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About

Leila Zouaghi’s research lies at the intersection of hybrid dynamical systems, probabilistic monitoring, and autonomous mobile robotics. Her core contribution is the development of the Modified Particle Petri Nets (MPPN) approach, a novel framework that fuses Petri nets with particle filtering to model, monitor, and diagnose complex systems operating under environmental uncertainty. This work, first detailed in her 2011 paper (11 citations), provides a powerful method for handling the hybrid (continuous-discrete) nature of real-time systems. Zouaghi extended this foundation to mobile robot navigation, creating online, mission-based probabilistic monitoring models that detect inconsistencies without halting system operation. Her 2012 paper (9 citations) and 2011 follow-up (8 citations) demonstrate how these models enable robust, real-time fault detection. Notably, her 2012 work on associative memory further enhances system resilience by allowing a robot to continue navigating safely after a fault occurs, rather than requiring an emergency stop. With a focused body of work that has garnered over 30 citations, Zouaghi’s research provides essential tools for ensuring the reliability and safety of autonomous systems in unpredictable environments.