Behavioural templates improve robot motion planning with social force model in human environments

Alessio Colombo 1 Daniele Fontanelli 1 Dhaval Gandhi 1 Antonella De Angeli 1 Luigi Palopoli 1 Sean Sedwards 2 Axel Legay 2
1 DISI - Dipartimento di Ingegneria e Scienza dell'Informazione
2 ESTASYS - Efficient STAtistical methods in SYstems of systems
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : An accurate model of human behaviour is crucial when planning robot motion in human environments. The Social Force Model (SFM) is such a model, having parameters that control both deterministic and stochastic elements. We have constructed an efficient motion planning algorithm by embedding the SFM in a control loop that determines higher level objectives and reacts to environmental changes. Low level predictive modelling is provided by the SFM fed by sensors; high level logic is provided by statistical model checking. To parametrise and improve our motion planning algorithm, we have conducted experiments to consider typical human interactions in crowded environments. We have identified a number of behavioural patterns which may be explicitly incorporated in the SFM to enhance its predictive power. In this paper we describe the results of these experiments and how we parametrise the SFM.
Keywords : Protocols Planning Predictive models behavioural patterns behavioural templates control loop crowded environments deterministic elements high level logic human behaviour model human environments human interactions low level predictive modelling robot motion planning algorithm social force model statistical model checking stochastic elements Force SFM stochastic processes statistical analysis path planning mobile robots human-robot interaction formal verification behavioural sciences Sensors Trajectory Visualization
Type de document :
Communication dans un congrès
2013 IEEE 18th Conference on Emerging Technologies \& Factory Automation (ETFA), Sep 2013, Cagliari, Italy. pp.1 - 6, 2013, IEEE Xplore Digital Library. 〈10.1109/ETFA.2013.6648081〉
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-01088063
Contributeur : Sean Sedwards <>
Soumis le : jeudi 27 novembre 2014 - 11:58:28
Dernière modification le : vendredi 16 novembre 2018 - 01:39:29
Document(s) archivé(s) le : lundi 2 mars 2015 - 09:23:08

Fichier

Colombo-et-al2013.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | IRISA | IRISA-D4 | UR1-UFR-ISTIC | UNIV-RENNES

Citation

Alessio Colombo, Daniele Fontanelli, Dhaval Gandhi, Antonella De Angeli, Luigi Palopoli, et al.. Behavioural templates improve robot motion planning with social force model in human environments. 2013 IEEE 18th Conference on Emerging Technologies \& Factory Automation (ETFA), Sep 2013, Cagliari, Italy. pp.1 - 6, 2013, IEEE Xplore Digital Library. 〈10.1109/ETFA.2013.6648081〉. 〈hal-01088063〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

664

Téléchargements de fichiers

223

Contact


archive-ouverte@inria.fr