Ordinateurs

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

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

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

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 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.

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

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

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,

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

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

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.