Papers
19
Total Citations
260
H-Index
10
About
Dr. Muhammad Umer Khan is a leading researcher at the intersection of robotics, cyber-physical systems (CPS), and precision agriculture. His work primarily focuses on multi-robot coordination, autonomous navigation, and bio-inspired control algorithms. Dr. Khan’s seminal contributions include developing CPS-oriented control designs for networked surveillance robots, where he modeled cyber-physical interactions to enable robust formation control and tracking amidst static obstacles and other robots. His highly cited work on the TobSet dataset (34 citations) is a landmark in precision agriculture, providing a specialized image repository for training convolutional neural networks to distinguish crops from weeds, enabling real-time, selective agrochemical spraying by agricultural robots. With over 200 total citations, his research has advanced path planning techniques using Particle Swarm Optimization (28 citations) and bacterial foraging algorithms, effectively solving local minima problems in cluttered environments. Dr. Khan has also made notable strides in reinforcement learning for mobile robot navigation in unknown settings and uncalibrated visual servoing for robotic arms. His editorial work on neural and bio-inspired processing further underscores his influence in shaping modern robot control paradigms.
Research Focus
Key Achievements
Top Papers
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- 7Uncalibrated eye‐in‐hand visual servoing: an LMI approach13 citations · 2011
- 8Mobile Robot Navigation Using Reinforcement Learning in Unknown Environments12 citations · 2019
- 9Editorial: Neural & Bio-inspired Processing and Robot Control11 citations · 2018
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