article · 2003
Obstacle Avoidance and Proscriptive Bayesian Programming
Carla Koike A, Cédric Pradalier, Pierre Bessière, Emmanuel Mazer, Carla Koike, Cédric Pradalier, Pierre Bessière, Emmanuel Mazer
Cited by 0Abstract and details only
Abstract
bInstitut National de Recherche en Informatique et en Automatique. cLab. Graphisme, Vision et Robotique. 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 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
Cite this paper
A, C. K., Pradalier, C., Bessière, P., Mazer, E., Koike, C., Pradalier, C., Bessière, P., & Mazer, E. (2003). Obstacle Avoidance and Proscriptive Bayesian Programming.
A, Carla Koike, et al. “Obstacle Avoidance and Proscriptive Bayesian Programming.” 2003.
A, Carla Koike, Cédric Pradalier, Pierre Bessière, Emmanuel Mazer, Carla Koike, Cédric Pradalier, Pierre Bessière, and Emmanuel Mazer. 2003. “Obstacle Avoidance and Proscriptive Bayesian Programming.”
A, C.K. et al. (2003) ‘Obstacle Avoidance and Proscriptive Bayesian Programming’.
@article{a2003obstacle,
title = {{Obstacle Avoidance and Proscriptive Bayesian Programming}},
author = {A, Carla Koike and Pradalier, Cédric and Bessière, Pierre and Mazer, Emmanuel and Koike, Carla and Pradalier, Cédric and Bessière, Pierre and Mazer, Emmanuel},
year = {2003}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.