Towards higher order fairness functionals for smooth path planning

Victor Parque*

*この研究の対応する著者

研究成果

1 被引用数 (Scopus)

抄録

Smoothness of mobile and vehicle navigation has become relevant to ensure the safety and the comfortability of riding. The robotics community has been able to render smooth trajectories in mobile robots by using non-linear optimization approaches and well-known fairness metrics considering the curvature variations along the path. In this paper, we introduce the possibility of computing smooth paths from observed mobile robot trajectories from higher order non-linear fairness functionals. Our approach is potential to enable the generation of simple and computationally-efficient path planning smoothing for navigation in mobile robots.

本文言語English
ホスト出版物のタイトルGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
出版社Association for Computing Machinery, Inc
ページ319-320
ページ数2
ISBN(電子版)9781450383516
DOI
出版ステータスPublished - 2021 7 7
イベント2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
継続期間: 2021 7 102021 7 14

出版物シリーズ

名前GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
国/地域France
CityVirtual, Online
Period21/7/1021/7/14

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • 計算理論と計算数学

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