaf density during forest navigation, offering a lightweight and cost-effective alternative to traditional sensing approaches. His more recent work expands into the domain of multi-robot coordination, where he developed a distributed layered planning and control framework for teams of quadrupedal robots, incorporating obstacle-aware nonlinear model predictive control to navigate uncertain, disturbance-rich environments. Together, these contributions reflect Imran's commitment to advancing intelligent, biologically inspired autonomous systems capable of operating in complex real-world settings. His growing citation record signals increasing recognition of his interdisciplinary contributions across aerial robotics, deep learning, and legged robot locomotion.
Research Focus
Key Achievements
Top Papers
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