3 offres trouvées dans Lyon

Title: Real-Time Dynamic-aware Trajectory Planning for Mobile Robots in Indoor Environments Abstract: Service robots are increasingly used in dynamic indoor spaces such as offices, hospitals, or universities, where people and moving objects constantly modify the environment. For safe and efficient operation, a robot must detect and react to these changes in real time. The TIAGo-2 robot (PAL Robotics) provides an ideal research platform for this task. It integrates LiDAR, RGB-D sensors, and a differential base, with full support for ROS 2, including mapping and navigation modules. However, these standard navigation pipelines-based on slam_toolbox and Navigation2-are primarily designed for static environments. When people or objects move within the robot's workspace, its local planner often fails to anticipate or smoothly avoid dynamic obstacles. This internship aims to extend the TIAGo-2 navigation stack by adding a LiDAR-based dynamic obstacle detection module and a reactive...

Résumé Le sujet de stage recherche proposé s'inscrit dans le cadre d'un paradigme émergent dans le domaine des systèmes de production manufacturiers nommé « Système Manufacturier Elastique » (SME). Le concept d'élasticité a été emprunté au domaine de l'informatique dans lequel une infrastructure informatique élastique a la capacité d'allouer dynamiquement des ressources (serveur, stockage, puissance de calcul…) selon les variations de la demande de ses clients pour optimiser les performances et les coûts. Par analogie, dans le contexte des systèmes manufacturier, un SME a la faculté de réaffecter « rapidement » ses ressources de production (poste de travail, machine, opérateurs…) et également d'augmenter et/ou de diminuer celles-ci afin de maintenir une activité de production performante (respect des délais, rentabilité…). L'objectif principal de ce stage est d'explorer le concept d'élasticité dans le cadre particulier d'un système de production à chemins multiples (Job-Shop...

Scientific fields: Artificial Intelligence, Machine Learning, Feature Selection, Digital Health. Keywords: Feature Selection, Multimodal Data, Real-Time, Monitoring, Fall-detection, Health. Internship Topic Stress and emotion monitoring systems play a crucial role in mental health management, especially given the global rise in psychological disorders. Wearable devices equipped with sensors such as ECG (electrocardiogram), PPG (photoplethysmogram), EDA (electrodermal activity), and motion sensors provide a rich source of data for stress and emotion analysis. However, the high dimensionality and noise present in these multimodal datasets pose challenges for efficient processing and interpretability. Feature selection is essential for building robust and interpretable models. Recent advancements in explainable AI (XAI) emphasize the need to ensure that selected features provide understandable information about emotional and stress states, which strengthens trust and adoption by...