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About

Rodrigo A. Carrasco’s research focuses on enhancing the reliability and autonomy of mobile robotic systems, particularly through advanced fault detection, isolation, and state estimation techniques. His work addresses a critical challenge: as robots are deployed in hazardous environments to protect human operators, system downtime from undetected faults undermines their benefits. Carrasco’s major contributions include developing multilayer architectures and dynamic observers for cooperative mobile robots, enabling real-time fault detection and isolation by leveraging both analytical and sensor redundancy. His most cited paper (30 citations) demonstrates how such architectures can significantly improve system reliability. He also pioneered the integration of fuzzy logic with nonlinear Kalman filters for accurate state estimation, reducing false alarms in fault detection systems. By optimizing Takagi-Sugeno fuzzy models using genetic algorithms, he advanced localization methods for mobile robots. Carrasco’s layered architecture approach, which combines analytical redundancy with sensor data, provides a scalable framework for increasing robustness in multi-robot teams. His work has laid important groundwork for dependable autonomous systems, with direct applications in search-and-rescue, exploration, and industrial automation.