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Title:Upravljanje kvadrokopterja z vmesnikom mišice-stroj
Authors:ID Kramberger, Matej (Author)
ID Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Kramberger_Matej_2018.pdf (6,00 MB)
MD5: 880ABE755313F5BEA9D463B82BE2CE80
PID: 20.500.12556/dkum/46e07de3-3870-4b6f-8b26-3e16c57cf685
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V delu smo zasnovali sistem za upravljanje kvadrokopterja v realnem času z uporabo vmesnika mišice-stroj. V programskem jeziku C# smo za operacijski sistem Windows izdelali aplikacijo, v kateri smo uporabili različne klasifikacijske algoritme iz odprtokodne knjižice Accord.NET. Klasifikacijo smo izvajali na računalniku s procesorjem Intel Core i7 2,8GHz ter 24 GB pomnilnika. Signale EMG smo zajeli s komercialno dostopno zapestnico Myo, ki omogoča zajem površinskih signalov EMG s podlahti. Uspešnost klasifikacije smo preizkusili na modelu kvadrokopterja Eachine E010, ki ga smo krmilili preko vmesnika nRF24L01 in mikrokontrolerja Atmel ATmega32u4 na razvojni plošči Arduino Micro. Klasificirane gibe smo uporabili za krmiljenje treh prostorskih stopenj kvadrokopterja. Giba ekstenzija in fleksija smo uporabili za nadzor naklona, pronacijo in supinacijo za nadzor nagiba ter ulnarno in radialno deviacijo za nadzor odklona. Za nadzor moči motorjev smo uporabili podatke inercijske merilne enote. Najboljše rezultate klasifikacije sta dajala algoritma SVM in k-NN, ki sta klasificirala s 95% pravilnostjo.
Keywords:elektromiogrami, kvadrokopter, vmesniki mišice-stroj, Arduino, zapestnica Myo
Place of publishing:[Maribor
Publisher:M. Kramberger
Year of publishing:2018
PID:20.500.12556/DKUM-69801 New window
UDC:[004.9:004.5]:629.735(043.2)
COBISS.SI-ID:21353238 New window
NUK URN:URN:SI:UM:DK:TFRLAXUG
Publication date in DKUM:05.04.2018
Views:1582
Downloads:183
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:27.02.2018

Secondary language

Language:English
Title:Quadcopter Control with Muscle-Machine Interface
Abstract:We have designed a system for real-time quadcopter control by using the muscle-machine interface. In programming language C#, we have developed a Windows desktop application in which we have used different classification algorithms from the open-source library Accord.NET. Classification was conducted on the computer with Intel Core i7 2.8 GHz processor and 24 GB of memory. EMG signals were captured by commercial Myo armband, that supports acquisition of surface EMG signals from the forearm. We tested the accuracy of classification on quadcopter model Eachine E010, which we controlled via nRF24L01 interface and Atmel ATmega32u4 microcontroller on the Arduino Micro development board. We used classified movements to control three spatial degrees of freedom of quadrocopter. Wrist extensions and flexions were used for controlling pitch, pronation and supination for controlling roll and ulnar and radial deviation for controlling yaw. We used the inertial measurement unit data to control engine thrust. Best classification results were obtained by SVM and k-NN algorithms, with accuracy rate of 95%.
Keywords:electromyogram, quadcopter, muscle-machine interface, Arduino, Myo armband


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