235 research outputs found
Bayesian Programming Multi-Target Tracking: an Automotive Application
A prerequisite to the design of future Advanced
Driver Assistance Systems for cars is a sensing system
providing all the information required for high-level driving
assistance tasks. In particular, target tracking is still
challenging in urban trafc situations, because of the large
number of rapidly maneuvering targets. The goal of this
paper is to present an original way to perform target position
and velocity, based on the occupancy grid framework. The
main interest of this method is to avoid the decision problem
of classical multi-target tracking algorithms. Obtained
occupancy grids are combined with danger estimation to
perform an elementary task of obstacle avoidance with an
electric car
Expressing Bayesian Fusion as a Product of Distributions: Application in Robotics
More and more fields of applied computer
science involve fusion of multiple data sources, such as sensor
readings or model decision. However incompleteness of the
models prevent the programmer from having an absolute
precision over their variables. Therefore bayesian framework
can be adequate for such a process as it allows handling of
uncertainty.We will be interested in the ability to express any
fusion process as a product, for it can lead to reduction of
complexity in time and space. We study in this paper various
fusion schemes and propose to add a consistency variable to
justify the use of a product to compute distribution over the
fused variable. We will then show application of this new
fusion process to localization of a mobile robot and obstacle
avoidance
Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform
Data fusion is a common issue of mobile robotics, computer assisted
medical diagnosis or behavioral control of simulated character for instance. However
data sources are often noisy, opinion for experts are not known with absolute
precision, and motor commands do not act in the same exact manner on the environment.
In these cases, classic logic fails to manage efficiently the fusion process.
Confronting different knowledge in an uncertain environment can therefore be adequately
formalized in the bayesian framework.
Besides, bayesian fusion can be expensive in terms of memory usage and processing
time. This paper precisely aims at expressing any bayesian fusion process as a
product of probability distributions in order to reduce its complexity. We first study
both direct and inverse fusion schemes. We show that contrary to direct models,
inverse local models need a specific prior in order to allow the fusion to be computed
as a product. We therefore propose to add a consistency variable to each local
model and we show that these additional variables allow the use of a product of the
local distributions in order to compute the global probability distribution over the
fused variable. Finally, we take the example of the Randomized Hough Transform.
We rewrite it in the bayesian framework, considering that it is a fusion process
to extract lines from couples of dots in a picture. As expected, we can find back
the expression of the Randomized Hough Transform from the literature with the
appropriate assumptions
Obstacle Avoidance and Proscriptive Bayesian Programming
Unexpected events and not modeled properties of the robot environment are some of
the challenges presented by situated robotics research field. Collision avoidance is a basic security
requirement and this paper proposes a probabilistic approach called Bayesian Programming, which
aims to deal with the uncertainty, imprecision and incompleteness of the information handled to
solve the obstacle avoidance problem. Some examples illustrate the process of embodying the
programmer preliminary knowledge into a Bayesian program and experimental results of these
examples implementation in an electrical vehicle are described and commented. A video illustration
of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac
Proscriptive Bayesian Programming Application for Collision Avoidance
Evolve safely in an unchanged environment
and possibly following an optimal trajectory is one big
challenge presented by situated robotics research field. Collision
avoidance is a basic security requirement and this
paper proposes a solution based on a probabilistic approach
called Bayesian Programming. This approach aims to deal
with the uncertainty, imprecision and incompleteness of the
information handled. Some examples illustrate the process
of embodying the programmer preliminary knowledge into
a Bayesian program and experimental results of these examples
implementation in an electrical vehicle are described
and commented. Some videos illustrating these experiments
can be found at http://www-laplace.imag.fr
The Problem of Marginality in Model Reductions of Turbulence
Reduced quasilinear (QL) and nonlinear (gradient-driven) models with scale
separations, commonly used to interpret experiments and to forecast turbulent
transport levels in magnetised plasmas are tested against nonlinear models
without scale separations (flux-driven). Two distinct regimes of turbulence --
either far above threshold or near marginal stability -- are investigated with
Boltzmann electrons. The success of reduced models especially hinges on the
reproduction of nonlinear fluxes. Good agreement between models is found above
threshold whilst reduced models would significantly underpredict fluxes near
marginality, overlooking mesoscale flow organisation and turbulence
self-advection. Constructive prescriptions whereby to improve reduced models is
discussed
PoPe (Projection on Proper elements) for code control: verification, numerical convergence and reduced models. Application to plasma turbulence simulations
The Projection on Proper elements (PoPe) is a novel method of code control dedicated to 1) checking the correct implementation of models, 2) determining the convergence of numerical methods and 3) characterizing the residual errors of any given solution at very low cost. The basic idea is to establish a bijection between a simulation and a set of equations that generate it. Recovering equations is direct and relies on a statistical measure of the weight of the various operators. This method can be used in any dimensions and any regime, including chaotic ones. This method also provides a procedure to design reduced models and quantify the ratio costs to benefits. PoPe is applied to a kinetic and a fluid code of plasma turbulence
Synergetic effects of collisions, turbulence and sawtooth crashes on impurity transport
This paper investigates the interplay of neoclassical, turbulent and MHD processes, which are simultaneously at play when contributing to impurity transport. It is shown that these contributions are not additive, as assumed sometimes. The interaction between turbulence and neoclassical effects leads to less effective thermal screening, i.e. lowers the outward flux due to temperature gradient. This behavior is attributed to poloidal asymmetries of the flow driven by turbulence. Moreover sawtooth crashes play an important role to determine fluxes across the q = 1 surface. It is found that the density profile of a heavy impurity differs significantly in sawtoothing plasmas from the one predicted by neoclassical theory when neglecting MHD events. Sawtooth crashes impede impurity accumulation, but also weaken the impurity outflux due to the temperature gradient when the latter is dominant
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
[No abstract available
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