Geometric and Bayesian Models for Safe Navigation in Dynamic Environments - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Intelligent Service Robotics Year : 2008

Geometric and Bayesian Models for Safe Navigation in Dynamic Environments

Abstract

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficcult research problems located on the cutting edge of the state of the art. Basically, these problems may be classiffied into three main categories: a) SLAM in dynamic environments; b) Detection, characterization, and behavior prediction of the potential moving obstacles; and c) On-line motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learning.

Domains

Other [cs.OH]
Fichier principal
Vignette du fichier
08-isr-laugier-etal-maison.pdf (1.49 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00180741 , version 1 (24-10-2007)
inria-00180741 , version 2 (28-02-2008)

Identifiers

  • HAL Id : inria-00180741 , version 2

Cite

Christian Laugier, Dizan Alejandro Vasquez Govea, Manuel Yguel, Thierry Fraichard, Olivier Aycard. Geometric and Bayesian Models for Safe Navigation in Dynamic Environments. Intelligent Service Robotics, 2008, 1 (1). ⟨inria-00180741v2⟩
137 View
271 Download

Share

Gmail Facebook X LinkedIn More