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

In part 2, we have seen the equations of the Bayes filter, which are the general equations which allow us to update the probability distribution, as the data from both proprioceptive sensors and

In this video we discuss the second two equations of the Kalman filter.

In this video I want to discuss the second two equations of the Kalman filter. And in particular I want to show that these actually perform a kind of weight mean.

In this video I want to explain the steps that we have to follow in order to implement an extended Kalman filter in robotics.

In this video, we are discussing the SLAM problem: simultaneous localization and mapping.

In this video we discuss a fundamental issue which arises when we deal with an estimation problem: understanding if the system contains enough information to perform the estimation of the state.

In this video, we discuss an automatic method which is analytical and allows us to answer the question if a state is observable or not: this method is the Observability Rank Criterion which has

In this video we want to apply the observability rank criterion to understand the observability properties of the system that we saw in the previous videos.

The fourth part of the course addresses perception, situation awareness and decision making. In this first video, we're giving an outline of the problem and introducing the new concept of dynamic

This video will show how to describe Bayesian occupancy filter concept.

This video addresses the problem of the practical implementation for the dynamic probabilistic grid.

This video is dedicated to the object level perception functions: Simultaneous Localization Mapping (SLAM) and Detection and Tracking surrounding Mobile Objects (DATMO).

This video adresses the Detection and Tracking of Mobile Objects (DATMO) problem.

This video addresses the question of model-based and grid-based approaches.

This video deals with embedded Bayesian perception and short-term collision risk.

This videos addresses the problem of situation awareness and motion prediction models.

This video addresses the problem of collision risk assessment and decision.

In this part of the course we are going to go deeper into situation awareness and in particular we are going to study how to model and learn human behavior which is a crucial task for social-aware

In this video, we are going to start working towards building a motion model.

In video 5.2 we showed how to apply the expectation maximization clustering algorithm to two-dimensional data. In this video we will learn how to apply it to trajectory data. And then we will be

In this video we are aiming to improve on the results we obtained in video 5.3a, in particular with respect to the greyed-out trajectories that are badly represented.

In this video we will review the base filter and we will study a particular instance of the Bayesian filter called Hidden Markov models which is a discrete version of a Bayesian filter.

In this video we are going to apply the concepts we have reviewed in the video 5.4 into real trajectories.

In video 5.5 we have defined an HMM in Python. In this video we are going to learn how to use it to estimate and predict motion.

In previous videos we have discussed how to implement the typical trajectories and motion patterns approach. In this video we are going to discuss what are the drawbacks of such an approach,

In this video we will review one of the alternatives we are proposing to the use of Hidden Markov models and typical trajectories: the Social Force model.

In this video we are going to study a second, and probably the most promising alternative for motion prediction: planning-based algorithms.