Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian Processes

Abstract : The paper describes a navigation algorithm for dynamic, uncertain environment. Moving obstacles are supposed to move on typical patterns which are pre-learned and are represented by Gaussian processes. The planning algorithm is based on an extension of the Rapidly-exploring Random Tree algorithm, where the likelihood of the obstacles trajectory and the probability of collision is explicitly taken into account. The algorithm is used in a partial motion planner, and the probability of collision is updated in real-time according to the most recent estimation. Results show the performance of the navigation algorithm for a car-like robot moving among dynamic obstacles with probabilistic trajectory prediction.
Type de document :
Communication dans un congrès
IEEE/RSJ 2008 International Conference on Intelligent RObots and Systems, Sep 2008, Nice, France. 2008
Liste complète des métadonnées

https://hal.inria.fr/inria-00332595
Contributeur : Chiara Fulgenzi <>
Soumis le : mardi 21 octobre 2008 - 11:32:41
Dernière modification le : vendredi 12 octobre 2018 - 01:18:08
Document(s) archivé(s) le : lundi 7 juin 2010 - 19:06:14

Fichier

paper_last_submission.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00332595, version 1

Collections

Citation

Chiara Fulgenzi, Christopher Tay, Anne Spalanzani, Christian Laugier. Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian Processes. IEEE/RSJ 2008 International Conference on Intelligent RObots and Systems, Sep 2008, Nice, France. 2008. 〈inria-00332595〉

Partager

Métriques

Consultations de la notice

440

Téléchargements de fichiers

1047