Behzad Moshiri
Papers
11
Total Citations
119
H-Index
6
About
Behzad Moshiri is a leading figure in robotics and sensor fusion, whose pioneering work has shaped how autonomous systems perceive and navigate their environments. His research centers on intelligent sensor fusion, optimal control, and multi-agent systems, with a particular emphasis on extending Bayesian and fuzzy logic approaches for mobile robotics. Moshiri’s most influential contribution is the introduction of the "pseudo information measure," a novel concept that expands Bayesian fusion for robotic map building—a foundational paper with 31 citations. He has also conducted seminal comparative studies of fuzzy, Dempster, and Bayesian sensor fusion methods for ultrasonic and laser arrays, demonstrating how these techniques improve environment perception and autonomous navigation. Beyond sensor fusion, Moshiri has advanced optimal control of robotic manipulators using wavelet-based neural networks and genetic algorithm-optimized fuzzy sliding mode controllers. His applied work includes designing the "Venus" landmine detection robot, which uses an ordered weighted averaging (OWA) fusion method to enhance detection efficiency. With over 100 citations across his top papers, Moshiri’s contributions have provided both theoretical foundations and practical solutions for robust, intelligent robotics, making him a respected authority in sensor data fusion and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3INTELLIGENT OPTIMAL CONTROL OF ROBOTIC MANIPULATORS USING WAVELETS15 citations · 2008
- 4Sensor fusion by pseudo information measure: A mobile robot application12 citations · 2002
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- 7A novel detection and navigation approach based on OWA fusion method6 citations · 2011
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- 10Tutorial A: Sensor data fusion, principles and applications4 citations · 2010