Mobile Robots and Autonomous Vehicles

The objective of this course is to show how to program the new generation of mobile robots. The course is primarily intended to students with engineering or Masters Degree, but any person with a

This is the first lesson of the course on mobile robots and autonomous vehicles. It addresses the objectives, the challenges, the state of the art and the technologies.

This video deals with technological evolutions of robotics and state of the art.

Introducing robots in human environments bring new challenges to robotics.

This video is presenting the decision and control architecture for autonomous mobile robots and intelligent vehicles. The question is how to control robot action in a dynamic world populated by

This video addresses sensing technologies. Sensing technologies is one of the key functions for autonomous robots. Sensing is performed using various internal and external sensors.

This video addresses sensing technology, and focuses on robot control and human-robot interaction applications.

This video presents the basic technologies for navigation in dynamic human environments. The objective is to achieve goal oriented navigation in open, dynamic and uncertain environments populated

This video introduces intelligent vehicles and presents more specifically the context and a state of the art.

This video presents the technical challenges and driving skills for intelligent vehicles.

In this part, we will talk about localization which is a fundamental problem that a robot has to be able to solve in order to accomplish almost any tasks. In particular, we will start by

Before deriving the equations of the Bayes filter, I want to remind you a few concepts in the theory of probability, and also some mathematical characterization for the statistical error of the

In this video, we want to discuss the case of a wheel encoders in 2D, and in particular the case of a robot equipped with a differential drive which is very popular in mobile robotics.

So far in the characterization of our sensor measurements, we didn't talk about the errors. This is precisely what we want to do in this video. In particular, we want to compute two probability

In this sequence I want to remind you a few concepts in the theory of probability and then in the next one we finally derive the equations of the Bayes filter. So the concept that I want to

The equations of the Bayes filters are the equation that allow us to update the probability distribution for the robot to be in a given configuration by integrating the information that are in the

Now that we have the equations of the Bayes filter, we need a method in order to implement in real cases these equations. So, in the following, I want to discuss two methods, which are commonly

We have seen the grid localization, and the advantage of this approach is that we can deal with any kind of probability distribution; in particular we don't need to do a Gaussian assumption. The