Special Issue on Unmanned Aerial Vehicles (UAVs)

Jung, Sunghun

doi:10.3390/app10228078

Cited by 2 publications

(2 citation statements)

References 26 publications

(45 reference statements)

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“…Unmanned airborne systems (UASs), especially small unmanned airborne systems (S-UASs), are used in military reconnaissance, search, and intelligence work in areas that are not safe for humans, as well as in remote sensing areas; data collection is used due to its data storage, analysis, and decision capabilities [ 9 ]. Work performed in areas that are not safe for humans and difficult to access can be performed thanks to human–UAV interactions [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. One of the areas where human UAV–interactions are used is computer-vision-based hand gesture recognition applications, which enable users to determine hand gestures using pattern recognition algorithms [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…As UAVs can work alone or move in a herd, they have achieved successful results in challenging missions [ 11 , 29 ]. Work that needs to be performed in unsafe and difficult-to-access areas can now be done using human–UAV interactions [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 43 ]. Human–UAV interactions are only used for information exchange in some applications, while some applications employ physical contact or force exchange.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves

Müezzinoğlu

Karaköse

2021

Sensors

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The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.

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