Tutorial: constraint programming for robotics


There are several ways to deal with state estimation in mobile robotics. The constraint programming approach consists in defining a problem as a set of rules and letting a solver perform the estimation. For mobile robotics, rules are constraints coming from state equations or uncertainties from the measurements. Efforts have been done to propose operators and solvers to apply these constraints. The goal of this tutorial is to learn how to use them and understand the efficiency of the approach on realistic robotic applications. We will see that some problems that are difficult to solve with conventional methods (Kalman filters, particle approaches) can be easily dealt with by constraint programming. This is for instance the case of poor observation measurements, time uncertainties, delays, or when the initial conditions of the system are not known. The tutorial will stand on the Tubex library, that provides tools for computations over sets of trajectories. It has been designed to deal with dynamical systems defined by non-linear differential equations and involving constraints such as trajectory evaluations, time uncertainties or delays. These computations stand on interval analysis, a well suited tool that reliably propagates uncertainties. The event will start with scientific presentations, providing the audience with elementary knowledge on constraint programming, interval analysis and tubes. Next presentations will concentrate on the python library so that the audience may be able to use the tools. A list of exercises will then be proposed, with realistic robotic applications involving both simulations and actual datasets.

Oct 25, 2020

This video introduces the tutorial about constraint programming for mobile robotics, proposed for the IROS'2020 conference. Simon Rohou and I jointly co-organize this tutorial. More details can be found on the tutorial website.

Click on the PDF button above to view the slides of this talk.
Raphael Voges
Raphael Voges
Doctor in Robotics

My research interests include error modeling, sensor fusion, SLAM, state estimation and AI in the context of autonomous driving.