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Automatic Control and Computer Sciences
Erschienen in: Automatic Control and Computer Sciences 5/2018

01.09.2018

Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions

verfasst von: S. Adarsh, K. I. Ramachandran

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 5/2018

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Abstract

Design and development of autonomous mobile robots attracts more attention in the era of autonomous navigation. There are various algorithms used in practice for solving research problems related to the robot model and its operating environment. This paper presents the design of data fusion algorithm using Adaptive Neuro Fuzzy Interface (ANFIS) for the navigation of mobile robots. Detailed analysis of various membership functions (MFs) provided in this paper helps to select the most appropriate MF for the design of similar navigation systems. The combined use of fuzzy and neural networks in ANFIS makes the measured distance value of the residual covariance consistent with its actual value. The data fusion algorithm within the controller of the mobile robot fuses the input from ultrasonic and infrared sensors for better environment perception. The results indicate that the data fusion algorithm provides minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared with that of the individual sensors.
Vorheriger Artikel Task Allocation Methods for Homogeneous Multi-Robot Systems: Feed Pushing Case Study
Nächster Artikel Integration of Computervision and Artificial Intelligence Subsystems with Robot Operating System Based Motion Planning for Industrial Robots
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Metadaten
Titel
Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions
verfasst von
S. Adarsh
K. I. Ramachandran
Publikationsdatum
01.09.2018
Verlag
Pleiades Publishing
Erschienen in
Automatic Control and Computer Sciences / Ausgabe 5/2018
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411618050036

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