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Title:Upravljanje kvadrokopterja z vmesnikom mišice-stroj
Authors:Kramberger, Matej (Author)
Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Kramberger_Matej_2018.pdf (6,00 MB)
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
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
Year of publishing:2018
Publisher:M. Kramberger
Source:[Maribor
UDC:[004.9:004.5]:629.735(043.2)
COBISS_ID:21353238 Link is opened in a new window
NUK URN:URN:SI:UM:DK:TFRLAXUG
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:404
Downloads:66
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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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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