An Adaptive Neuro-Fuzzy-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Paulino, Angelo de Carvalho; Shiguemori, Elcio Hideiti; Guimarães, Lamartine Nogueira Frutuoso

doi:10.5753/eniac.2018.4472

Cited by 2 publications

(2 citation statements)

References 14 publications

(29 reference statements)

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“…Paulino et al (2018) proposed the Adaptive Network‐based Fuzzy Inference System for data fusion application, which can improve the precise location of such networks. Ji and Luo (2019) proposed an optimized model method that fuses high‐resolution images taken from UAVs with laser point clouds for landslide topography.…”

Section: Data Collection Through Uavs For Various Dm Scenariosmentioning

confidence: 99%

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Khan

Gupta

2022

Journal of Field Robotics

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In recent years, Unmanned Aerial Vehicles (UAVs) have gained significant interest in research groups due to the wide range of applications, such as disaster surveillance and monitoring, rescue operations, military, civil, and so forth. UAVs are most often used to fulfill both user's services and requirements such as wireless communication facilities to end-users, as a relay node to extend the coverage of the core network, and so forth. UAVs are versatile in design and can cover larger areas, contrary to the Tethered Balloon and Loon Balloon systems. Generally in any natural or humanmade disaster, there is a high potential risk of damage to buildings, transport systems, communication systems, and basic services. During heavy disasters like landslides, forest fires, floods, earthquakes, and so forth, the conventional terrestrial communication system gets destroyed, and people face many problems. In this case, UAVs prove to offer a better solution to provide fast, cost-effective, easy to deploy, and secure wireless communication to the victims. But there are some issues like interference between UAVs and other base stations, coordination between UAVs, Quality of Service requirements, Size, Weight, and Power limitation, delay, coverage, positioning of UAVs, and so forth. This study article mainly highlighted these issues and try to present the recent developments of the state-of-the-art to overcome these issues. In UAV communications, with an increasing emphasis on how UAVs can be integrated with different technologies, such as the Internet of Things, Wireless Sensor Network, Heterogeneous Network, and Cloud computing. The primary aim of this article is to examine how UAVs can assist survivors in floods, earthquakes, tsunamis, or in any natural or human-made disaster situations, either in the present or soon. Also, it focused on various applications of UAVs in disaster management (DM). It underlines the significance of UAVs in DM and their advantages. It also focuses on the various issues and challenges faced by the UAV-based infrastructure and security issues and gives future directions.

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Section: Data Collection Through Uavs For Various Dm Scenariosmentioning

confidence: 99%

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Khan

Gupta

2022

Journal of Field Robotics

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“…The Data Fusion approach has shown to be effective in reducing the imprecision of the positioning estimation process. Nevertheless, it is worth mentioning that the only papers found regarding the improvement of the autonomous navigation of a UAV using ANFIS as the main estimator was published by the authors in [46] and [47].…”

Section: Introductionmentioning

confidence: 99%

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

Paulino

Guimarães

Shiguemori

2019

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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 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 low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -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 considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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An Adaptive Neuro-Fuzzy-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Cited by 2 publications

References 14 publications

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

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

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