Learning-Based External Wrench Estimation for Quadrotors

Yi Wei Dai, Wei Yuan Ye, Chen Huan Pi, Stone Cheng

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

Abstract

In this work, we present a quadrotor external disturbance estimator without any preliminary knowledge about system dynamic using machine learning. Traditionally, measuring the external wrench directly requires large demands on sensor accuracy and would increase the take-off weight of quadrotors. This paper presents an estimation method based on machine learning using system states measured by sensors such as position, velocity, attitude and angular velocity. This method can train a highly accurate estimator without further information about the quadrotors dynamic to predict external wrench under simulation environment. Our proposed method is verified through simulation and compare to a traditional wrench estimator.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019
PublisherIEEE Computer Society
Pages245-249
Number of pages5
ISBN (Electronic)9781728134802
DOIs
StatePublished - Aug 2019
Event2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019 - Kusatsu, Shiga, Japan
Duration: 26 Aug 201928 Aug 2019

Publication series

NameInternational Conference on Advanced Mechatronic Systems, ICAMechS
Volume2019-August
ISSN (Print)2325-0682
ISSN (Electronic)2325-0690

Conference

Conference2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019
CountryJapan
CityKusatsu, Shiga
Period26/08/1928/08/19

Keywords

  • estimation
  • machine learning
  • Quadrotor

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