Emmanuel Grolleau

Emmanuel Grolleau is a Full Professor at ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace - École Nationale Supérieure de Mécanique et d'Aérotechique) specializing in real-time systems. He is affiliated with the LIAS laboratory (Laboratoire d'Ingénierie des Applications de la Sensorique) where he leads the Real Time Team. His work bridges theoretical scheduling principles with practical embedded system implementations across multiple domains.

Professor Grolleau's primary research interests include:

• Real-Time Scheduling: uniprocessor, multiprocessor, and distributed scheduling with practical considerations like transactions and precedence constraints
• Model-Based Systems Engineering (MBSE): developing bridges between UML-MARTE and AADL to real-time scheduling tools
• Unmanned Aerial Vehicles (UAVs): autopilot architecture design and optimization
• Energy Systems: co-heading the LabCom ANR Laboratoire d'Insertion des Énergies Nouvelles et d'Optimisation des Réseaux (LIENOR)

His publication record demonstrates a clear progression from fundamental scheduling theory to applied work spanning avionics, drone technology, and power systems. Recent work shows strong focus on UAV autopilot architectures, model-based frameworks for real-time systems, and energy management in power distribution networks.

Professor Grolleau serves on multiple prestigious program committees including Real-Time Networks & Systems (RTNS) since 2012, ACM/SIGAPP Symposium On Applied Computing (SAC) since 2015, DETECT since 2018, and DroneSE in 2023. He has led significant research projects such as PIA CORAC Panda and FUI WARUNA, which developed the Time4Sys pivot meta-model to connect theoretical scheduling with practical implementation.

His collaborative work extends across multiple institutions and industries, with publications spanning real-time scheduling theory, UAV systems, energy management, and avionic architectures. The consistent thread through his work is the practical application of real-time scheduling principles to solve complex engineering problems in safety-critical systems.