DESCRIPTION :
Robots are physical agents that interact with their physical environment. Accordingly, their sensorimotor capabilities are essential and largely determine the activities that robots can perform. In recent years, great progress has been made in sensory capabilities thanks to significant advances in machine learning and dedicated hardware. In contrast, much less progress has been made in motor skills. Examples of promising approaches in the current scientific literature are Model Predictive Control (MPC) [1] and Model Predictive Path Integral (MPPI) control [2], where control actions are optimized over a finite time horizon, considering the time evolution of robot dynamics to optimize a given cost or reward function that describes the robot motion. Such algorithms are particularly suited for optimizing control trajectories and planning horizons in real time due to their ability to handle dynamic environments.
From a control perspective, planning a horizon that is as long as possible to manage complex trajectories while considering the environment is essential. Additionally, maintaining a high control frequency is crucial to meet the real-time demands imposed by real-world physics and, if necessary, to adjust the sequence of movements. In the resource-constrained context of small-scale UAVs, such control algorithms are crucial as they enable optimal trajectory generation and real-time decision-making in complex, dynamic, and uncertain environments. However, particularly for battery-powered UAVs, achieving a high control frequency while planning for a long horizon is difficult due to limited computational power and energy constraints [3], and conventional GPU acceleration often requires excessive energy consumption.
In recent years, hardware acceleration [4] has become increasingly popular, using dedicated platforms such as FPGAs (Field Programmable Gate Arrays) and ASICs (Application-specific Integrated Circuits), increasing energy efficiency by orders of magnitude [5]. However, dedicated hardware acceleration for small-scale UAV control has not been proposed.
The Phd is in collaboration between the computer architecture team (TARAN) and the robotics team (RAINBOW) at Inria Centre at Rennes University.
Prospective candidates must manifest their interest before June 1, 2025, to prepare for the doctoral audition, scheduled on June 12, 2025 (remotely or in person).
Code d'emploi : Pilote de Drone (h/f)
Domaine professionnel actuel : Circulation et Transport (autre)
Niveau de formation : Bac+5
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : C ++ (Langage de Programmation), Programmation Informatique, Génie Informatique, FPGA, Conception de Matériel, Python (Langage de Programmation), Machine Learning, Verilog, Hardware Description Language Vhsic (Vhdl), Hardware Acceleration, Anglais, Prise de Décision, Optimisme, Enthousiasme, Esprit d'Équipe, Motivation Personnelle, Algorithmes, Systèmes Embarqués, Circuits Intégrés, Sciences Physiques, Analyses Prédictives, Conception et Réalisation en Robotique, Documentation Scientifique, Horizon Temporel, Control Methods, Véhicule Aérien sans Pilote, Modèle de Contrôle Prédictif, Architecture Matérielle, Performance Energétique
Courriel :
marcello.traiola@inria.fr
marco.tognon@inria.fr
tommaso.belvedere@inria.fr
Téléphone :
0299847100
Type d'annonceur : Employeur direct