Papers
11
Total Citations
121
H-Index
7
About
Muhammad Amar is a robotics and intelligent systems researcher whose work centers on autonomous mobile robot navigation, embedded control systems, and the practical implementation of soft computing techniques. His research is distinguished by a commitment to low-cost, real-world solutions, translating sophisticated algorithms into affordable microcontroller-based platforms accessible to resource-constrained developers and institutions. Amar's most significant contributions lie in applying fuzzy logic and neural network controllers to mobile robotics challenges, including hurdle avoidance, wall following, path tracking, and line following. His 2010 papers on fuzzy logic-based wall following and neural network-controlled autonomous vehicles each garnered 22 citations, establishing him as a productive voice in intelligent robotics during that period. His 2014 work on neural network-based mobile robot navigation in unknown environments similarly achieved 22 citations, demonstrating sustained influence across half a decade of research. Beyond ground-based navigation, Amar extended his expertise to vision-based road following simulation and self-balancing robots, reflecting a broad curiosity within autonomous systems. His comparative analyses of fuzzy logic versus neural network approaches offer students and engineers practical guidance for controller selection. Collectively, his body of work provides a foundational reference for researchers pursuing intelligent, affordable robotics solutions in dynamic and uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1Microcontroller Based Neural Network Controlled Low Cost Autonomous Vehicle22 citations · 2010
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- 5Fuzzy Logic Based Path Tracking Controller for Wheeled Mobile Robots11 citations · 2014
- 6Fuzzy Logic Reasoning System for Line Following Robot10 citations · 2014
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- 8A 2D simulator for vision based autonomous road following4 citations · 2013
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