Adaptive functional module selection using machine learning: Framework for intelligent robotics

Martin Lukac, Michitaka Kameyama

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

In robotics, it is a common problem that for a given task many algorithms are available. For a particular environmental context and some computational constraints some algorithms will perform better and others will perform worse. Consequently, a robot, evolving in a real world environment where both the context and the constraints change in real time, should be able to select in real time algorithms that will provide it with the most accurate world description as well as will allow it to extract the currently most vital information and artifacts. In this paper we propose a machine learning based approach for the real-time selection of computational resources (algorithms) based on both the high level objectives of the robot as well as on the low level environmental requirements (image quality, etc.). The learning mechanism described is using a Genetic Algorithm and the learning method is based on supervised learning; an initial set of algorithms with input data is provided as examples that are used for learning.

本文言語English
ホスト出版物のタイトルSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
出版社Society of Instrument and Control Engineers (SICE)
ページ2480-2483
ページ数4
ISBN(印刷版)9784907764395
出版ステータスPublished - 2011 1 1
イベント50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
継続期間: 2011 9 132011 9 18

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
国/地域Japan
CityTokyo
Period11/9/1311/9/18

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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