Papers
3
Total Citations
9
H-Index
2
About
Mahmoud Almasri’s research centers on advancing fault detection and isolation (FDI) for mobile robots, with a focus on enhancing autonomous system reliability. His major contributions include developing a parameter estimation-based FDI method improved by mixed particle filters, which boosts diagnostic accuracy in noisy environments. He also pioneered multiple model adaptive estimation to detect blocked wheel faults, enabling robots to maintain stability during locomotion failures. Almasri’s work systematically evaluates 14 FDI techniques, proposing an optimal assignment framework that matches diagnosis methods to specific robot faults—a critical step toward fully autonomous operation. Though his citation counts (5, 2, and 2) reflect an emerging career, his targeted studies address foundational challenges in mobile robotics, such as real-time fault tolerance and adaptive estimation. His 2020 paper on mixed particle filters represents a notable achievement, offering a robust solution for parameter estimation under uncertainty. Almasri’s research is particularly valuable for students and engineers working on autonomous navigation and robotic safety, as it bridges theoretical FDI methods with practical mobile robot applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Toward an optimal assignment of diagnosis method to mobile robots faults2 citations · 2018