Realizing Body-Machine Interface for Quadrotor Control through Kalman Filters and Recurrent Neural Network

Alexander Menshchikov, Daniil Lopatkin, Evgeny Tsykunov, Dzmitry Tsetserukou, Andrey Somov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Unmanned Aerial Vehicles (UAV) have been recently applied in several various civilian applications. Based on this, there is a growing need for intuitive UAV control interfaces. In this work, we report on the Body-Machine Interface (BMI), helping a human operator to control a quadrotor through the gesture commands. We perform the human motion capture through wearable sensors and Kalman filter to reduce the noise. For the gesture command recognition, we designed the Recurrent Neural Network recognizing gestures within 65 ms. For the quadrotor orientation estimation, we designed the Extended Kalman Filter (EKF). We assess the proposed BMI via the simulations and experiments: the standard deviation of the trajectories varies for up to 10 cm.

Original languageEnglish
Title of host publicationProceedings - 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages595-602
Number of pages8
ISBN (Electronic)9781728189567
DOIs
Publication statusPublished - Sep 2020
Event25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020 - Vienna, Austria
Duration: 8 Sep 202011 Sep 2020

Publication series

NameIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2020-September
ISSN (Print)1946-0740
ISSN (Electronic)1946-0759

Conference

Conference25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020
Country/TerritoryAustria
CityVienna
Period8/09/2011/09/20

Keywords

  • artificial intelligence
  • Body-machine interface
  • Kalman filter
  • machine learning
  • recurrent neural network
  • wearable sensing

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