creators_name: Koike, C
creators_name: Pradalier, C
creators_name: Bessiere, P
creators_name: Mazer, E
type: confpaper
datestamp: 2004-08-10
lastmod: 2011-03-11 08:55:39
metadata_visibility: show
title: Proscriptive Bayesian Programming Application for Collision Avoidance
ispublished: pub
subjects: comp-sci-robot
full_text_status: public
abstract: 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.
date: 2003
date_type: published
refereed: FALSE
citation:   Koike, C and Pradalier, C and Bessiere, P and Mazer, E  (2003) Proscriptive Bayesian Programming Application for Collision Avoidance.  [Conference Paper]     
document_url: http://cogprints.org/3757/1/Koike03a.pdf