Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

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ID: 281364
2019
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Abstract
Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve the navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence - the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms - that have been shown able to improve the accuracy of current positioning estimation systems for real-time UAV autonomous navigation. In addition, the proposed methodology outperformed two other Computational Intelligence techniques.
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paulino2019hybridinteligencia Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Paulino, Ângelo de Carvalho;Dr., Lamartine Nogueira Frutuoso Guimarães,;Dr., Elcio Hideiti Shiguemori,;
Journal inteligencia artificial
Year 2019
DOI
DOI not found
URL
https://journal.iberamia.org/index.php/intartif/article/view/237
Keywords
Medicine Computer applications to medicine. Medical informatics dermatology architecture genetics cytology electronic computers. computer science geology

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