Menouer Bennaoum

Université Mustapha Stambouli de Mascara

Papers

Total Citations

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About

Menouer Bennaoum is a researcher at the forefront of advancing safe and efficient industrial robotics. His work centers on two critical challenges: ensuring human safety during physical human-robot interaction and optimizing robot performance for sustainability. In his highly cited 2024 paper, Bennaoum introduced a novel "Sensorless robot collision detection based on fuzzy momentum observer," a model-based method that detects unwanted collisions without additional sensors—a key step toward safer, more collaborative automation. This work has already garnered 3 citations, reflecting its immediate relevance to the robotics safety community. More recently, in 2025, Bennaoum tackled the pressing issue of energy consumption in manufacturing with his paper on "Optimal trajectory planning for industrial robots," where he leverages LSTM neural networks to model energy profiles. By minimizing time, jerk, and energy use simultaneously, his approach promises significant cost savings and operational efficiency for modern industry. With a clear focus on practical, data-driven solutions, Bennaoum’s contributions are shaping the next generation of intelligent, human-aware, and energy-conscious robotic systems—work that is already attracting attention from peers and practitioners alike.