Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics

Victor Parque*

*この研究の対応する著者

研究成果: Conference contribution

抄録

The quest for the efficient adaptation of multilegged robotic systems to changing conditions is expected to render new insights into robotic control and locomotion. In this paper, we study the performance frontiers of the enumerative (factorial) encoding of hexapod gaits for fast recovery to conditions of leg failures. Our computational studies using five nature-inspired gradient-free optimization heuristics have shown that it is possible to render feasible recovery gait strategies that achieve minimal deviation to desired locomotion directives with a few evaluations (trials). For instance, it is possible to generate viable recovery gait strategies reaching 2.5 cm, (10 cm.) deviation on average with respect to a commanded direction with 40 - 60 (20) evaluations/trials. Our results are the potential to enable efficient adaptation to new conditions and to explore further the canonical representations for adaptation in robotic locomotion problems.

本文言語English
ホスト出版物のタイトル2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665467087
DOI
出版ステータスPublished - 2022
イベント2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Padua, Italy
継続期間: 2022 7月 182022 7月 23

出版物シリーズ

名前2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings

Conference

Conference2022 IEEE Congress on Evolutionary Computation, CEC 2022
国/地域Italy
CityPadua
Period22/7/1822/7/23

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 計算数学
  • 制御と最適化

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