Juan Carlos Peris

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About

Juan Carlos Peris is a robotics researcher whose work centers on sensor data integration, fuzzy logic, and environmental interpretation for autonomous systems. His most-cited paper, "Fuzzy Distance Sensor Data Integration and Interpretation" (2011), introduces a novel approach that transforms raw sensor readings into fuzzy distance zone patterns, enabling robust interpretation of a robot’s surroundings. A key contribution of this work is its ability to detect malfunctioning sensors by comparing these patterns, thereby enhancing system reliability in uncertain or noisy environments. With 5 citations, this research has provided foundational techniques for improving sensor fusion and fault tolerance in robotics. Peris’s achievements lie in advancing how robots perceive and adapt to their environment through fuzzy logic, offering practical solutions for autonomous navigation and safety. His work is particularly valuable for students and researchers exploring sensor integration, fuzzy systems, and resilient robotic design, demonstrating how intelligent data interpretation can overcome real-world sensing challenges.