Neural networks for sonar and infrared sensors fusion

Barberá, Humberto Martínez; Skármeta, Antonio; Izquierdo, M.Z.; Blaya, J.B.

doi:10.1109/ific.2000.859830

Cited by 18 publications

(11 citation statements)

References 16 publications

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“…Nevertheless, combining information from a couple of different sensors located with the same orientation results in more reliable distance measures. The process of merging data from different kind of sensors in order to obtain a better measure is called sensor fusion [11], [12]. Our problem is to generate a FIS for approximating a supposed function: that obtains the real distance starting from the sonar an infrared ( ) measures.…”

Section: Fuzzy Modeling For Sensor Fusionmentioning

confidence: 99%

Some Applications of Hybrid Fuzzy Modeling

Valdés-Vela¹,

Botía²,

Gómez‐Skarmeta³

2005

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.

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Fuzzy Modeling is an effective approach for System Identification. It is based on fuzzy sets and logic and describes the system behaviour by means of fuzzy rules. In its turn, Data Driven Fuzzy Modeling (DDFM) extracts these models from a set of input-output observations about the system. Three main stages compose DDFM: rules number identification, rules generation and parameter optimization. One way to carry out a DDFM process is by means of a combination of techniques, each one solving one of the DDFM phases. In this paper, we apply hybridizations of clustering algorithms and neural networks (NN) in order to solve several regression problems from different domains showing up the suitability and success of hybridization in DDFM.

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Section: Fuzzy Modeling For Sensor Fusionmentioning

confidence: 99%

Some Applications of Hybrid Fuzzy Modeling

Valdés-Vela¹,

Botía²,

Gómez‐Skarmeta³

2005

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.

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“…In 17], an automated guided vehicle detects unknown obstacles by means of an \electronic stick" consisting of infrared sensors, using a strategy similar to that adopted by a blind person. Other researchers have dealt with the fusion of information from infrared and sonar sensors 18,19] and infrared and radar systems 20,21].…”

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confidence: 99%

Improved range estimation using simple infrared sensors without prior knowledge of surface characteristics

Yuzbasioglu

Barshan

2005

Meas. Sci. Technol.

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This thesis describes a new method for range estimation using low-cost infrared sensors. The intensity data acquired with infrared sensors depends highly on the surface properties and the con guration of the sensors with respect to the surface. Therefore, in many related studies, either the properties of the surface are determined rst or certain assumptions about the surface are made in order to estimate the distance and the orientation of the surface relative to the sensors. We propose a novel method for position estimation of surfaces with infrared sensors without the need to determine the surface properties rst. The method is relatively independent of the type of surface encountered since it is based on searching the maximum value of the intensity rather than using absolute intensity values for a given surface which would depend on the surface type. The method is veri ed experimentally with planar surfaces of di erent surface properties. An intelligent feature of our system is that its operating range is made adaptive based on the intensity of the detected signal. Three di erent ways of processing the intensity signals are considered for range estimation. The overall absolute mean error in the range estimates has been calculated as 0.15 cm in the range from 10 to 50 cm. The cases where the azimuth and elevation angles are nonzero are considered as well. The results obtained demonstrate that infrared sensors can be used for localization to an unexpectedly high accuracy without prior knowledge of the surface characteristics.

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“…In [9], IR sensors are employed to locate edges of doorways in a complementary manner with sonar sensors. Other researchers have also dealt with the fusion of information from IR and sonar sensors [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Target classification with simple infrared sensors using artificial neural networks

Aytaç

Barshan

2008

2008 23rd International Symposium on Computer and Information Sciences

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Abstract-This study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90• corners, and 90• edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90• edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for.

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Neural networks for sonar and infrared sensors fusion

Cited by 18 publications

References 16 publications

Some Applications of Hybrid Fuzzy Modeling

Some Applications of Hybrid Fuzzy Modeling

Improved range estimation using simple infrared sensors without prior knowledge of surface characteristics

Target classification with simple infrared sensors using artificial neural networks

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