Fuzzy logic based nonlinear Kalman filter applied to mobile robots modelling

Carrasco, Rodrigo A.; Cipriano, Aldo

doi:10.1109/fuzzy.2004.1375393

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…It is an effective and good indicator for normal operation of a complex system such as WMRs. On the other hand, as we are aware, some other fault-diagnostic methods do not need analytical models and these methods include those using neural networks and/or fuzzy logic systems to classify the current state measurements into known faulty classes [7] [8], or represent the vehicle behaviours in different faulty situations with a well-trained neural network [9] or fuzzy logic [10]. However, these methods require a huge amount of measurement data, particularly those from the faulty situations, which are difficult to obtain.…”

Section: Fig 2 a Quarter Car Modelmentioning

confidence: 99%

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Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

Luo

Wang

Pham

et al. 2005

31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.

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This paper presents a survey on model-based fault diagnostics and prognostics for wheeled mobile robots (WMRs). Numerous cases of faults diagnosis and prognosis solutions show that, for a complex machine like a WMR, model-based methods that utilize analytical models of the system are desirable because analytical models can well represent the nominal behaviours of the system. On the other hand, datadriven methods are not practical for WMRs since they require a lot of failure cases and data that are too costly, if possible at all, to obtain. Fault prognosis for a system in general and for WMRs in particular is still at the preliminary stage. System models can also be used to predict the remaining useful life of the system.

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Section: Fig 2 a Quarter Car Modelmentioning

confidence: 99%

“…A meaningful variable, termed damage variable is selected from the state-space model (10) to describe the health of the system. Thus, the system's RUL is determined by predicting the time interval required for the damage variable to exceed a pre-designed threshold level that corresponds to a system failure.…”

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

Luo

Wang

Pham

et al. 2005

31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.

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“…Early works on KF for sensor fusion include the parallel Kalman filtering method [27], decentralized Kalman filters [11], [17], and the federated Kalman filter [5]. More recently, KF based localization has been investigated in [6], [22].…”

Section: A Backgroundmentioning

confidence: 99%

¿-Filter: An Evidence Theoretic Approach to Unmanned Ground Vehicle Localization

Vibeeshanan¹,

Subbarao

Huff³

2007

2007 International Symposium on Computational Intelligence in Robotics and Automation

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In this paper, we present a novel evidence theoretic fusion filter, and its application to the Unmanned Ground Vehicle (UGV) localization problem. The various components of the sensor fusion framework such as the adaptive pre-processing unit, the evidence extraction and combination unit, and the extended Kalman filter are described in detail. The crux of this architecture is the evidence extraction and combination unit that employs a twopronged approach, one to switch between parametric models, and another to adaptively vary the measurement noise covariance matrix. The process of evidence extraction using fuzzy-type or rule-based techniques, and their subsequent combination using the Dempster's rule for combination are detailed. An experiment is conducted to demonstrate the merits of this UGV localization approach. Finally, we conclude with a brief summary of the results.

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“…The ones that show lower agreement with the FMO can be removed. Another way to perform the representation fusion is reported in [2], where a collection of KF is used with diverse noise covariances, the resulting estimation is fused by a Takagi-Sugeno Fuzzy System, providing an improved traking in mobile localization.…”

Section: Information Fusionmentioning

confidence: 99%

Review of Hybridizations of Kalman Filters with Fuzzy and Neural Computing for Mobile Robot Navigation

Graña

Villaverde

Guede

et al. 2009

Lecture Notes in Computer Science

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Abstract. Kalman Filters (KF) are at the root of many computational solutions for autonomous systems navigation problems, besides other application domains. The basic linear formulation has been extended in several ways to cope with non-linar dynamic environments. One of the latest trend is to introduce other Computational Intelligence (CI) tools, such as Fuzzy Systems or Artificial Neural Networks inside its computational loop, in order to obtain learning and advanced adaptive properties. This paper offers a short review of current approaches.

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Fuzzy logic based nonlinear Kalman filter applied to mobile robots modelling

Cited by 9 publications

References 12 publications

Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

¿-Filter: An Evidence Theoretic Approach to Unmanned Ground Vehicle Localization

Review of Hybridizations of Kalman Filters with Fuzzy and Neural Computing for Mobile Robot Navigation

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