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Conference papers

Obstacle Avoidance and Proscriptive Bayesian Programming

Carla Koike 1 Cédric Pradalier 1 Pierre Bessiere 1 Emmanuel Mazer 1 
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : 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 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 vehicule are described and commented.
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Contributor : Pierre Bessiere Connect in order to contact the contributor
Submitted on : Friday, March 10, 2006 - 11:56:48 AM
Last modification on : Thursday, January 20, 2022 - 4:12:44 PM
Long-term archiving on: : Saturday, April 3, 2010 - 10:29:51 PM


  • HAL Id : hal-00019258, version 1



Carla Koike, Cédric Pradalier, Pierre Bessiere, Emmanuel Mazer. Obstacle Avoidance and Proscriptive Bayesian Programming. --, 2003, France. ⟨hal-00019258⟩



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