Muhammad Moaz
Papers
2
Total Citations
6
H-Index
2
About
Muhammad Moaz is a robotics researcher whose work centers on intelligent control systems for autonomous mobile robots, with a particular focus on path tracking and navigation in challenging environments. His key research areas include bio-inspired optimization algorithms, fuzzy logic control, and their application to unicycle-type differential drive robots. Moaz’s major contributions lie in developing hybrid controllers that combine evolutionary optimization techniques, such as the Bacterial Foraging Optimisation Algorithm (BFA) and Hybrid Spiral Dynamic Bacterial Chemotaxis (HSDBC), to enhance robot performance on irregular terrains—a significant advancement over prior studies limited to regular surfaces. His most-cited paper, “BFA optimized intelligent controller for path following unicycle robot over irregular terrains” (2015, 4 citations), demonstrates how BFA-tuned fuzzy logic systems improve trajectory tracking accuracy and stability in non-ideal conditions. A related work, “Intelligent Path Tracking Hybrid Fuzzy Controller For A Unicycle-Type Differential Drive Robot” (2015, 2 citations), further explores HSDBC-optimized controllers for robust navigation. While his citation counts are modest, Moaz’s research addresses a critical gap in off-road robotics, offering practical solutions for autonomous systems in agriculture, search-and-rescue, and exploration. His work exemplifies the integration of nature-inspired algorithms with control theory, making him a notable contributor to the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 2