Baghdadi Rezali

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About

Baghdadi Rezali is a robotics researcher dedicated to making industrial and collaborative robots safer, smarter, and more energy-efficient. His work centers on two critical challenges in modern robotics: ensuring human safety during physical human-robot interaction and optimizing robot performance for sustainable manufacturing. Rezali’s key contributions include developing a sensorless collision detection system based on a fuzzy momentum observer, a model-based method that detects unwanted collisions without requiring additional sensors—a significant advancement for safe human-robot collaboration. This work has already garnered attention in the field. More recently, he has pioneered an optimal trajectory planning approach for industrial robots that simultaneously minimizes time, jerk, and energy consumption, using Long Short-Term Memory (LSTM) networks to accurately model energy profiles. This integrated method addresses the pressing industrial need for energy conservation while maintaining precision and smooth motion. Though early in his career, Rezali’s innovative fusion of fuzzy logic, deep learning, and robotics control is establishing him as a promising voice in the next generation of intelligent, human-aware automation.