Cartographie et localisation simultanées multirobots

Cet article traite des problèmes de localisation et cartographie simultanées (SLAM en anglais : Simultaneous Localization And Mapping) dans un contexte multirobot. Dès lors que plusieurs robots agissent ensemble dans un même environnement, diverses questions se posent : comment localiser les robots ? Doit-on utiliser un système centralisé ? Quelles informations échanger entre robots ? La première partie de l\u27article est une vue d\u27ensemble des principaux systèmes de localisation et cartographie existants. La seconde traite des spécifications des systèmes multirobots et des stratégies de déploiement. La troisième partie présente les principales approches de SLAM multirobots avant d\u27illustrer l\u27article avec quelques exemples d\u27applications industrielles

Une contribution ignorée d’Antoine-César Becquerel : l’analyse économétrique du marché du blé en France

International audienc

À partir de contributions d'Antoine-César Becquerel (1853-1865), une étude économétrique du marché du blé en France de 1815 à 1863

Dans deux Mémoires publiés en 1853 et 1865, A.-C. Becquerel a étudié le marché du blé en France en en dégageant les évolutions de long terme et en analysant les fluctuations de la récolte et du prix. L\u27article présente d\u27abord ces travaux novateurs en les situant par rapport aux recherches de leur époque et en en montrant les insuffisances. Les données statistiques rassemblées par Becquerel sont ensuite traitées à l\u27aide de différentes techniques économétriques, modèle à correction d\u27erreurs, modèle VAR, analyse spectrale. Si les deux dernières techniques montrent peu de liaisons entre les évolutions des différentes variables, le modèle à correction d\u27erreurs montre que l\u27on peut expliquer les variations du prix du blé à partir des variations de la superficie ensemencée en blé et du rendement en blé à l\u27hectare, ce qui rejoint les explications en termes de cycles de récoltes proposées par de nombreux économistes et agronomes au XIXe siècle

Strongly coupled fluid-particle flows in vertical channels. II. Turbulence modeling

In Part I, simulations of strongly coupled fluid-particle flow in a vertical channel were performed with the purpose of understanding, in general, the fundamental physics of wall-bounded multiphase turbulence and, in particular, the roles of the spatially correlated and uncorrelated components of the particle velocity.The exact Reynolds-averaged (RA) equations for high-mass-loading suspensions were presented, and the unclosed terms that are retained in the context of fully developed channel flow were evaluated in an Eulerian–Lagrangian (EL) framework. Here, data from the EL simulations are used to validate a multiphase Reynolds-stress model (RSM) that predicts the wall-normal distribution of the two-phase, one-point turbulence statistics up to second order. It is shown that the anisotropy of the Reynolds stresses both near the wall and far away is a crucial component for predicting the distribution of the RA particle-phase volume fraction. Moreover, the decomposition of the phase-average (PA) particle-phase fluctuating energy into the spatially correlated and uncorrelated components is necessary to account for the boundary conditions at the wall. When these factors are properly accounted for in the RSM, the agreement with the EL turbulence statistics is satisfactory at first order (e.g., PA velocities) but less so at second order (e.g., PA turbulent kinetic energy). Finally, an algebraic stress model for the PA particle-phase pressure tensor and the Reynolds stresses is derived from the RSM using the weak-equilibrium assumption

Numerical Description of Dilute Particle-Laden FLows by a Quadrature-Based Moment Method

The numerical simulation of gas-particle flows is divided into two families of methods. In Euler-Lagrange methods individual particle trajectories are computed, whereas in Euler-Euler methods particles are characterized by statistical descriptors. Lagrangian methods are very precise but their computational cost increases with instationarity and particle volume fraction. In Eulerian methods (also called moment methods) the particle-phase computational cost is comparable to that of the fluid phase but requires strong simplificaions. Existing Eulerian models consider unimodal or close-to-equilibrium particle velocity distributions and then fail when the actual distribution is far from equilibrium. Quadrature-based Eulerian methods introduce a new reconstruction of the velocity distribution, written as a sum of delta functions in phase space constrained to give the right values for selected low-order moments. Two of the quadrature-based Eulerian methods, differing by the reconstruction algorithm, are the focus of this work. Computational results for two academic cases (crossing jets, Taylor-Green flow) are compared to those of a Lagrangian method (considered as the reference solution) and of an existing second-order moment method. With the quadrature-based Eulerian methods, significant qualitative improvement is noticed compared to the second-order moment method in the two test cases

Le songe biblique comme hypothèse heuristique : le songe des vaches maigres et des vaches grasses et les analyses de la périodicité des mouvements du prix du blé

Depuis 1850, le songe biblique des vaches grasses et des vaches maigres est utilisé dans l\u27étude de la périodicité des récoltes. Ainsi, Hugo (1853), Briaune (1857) et Duffaud (1862) fondent sur lui leurs analyses de la périodicité. Cette démarche est rejetée par Benner (1875), Leslie (1864) et Juglar (1889). Plus tard, Mandelbrot (1968 fonde sur ce songe et la statistique des crues du Nil l\u27étude de la mémoire longue dans les séries temporelles. L\u27analogie est confirmée en comparant la série de prix utilisée par Duffaud à une série des niveaux du Nil

Cart-O-matic project : autonomous and collaborative multi-robot localization, exploration and mapping

International audienceThe aim of the Cart-O-matic project was to design and build a multi-robot system able to autonomously map an unknown building. This work has been done in the framework of a French robotics contest called Defi CAROTTE organized by the General Delegation for Armaments (DGA) and the French National Research Agency (ANR). The scientific issues of this project deal with Simultaneous Localization And Mapping (SLAM), multi-robot collaboration and object recognition. In this paper, we will mainly focussed on the two first topics : after a general introduction, we will briefly describe the innovative simultaneous localization and mapping algorithm used during the competition. We will next explain how this algorithm can deal with multi-robots systems and 3D mapping. The next part of the paper will be dedicated to the multi-robot pathplanning and exploration strategy. The last section will illustrate the results with 2D and 3D maps, collaborative exploration strategies and example of planned trajectories

Diagnosing observation error correlations for Doppler radar radial winds in the Met Office UKV model using observation-minus-background and observation-minus-analysis statistics

With the development of convection-permitting numerical weather prediction the efficient use of high-resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Doppler radar radial winds (DRWs), is now common, though to avoid violating the assumption of uncorrelated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution UK model using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam observation error statistics. The new results obtained show that the DRW error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly the estimated observation error correlation length-scales are longer than the operational thinning distance. They are dependent both on the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the background error covariance matrix used in the assimilation, although they are, in part, a result of using superobservations and a simplified observation operator. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and hence better use of the high-resolution observations