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  • Mobile Robots and Autonomous Vehicles

Mobile Robots and Autonomous Vehicles

Ref. 41005
CategoryComputer science and programmingCategoryDigital and technology
  • Duration: 5 weeks
  • Effort: 10 hours
  • Pace: ~2 hours/week
Enrollment
From Nov. 12, 2015 to ...
Course
From Feb. 8, 2016 to March 27, 2016
Languages
English

Description

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.

Course Objective

The objective of this course is to introduce the key concepts required to program mobile robots and autonomous vehicles. The course presents both formal and algorithmic tools, and for its last week's topics (behavior modeling and learning), it will also provide realistic examples and programming exercises in Python.

This course 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.

Format

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

Prerequisites

Basic notions of robotics, probabilities, linear algebra and Python (only for week 5).

Targeted Audience

The course 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 course can also complement the skills of engineers and researchers working in the field of mobile robots and autonomous vehicles.

Course plan

  • 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 (with examples and exercises in Python)

Other course runs

Archived

  • From May 18, 2015 to June 21, 2015

Course team

Christian Laugier

Categories

Dr. Christian Laugier is first class Research Director at Inria (Institut National de Recherche en Informatique et en Automatique – France).

Agostino Martinelli

Categories

Agostino Martinelli rereceived a M.Sc. degree in theoretical physics (1994) and a Ph.D. in astrophysics (1999).

Dizan Vasquez

Categories

Dizan Vasquez received his PhD in Computer Graphics, Computer Vision and Robotics from Institut National Polytechnique – Grenoble, France.

Organizations

Inria

License

License for the course content

Attribution-NonCommercial-NoDerivatives

You are free to:

  • Share — copy and redistribute the material in any medium or format

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.

License for the content created by course participants

Attribution-NonCommercial-NoDerivatives

You are free to:

  • Share — copy and redistribute the material in any medium or format

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
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