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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.

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

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

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

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

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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

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 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

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