| Titre : | Self-Adaptative.. | | Type de document : | texte imprimé | | Auteurs : | Lei ZHANG, Auteur | | Année de publication : | 2010 | | Langues : | Anglais (eng) | | Tags : | Robotique, localisation, multi-robot, filtres particulaires, Monte Carlo, méthodes stochastiques | | Index. décimale : | THE Thèses de doctorat | | Résumé : | In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite.
In this thesis, we first study and compare three regular Markov localization algorithms by simulations.
Then we propose an improved Monte Carlo localization algorithm using self-adaptive samples,
abbreviated as SAMCL. By employing a pre-caching technique to reduce the on-line computational
burden, SAMCL is more efficient than regular MCL. Further, we define the concept of similar energy
region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot
space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL
obtains a better performance in localization. Position tracking, global localization and the kidnapped
robot problem are the three sub-problems of the localization problem. Most localization approaches
focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems
together thanks to self-adaptive samples that can automatically separate themselves into a global
sample set and a local sample set according to needs.
Cooperative localization among multiple robots is carried out by exchanging localization information
derived from cooperation. We devise the Position Mapping (PM) algorithm to integrate this information,
which can merge into the SAMCL algorithm as an extension.
The validity and the efficiency of our algorithm are demonstrated by experiments carried out with a
real robot in a structured and known environment. Extensive experiment results and comparisons are
also given in this thesis.
__________________________________________________________________________________ | | Directeur(s) de thèse : | ZAPATA René | | Rapporteur(s) : | JAULIN Luc/DEVY Michel | | Examinateur(s) : | LAPIERRE Lionel/RAMDANI Nacim/JOUVENCEL Bruno/LAVAREC Erwan | | Date de soutenance : | 15/01/2010 | | En ligne : | http://tel.archives-ouvertes.fr/tel-00491010/fr/ |
Self-Adaptative.. [texte imprimé] / Lei ZHANG, Auteur . - 2010. Langues : Anglais ( eng) | Tags : | Robotique, localisation, multi-robot, filtres particulaires, Monte Carlo, méthodes stochastiques | | Index. décimale : | THE Thèses de doctorat | | Résumé : | In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite.
In this thesis, we first study and compare three regular Markov localization algorithms by simulations.
Then we propose an improved Monte Carlo localization algorithm using self-adaptive samples,
abbreviated as SAMCL. By employing a pre-caching technique to reduce the on-line computational
burden, SAMCL is more efficient than regular MCL. Further, we define the concept of similar energy
region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot
space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL
obtains a better performance in localization. Position tracking, global localization and the kidnapped
robot problem are the three sub-problems of the localization problem. Most localization approaches
focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems
together thanks to self-adaptive samples that can automatically separate themselves into a global
sample set and a local sample set according to needs.
Cooperative localization among multiple robots is carried out by exchanging localization information
derived from cooperation. We devise the Position Mapping (PM) algorithm to integrate this information,
which can merge into the SAMCL algorithm as an extension.
The validity and the efficiency of our algorithm are demonstrated by experiments carried out with a
real robot in a structured and known environment. Extensive experiment results and comparisons are
also given in this thesis.
__________________________________________________________________________________ | | Directeur(s) de thèse : | ZAPATA René | | Rapporteur(s) : | JAULIN Luc/DEVY Michel | | Examinateur(s) : | LAPIERRE Lionel/RAMDANI Nacim/JOUVENCEL Bruno/LAVAREC Erwan | | Date de soutenance : | 15/01/2010 | | En ligne : | http://tel.archives-ouvertes.fr/tel-00491010/fr/ |
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