MOOCs gratuits : calendrier des cours en ligne ouverts et massifs

MOOC Mobile robots and autonomous vehicles

Inscriptions ouvertes jusqu'au 13 mars 2016

Using realistic examples, mathematical exercices and programming activities in Python, this MOOC analyses the main problems a mobile robot has to deal with in a dynamic human environment.

MOOC sur FUN-MOOC - du 8.02.16 au 13.03.2016
Auteur(s): Christian Laugier, Agostino Martinelli, Dizan Vasquez

Présentation du MOOC Mobile Robots and Autonomous Vehicles (session 2)

Informations pratiques sur le MOOC Mobile robots and autonomous vehicles

  • Type: MOOC, cours en ligne, quiz et exercices
  • Temps d'apprentissage: 5 semaines
  • Niveau: à partir du niveau master
  • Durée: 02:00h/semaine
  • Langues: anglais
  • Contenu: vidéos
  • Public cible: étudiants, ingénieurs et chercheurs
  • Age attendu: 21 et +
  • Droits: Creative Commons Licence BY-NC-ND

Description du MOOC Mobile Robots and Autonomous Vehicles

Mobile Robots are increasingly working in close interaction with human beings in environments as diverse as homes, hospitals, public spaces, public transportation systems and disaster areas. The situation is similar when it comes to Autonomous Vehicles, which are equipped with robot-like capabilities (sensing, decision and control).
Such robots must balance constraints such as safety, efficiency and autonomy, while addressing the novel problems of acceptability and human-robot interaction. Given the high stakes involved, developing these technologies is clearly a major challenge for both the industry and the human society.

The MOOC presents both formal and algorithmic tools, illustrated using realistic examples and programming exercises in Python.

This MOOC is designed around a real-time decision architecture using Bayesian approaches. It covers topics such as :

  • Sensor-based mapping and localization : presentation of the most popular methods to perform robot localization, mapping and to track mobile objects.
  • Fusing noisy and multi-modal data to improve robustness : introduction of both traditional fusion methods as well as more recent approaches based on dynamic probabilistic grids.
  • Integrating human knowledge to be used for scene interpretation and decision making : discussion on how to interpret the dynamic scene, predict its evolution, and evaluate the risk of potential collisions in order to take safe and efficient navigation decisions.

Every week consists in approximately 10 sessions composed of a video lecture, supplementary ressources, associated quiz and applicative exercises.

Plan du MOOC Mobile Robots and Autonomous Vehicles

  • Week 1 : Objectives, challenges, state of the art
  • Week 2 : Bayes & kalman filters
  • Week 3 : Extended kalman filters
  • Week 4 : Perception & situation awareness & decision making
  • Week 5 : Behavior modeling and learning

Évaluation

At the end of the MOOC a statement of completion will be provided for learners having obtained the required score to the quiz and exercises.

Informations pédagogiques

Public

  • Public visé : The MOOC is primarily intended for students with an engineering or masters degree, but any person with basic familiarity with probabilities, linear algebra and Python can benefit from it.
  • The MOOC can also complement the skills of engineers and researchers working in the field of mobile robots and autonomous vehicles.
  • Pré-requis : Basic notions of robotics, probabilities, linear algebra and Python.

Objectif pédagogique du MOOC Mobile Robots and Autonomous Vehicles

  • Objectif : The objective of this MOOC is to introduce the key concepts required to program mobile robots and autonomous vehicles.

Édition et diffusion du MOOC Mobile robots and autonomous vehicles (session 2)

Édition

Diffusion

Conditions d'utilisation

  • The course material and the contents produced by users are provided under Creative Commons License BY-NC-ND : the name of the author should always be mentioned ; the user can exploit the work except in a commercial context and he cannot make changes to the original work.
Publication : 18.11.2015