Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by a...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 1998-10, Vol.23 (2-4), p.201-216
Main Authors: Kobayashi, Futoshi, Arai, Fumihito, Fukuda, Toshio, Shimojima, Koji, Onoda, Makoto, Marui, Norimasa
Format: Article
Language:English
Subjects:
Algorithms
Estimating
Fuzzy
Fuzzy logic
Fuzzy set theory
Learning
Mathematical models
Sensors
Studies
Online Access:Get full text
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Summary:In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.[PUBLICATION ABSTRACT]
ISSN:0921-0296
1573-0409
DOI:10.1023/A:1008031614352