Experimental vehicle localization by bounded-error state estimation using interval analysis

Emmanuel, Sabu; Kieffer, Michel; Lambert, Alain; Walter, Éric; Maurin, T.

doi:10.1109/iros.2005.1545155

Cited by 24 publications

(18 citation statements)

References 14 publications

(15 reference statements)

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“…A cooperative and constraint based object search strategy for a group of robots is described in [7]. Another localization method using bounded-error state estimation was introduced for a small truck or vehicle, which was equipped with ultrasonic sensors in [65,108]. Methods applied within this work where interval mathematics [84,57].…”

Section: Theory Of Constraint Based Modelingmentioning

confidence: 99%

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Göhring

2010

Künstl Intell

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Mobile autonomous robotics is a very young and complex field of research. Only in recent decades have robots become able to explore, to move, navigate and to interact with their environment.Since the world is uncertain and since robots can only gain partial information about it, probabilistic navigation algorithms have become very popular whenever a robot has to localize itself or surrounding objects. Furthermore, cooperative exploration and localization approaches have become very relevant lately, as robots begin to act not just alone but in groups. Within this thesis a new approach using the concept of spatial percept-relations for cooperative environment modeling is presented and evaluated. As a second contribution, constraint based localization techniques will be introduced for having a robot or a group of robots efficiently localized and to model their environment.v Zusammenfassung Die mobile Robotik stellt ein sehr junges und komplexes Forschungsfelder unserer Zeit dar. Innerhalb der letzten Jahrzehnte wurde es Robotern möglich, sich innerhalb ihrer Umgebung zu bewegen, zu navigieren und mit ihrer Umwelt zu interagieren.Aufgrund der Tatsache, dass die Welt von Unsicherheit geprägt ist und ein Roboter immer nur partielle Information über sie erhalten kann, wurden probabilistische Navigationsverfahren entwickelt, mit denen sich Roboter lokalisieren und Objekte ihrer Umgebung modellieren können. Weiterhin wurden in letzter Zeit Verfahren untersucht, die die kooperative Exploration der Umgebung durch eine Gruppe von Robotern zum Ziel haben. In der vorliegenden Arbeit wird ein neuartiges Konzept, welches sich Perzeptrelationen für die kooperative Umweltmodellierung zu Nutze macht, vorgestellt und evaluiert. Einen zweiten Beitrag der Arbeit stellen constraintbasierte Lokalisierungstechniken dar, die es einem oder mehreren Robotern auf effiziente Art und Weise ermöglichen, sich zu lokalisieren und ihre Umwelt zu modellieren.vii

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Section: Theory Of Constraint Based Modelingmentioning

confidence: 99%

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Göhring

2010

Künstl Intell

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“…• a mobile robot's localization and navigation (Ashokaraj et al, 2004;Clerentin et al, 2003;Kieffer et al, 2000;Seignez et al, 2005), and simultaneous localization and mapping (SLAM) (Drocourt et al, 2003),…”

Section: Robotics and Certificationmentioning

confidence: 99%

Interval Analysis for Certified Numerical Solution of Problems in Robotics

Merlet¹

2009

International Journal of Applied Mathematics and Computer Science

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Interval analysis is a relatively new mathematical tool that allows one to deal with problems that may have to be solved numerically with a computer. Examples of such problems are system solving and global optimization, but numerous other problems may be addressed as well. This approach has the following general advantages: (a) it allows to find solutions of a problem only within some finite domain which make sense as soon as the unknowns in the problem are physical parameters; (b) numerical computer round-off errors are taken into account so that the solutions are guaranteed; (c) it allows one to take into account the uncertainties that are inherent to a physical system. Properties (a) and (c) are of special interest in robotics problems, in which many of the variables are parameters that are measured (i.e., known only up to some bounded errors) while the modeling of the robot is based on parameters that are submitted to uncertainties (e.g., because of manufacturing tolerances). Taking into account these uncertainties is essential for many robotics applications such as medical or space robotics for which safety is a crucial issue. A further inherent property of interval analysis that is of interest for robotics problems is that this approach allows one to deal with the uncertainties that are unavoidable in robotics. Although the basic principles of interval analysis are easy to understand and to implement, this approach will be efficient only if the right heuristics are used and if the problem at hand is formulated appropriately. In this paper we will emphasize various robotics problems that have been solved with interval analysis, many of which are currently beyond the reach of other mathematical approaches.

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“…To solve this problem a set-membership approach of the localization problem based on interval analysis is considered (E. Seignez, 2005;Jaulin, 2009). In this context the the LUVIA algorithm (Localisation Updating with Visibility and Interval Analysis) has been developed to solve the pose tracking problem.…”

Section: Introductionmentioning

confidence: 99%