TY - GEN
ID - cogprints3757
UR - http://cogprints.org/3757/
A1 - Koike, C
A1 - Pradalier, C
A1 - Bessiere, P
A1 - Mazer, E
TI - Proscriptive Bayesian Programming Application for Collision Avoidance
Y1 - 2003///
N2 - 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.
AV - public
ER -