Ameer Hamza Khan
Papers
16
Total Citations
670
H-Index
8
About
Ameer Hamza Khan is a robotics and control systems researcher whose work sits at the intersection of neural computing, metaheuristic optimization, and advanced robotic manipulation. He is best known for pioneering the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) framework, a biologically inspired control architecture that enables redundant robotic manipulators to simultaneously track predefined paths and avoid obstacles in real time — a contribution that has garnered over 270 citations and established him as a leading voice in RNN-based robot control. His research systematically extends this metaheuristic paradigm across mobile manipulators, remote center-of-motion constraints, and repetitive motion planning, collectively accumulating hundreds of citations that reflect broad community adoption. Equally notable is his work on soft robotics, where he has developed novel sliding mode controllers with PID sliding surfaces for active vibration damping in pneumatically actuated systems, complemented by rigorous experimental comparisons of PID variants — contributions that provide practical guidance for both researchers and engineers deploying soft robots in human-centered environments. More recently, Khan has expanded into neuromorphic computing, authoring a comprehensive survey on spiking neural networks. With over 650 total citations across a focused and coherent body of work, his research continues to bridge biological intelligence and real-world robotic control.
Research Focus
Key Achievements
Top Papers
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- 6Repetitive Motion Planning of Robotic Manipulators With Guaranteed Precision50 citations · 2020
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- 8Editorial: Neural & Bio-inspired Processing and Robot Control11 citations · 2018
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