nach oben

Soft Computing

Erschienen in:

01.02.2010 | Original Paper

T–S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

verfasst von: Chandima Dedduwa Pathiranage, Keigo Watanabe, Kiyotaka Izumi

Erschienen in: Soft Computing | Ausgabe 4/2010

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper describes a Takagi–Sugeno (T–S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T–S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter–SLAM algorithm.

Vorheriger Artikel Anteriority index for managing fuzzy dates in archæological GIS

Nächster Artikel Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Aidala VJ (1979) Kalman filter behavior in bearings-only tracking applications. IEEE Trans Aerospace Electron Syst 15(1):29–39CrossRef

Bailey T (2002) Mobile robot localization and mapping in extensive outdoor environments. PhD dissertation, Australian Center for Field Robotics, Dept. Aerosp., Mech. Mechatron. Eng., Univ. of Sydney, Sydney, Australia

Bailey T, Durrant-Whyte H (2006) Simultaneous localization and mapping (SLAM): Part II. IEEE Robot Automat Mag 13(3):108–117CrossRef

Bar-Shalom Y, Fortman TE (1988) Tracking and data association. Mathematics in science and engineering, vol 179. Academic Press, Orlando

Blanco JL, Gonzalez J, Fernandez-Madrigal JA (2008) A pure probabilistic approach to range-only SLAM. In: Proc. IEEE int. conf. robot. automat., Pasadena, CA, USA

Chen G, Xie Q, Shieh LS (1998) Fuzzy Kalman filtering. J Inf Sci 109:197–209MATHCrossRefMathSciNet

Chen H, Kirubarajan T, Bar-Shalom Y (2002) Tracking of spawning targets with multiple finite resolution sensors, vol 2, pp 943–950. In: Proc. 5th int. conf. inf. fusion, Annapolis, MD

Dezert J, Bar-Shalom Y (1993) Joint probabilistic data association for autonomous navigation. IEEE Trans Aerosp Electron Syst 29(4):1275–1286CrossRef

Dissanayake MWMG, Newman P, Clark S et al (2001) A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans Robot Automat 17(3):229–241CrossRef

Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: Part I. IEEE Robot Automat Mag 13(2):99–108CrossRef

Frese U, Larsson P, Duckett T (2005) A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Trans Robot Automat 21(2):196–207

Frintrop S, Jensfelt P (2008) Active gaze control for attentional visual SLAM. In: Proc. IEEE int. conf. robot. automat., Pasadena, CA, USA

Grisetti G, Stachniss C, Burgard W (2005) Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In: Proc. IEEE int. conf. robot. automat., Barcelona, Spain, pp. 2443–2448

Holmes S, Klein G, Murray D (2008) A square root unscented Kalman filter for visual monoslam. In: Proc. IEEE int. conf. robot. automat., Pasadena, CA, USA

Leonard JJ, Durrant-White HF (1991) Mobile robot localization by tracking geometric beacons. IEEE Trans Robot Automat 7(3):376–382CrossRef

Li XR, Jikov VP (2001) A survey of maneuvering target tracking—part III: measurement models. In: Proc. SPIE conference on signal and date processing of small targets, San Diego, CA, USA

Li X, Luo ZQ, Wong KM, Bosse E (1999) An interior point linear programming approach to two-scan data association. IEEE Trans Aerosp Electron Syst 35(2):474–490CrossRef

Lindgren AG, Gong KF (1978) Position and velocity estimation via bearing observations. IEEE Trans Aerospace Electron Syst 14(4):564–577CrossRef

Maksarov D, Durrant Whyte H (1995) Mobile vehicle navigation in unknown environments: a multiple hypothesis approach. In: Proc. IEE Control Theory Appl. 142(4):385–400

Montemerlo M, Thrun S, Koller D, Wegbreit B (2002) FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proc. AAAI national conf. artif. intell., Edmonton, AB, Canada

Montemerlo M, Thrun S, Koller D et al (2003) FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proc. 18th int. joint conf. artif. intell., Acapulco, Mexico

Poore AB, Rijavec N (1991) Multi-target tracking and multidimensional assignment problems. In: Proceeding of the SPIE signal and data processing of small targets, Orlando, FL, pp 345–356

Santosh D, Achar S, Jawahar CV (2008) Autonomous image-based exploration for mobile robot navigation. In: Proc. IEEE int. conf. robot. automat., Pasadena, CA, USA

Simon D (2003) Kalman filtering for fuzzy discrete time dynamic systems. Appl Soft Comput 3:191–207CrossRef

Smith R, Self M, Cheeseman P (1987) A stochastic map for uncertain spatial relationships. In: Proc. 4th int. symp. robot. res., pp 467–474

Smith R, Self M, Cheeseman P (1990) Estimating uncertain spatial relationships in robotics. In: Cox IJ, Wilfon GT (eds) Autonomous robot vehicles. Springer, New York, pp 167–193

Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132MATH

Tanaka K, Ikeda T, Wang H (1996) Robust stabilization of a class of uncertain nonlinear system via fuzzy control. IEEE Trans Fuzzy Syst 4:1–13CrossRef

Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge

Tully S, Moon H, Kantor G, Choset H (2008) Iterated filters for bearing-only SLAM. In: Proc. IEEE int. conf. robot. automat., Pasadena, CA, USA

Whitcombe DW (1972) Pseudo state measurements applied to recursive nonlinear filtering. In: Proc. 3rd symp. nonlinear estimation theory and its application, San Diego, CA, pp 278–281

Ye Y, Todd M, Mizuno S (1994) An \(O(\sqrt{n}L)\) iteration homogeneous and self-dual linear programming algorithm. Math Oper Res 19(1):53–67

Zhang Y (1995) Solving large scale linear programs by interior point methods under MATLAB environment. Tech. Rep. TR96-01. Dept. Math. Statist., Univ. Maryland, Baltimore

Titel: T–S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots
verfasst von: Chandima Dedduwa Pathiranage
Keigo Watanabe
Kiyotaka Izumi
Publikationsdatum: 01.02.2010
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 4/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0409-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2010

Anteriority index for managing fuzzy dates in archæological GIS

Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training

Riesz ideals in generalized pseudo effect algebras and in their unitizations

Taximeter verification with GPS and soft computing techniques

SAS/OWA: ordered weighted averaging in SAS optimization

The revised algorithms of fuzzy variance and an application to portfolio selection